Article

Advances in control technologies for wastewater treatment processes: Status, challenges, and perspectives

Authors:
  • University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj
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Abstract

This paper presents a thorough review of control technologies that have been applied to wastewater treatment processes in the environmental engineering regime in the past four decades. It aims to provide a comprehensive technological review for both water engineering professionals and control specialists, giving rise to a suite of up-to-date pathways to impact this field in light of the classified technology hubs. The assessment was conducted with respect to linear control, linearizing control, nonlinear control, and artificial intelligence-based control. The application domain of each technology hub was summarized into a set of comparative tables for a holistic assessment. Challenges and perspectives were offered to these field engineers to help orient their future endeavor.

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... The modeling of process dynamics plays a vital role in designing control strategies utilized to help maintain process efficiencies and safety, along with minimize process disturbances. Meanwhile, industrial wastewater treatment processes are increasingly confronted with monitoring and standardizing requirements in effluent quality and reducing cost [12,13]. Despite current efficiency measures of degradation processes, AOPs require monitoring and controlling parameters that can be determined more rapidly to act fast to the changing influent conditions. ...
... Hence, it is a lofty design and a set of operating conditions that are not consistently maintained due to chemical processes' dynamic behavior. In a dynamic process, steady-state models can express the effect of different factors onto process efficiencies using algebraic equations and, most importantly, the static objectives of a process known as the setpoints [12,70]. Contradictory, dynamic modeling seeks to study the dynamic, transient behavior of chemical processes. ...
... Linear control deals with systems modeled in continuous or discrete forms, which are the most common methods in automatic control. Most wastewater treatment systems are controlled by conventional linear controllers [12]. Linearizing control deals with non-linearity in the process models through different strategies and control algorithms depending on the application. ...
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Polyvinyl alcohol (PVA) is an emerging pollutant commonly found in industrial wastewater, owing to its extensive usage as an additive in the manufacturing industry. PVA’s popularity has made wastewater treatment technologies for PVA degradation a popular research topic in industrial wastewater treatment. Although many PVA degradation technologies are studied in bench-scale processes, recent advancements in process optimization and control of wastewater treatment technologies such as advanced oxidation processes (AOPs) show the feasibility of these processes by monitoring and controlling processes to meet desired regulatory standards. These wastewater treatment technologies exhibit complex reaction mechanisms leading to nonlinear and nonstationary behavior related to variability in operational conditions. Thus, black-box dynamic modeling is a promising tool for designing control schemes since dynamic modeling is more complicated in terms of first principles and reaction mechanisms. This study seeks to provide a survey of process control methods via a comprehensive review focusing on PVA degradation methods, including biological and advanced oxidation processes, along with their reaction mechanisms, control-oriented dynamic modeling (i.e., state-space, transfer function, and artificial neural network modeling), and control strategies (i.e., proportional-integral-derivative control and predictive control) associated with wastewater treatment technologies utilized for PVA degradation.
... 5 To address those challenges in wastewater treatment plants (WWTPs), numerous control strategies have been put forward over the past years. Typical methods include proportional-integral-derivative (PID) control, 6,7 multi-objective optimal control, 8 multivariable control, [9][10][11] and nonlinear model predictive control (NMPC). [12][13][14] The PID control, a commonly used approach in industrial processes, is also popular in wastewater treatment processes. ...
... However, nonlinearities and disturbances greatly degrade the performance of PID. 6 Multivariable control 9 can get satisfied effects. However, it largely depends on the accuracy of a neural network model, and the structure of neural network is determined by trial and error. ...
... For example, it simply integrates constraints into an optimization problem. 6 However, NMPC is a data-driven control, whose robustness may be deteriorated owing to uncertainties in the WWTPs. 16 Actually, if nonlinearities, uncertainties, and external disturbances-such as coupling, influent temperature, influent rate, and constituents-are considered as time-varying disturbance, desired performance can be obtained by estimating and eliminating disturbance by an advanced control algorithm. ...
Article
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In wastewater treatment processes, the concentration of dissolved oxygen affects the performance of wastewater treatment directly. It is one of the key factors that determines effluent quality of the wastewater treatment. However, a simple closed-loop control has a high-energy consumption, and it cannot guarantee the effluent quality due to large perturbations in wastewater treatment plants, such as the influent rate, the temperature, and the complex biochemical reactions. In this paper, a new disturbance rejection controller is designed to address those perturbations. Dynamics of dissolved oxygen is transformed into a controllable canonical form. Discrepancy between the dissolved oxygen dynamics and the controllable canonical form is estimated by a disturbance observer and compensated by a control law. Stability and the bound of tracking errors are obtained. Finally, numerical results on the benchmark simulation model number 1 are presented to confirm the proposed method.
... During the past decades, several works have been proposed to develop control systems for bioprocesses, different types of control techniques and structures were developed to achieve their control objectives and desired targets [3,4]. Liu et al. proposed a cascade model predictive control (MPC) and proportional integral derivative (PID) control strategy for the Benchmark Simulation Model 1 (BSM1) [5]. ...
... e corresponding nominal steady-state pair obtained by solving equation (3) can be written in a compact form as: ...
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A two-stage anaerobic digestion (AD) process has been applied to improve the efficiency of methane production from various organic materials. However, the performance of traditional process controllers may be limited by differences in the rate of biochemical reactions, process uncertainties, and the consequences of interconnection between the two bioreactors. In this work, a nonlinear optimization-based control strategy that applies an analytical model predictive control (AMPC) scheme with an adaptive optimal set-point is proposed for the control of the two-stage AD system. The objectives of the proposed control system are to stabilize the system under uncertain operating conditions and maximize biomethane production. The optimal set-points for the controller are adapted in real-time operation, and then the control system is performed to manipulate the controlled output to the optimal trajectories. Compensators and nonlinear state observers are applied to handle the process/model mismatch and estimate unmeasured variables. The proposed control system is applied to the process with disturbances, fluctuations of inlet stream concentrations, and changes in the bacterial growth rate, and the control performance is investigated. Simulation results show that the developed control scheme automatically adjusts the optimal set-points and provides adequate control actions to maintain the maximum rate of methane production. The results of this investigation demonstrate that the control strategy promotes different biochemical reactions, avoids the inhibition effect, and handles the mutual effects between acidogenic and methanogenic bioreactors for methane production effectively.
... Because of the mutual coupling and effects among multiple variables in WWTPs [3], actuator faults will lead to control loop fluctuations, performance degradation, affecting other related control loops, resulting in the whole factory control system fluctuations and even exceeding the quality of wastewater purification standard. Additionally, working actuator may not exhibit a linear characteristic in all operating domains, i.e, actuator is subject to input constraints incurred by the consideration of safety and physical limits of its actuation values [22]. Up till now, numerous researchers have long worked on the fault-tolerant and actuator constraint control, and the constructed intelligent control techniques have been employed in different engineering fields, for example, ocean surface vessels [23], spacecraft attitude [24], unmanned aerial vehicle [25], etc. ...
... Substituting (24)-(28) into (22), one haṡ ...
Article
This paper investigates the performance-guaranteed adaptive self-healing control for wastewater treatment processes (WWTPs), in which the non-ideal actuator, i.e., the actuator suffering from faults and constraints is considered. Firstly, an error conversion dynamic model based on a new prespecified-time performance function is presented to guarantee that the tracking error is quickly maintained in the user-defined area regardless of the occurrence of actuator faults and constraints. Secondly, a novel smooth approximation function is constructed to imitate the non-smooth characteristics of actuator constraints. Thirdly, the unknown bounds of non-ideal actuator parameters are estimated online in place of parameters themselves. Meanwhile, the lumped disturbances induced by the external disturbance and the fuzzy approximation error are suppressed via H ∞ control theory. In addition, the stability analysis is carried out by the Lyapunov stability criterion to guarantee that all closed-loop system signals are bounded. Finally, comparative simulation results demonstrate that the proposed method has the superiorities in terms of preferable dynamic tracking performance with fast transient convergence, strong robustness and performance recovery under non-ideal actuator.
... Wastewater treatment plants (WTP) have been widely used in the field of state estimation and control ( Revollar et al., 2017 ;Cristea et al., 2011 ). WTP processes are often subjected to external perturbations and therefore are difficult to model properly ( Iratni and Chang, 2019 ). Developing a mechanistic model that can completely represents the dynamic of the process is challenging ( Iratni and Chang, 2019 ). ...
... WTP processes are often subjected to external perturbations and therefore are difficult to model properly ( Iratni and Chang, 2019 ). Developing a mechanistic model that can completely represents the dynamic of the process is challenging ( Iratni and Chang, 2019 ). When these highly nonlinear processes are subjected to external perturbations, the process uncertainties and measurement noises present in the system are less likely to follow their expected probability density functions. ...
Article
Industrial processes are often subject to unexpected process uncertainties or measurement noises such that their distributions may become non-Gaussian and unforeseeable. A Moving Horizon Estimation (MHE) framework that can explicitly accommodate unknown non-Gaussian distributions is absent. This study presents a novel robust MHE (RMHE) scheme that approximates the unknown non-Gaussian distributions of uncertainties or noises using an optimal Gaussian mixture model that is adapted online. The proposed RMHE considers additional constraints and decision variables than in the standard MHE framework, which are needed to approximate the distributions of the uncertainties (or noises) to Gaussian mixture models online. Therefore, RMHE increases the robustness of the estimation with respect to the unexpected noises or uncertainties occurring in the process. RMHE is an efficient scheme as it does not increase significantly the computational costs required by the standard MHE. Case studies involving multiple scenarios are presented to illustrate the benefits of RMHE.
... Real-time automated control and monitoring systems, such as Supervisory Control and Data Acquisition (SCADA), should be implemented in the water service system, at the source, water and wastewater treatment plants, and other networks to reduce the need for human labor during a pandemic [49]. Automatic monitoring of key water quality parameters, particularly virus concentrations, in lakes and rivers to ensure good water quality should also be initiated at the regional levels [50]. This will serve as a criterion for determining the appropriate level of disinfection achieved at water treatment plants. ...
