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Chemical Process Control: An Introduction to Theory and Practice / G. Stephanopoulos.
... In states that 98% of control loops in pulp and paper industrials are controller by (Single Input Single Output) SISO PI controllers and that, in process control application, more than 95% of the controllers are of PID type [2]. ...
... Time response for three rules tuning method group (Time response for three rules tuning method group (Frequency response for three rules tuning method group (1) model(2) .38) Frequency response for three rules tuning method group (2) model(2)xi # ofFigure Page ...
... .32): Time response for three rules tuning method group (1) model(2) ...
Modeling, simulation of first order systems and different tuning methods are
represented in this project. The first part of the project is concentrated on
modeling and simulation of different physical system with first order model.
Matlab simulation package is used to simulate and test different first order models.
Response of different first order systems with different parameters are given in
time domain and frequency domain.
The second part of the project is concentrated on PID controller. A summary of
different tuning rules for the PI and PID controller of single input single output
(SISO) are given. A bout 63 different rules of tuning are given. Thus rules are used
to design PI and PID controller of two industrial process with first order with time
delay.
These process are linear model of distillation column. Time domain and frequency
domain data comparison the rules are tabled. The simulation study is completed
using Matlab simulation package.
The simulation results shows the difference control system time domain and
frequency domain performance to find different PID tuning rules.
tuning using PID controller in industrial system has been carried out on flow
process model 3502. The PID controller is used to controller water flow rate of
single tank system.
... For the average adsorbed phase concentration one can proceed in the same manner starting from Eq. 21 or, remembering that the transfer function is also the derivative with respect to time of the solution to a step input [18], one can use the dimensionless column mass balance, Eq. 11, to obtain more easily ...
... The time domain expression for the transfer function is the solution for the case where the inlet concentration is a pure pulse, i.e. the Dirac delta function [18]. To obtain the solution for the desorption curve after equilibration one can take the integral of this solution, Eq. ...
... To obtain the concentration profiles one can proceed from the transfer function of the adsorbed phase concentration relative to the concentration at the surface. [18] ...
Mass transport in nanoporous materials is a key property that allows to improve the performance of many gas separation processes and design more efficient heterogeneous catalytic reactors. In many instances a combination of surface resistance and internal diffusion are present. The combined model for surface barrier and diffusion in a ZLC system is discussed in detail and the analytical solutions valid for the traditional and the partial loading experiments have been derived for the spherical and slab geometries. The model reduces to the limiting forms of pure diffusion when , and pure surface barrier when . This study has shown that most literature studies have analysed ZLC responses incorrectly based on an effective combined dimensionless parameter. Two methods are described to obtain the parameters from the long-time asymptotic behaviour of the response curves. Both approaches have been demonstrated on curves generated from the full model solution and experimental data on an etched sample of Y zeolite. Both the analysis of the model and of the experimental results confirm that to characterize combined surface barriers and diffusion one should perform at least experiments at two different flowrates where the system is kinetically controlled, and crucially a partial loading experiment with a time to the switch which should be at least an order of magnitude smaller than the smallest of the diffusion and surface barrier times.
... At every cycle, such error ϵ is then elaborated through the characteristic proportional-integralderivative (PID) functions that define a signal, i.e., current i, to send to the final control element, i.e., the Peltier module. In this way, the cooling power of the Peltier module is modified to accommodate the increase in the temperature generated by the disturbance [28]. A schematic of the feedback control loop of the temperature control system is given in Figure 1. ...
... The proportional action actuates an output proportional to the error. The higher the gain K p is, the higher will be the sensitivity of the signal to the deviation of the error [28]. The integral action instead causes the controller to repeat the proportional action with a period τ i . ...
... The integral action instead causes the controller to repeat the proportional action with a period τ i . Such action is acting until an error is generated, resulting in the removal of small errors [28]. By contrast, the derivative action is capable of predicting the value of the error in the immediate future working as anticipatory control [28]. ...
Acoustofluidics is an emerging research field wherein either mixing or (bio)-particle separation is conducted. High-power acoustic streaming can produce more intense and rapid flow patterns, leading to faster and more efficient liquid mixing. However, without cooling, the temperature of the piezoelectric element that is used to supply acoustic power to the fluid could rise above 50% of the Curie point of the piezomaterial, thereby accelerating its aging degradation. In addition, the supply of excessive heat to a liquid may lead to irreproducible streaming effects and gas bubble formation. To control these phenomena, in this paper, we present a feedback temperature control system integrated into an acoustofluidic setup using bulk acoustic waves (BAWs) to elevate mass transfer and manipulation of particles. The system performance was tested by measuring mixing efficiency and determining the average velocity magnitude of acoustic streaming. The results show that the integrated temperature control system keeps the temperature at the set point even at high acoustic powers and improves the reproducibility of the acoustofluidic setup performance when the applied voltage is as high as 200 V.
