The Activated Sludge Model No. 1 (ASM1) considers that nitrification and denitrification are single step processes and nitrite nitrogen (NO2–N), which is an intermediate for the two processes, is not accounted for. The first part of this paper presents the development of an enhanced ASM1 with two step nitrification/denitrification processes and its implementation in the Benchmark Simulation Model No.1 wastewater treatment plant (WWTP). The secondary settler was considered to be reactive in order to achieve a better fit between the simulation model and the behavior of the real WWTP. The second part presents the investigation of Model Predictive Control approach for the advanced control of the WWTP. Two control strategies are implemented for the wastewater treatment plant and they are analyzed from the perspective of the benefits brought to the WWTP operation. The proposed control strategy shows a reduction of the operational costs and the improvement of the effluent quality index.
"The high capital and operational costs associated to these facilities (Vanrolleghem et al., 1996; Liu et al., 2011; Rodriguez-Garcia et al., 2011) have fostered the use of simulation models to optimise their performance , and in this sense, the ASM family models (Henze et al., 2000) have become a standard. In the last years, many publications have illustrated the usefulness of simulation models for WWTP design (Bixio et al., 2002; Benedetti et al., 2010; Rivas et al., 2008); operation (Ostace et al., 2011) and control (Ayesa et al., 2006; Nopens et al., 2010; Flores-Alsina et al., 2008; Yong et al., 2006). "
[Show abstract][Hide abstract] ABSTRACT: This paper makes a critical review of the available techniques for analysing, completing and generating influent data for WWTP modelling. The solutions found in literature are classified according to three different situations from engineering practice: 1) completing an incomplete dataset about the quantity and quality of the influent wastewater; 2) translating the common quality measurements (COD, TSS, TKN, etc.) into the ASM family components (fractionation problem); 3) characterising the uncertainty in the quality and quantity of the influent wastewater. In the first case (Situation 1), generators based on Fourier models are very useful to describe the daily and weekly wastewater patterns. Another specially promising solution is related to the construction of phenomenological models that provide wastewater influent profiles in accordance with data about the catchment properties (number of inhabitant equivalents, sewer network, type of industries, rainfall and temperature profiles, etc.). This option has the advantage that using hypothetical catchment characteristics (other climate, sewer network, etc.) the modeller is able to extrapolate and generate influent data for WWTPs in other scenarios. With a much lower modelling effort, the generators based on the use of databases can provide realistic influent profiles based on the patterns observed. With regard to the influent characterisation (Situation 2), the WWTP modelling protocols summarise well established methodologies to translate the common measurements (COD, TSS, TKN, etc.) into ASM family components. Finally, some statistical models based on autoregressive functions are suitable to represent the uncertainty involved in influent data profiles (Situation 3). However, more fundamental research should be carried out to model the uncertainty involved in the underlying mechanisms related to the wastewater generation (rainfall profiles, household and industries pollutant discharges, assumed daily and weekly patterns, etc.). (C) 2014 Published by Elsevier Ltd.
"The current state of biological wastewater treatment modeling consists in the Activated Sludge Model Suite which includes the Activated Sludge Model No. 1, 2, 2d and 3 (ASM1, ASM2, ASM2d, ASM3)    . These models have suffered extensions over the last decade           . "
[Show abstract][Hide abstract] ABSTRACT: The first part of this paper presents the development of a 4-stage Bardenpho wastewater treatment plant simulator model. The simulator model is an adaptation of the Benchmark Simulation Model No. 1 for the 4-stage Bardenpho process, and it consists of seven reactor arranged in series followed by a secondary settler. The bio-kinetic model used to describe the biological processes in the reactors is a modified Activated Sludge Model No. 3 that considers parallel growth of the heterotrophic biomass on both biodegradable substrate and internal storage products. The secondary settler was considered to be reactive and the biological processes that occur in the settler were accounted for. The second part of the paper focuses on the analysis of the benefits that decentralized control has on the operation of the 4-stage Bardenpho process. Four control strategies of the 4-stage Bardenpho are analyzed from a control performance, operational costs and effluent quality perspective. The control strategies are based on PI controller designed using the Internal Model Control principle. In order to make the simulation more realistic the sensors and reactors were not considered being ideal and their complex behavior was considered in the simulator model.
16th International Conference on System Theory, Control and Computing (ICSTCC 2012); 10/2012
"However, an advanced control algorithms may highly beneficial when the plant is strongly loaded. In addition, concentration on minimizing the operational cost in wastewater system was addressed too in (Ostace et al., 2011). "
[Show abstract][Hide abstract] ABSTRACT: The objective of the current study is to investigate various control strategies implemented
to wastewater treatment plants. The paper starts with discussion in modeling part of wastewater system
and continues with designation of control objectives and control parameters. Subsequently, the
implementations of common control structures including feedback, feedforward-feedback, supervisory
and hierarchical controls are explained. The study is exclusively emphasized on four control
techniques. Model predictive control performs superior control in optimizing nitrogen removal based
on predictions of future behavior of wastewater systems. The performances of PID control in dissolve
oxygen and nitrate control is improved significantly with multivariable configuration. Similar results
achieved by data-driven approach compared to default PI simulation. Finally, artificial neural networks
are commonly suggested for modeling and prediction purposes. A study is emphasized on Benchmark
Simulation Model No. 1. The paper serve as a reference and for future research improvements in
developing new advanced control techniques for wastewater field that aims in achieving stringent
effluent quality standards.
Key words: Wastewater treatment plant, control strategies, BSM1 benchmark.
Australian Journal of Basic and Applied Sciences 08/2011; 5(8):446-455.
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