... This will serve as a criterion for determining the appropriate level of disinfection achieved at water treatment plants. Real-time automated central control of the processing flow and dose at treatment plants can help in reducing the workers workload, while providing a consistent supply of high-quality water [50]. ...
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In China, the notion of a water sensitive city has gained popularity in urban water management as a result of the detrimental effects of flooding and pollution caused by developmental activities. Urban systems and their interrelationships are critical for long-term urban water management and water sensitivity. This article is a case study considering how a strength, weakness, opportunities, and threat (SWOT) analysis-based approach to urban water management interventions in Guangzhou and Kunming cities (China) enables decision makers to identify solutions for cities to become more water-sensitive and resilient. The similar difficulties and rewards with respect to the contexts of both cities were synthesized using SWOT analysis. The contextual SWOT analysis, in conjunction with the comprehensive inclusion of Sustainable Development Goals (SDGs) in intervention planning in these cities, revealed that a water-sensitive-cities approach requires the establishment of a comprehensively multi-objective rainwater management system; this approach would have the goals of reducing rainwater draining sources, controlling processes and adaptive measures, and governing the system to make it more resilient. The water strategy should be holistic and adaptive, capable of providing a broad range of ecological services and other social benefits consistent with the fulfilment of the Sustainable Development Goals, and adaptable to other Chinese cities seeking to achieve water sensitivity.
... For this reason, real-time monitoring and control systems are highly desirable to maintain the optimum conditions. In order to achieve this in practice, the use of machine learning algorithms (MLAs) and dynamic models has shown promising results for optimizing the algal system performance, as it gives a better understanding of the biological processes in terms of uncertainty than the conventional kinetic or phenomenological models (Iratni and Chang 2019). Moreover, applying mathematical models provides a deep insight on the algal-bacterial metabolism, wherein the light intensity, temperature, N limitation, and dissolved inorganic carbon play key and distinguished roles . ...
... algal-bacterial bioreactors) . Nevertheless, the sensitivity of these sensors to measure the key parameters during transient conditions still remains a major challenge (Iratni and Chang, 2019). Therefore, hybrid models such as the GPR, ANNs integrated with PCA or RL should be tested under full-scale conditions so that any anomalies in fault detection/diagnosis can be overcome. ...
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Biological wastewater treatment using algae–bacteria consortia for nutrient uptake and resource recovery is a ‘paradigm shift’ from the mainstream wastewater treatment process to mitigate pollution and promote circular economy. The symbiotic relationship between algae and bacteria is complex in open or closed biological wastewater treatment systems. In this regard, machine learning algorithms (MLAs) have found to be advantageous to predict the uncertain performances of the treatment processes. MLAs have shown satisfactory results for effective real-time monitoring, optimization, prediction of uncertainties and fault detection of complex environmental systems. By incorporating these algorithms with online sensors, the transient operating conditions during the treatment process including disruptions or failures due to leaking pipelines, malfunctioning of bioreactors, unexpected fluctuations of organic loadings, flow rate, and temperature can be forecasted efficiently. This paper reviews the state-of-the-art MLA approaches for the integrated operation of biological wastewater treatment systems combining algal biomass production and nutrient recovery from municipal wastewater. Graphic abstract
... PID control, a commonly used approach, has also been widely utilized in the DO concentration control. 5 However, the performance of the PID control degrades significantly for strong nonlinearities and disturbances in WWTPs. 5 To improve the tracking performance, a feedback linearization-based PI controller was designed. ...
... 5 However, the performance of the PID control degrades significantly for strong nonlinearities and disturbances in WWTPs. 5 To improve the tracking performance, a feedback linearization-based PI controller was designed. 6 Drawback of the feedback linearization method is that the robustness cannot be guaranteed for the existing uncertainties. ...
Article
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Nonlinearities, uncertainties and external disturbances commonly exist in a wastewater treatment process (WWTP). Those issues present great challenges to the control of the dissolved oxygen (DO) concentration in a WWTP. In this paper, an active disturbance rejection control (ADRC) is utilized to estimate the total disturbance and drive the DO concentration to track the set-value. Simultaneously, an iterative learning strategy is employed to adjust the parameters of an extended state observer (ESO) to improve the accuracy of the estimation and reduce the dependence on experience in determining parameters. By combining the advantages of the ADRC and the iterative learning strategy, an iterative learning based active disturbance rejection control (ILADRC) is constructed, and the close-loop stability is analyzed. The benchmark simulation model No.1 (BSM1) is utilized to confirm the ILADRC. Numerical results show that the ILADRC is more effective in the DO concentration control.
... A survey of these issues can be observed in Jacobs et al. (2021), wherein authors have aggregated these issues, and suggested recommendations for countering them. Based on these recommendations, work in Iratni & Chang (2019) suggests different control techniques including proportional integral control (PI), PI differential control (PID), feedback-based control, etc. These strategies will assist in mitigating genetic issues by analyzing and preserving water samples which pose privacy threats to the governments, and other monitoring agencies. ...
Article
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Wastewater treatment is an environmental issue of utmost importance. Various models and architectures for wastewater treatment exist, each having its unique characteristics. In this study, an application-specific review of various treatment models is performed. Extensions to existing treatment models are discussed for improvement in process performance. The treatment models are compared statistically based on their performance metrics, namely the quality of treated water (Q), sludge percentage at output (SL), complexity of treatment (C), time needed for treatment (T), and deployment cost (DC). A novel parameter, model rank, is proposed that combines all the performance metrics into a single number so that the treatment models can be analyzed effectively. Results show that advanced oxidation processes with ozone treatment (AOPO), Kernel principal components analysis-based one-class support vector machine (KPCA SVM), electrochemical processes (EPs), membrane and absorption (MA), Nondominated Sorting Genetic Algorithm-based Optimal Controller (NSGAOC), and wet-type nonthermal plasma reactor (WTNPR) models have a rank above 3.5. The AOPO model has the highest model rank of 3.85 and thus has better overall performance than others. This study might aid major stakeholders in waste treatment industry including researchers in selecting the appropriate wastewater treatment method according to their requirements.
... The increase of severe environmental regulations and the attention over social and political aspects of environmental protection has consolidated all the research efforts on more sustainable processes and ways to overcome pollution [1]. These quality standards exhort the need to eliminate and reprocess hazardous pollutants of which recalcitrant compounds stand as a treat that demands other non-biological technologies for their treatment. ...
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Advanced oxidation processes can counteract the hazardous effects of polluted effluents in a highly efficient way, in many cases limited by the adsorption capacity of the nanocatalyst that depends on their size, internal structure and coating. Here, magnetic iron oxide nanocatalysts consisting on single core (SC), multicore (MC) and core-shell (CS) structures, stabilized with citrate and silica, have been evaluated for the degradation of anionic acid orange 8 (AO8) and cationic methylene blue (MB). It was observed that the adsorption is a limiting parameter, as expected in a mainly heterogeneous process involving molecular adsorption, reaction, and desorption at the catalyst surface. Thus, for the anionic dye, AO8, no degradation is observed by any of the nanocatalysts considering their negative surface charge. However, for MB loaded SC or CS nanocatalysts, highest degradation yields (almost 100% after 180 min at 90 ºC) were achieved through a homogeneous and heterogeneous catalysis in the case of SC and a pure heterogeneous process in the case of CS. MC presents the lager aggregate size due to the lack of coating and low surface charge, leading to poor capacity of adsorption and degradation. On the other hand, magnetic induction heating promotes the degradation of MB (up to ≈50%, respect to room temperature). The results show that iron oxide nanocatalysts through Fenton reactions are an interesting alternative for wastewater treatment considering also that iron is non-toxic and one of the most abundant elements on Earth and can be recovered simply by applying a magnetic field.
... Multiple-input multiple-output (MIMO) structures present another daunting challenge because of the different volumes and compositions of the contaminated waste streams in the structure (Li and Yamamoto, 2017). These systems often require specialized MIMO control systems to assist in the treatment process (Iratni and Chang, 2019). However, having a WWT system that can take multiple input contaminant streams and treat them to meet multiple outlet specifications can be advantageous, especially when implementing recycling systems. ...
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Designing effective wastewater treatment networks is challenging because of the large number of treatment options available for performing similar tasks. Each treatment option has variability in cost and contaminant removal efficiency. Moreover, their mathematical models are highly nonlinear, thus rendering them computationally intensive. Such systems yield mixed-integer nonlinear programming models which cannot be solved properly with contemporary optimization tools that may result in local optima or may fail to converge. Herein, the P-graph framework is employed, thus generating all potentially feasible process structures, which results in simpler, smaller mathematical models. All potentially feasible process networks are evaluated by nonlinear programming resulting in guaranteed global optimum, furthermore, the ranked list of the n-best networks is also available. With the proposed tool, better facilities can be designed handling complex waste streams with minimal cost and reasonable environmental impact. The novel method is illustrated with two case studies showing its computational effectiveness.
... A review of the various strategies that have been proposed for the control of WWTPs has been presented in [1]. Due to the fact that aeration contributes from 45% to 75% to the overall * Corresponding author. ...
Article
Wastewater Treatment Plants (WWTPs) are industrial facilities, which are important for the protection of the environment, because they remove pollutants from wastewater, before it reaches natural bodies of water. WWTPs consist of complex physical, chemical, and biological energy-intensive processes, which are subject to significant disturbances and uncertainties, due to large variations in the load and quality of the influent. Rising energy prices and increasingly stringent effluent requirements have amplified the need of developing more efficient control schemes for WWTPs. In this paper a novel Economic Dynamic Matrix Control (EDMC) configuration is proposed for WWTPs, where the objective is to minimize the plant’s operating costs in terms of energy savings, while maintaining the effluent quality within acceptable regulatory limits. The novelty of the proposed scheme lies in the combination of the standard Dynamic Matrix Control (DMC) methodology, with economic oriented control strategies. The EDMC predictive models are derived from the application of step tests on the COST/IWA Benchmark Simulation Model No. 1 (BSM1). Based on the BSM1 model, the proposed method is compared to standard Multiple Input–Multiple Output (MIMO) DMC controllers, to the default BSM1 control strategy and to other economic control methods, which have been proposed in the literature. The results illustrate that the proposed EDMC scheme is superior to alternative control strategies in terms of minimizing the energy consumption while, the effluent quality of the plant is maintained at acceptable levels.
... Therefore, control systems and realtime monitoring, through machine learning algorithms (MLAs) and dynamic models are presented as a solution to preserve optimum conditions. Their application brought some interesting results for the optimizing study of algal system performance, which provides a better understanding of the biological processes when compared to the conventional kinetic or phenomenological models [214]. ...
Chapter
The world consumption of dyes has been increasing, mainly due to the textile industry (colorization of fibers). The textile industry generates a massive amount of wastewater. The incorrect disposal of colored effluent into the environment leads to the derangement of aquatic life. Several techniques have been applied to reduce this impact, including adsorption, coagulation, and filtration, among others that, on the one hand, are efficient; on the other hand, require additional management (e.g., a large volume of sludge). In this sense, specific biological pathways for dye degradation and wastewater discoloration have drawn attention to the industry since they can increase wastewater treatment yields. Therefore, this chapter describes the fundamental concepts of dye-containing textile wastewater treatments, particularly microbial and enzymatic approaches, including the most usual textile wastewater treatments and their trends (modern technology).KeywordsAzo dyesBioremediationBacterialFungalMicroalgaeGenetically modified organismsCombined treatments systemsBiofilmsResource recovery strategyMachine learning
... This work will address issues related to the modelling and control of biotechnological processes (bioprocesses) within WWTPs, i.e., from the secondary treatment stage. These topics have been important research subjects in the last decades [1,2]. ...
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The design and implementation of a simulator, as a real-time application, for a complex process from the biological treatment stage of a wastewater treatment plant (WWTP), is addressed. More precisely, this emulator was achieved as a software tool that can be later integrated into a more complex SCADA (supervisory control and data acquisition) system of the WWTP Făcăi, Romania. The basic idea is to implement and validate a reduced-order model of the activated sludge process (ASP), initially simulated in the Matlab/Simulink environment (The MathWorks, Inc., Natick, MA, USA). Moreover, an advanced multivariable adaptive control scheme of the ASP is addressed. This software tool can be made to work in parallel with the evolution of the process and can have as input signals measured directly at the process level, possibly following parametric or model adaptations. The software emulator is developed in the LabWindows/CVI programming environment (National Instruments), which offers low-level access to hardware or software systems that have minimal open-architecture facilities. This environment provides versatile drivers and software packages that can facilitate the interaction with software tools developed within some earlier SCADA systems. The structure and the graphical interface of the emulator, some functionalities, experiments, and evolution of main variables are presented.
... Wastewater treatment is absolutely essential for municipal civilization and is of huge significance to smart cities. Nowadays, biological wastewater treatment is the most feasible one, which is widely applied to a municipal wastewater treatment process (WWTP). Due to its complex nonlinear dynamics with large disturbances and uncertain time-delay, water quality soft-sensing of WWTP is a challenging task (Wang et al. 2020a;Iratni and Chang 2019;Najafzadeh and Ghaemi 2019;Guo et al. 2015). Under the strict environmental regulations, accurate soft-sensing models for water quality of wastewater are vital (Zeinolabedini and Najafzadeh 2019; Han and Qiao 2014;Qiao et al. 2017). ...
Article
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This paper aims to present a comprehensive survey on water quality soft-sensing of a wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We mainly present problem formulation of water quality soft-sensing, common soft-sensing models, practical soft-sensing examples and discussion on the performance of soft-sensing models. In details, problem formulation includes characteristic analysis and modeling principle of water quality soft-sensing. The common soft-sensing models mainly include a back-propagation neural network (BPNN), radial basis function neural network (RBFNN), fuzzy neural network (FNN), echo state network (ESN), growing deep belief network (GDBN) and deep belief network with event-triggered learning (DBN-EL). They are compared in terms of accuracy, efficiency and computational complexity with partial-least-square-regression DBN (PLSRDBN), growing ESN (GESN), sparse deep belief FNN (SDB-FNN), self-organizing DBN (SODBN), wavelet-ANN (W-ANN) and self-organizing cascade neural network (SCNN). In addition, this paper generally discusses and explains what factors affect the accuracy of the ANNs-based soft-sensing models. Finally, this paper points out several challenges in softsensing models of WWTP, which may be helpful for researchers and practitioner to explore the future solutions for their particular applications.
... The activated sludge method is a biological sewage treatment method commonly used in the wastewater treatment processes (WWTP) [1,2]. Through biochemical reaction, the pollutants in the sewage are adsorbed, decomposed and oxidized, so the pollutants are degraded and separated from the sewage to achieve the purification of the sewage [3][4][5][6]. To ensure that the effluent water quality reaches the standard, it is necessary to fill the aeration tank with appropriate oxygen through the blower to maintain the concentration of dissolved oxygen (S O ) in the aerobic area, and use the reflux pump to maintain the concentration of nitrate nitrogen (S NO ) in the anoxic zone [7]. ...
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To solve the problem of high-energy consumption in activated sludge wastewater treatment, a reinforcement learning-based particle swarm optimization (RLPSO) was proposed to optimize the control setting in the sewage process. This algorithm tries to take advantage of the valid history information to guide the behavior of particles through a reinforcement learning strategy. First, an elite network is constructed by selecting elite particles and recording their successful search behavior. Then the network is trained and evaluated to effectively predict the particle velocity. In the periodic wastewater treatment process, the RLPSO runs repeatedly according to the optimized cycle. Finally, RLPSO was tested based on Benchmark Simulation Model 1 (BSM1) of sewage treatment, and the simulation results showed that it could effectively reduce the energy consumption on the premise of ensuring qualified water quality. Furthermore, the performance of RLPSO was analyzed using the benchmarks with higher dimension, which verifies the effectiveness of the algorithm and provides the possibility for RLPSO to be applied to a wider range of problems.
... These methods, however, almost did not improve the system performance indexes: operating cost index (OCI) and output wastewater quality index (EQI). Thanks to the good work in [15] that addresses many control technologies for wastewater treatment processes. In ref. [16], some common techniques are compared among the bee colony optimization (BCO), differential evolution (DE), harmony search (HS) algorithms, type-1 fuzzy logic system (T1FLS) and show the promissing point related to fuzzy controller. ...
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A wastewater treatment plant facilitates various processes (e.g., physical, chemical and biological) to treat industrial wastewater and remove pollutants. This topic recently encourages much attention in different fields to explore suitable methods to be able to remove chemical or biological elements from wastewater. This paper presents a novel genetic based control algorithm for biological wastewater treatment plants, intending to improve the quality of the effluent, and reduce the costs of operation. The proposed controller allows adjusting the dissolved oxygen in the last basin, SO,5, according to the operational conditions, instead of maintaining it at a constant value. genetic algorithm (GA) is used in the higher-level control design to verify the desired value of the lower level based on the Ammonium and ammonia nitrogen concentration in the fourth tank, SNH,4, concentration values in the fourth tank. In order to modify the tuning parameters of the higher level, an adjustment region is determined. Consequently, the effluent quality is improved, help to decrease the total operational cost. Simulation results demonstrate the advantages of the proposed method.
... Nowadays, biological wastewater treatment is the most feasible one, which is widely applied to a municipal Wastewater Treatment Process (WWTP). Due to its complex nonlinear dynamics with large disturbances and uncertain time-delay, WWTP modeling is a challenging task [26,9]. Under the strict environmental regulations, soft-sensing models for effluent quality of wastewater are so vital [6,30,19]. ...
Article
Due to the complex dynamic behavior of a wastewater treatment process (WWTP), the existing soft-sensing models usually fail to efficiently and accurately predict its effluent water quality. Especially when a lot of practical data is provided and we do not know which data-pair is more valuable, WWTP modeling becomes a time-consuming process. The main reason is that the existing soft-sensing models update their parameters at each data-pair in one iteration, while some update operations are meaningless. To address this thorny problem, this paper proposes a Deep Belief Network with Event-triggered Learning (DBN-EL) to improve the efficiency and accuracy of soft-sensing model in WWTP. First, some events are defined according to different running condition during the process of training DBN-based soft-sensing model. The different running condition is dominated by the fluctuation of error-reduction rate. Second, an event-triggered learning strategy is designed to construct DBN-EL, whose parameters are updated only when a positive event is triggered. Thirdly, we present the convergence analysis of DBN-EL based on the optimization in Markov process. Finally, the effectiveness of DBN-EL is demonstrated on soft-sensing of total phosphorus concentration in a practical WWTP system. In experiment, DBN-EL is compared with nine different models on soft-sensing of WWTP. The experimental results show that the efficiency of DBN-EL is 27.6%-64.9% higher than that of nine competitive models, which indicates that the proposed model is readily available for industrial deployment.
... For instance, recent reviews provide a summary of the current state of nutrient monitoring methods (nitrogen and phosphorus) 18 as well as insight into the potential for use of biosensors 19 and fluorescence spectroscopy 20 in wastewater monitoring. Wastewater process control technologies 21 and methods 22,23 have also been discussed, increasingly promoting the use of big data and statistical process control to address unique characteristics in wastewater treatment. 24,25 Different teams have focused on the wide range of options available in leveraging big data, e.g., regression models for biological process variable prediction 26,27 or resource recovery, 28 plant-wide process modeling and monitoring, 29 and development of multiple sensor-driven inference models. ...
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The primary mandate of wastewater treatment facilities is the limitation of pollutant discharges, however both continued tightening of permit limits and unique challenges associated with improving sustainability (i.e., resource recovery) demand innovation. Enabling increasingly sophisticated treatment processes in a cost-effective and energy-efficient way requires expanding capabilities for rapid, accurate real-time quantification of a broadened range of wastewater constituents as well as envisioning novel feedback control strategies based on these signals. This manuscript quantitatively compares results of early adoption of instrumentation and process upgrades at operating wastewater treatment facilities and proof-of-concept research results, with a focus on leveraging real-time sensing of wastewater chemistry for process monitoring and control. Up to 10% improvement in nutrient removal and energy savings are already being achieved, yet shortfalls in hardware readiness, lack of field-relevant context of research results, and a widening gap between the training of environmental engineers and the skillsets required to develop and maintain sensor-driven solutions present challenges. A forward-looking roadmap highlights opportunities for accelerating innovation, including (1) ensuring research results are published in units and context that allow operators to make an informed cost–benefit analysis with explicit comparison to existing operational baselines, (2) promoting integrated design of hardware and software to generate novel approaches for improved sensing of target analytes, (3) strengthening partnerships nationally, including for data sharing, field testing of new hardware, and expanding educational curricula, and (4) building forums for sharing of expertise and data among plant operators to enable smaller facilities to more cost-effectively collect information required to design and evaluate upgrades.
... For conventional control, the differential equations derived are then transformed into a standard transfer function or state space and the control laws are then applied [7][8][9][10]. However, the principal control problems of ASP as found in literature can be briefly illustrated by the variability of the kinetic parameter [11][12], time-varying influent conditions, nonlinearities [13], delays, lack of accurate sensors [14], and the limited availability of the online measurements [15]. Therefore, adaptive control principles are the preferred design option [16][17][18]. ...
Article
Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.
... A review of control technologies applied to water reclamation facilities was performed by Iratni and Chang (2019). Fermentate addition and ABAC were implemented to maintain long-term nitritation in batch laboratory scale operated sequentially under anaerobic, aerobic, and anoxic conditions (Melin & Coats, 2019). ...
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A review of the literature published in 2019 on topics relating to water resource recovery facilities (WRRF) in the areas of modeling, automation, measurement and sensors and optimization of wastewater treatment (or water resource reclamation) is presented.
... Under the situation of global environmental degradation, freshwater resources shortage has become a widely serious phenomenon and wastewater recycling has been regarded an effective channel to address this issue (Iratni and Chang 2019;Han et al. 2019). As one of the core attentions for treating wastewater, ensuring effluent quality is actually an optimization problem to a large extent. ...
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In this paper, by integrating neural network approximators, a data-based composite control technique is developed with critic learning implementation and wastewater treatment verification. The iterative adaptive critic framework is established involving dual heuristic dynamic programming (DHP), so as to obtain an intelligent optimal controller. Besides, a steady control input is computed with the help of the neural identifier. Then, by combining the DHP controller and the steady control input, an effective composite control strategy is derived and applied to the proposed wastewater treatment platform. Through conducting experiments, it is observed that the dissolved oxygen concentration and the nitrate level can be maintained at setting points successfully, which results in an intelligent wastewater treatment system.
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Dear Editor, This letter is concerned with the data-driven fault compensation tracking control for a coupled wastewater treatment process (WWTP) subject to sensor faults. Invariant set theory is introduced to eliminate the completely bounded and differentiable conditions of coupled non-affine dynamics and to explicitly express the control inputs. An adaptive fault compensation mechanism is constructed to accommodate the effects of sensor faults. By employing a cubic absolute-value Lyapunov criteria, it is shown that all the signals are bounded and the tracking error converges to an adjustable neighborhood near the origin. Experiment studies are executed on a standardized platform of WWTP to illustrate the effectiveness of the proposed strategy.
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Dissolved oxygen (DO) is one of the most important water quality factors. Maintaining the DO concentration at a desired level is of great value to both wastewater treatment plants (WWTPs) and aquaculture. This review covers various DO control strategies proposed by researchers around the world in the past 20 years. The review focuses on published research related to determination and control of DO concentrations in WWTPs in order to improve control accuracy, save aeration energy, improve effluent quality, and achieve nitrogen removal. The strategies used for DO control are categorized and discussed through the following classification: classical control such as proportional-integral-derivative (PID) control, advanced control such as model-based predictive control, intelligent control such as fuzzy and neural networks, and hybrid control. The review also includes the prediction and control strategies of DO concentration in aquaculture. Finally, a critical discussion on DO control is provided. Only a few advanced DO control strategies have achieved successful implementation, while PID controllers are still the most widely used and effective controllers in engineering practice. The challenges and limitations for a broader implementation of the advanced control strategies are analyzed and discussed. HIGHLIGHTS The application of control strategies of dissolved oxygen for water treatment was reviewed.; Systemically summarized the various dissolved oxygen control strategies in wastewater treatment process.; Surveyed the prediction and control methods of dissolved oxygen in aquaculture.; Provided a critical thinking on DO control.;
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Different time scales are inevitable for the controlled variables of wastewater treatment process (WWTP), which may degrade the operation performance or even destroy the stability of the closed-loop system. In this paper, a hierarchical nonlinear model predictive control (HNMPC) strategy is developed to deal with the different time scales of controlled variables to improve the operation performance of WWTP. The main merits of HNMPC are three-fold. First, a hierarchical control structure is developed to obtain reasonable control laws. Then, the controlled variables with different time scales can be tracked by different level controllers. Second, a gradient method is developed to solve the hierarchical optimization problem of HNMPC to reduce the computational cost. Then, the fast response of HNMPC can be achieved to improve the operation performance. Third, the stability of HNMPC is proved in theory. Then, the corresponding stability conditions are given to guide the practical application. Finally, the testing results on the benchmark simulation model verify that the proposed HNMPC can achieve suitable operation performance in terms of control accuracy.
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Strong nonlinearities, uncertainties, and disturbances present great challenges to the control of the dissolved oxygen (DO) in a wastewater treatment process (WWTP). To deal with those undesired issues, in this article, a scalable-bandwidth extended state observer (SESO) is proposed, and the SESO-based adaptive sliding-mode control (ASMC) is designed. By the SESO, the time-varying total disturbance can be estimated more accurately and compensated more effectively. For the disturbances that are not addressed completely, an ASMC is employed to suppress them. Due to the advantages of both SESO and ASMC, the DO can be regulated more desirably. The benchmark simulation model Number 1 is taken to verify the proposed SESO-based ASMC. Comparative simulation results highlight the advantages of the proposed approach.
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Affected by multiple operation conditions, wastewater treatment process (WWTP) is a complex industrial process with strong nonlinearity and disturbance. How to enhance the rapid tracking response-ability and robustness of the controller is still a challenge when the operation conditions change. To solve this problem, a type-2 fuzzy broad learning controller (T2FBLC) is proposed in this paper. First, a type-2 fuzzy broad learning system (T2FBLS) is constructed in T2FBLC by replacing nodes in feature window with a group of interval type-2 fuzzy submodules. Then, the proposed T2FBLC can take tracking error as inputs while its outputs acting on WWTP to directly obtain a control law, and the controller makes a quick tracking response in different operation conditions. Second, the weight parameters of T2FBLC are adjusted by using the gradient descent method to ensure the control performance. In this way, the developed T2FBLC can realize online learning to reduce tracking errors. Third, according to the Lyapunov function theory, the stability of control strategy is proved. Finally, benchmark simulation model 1 (BSM1) is adopted to verify the effectiveness of T2FBLC. The experimental results prove the applicability and superior tracking performance of the proposed method. Index Terms: Type-2 fuzzy broad learning system; rapid tracking response; wastewater treatment process; multiple operation conditions; stability analysis
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An enhanced biological phosphorus removal (EBPR) system based on anaerobic side-stream phosphorus recovery was operated to investigate the nutrient removal performance of the mainstream system under low dissolved oxygen (DO = 1.0, 0.6, 0.2 mg L⁻¹) and the corresponding phosphorus recovery efficiency with different extracting ratio (m = 0, 1/4, 1/3, 1/2) of anaerobic supernatant within 310 days. The results showed that the nutrient removal efficiency remained stable even though DO was extremely low. Nevertheless, phosphorus removal performance was found to deteriorate as side-stream ratio increased to 1/3 at DO = 0.2 mg L⁻¹, suggesting that extracting a higher ratio of supernatant was not favorable for phosphorus removal in the mainstream process at ultra-low DO. The stoichiometric ratios of Prelease/HAcuptake during anaerobic phase which decreased from 0.125 to 0.020 P-mol/C-mol as DO decreased with increasing side-stream ratio were lower than that of typical PAO metabolism. It was very likely that the metabolic mode of PAOs changed due to long-term deprivation of phosphate. Consequently, the mainstream EBPR system failed to remove phosphorus efficiently. It was also observed that phosphorus recovery efficiency was considerable at DO ≥ 0.6 mg L⁻¹ coupled with high side-stream ratio. Therefore, it was feasible and energy-saving to extract appropriate ratio of anaerobic supernatant for recovering phosphorus and removing nutrient efficiently in an EBPR process under low DO.
Chapter
The biorefineries, with their recognized capacity to convert in a sustainable manner the biomass into a wide range of bio-based products and bioenergy, are used on an increasing scale in bioindustry. One of the aims of biorefineries is to contribute to the conservation of resources and to the reduction of pollutants. The biorefineries are complex plants, with many challenging problems related to their operation and control. The present chapter addresses innovative issues related to the control of a modern type of biorefinery, which includes two interconnected reactors: an anaerobic digester and a photobioreactor. The anaerobic digestion is used as a main process in wastewater treatment plants. In the photobioreactor, by using the solar energy and a specific microalgae population, the carbon dioxide produced by anaerobic fermentation is converted into organic components. To gradually present the control concepts for the biorefinery, in the first part of the chapter, a description of basic modeling, estimation and control problems, and techniques for bioprocesses is approached. The second part investigates control schemes that are based on special types of software sensors, resulting thus advanced adaptive and robust-adaptive control methods, able to cope with nonlinear, complex, and uncertain plants. Several numerical simulations illustrate the behavior of the estimation and control schemes.
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In this work, we investigate the performance of Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) in a feedback control system subject to different Arrival Cost (AC) approximation methods, process uncertainties with non-Gaussian distributions and plant designs. In particular, we investigate the performance of Extended Kalman Filter (EKF) as an AC estimator for large and/or complex applications. Considering the significant impact of state estimations as the initial condition of the NMPC problem, together with the importance of the AC approximation in the success of the MHE framework, it is expected that a poor approximation of the AC may lead to poor closed-loop performance. Different arrival cost estimation methods including the traditional EKF and constrained Particle Filtering were evaluated in this work. The closed-loop framework was tested on two industrial applications: a wastewater treatment plant and a high impact polystyrene process. An error analysis on the convergence of the EKF-based AC estimator is presented in this work to provide insights on the performance of EKF as AC estimator under different scenarios. The results show that an appropriate arrival cost estimation method such as EKF is adequate to maintain the operation of large and challenging systems in closed-loop using an MHE-NMPC framework.
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A non-integer order model is developed for a biological wastewater treatment plant (WWTP) represented in benchmark simulation model (BSM1) scenario. In BSM1 framework, sufficient nitrification is maintained by providing a constant aeration flow rate, by controlling the dissolved oxygen (DO) in the fifth reactor at pre-selected set-point. The nitrates in the anoxic reactor-2 are controlled by manipulating internal recirculation flow. Initially, a state-space model for both the loops is developed by varying both the manipulated variables by ± 10% around the nominal operating point. Then, fractional order models are developed for both the loops around the operating point. After obtaining the fractional order model, fractional IMC-PID-based controllers are designed for controlling the WWTPs. The designed fractional order simple PI controllers provided improved plant as well as controller performance when compared to the traditional integer order PI controllers.
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In this research, hierarchical control strategies are developed for biological wastewater treatment plants (WWTPs) to reduce the operational expenses. The benchmark simulation model no. 1 (BSM1) is used as the working platform which is developed based on activated sludge model no. 1 (ASM1) to control dissolved oxygen in aerobic reactors and nitrate levels in anoxic reactors. Fractional PI (FPI) controllers are designed at the lower level and model predictive control (MPC) and a fuzzy controller are designed at higher level in order to achieve enhanced set-point tracking. Initially, linear state space model is developed around the operating point using prediction-error method for lower level. For identification of the higher level model, the lower level control loop is closed in a feedback sense with the designed controller. Based on the identified model in the higher level, the controllers are designed. This paper presents two combinations of hierarchical control strategies: FPI-Fuzzy and FPI-MPC. It is observed that FPI controller at the lower level and MPC controller at the higher level results in better plant performance with better set-point tracking with reduced operational costs. It is observed that FPI-Fuzzy control strategy results in better EQI of 7041.7 for storm weather condition which also resulted in 48% reduction in total nitrogen violations and FPI-MPC results in better OCI of 17119.6 and this strategy resulted in nearly 38% reduction in total nitrogen violations. A significant improvement in the plant performance is observed for dry and rain weather conditions as well.
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Waste water treatment process (WWTP) control has been attracting more and more attention. However, various undesired factors, such as disturbance, uncertainties, and strong nonlinear couplings, propose big challenges to the control of a WWTP. In order to improve the control performance of the closed-loop system and guarantee the discharge requirements of the effluent quality, rather than take the model dependent control approaches, an active disturbance rejection control (ADRC) is utilized. Based on the control signal and system output, a phase optimized ADRC is designed to control the dissolved oxygen and nitrate concentration in a WWTP. The phase advantage of the phase optimized extended state observer (POESO), convergence of the POESO, and stability of the closed-loop system are analyzed from the theoretical point of view. Finally, a commonly accepted benchmark simulation model no. 1. (BSM1) is utilized to test the POESO and POADRC. Linear active disturbance rejection control (LADRC) and the suggested proportion-integration (PI) control are taken to make a comparative research. Both system responses and performance index values confirm the advantage of the POADRC over the LADRC and the suggested PI control. Numerical results show that, as a result of the leading phase of the total disturbance estimation, the POESO based POADRC is an effective and promising way to control the dissolved oxygen and nitrate concentration so as to ensure the effluent quality of a WWTP.
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The wastewater treatment is an important avenue of resources cyclic utilization when coping with the modern urban diseases. However, there always exist obvious nonlinearities and uncertainties within wastewater treatment systems, such that it is difficult to accomplish proper optimization objectives towards these complex unknown platforms. In this paper, a data-driven iterative adaptive critic (IAC) strategy is developed to address the nonlinear optimal control problem. The iterative algorithm is constructed with a general framework, followed by convergence analysis and neural network implementation. Remarkably, the derived IAC control policy with an additional steady control input is also applied to a typical wastewater treatment plant, rendering that the dissolved oxygen concentration and the nitrate level are maintained at desired setting points. When compared with the incremental proportional-integral-derivative method, it is found that faster response and less oscillation can be obtained during the IAC control process.
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Anaerobic absorption is a sequence of processes by which microbes separate decomposable wastes without oxygen. They convert the decomposable wastes in the form of carbon dioxide and water. Due to the nonlinear nature of the anaerobic process several control strategies are used to adjust the manipulated inputs to maintain the process outputs at desired set points. The objective of this work is to achieve the desired sludge temperature level of the anaerobic digester using PI (Proportional Integral), PID (Proportional-Integral-Derivative) Control and Fuzzy Logic Control (FLC). The comparative study indicates that the Fuzzy Logic Control is enhanced over conventional PID Control. The designed system can also be implemented using BFOA (Bacterial Foraging Optimization Algorithm) to optimize the operation and in tuning of PID controller parameters for an anaerobic digester.
Chapter
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This chapter is focused on the development and implementation of a distributed and hierarchized control system for the wastewa ter treatment plant (WTP) Calafat, Roma- nia. The primary control loops for both treatment lines (water and activated sludge) are developed and analyzed. Also, the distribut ed control system (DCS) architecture of the wastewater treatment plant is presented, and the advantages of the proposed control structure are highlighted. In order to increase the performance of the overall control system, some advanced control solutions are investigated. More precisely, multivari- able adaptive and robust control algorithms are proposed for the activated sludge bioprocess. Several realistic simulation exp eriments are performed, and the obtained results are analyzed.
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Control of the activated sludge process (ASP) is a challenging problem due to the complexity of the biological and chemical reactions, and large variations in the influent flow. Herein, the ASP is presented as a linear parametric varying (LPV) model to account for the parameter changes in the system dynamics. Since the influent flow is the main source of parametric uncertainty but is variable and easily measured, it is chosen as the scheduling parameter. Based on this LPV model with a scheduling parameter, a robust gain-scheduled controller is synthesized to deal with the large uncertainties and neglected dynamic behaviors of the process. Accordingly, the dynamic performance of the controller changes with respect to the scheduling parameter. The ultimate goal is to regulate the dissolved oxygen (DO) concentration and control the biomass concentration. The extensive simulation results show that the robust controller can effectively deal with large uncertainties and unavoidable sensor noises for both linear and nonlinear ASP models, which helps to improve the system performance and saves energy. This study contributes a potential control approach for more robust wastewater treatment plants, which is currently an active research area.
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The main scope of this paper is the proposal of a new single layer Nonlinear Economic Closed-Loop Generalized Predictive Control (NECLGPC) as an efficient advanced control technique for improving economics in the operation of nonlinear plants. Instead of the classic dual-mode MPC (model predictive controller) schemes, where the terminal control law defined in the terminal region is obtained offline solving a linear quadratic regulator problem, here the terminal control law in the NECLGPC is determined online by an unconstrained Nonlinear Generalized Predictive Control (NGPC). In order to make the optimization problem more tractable two considerations have been made in the present work. Firstly, the prediction model consisting of a nonlinear phenomenological model of the plant is expressed with linear structure and state dependent matrices. Secondly, instead of including the nonlinear economic cost in the objective function, an approximation of the reduced gradient of the economic function is used. These assumptions allow us to design an economic unconstrained nonlinear GPC analytically and to state the NECLGPC allow for the design of an economic problem as a QP (Quadratic Programing) problem each sampling time. Four controllers based on GPC that differ in designs and structures are compared with the proposed control technique in terms of process performance and energy costs. Particularly, the methodology is implemented in the N-Removal process of a Wastewater Treatment Plant (WWTP) and the results prove the efficiency of the method and that it can be used profitably in practical cases.
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A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent "time" variables, namely, the run time during a batch and the batch index. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This chapter describes the various control and optimization strategies available for the operation of batch processes. These include online and run-to-run control on the one hand, and repeated numerical optimization and optimizing control on the other. Several case studies are presented to illustrate the various approaches.
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This paper addresses the implementation of economic-oriented model predictive controllers for the dynamic real-time optimization of the operation of wastewater treatment plants (WWTP). Both the economic-optimizing controller (pure-EMPC) and the economic-oriented tracking controller (Hybrid-EMPC, or HEMPC) formulations are validated in the benchmark simulation model (BSM1) platform that represents the behavior of a characteristic activated sludge process. The objective of the controllers is to ensure the appropriate operation of the plant, while minimizing the energy consumption and the fines for violations of the limits of the ammonia concentration in the effluent along the full operating period. A non-linear reduced model of the activated sludge process is used for predictions to obtain a reasonable computing effort, and techniques to deal with model-plant mismatch are incorporated in the controller algorithm. Different designs and structures are compared in terms of process performance and energy costs, which show that the implementation of the proposed control technique can produce significant economic and environmental benefits, depending on the desired performance criteria.
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Aeration of the biological reactor in wastewater treatment plants (WWTPs) represents one of the major cost items, which may account for more than 50% of the total energy consumption. Therefore, airflow rate must be supplied based on the real needs of the biological reactions and the goals to be achieved in terms of removal efficiency and effluent quality. Among the different strategies available to optimize energy consumption of air supply, the Oxy Fuzzy logic and oxidation reduction potential (ORP)-based control systems have proven to be efficient and reliable. The present study compares the effects of these two control systems in terms of energy consumption and efficiency of COD and ammonia oxidation in the activated sludge reactors of two WWTPs for domestic sewage. Both systems allowed to largely comply with the limits set on the effluent for COD and ammonia in spite of the dynamic pattern of the influent load. The Oxy Fuzzy system led to reducing energy consumption by 13% while the ORP control system only by 2%, as average per year. The Oxy Fuzzy system showed higher flexibility, being more capable of adapting the set-points in relation to the influent load. The ORP system seemed to be more suitable for plants where the influent load does not change significantly: the set-points are fixed and the input load can be properly managed only for limited variations.
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The paper presents the design of the integrated control system applied to Sequencing Batch Reactor (SBR) in a biological Wastewater Treatment Plant (WWTP) in Swarzewo, which operates under activated sludge technology. Based on the real data records, ASM2d biological processes model and aeration system model, hierarchical control system for dissolved oxygen tracking and cycle management is designed. Internal Model Controller (IMC) was applied to control the air flow at the lower control level. Higher level dissolved oxygen controller is based on Direct Model Reference Adaptive Control (DMRAC) method. The supervisory system performs management of reactor work cycle, determines the phase length, controls sludge age, calculates setpoint of dissolved oxygen and adapts parameters of the lower control layer. Proposed control system allowed to: increase the efficiency, improve the quality of outflow and reduce the cost of aeration and chemical treatment plant, in relation to existing solutions in case study plant.
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Wastewater treatment involves removing nutrients from wastewater before discharging it to water courses. Many technologies have been developed for this purpose, such as the activated sludge process and others. This process is a biological method performed by a mixed community of microorganisms in an aerobic aquatic environment. These microorganisms derive energy from carbonaceous organic matter in aerated wastewater for the production of new cells in a process known as synthesis, while simultaneously releasing energy through the conversion of this organic matter into compounds that contain lower energy, such as carbon dioxide and water, in a process called respiration. As well, a variable number of microorganisms in the system obtain energy by converting ammonia nitrogen to nitrate nitrogen in a process termed nitrification. This consortium of microorganisms, the biological component of the process, is known collectively as activated sludge. EBPR (Enhanced biological phosphorus removal) is a wastewater treatment configuration applied to activated sludge systems for the removal of phosphate. The common element in EBPR implementations is the presence of an anaerobic stage (nitrate and oxygen are absent) prior to the aeration stage. Under these conditions a group of heterotrophic bacteria, called polyphosphate-accumulating organisms (PAO) are selectively enriched in the bacterial community within the activated sludge. These bacteria accumulate large quantities of polyphosphate within their cells and the removal of phosphorus is said to be enhanced (R. G. Benedict et al. 1971). SBR (Sequencing batch reactors) is a fill-and-draw activated sludge system which is used in most lab-scale systems in order to enrich the sludge with PAO. The management of these reactors is mostly based on off-line measurements such as volatile fatty acids and phosphorus. However, off-line monitoring of the SBR cycle implies low frequency data sampling and delay between sampling and availability of the results. This is an obstacle for a proper process monitoring and makes difficult the application of control strategies to the process. For this reason, the on-line monitoring of the SBR cycle would improve the daily process management, as well as facilitate the "on-line" detection of abnormal situations and the implementation of new control strategies. Moreover the control of these processes has become more complex and the demand on continuous monitoring has increased. This may cause difficulties if this is handled manually and an online control system should make the process more controllable and less complex. In recent years there has been a lack of a proper sensor which can be used for on-line real time monitoring. This paper will review the available technologies for online monitoring outlining their advantages and disadvantages. The paper will also present current developments at Liverpool John Moores University with regards to the development of such a sensor and investigate the possibility of applying it to in-situ applications. In addition, the use of microwave technology is investigated for enhancing sensor performance.
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The paper deals with new trends in research, development and applications of advanced control methods and structures based on the principles of optimality, robustness and intelligence. Present trends in the complex process control design demand an increasing degree of integration of numerical mathematics, control engineering methods, new control structures based of distribution, embedded network control structure and new information and communication technologies. Furthermore, increasing problems with interactions, process non-linearities, operating constraints, time delays, uncertainties, and significant dead-times consequently lead to the necessity to develop more sophisticated control strategies. Advanced control methods and new distributed embedded control structures represent the most effective tools for realizing high performance of many technological processes. Main ideas covered in this paper are motivated namely by the development of new advanced control engineering methods (predictive, hybrid predictive, optimal, adaptive, robust, fuzzy logic, and neural network) and new possibilities of their SW and HW realizations and successful implementation in industry.
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The paper deals with the robust control of the dissolved oxygen concentration in the case of wastewater treatment processes. The Gain Scheduling control method has been chosen for the dissolved oxygen concentration control. Firstly the dissolved oxygen control loop was experimental identified in various operating points. The Gain Scheduling strategy was designed so that the behaviour of the closed loop system to be the same around the mentioned operating points. The Gain Scheduling control strategy of the dissolved oxygen concentration was tested by simulation and using an experimentally pilot plant that is also presented in the paper.
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Two control strategies, relying on different principles, are used in this paper for improving the performance of an Activated Sludge Process (ASP): gain scheduling PI control (GS-PI) and model predictive control (MPC). Among the numerous existing control strategy, PI control and MPC control are the most frequently ones used successfully in industrial applications. The ASP is described by a nonlinear multivariable model with two inputs and two outputs. The main objective is to obtain a substrate concentration in the effluent within the standard limits established by legislation on wastewater treatment, especially the strict EU Guideline Urban Wastewater Directive 91/271/EEC. This goal is achieved by controlling the dissolved oxygen (DO) concentration to setpoint values established by preliminary tests. Simulations are carried out on the nonlinear model to show the effectiveness of GS-PI and MPC control methods. The contribution of the paper can be summarized to the fact that the effluent substrate concentration is controlled through two different methods without using measurements of the substrate, but only measurements of the DO concentration. This is more reliable and less expensive. Additionally, the performance of wastewater treatment process is analyzed in terms of energy efficiency. This is done by considering the volume of treated water in relation to the consumed electricity.
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More stringent effluent and cost requirements are increasing the need for better control of wastewater treatment plants. In an activated sludge process, the nitrogen removal efficiency may be improved by adding an external carbon source. In this paper, automatic control of the nitrate level by regulating external carbon flow is discussed. More specifically, an iterative tuning procedure for the controller is outlined. Iterative controller design schemes aim at tuning high performance controllers of low complexity using closed loop data. The basic strategy used in this paper is an iterative pole placement controller design procedure. The suggested approach is compared with conventional design in a simulation study.
Article
During the last 20 years, control of wastewater treatment plants has developed from very simple to advanced computer control methods based on on-line measurements. Fortunately, both on-line equipment and computer system technology are still developing fast and have become applicable at WWTPs. The use of on-line measurements for real time control is advantageous at most plants over 15.000 personal equivalent performing biological nutrient removal. Control strategies have demonstrated their efficiency at several Scandinavian wastewater treatment plants. Experience shows that the applied control strategies are efficient and robust for practical implementation, provide savings in energy and chemical consumption and decreases the nitrogen content in the effluent. Experience from a recirculating BNR plant using the STAR concept for advanced real time control is described. The on-line nutrient measurements and excitation of the processes through control strategies have improved the process understanding. Control has reduced the consumption of energy by about 30% and eliminated the need for external carbon addition, as well as improved the effluent quality of total nitrogen from the plant. The new measurements and data handling methods will make it possible to perform dynamic identification of activity in recirculating plants (Nielsen et.al., 1994), and hence give information as to which control strategies improve the biomass activity or favour desired cultures of micro-organisms.
Article
Nine NH4 automatic analysers or monitors were tested in June-July 1995 (among them 2 prototypes): - 5 based on ion electrode; ABB, Applikon, Contronic, Hydro-Environnement, STIP, - 4 based on colorimetry; Danfoss, Data Link (UV absorption), Meerestechnik, Skalar Laboratory tests are aimed to determine response time, repeatability, response linearity, short-term stability, influence of various factors on the measurement. The field test relates to real conditions: all the sensors were installed in parallel at the discharge point of a Wastewater treatment plant (WWTP). Recorded outputs were compared with conventional laboratory analysis of average hourly samples. Response time range from 2 to 21 minutes. Repeatability varies from 1 to 10%, stability from 1 to 17%. Linearity is always good and detection limits (about 0.2 mg/l) do not seem to be critical for use in a WWTP. Among factors of influence, power voltage has limited effect, sample temperature is affecting some monitors, chemical interferents have nearly no effect excepted for one monitor. Field tests have shown that NH4 monitors are still very sensitive and that installation is crucial. Each monitor suffered several failures, some of them required high maintenance and used costly reagents.
Article
Sensor prototypes for measurement of ammonium, nitrate and phosphate in wastewater treatment plants are described together with the results obtained in laboratory and pilot scale wastewater treatment plants. A functional description of the sensor principles is presented together with the installation and operation procedures. Basically the measurements are done using membrane technology in combination with semi-micro Continuous Flow Analysis (μCFA) with classic colorimetry. Because of this the sensors can be installed directly in the aeration tanks without any need for sampling, filtration, etc. Furthermore, the semi-micro scale is used in such a way, that handling of chemicals and waste is a closed loop in a package to be changed once a month. The sensors have been tested thoroughly in a pilot scale waste water treatment plant (recirculation) using real raw wastewater as well as artificial wastewater. The sensors have been placed directly in the aeration tank or in the anoxic tank of the pilot plant. The tests show very little, if any, fouling problems due to the membrane material used. The test results show a good reproducibility and most important, compared to other available sensors/analyzers on the market, very low response times, less than 5 minutes. Owing to these low response times, experiments with direct measurement of nitrification and denitrification rates were carried out.
Article
Reverse Osmosis (RO) processes are readily used for removing pollutants, such as dimethylphenol from wastewater. A number of operating parameters must be controlled within the process constraints to achieve an efficient removal of such pollutants. Understanding the process dynamics is absolutely essential and is a pre-step for designing any effective controllers for any process. In this work, a detailed distributed two-dimensional dynamic (x and y dimensions and time) model for a spiral-wound RO process is developed extending the 2-D steady state model of the authors published earlier. The model is used to capture the dynamics of the RO process for the removal of dimethylphenol from wastewater. The performance of the 2-D model is compared with that obtained using 1-D dynamic model before the model is being used to investigate the performance of the RO process for a range of operating conditions.
Article
The application of control strategies is increasingly used in wastewater treatment plants with the aim of improving effluent quality and reducing operating costs. Due to concerns about the progressive growth of greenhouse gas emissions (GHG), these are also currently being evaluated in wastewater treatment plants. The present article proposes a fuzzy controller for plant-wide control of the biological wastewater treatment process. Its design is based on 14 inputs and 6 outputs in order to reduce GHG emissions, nutrient concentration in the effluent and operational costs. The article explains and shows the effect of each one of the inputs and outputs of the fuzzy controller, as well as the relationship between them. Benchmark Simulation Model no 2 Gas is used for testing the proposed control strategy. The results of simulation results show that the fuzzy controller is able to reduce GHG emissions while improving, at the same time, the common criteria of effluent quality and operational costs.
Article
In this paper, an improved multiobjective optimal control (MOOC) strategy is developed to improve the operational efficiency, satisfy the effluent quality (EQ) and reduce the energy consumption (EC) in wastewater treatment process (WWTP). First, the adaptive kernel function models of the process, which can describe the complex dynamics of EQ and EC, are developed for the proposed MOOC strategy. Meanwhile, a multiobjective optimization problem is constituted to account for WWTP. Second, an improved multiobjective particle swarm optimization (MOPSO) algorithm, using a self-adaptive flight parameters mechanism and a multiobjective gradient (MOG) method, is designed to minimize the established objectives. And then the optimal set-points of dissolved oxygen (SO) and nitrate (SNO) are obtained in the treatment process. Third, an adaptive fuzzy neural network controller (FNNC) is applied for realizing the tracking control of the obtained set-points in the proposed MOOC strategy. Finally, Benchmark Simulation Model No.1 (BSM1) is introduced to evaluate the effectiveness of the proposed MOOC strategy. Experimental results show the efficacy of the proposed method.
Book
The book is intended to present various examples for reactor and process modeling and control as well as for metabolic flux analysis and metabolic design at an ad­ vanced level. In Part A, General principles and techniques with regard to reactor and process models, process control, and metabolic flux analysis are presented. In addition the accuracy, precision, and reliability of the measured data are discussed which are ex­ tremely important for process modeling and control. A virtual bioreactor system is presented as well, which can be used for the training of students and operators of industrial plants and for the development of advanced automation tools. In Part B, the General principles are applied for particular bioreactor models. It covers the application of the computational fluiddynamic (CFD) technique to stirred tank and bubble column bioreactors. Different solution methods are presented: the Reynolds-averaging of the turbulent Navier-Stokes equations and modeling of the Reynolds stresses with an appropriate turbulence (k-ee) model, and the Euler (two fluid model), as well as the Euler-Langrange approaches.
Article
This paper analyzes the effect of common control actions on the performance of wastewater treatment plants, performance defined by criteria such as: effluent quality, economical cost and greenhouse gas emissions. At the same time the paper proposes to use a quantitative indicator which incorporates all the mentioned performance criteria allowing a more accurate evaluation of the control actions. This study was performed by numerical simulation using a benchmark model that incorporates a dynamic model of the greenhouse gas emissions in all the units of the wastewater treatment plant.
Article
This study investigates an adaptive fuzzy neural network control system for the multiobjective operation of wastewater treatment process (WWTP) with standard effluent quality (EQ) as well as low energy consumption (EC). The control system consists of an optimization module with the adaptive multiobjective differential evolution (AMODE) algorithm and a control module with the adaptive fuzzy neural network (AFNN). First, an AMODE algorithm, using the adaptive adjustment strategies for selecting the suitable scaling factor and crossover rate, is developed to optimize all objectives simultaneously. Then, the optimal set-points of the dissolved oxygen concentration in the fifth tank (S O5) and the nitrogen nitrate concentration in the second anoxic tank (S NO2) of WWTP can be obtained by the AMODE algorithm. Second, an AFNN controller, based on an adaptive second order algorithm, is employed to trace the set-points of S O5 and S NO2 for achieving the process performance. Finally, the proposed control system is applied on the Benchmark Simulation Model 1 (BSM1). The performance comparison with other algorithms indicates that the proposed control system yields better effluent qualities and lower average operation consumption.
Conference Paper
This paper describes the application of an offset free nonlinear model predictive controller (NMPC) to regulate the self-optimizing variables and active constraints in a wastewater treatment plant, particularly the activated sludge process using the benchmark simulation model No. 1 (BSM1). A set of terminal constraints have been added to the NMPC formulation in order to ensure stability. The procedure to find the self-optimizing variables as the best controlled variables in an economic sense, has also been described briefly, considering the most important measurements of the process. This methodology provides the optimum combination of measurements to keep constant with minimum economic loss. The simulation results show good reference tracking for typical average load disturbances.
Article
The on-line monitoring of Chemical oxygen demand (COD) and total phosphorus (TP) restrains wastewater treatment plants to achieve better control of aeration and chemical dosing. In this study, we applied principal components analysis (PCA) to find out significant variables for COD and TP prediction. Multiple regression method applied the variables suggested by PCA to predict influent COD and TP. Moreover, a model of full-scale wastewater treatment plant with moving bed bioreactor (MBBR) and ballasted separation process was developed to simulate the performance of wastewater treatment. The predicted COD and TP data by multiple regression served as model input for dynamic simulation. Besides, the wastewater characteristic of the wastewater treatment plant and MBBR model parameters were given for model calibration. As a result, R(2) of predicted COD and TP versus measured data are 81.6% and 77.2%, respectively. The model output in terms of sludge production and effluent COD based on predicted input data fitted measured data well, which provides possibility to enabled model predictive control of aeration and coagulant dosing in practice. This study provide a feasible and economical approach to overcome monitoring and modelling restrictions that limits model predictive control of wastewater treatment plant.
Conference Paper
Operation of Wastewater Treatment Plants (WWTP) is a really challenging problem from the point of view of being an industry process that does not deliver any turnover. Instead, the costs of operation are always to be faced against the environmental benefits. It is from this point of view that any control and operation formulation that includes economic considerations may provide useful perspectives on this respect. This work proposes the implementation of Dynamic Real Time Optimization (D-RTO) integrating the economic optimization into Non-linear Model Predictive Control (NMPC) techniques for improving the operation of WWTPs.
Article
This paper deals with the design of adaptive nonlinear control algorithms of biological wastewater treatment processes. The control design is based on the dynamical mass balance equations of the process and includes the on-line estimation of uncertain parameters (specific growth rates and yield coefficients). The procedure is illustrated by two examples (activated sludge process, anaerobic digestion).
Chapter
Exothermic, continuous stirred tank reactors (CSTR's) present some of the more challenging chemical process control problems. In this paper an optimization-based approach is used to control exothermic CSTR's. It is shown that an open-loop observer can be used at an open-loop unstable operating point, under certain conditions, although this is not recommended in practice. Process measurements can be used to reset the model states, when there is no plant/model mismatch; performance is poor, however, when there is mismatch. With plant/model mismatch, a nonlinear programming-based process identification scheme is used in combination with NLPC to achieve much tighter performance than is possible without estimation.
Chapter
In this paper we present a nonlinear adaptive approach of L/A type to control fermentation processes. The basic idea is to operate on an elementary nonlinear relation (ratio: set-point/output) instead of a linear one (difference: set point - output) and to use the power of the ratio as a (self)tuning parameter of the control algorithm. Algorithms, able to control biomass and substrate concentrations, are obtained. In a simple way they take into account basic physical limits of the real process (i.e. positivity of control and state variables). Their minimal formulation and tuning simplicity facilitate the application and computer implementation.
Chapter
Fuzzy sets, the foundation of fuzzy control, were introduced thirty years ago, (Zadeh, 1965), as a way of expressing non-probabilistic uncertainties. Since then, fuzzy set theory has developed and found applications in database management, operations analysis, decision support systems, signal processing, data classifications, computer vision, etc. The application area that has attracted most attention is, however, control. In 1974, the first successful application of fuzzy logic to control was reported (Mamdani, 1974). Control of cement kilns was an early industrial application (Holmblad and Østergaard, 1982). Since the first consumer product using fuzzy logic was marketed in 1987, the use of fuzzy control has increased substantially. A number of CAD environments for fuzzy control design have emerged together with VLSI hardware for fast execution. Fuzzy control is being applied industrially in an increasing number of cases, e.g., (Froese, 1993; Hellendoorn, 1993; Bonissone, 1994; Hirota, 1993; Terano et al., 1994).
Article
The paper describes the application of an adaptive algorithm for the monitoring and control of anaerobic digestion. Results are presented on the performance of the model for controlling shock loads applied to laboratory-scale anaerobic filters. Instrumentation and sensors used were on-line conductivity, suspended solids, gas flow and quality. Data are also presented on the steady-state performance of the upflow anaerobic filter for the treatment of dairy and coffee effluent.
Article
The biodegradation of municipal solid waste (MSW) was investigated in simulated bioreactor landfills under aerobic and anaerobic conditions. The bioreactors were operated to determine the quantify of moisture and sludge that would optimize waste degradation. The leachate generated was recycled over 47 and 63 weeks for aerobic and anaerobic bioreactors, respectively. Leachate samples were collected on a weekly basis and analyzed for pH and organic concentration measured in terms of BOD and COD. In addition, the generation of the biogas was monitored during the operation of the bioreactors. Leachate recirculation and sludge addition rate of 650 and 65 mL/kg of waste/d in the aerobic bioreactor proved to be sufficient to enhance the MSW degradation within 31 weeks where COD concentration was below 1000 mg/L. While in the anaerobic bioreactors, at the same rates of leachate recirculation and sludge addition, the MSW was stabilized within 51 weeks. This showed that aerobic degradation, with the addition of air at the rate of 84 L/kg of waste/d, provided greatly enhanced decomposition compare to the degradation under anaerobic operation. Reduction of leachate recirculation and sludge addition to 325 and 100 mL/kg of waste/d increased the stabilization period from 33 to 47 weeks in the aerobic bioreactors. While in the anaerobic bioreactors, after 63 weeks, MSW stabilization was still in progress. These results revealed that addition of supplemental material and air have a positive effect on the rate of biodegradation of MSW.
Article
Biological nitrogen removal in an activated sludge process is obtained by two biological processes; nitrification and denitrification. Denitrifying bacteria need an anoxic environment and access to an organic energy source in order to convert nitrate to nitrous oxide. Often an external carbon source has to be added in order to achieve a high denitrification rate (giving a low effluent nitrogen discharge). Both for process efficiency and economy it is of interest to control the flow rate of external carbon. In this paper, a model based feedback-feedforward controller with measurements of the states is presented for controlling the nitrate concentration using the external carbon flow rate. The main approach is to use exact linearization on a simplified IAWQ Activated Sludge Model no 1. By using some further simplifications, some measurements can be avoided and others easily estimated from the output leading to a more feasible output feedback feedforward controller which still has a similar performance as the feedforward-feedback linearizing controller. Simulation results using the full IAWQ Activated Sludge Model no 1 is used to illustrate the controllers performances.
Article
This paper examines the problem of building a machine which adjusts itself automatically to control an arbitrary dynamic process. The design of a small computer which acts as such a machine is presented in detail. A complete set of equations describing the machine is derived and listed; engineering features of the computer are discussed briefly. This machine represents a new concept in the development of automatic control systems. It should find widespread application in the automation of complex systems such as aircraft or chemical processes, where present methods would be too expensive or time-consuming to apply.
Conference Paper
A switching scheme based on Takagi-Sugeno fuzzy inference system is proposed in this paper to address the problems of poor transient response, rapid oscillation of plant output and long duration of switching time associated with the multiple model predictive control (MMPC) based on hard switching. A set of piecewise linear models is used to represent the system under consideration at different operating regimes. Corresponding MPC local controller is developed for each model. At each instant, the fuzzy switching system selects the appropriate model/controller pair for the system. The proposed MMPC strategy is applied to a coagulation chemical dosing unit for water purification plants. Simulation results of the proposed control scheme are promising and positive. Control of Nonlinear Systems, Modeling and Identification of Nonlinear Systems, Disturbance Rejection
Book
Cette troisième édition a été enrichie par l'introduction de nouveaux exemples et de méthodes récentes. En un volume unique, ce livre propose une synthèse progressive et approfondie des principales méthodes de commande exposées sous forme théorique et illustrées par des exemples variés de procédés : réacteurs chimiques, biologiques, de polymérisation, craqueur catalytique, colonne de distillation. Ces exemples sont détaillés, y compris numériquement, afin que les raisonnements utilisés puissent être vérifiés et repris par le lecteur. Les six parties couvrent : * la modélisation et la commande continue monovariable * la commande multivariable par fonction de transfert * l'identification et la commande en temps discret * la commande optimale et prédictive multivariable * la commande non linéaire * les observateurs d'état. Cet ouvrage s'adresse aussi bien aux étudiants de 2e et 3e cycle qu'aux chercheurs, enseignants et ingénieurs.
Article
The IMC-PID tuning rules reduce the tuning process to the selection of one tuning parameter as opposed to three. In addition, the single parameter is directly related to the closed-loop speed of response and robustness. Process models can be developed directly from open-loop tests or combinations of tests and first-principle equations. Disturbance rejection can be significantly improved for long time-constant-to-deadtime processes by assuming they are deadtime-plus-integrator forms. Finally, the rules are being successfully applied to industrial processes. The first example given illustrated how the application of the rules quickly led to high-quality tuning parameters for a troublesome level control loop. The second illustrated how the rules were applied to obtain very responsive tuning for control of composition for a high-purity distillation column.
Article
An inventory of the European market showed that 12 automatic nitrate analyzers were available using measuring techniques based on ion specific electrodes, colorimetry and UV absorption. Among these analyzers the following six - Data Link, Dr Lange, M.E, Phox, Polymetron, Process Styrning - were put through two series of tests:- metrological characteristics of the instruments,- behaviour in waste water and comparison with analyses of samples in laboratory. The metrological quality of all the analyzers is sufficient, but all the ion specific electrode analyzers showed contamination and rapid drift when used in waste water. The others show greater resistance. UV absorption systems are simpler and quicker to use. They are also less demanding with regard to sample conditioning than colorimetric apparatus.
Article
A manual describing design procedures and guidelines for the selection of aeration equipment and dissolved oxygen (DO) control systems for activated sludge treatment plants is summarized. Aeration methods, equipment, and application techniques are examined and selection procedures presented. Various DO control systems are described with recommendations for system applications to various aeration equipment types and process configurations. Automatic DO control systems are presented for various size hypothetical activated sludge plants. Finally, the economics of manual and automatic DO control systems are explored using data derived from a study of 12 operating activated sludge plants. The conclusion is drawn that the capital and operating cost of automatic DO control systems is justified for activated sludge plants larger than 1 mgd, provided equipment is selected and applied in accordance with the guidelines of the design manual and a power cost equal to or greater than the national average power rate is applicable.
Conference Paper
In this study a model-based control designed for the operation of a solar power plant is discussed. The simplified physically based model of the plant was developed on the basis of the energy balances including the solar insulation as an input, the heat transferred by the flow of the oil as a working media and the overall heat loss from the plant. The outlet temperature of the collector field is the reference/set-point value while the volumetric flow rate of the oil is the manipulating variable to the controller. The solar radiation, the ambient temperature and the inlet oil temperature to the collector field are assumed as the disturbances. The model developed and identified is an internal part of the controller. The initial control experimental results are also presented.
Conference Paper
The paper addresses design, implementation and simulation of a novel type of softly switched Takagi-Sugeno fuzzy PI control system for dissolved oxygen concentration (DO) tracking at wastewater treatment plant (WWTP). The proposed control system is designed, including tuning the PI controllers, entirely based on the experimental data. This control system is validated by simulation. Copyright © 2008 IFAC
Article
The current study endeavors to put together evolution, developments, and future prospects of internal model control strategy. Due efforts have been made to include all the techniques and philosophies used to that end. The brief account of the most recent techniques for near optimal and robust control has been provided along with a summary of conventional control schemes. At the same time, more emphasis is given on the issues in IMC design for single-input, single-output systems, like quantitative filter selection and tuning guidelines, modified structures, and related aspects of contemporary research developments. A set of references of all concerned papers, and a brief summary in them is provided. This paper also presents the results of these techniques as provided in the respective references. Illustrative example is given to show the effectiveness and the merits of the IMC based various forms.
Conference Paper
This paper proposes an adaptive neural network control for an activated sludge bioreactor used for waste-water treatment. The novel method prevents weight drift and associated bursting when a persistent disturbance affects the system, without sacrificing performance - unlike traditional e-modification. The neural adaptive method outperforms two types of PI controllers when tracking arbitrary set points, of organic substrate and dissolved oxygen, when appropriate feedforward terms are unknown. The method also outperforms a feedback linearizing controller using model parameter estimates when an observer is used to provide an estimate of unmeasured substrate concentration.
Article
Most reported applications of model-predictive control (MFC) have a narrow scope, with 1-5 variables to be regulated and a comparable number of manipulated variables. Several authors have claimed that global-optimization versions of MPC (such as DMC and QDMC) should be more useful for problems in which an entire system can be operated to achieve an economic and/or technical objective. In this paper, we describe the application of MPC to a large-scale, constraint-dominated problem: the minimization of combined-sewer overflows (CSOs) in the Seattle metropolitan area. The key decision variables are flowrates at 23 locations throughout the sewer network. There are approximately 40 output variables that must be kept between lower and upper bounds. The main issues addressed in the application are: (1) definition of an appropriate objective function for on-line optimization; (2) creation and maintenance of complex system models; and (3) use of state estimation to minimize the impact of disturbances and model errors. MPC performance is compared with that of an existing heuristic (rule-based) control strategy for seven design storms, selected from historical records. A realistic, nonlinear simulation of the sewer system acts as the plant. MPC reduces CSOs by 26% (on a yearly basis) relative to the existing control strategy. This was sufficient incentive for the sewer agency to replace their heuristic control strategy with MPC.
One of the most important objectives of a Wastewater Treatment Plant (WWTP) is to protect the water environment from negative effects produced by residual water, controlling the maximum concentration of pernicious substances. This paper presents the design of a two loops control strategy, based on the Quantitative Feedback Theory (QFT), and their implementation on the Activated Sludge Wastewater Treatment Plant of Crispijana (Vitoria, Spain), with a Nitrification-Denitrification (D-N) configuration. The overall control objective is to minimize simultaneously the effluent nitrogen compounds (Ammonia-SNH- and Nitrates-SNO-) discharged from the plant, in spite of disturbances. Nitrification is the bacterial oxidation from ammonia to nitrates by nitrificant bacteria. On the other hand, Denitrification is the process that reduces nitrates to gas compounds of nitrogen by micro-organisms which use these components instead of oxygen in the respiration process when oxygen falls short. The biological reactor presents two zones. The first one (D zone) has no aeration system. It must eliminate the organic material in the influent water using the nitrates as an oxidising agent (Denitrification). The second zone (N zone) is aerated and eliminates the rest of the organic material and the ammonia (Nitrification). The plant configuration needs an internal recycle to support the Denitrification process. This recycle supplies nitrates from the nitrification stage to the denitrification zone.