... Due to the non-isothermal nature of the reactor, the process dynamics are anticipated to be nonlinear, making it challenging to measure the conversion of the reactants. As a result, we aim to establish a control system for the reactor's conversion utilizing inferential control methodology for unmeasured quantities (George Stephanopoulos, 1984), and adaptive gain scheduling control techniques to mitigate non-linearity challenges (Shamma, 1988). ...
... The inferential control approach has been proven to be effective for controlling unmeasured quantities in various processes (George Stephanopoulos, 1984). Moreover, the adaptive gain scheduling control method enables the controller to dynamically adjust the control parameters based on changes in operating conditions (Shamma, 1988). ...
... In Fig. 7, τ represents the time constant. For first-order processes, after 5τ the system reaches steady state (Seborg et al., 2017;Coughanowr and LeBlanc, 2009;George Stephanopoulos, 1984). The coded values for the interactions between Factors 1 and 2 are represented in Fig. 8 (Montgomery, 2017). ...
In the large-scale production of Diethyl Oxalate (DEO), numerous control and optimization challenges are faced. These issues are targeted in this study by implementing a gain scheduling approach to control a catalytic fixed bed multi-tubular reactor designed for DEO production. A novel inferential modeling method, developed using Python and response surface methodology, was applied to simulate the dynamics of the reactor, and the inlet coolant temperature was identified as the most influential parameter. An inferential control system was implemented that utilized gain scheduling for Proportional Integral Derivative (PID)
controllers. The results showed that the proposed PID controller gains offered fast and stable responses, even at high conversion setpoints. However, minimization of response overshoot at
high amplitudes of step changes proved challenging, leading to a decrease in conversion estimator accuracy. The use of a quadratic model from response surface methodology for the
regression of the inferential estimator was proposed, showing improved prediction accuracy over the linear model. These findings provide valuable insights and guide future optimization in the design and operation of reactors for large-scale DEO production.
... O reator químico é o centro dos processos químicos sendo essencial no qual a alimentação é convertida nos produtos desejados e é o local do processo onde se possui maior valor agregado (os alimentos de menor valor são convertidos em produtos de maior valor), seu projeto e operação determinam o sucesso ou o fracasso de todo o processo. No processo geral, as matérias-primas são entregues ao reator químico na temperatura, pressão e concentrações de espécies apropriadas (Stephanopoulos, 1983). Diante dessa informação esse trabalho tem como objetivo desenvolver um sistema de monitoramento inteligente da integridade do reator baseado em sistemas imunológicos artificiais para que o mesmo possa operar em máxima produção e condições de segurança identificando falhas no comportamento. ...
... Os reatores químicos são equipamentos das mais variadas formas e tamanhos onde acontecem reações em escala industrial para transformação de matérias-primas em produtos comercializáveis. Este trabalho apresenta o modelo matemático do reator tubular com escoamento pistonado (PFR), para se obter resultados rápidos e seguros sem a realização de testes em uma planta real (Fogler, 2012;Stephanopoulos, 1983). Uma introdução ao problema que se propõe neste trabalho é ilustrada na Figura 3. ...
... Modelando o problema ilustrado na Figura 1, o reator PFR, faz-se um balanço molar para o reagente A, utilizando a lei de conservação da matéria (Acúmulo -Entra -Saída + Gerado -Consumido) e considerando que não há acúmulo nem geração (Fogler, 2012;Stephanopoulos, 1983). ...
A indústria 4.0 utiliza princípios de novas tecnologias para obter resultados de melhor qualidade e produtividade, o qual a computação inteligente é uma delas. Neste trabalho é proposto uma metodologia inteligente em que o sistema imunológico artificial separa o sinal em grupamentos e determina a classificação baseado em prognose de falhas pelo grau de severidade de um reator tubular com escoamento pistonado. O processo é desenvolvido da seguinte forma: basicamente, após a obtenção dos sinais de vibração do reator através de um modelo numérico, é utilizada a transformada rápida de Fourier para transformar os sinais no domínio da frequência. Posteriormente, um sistema imunológico artificial de seleção negativa realiza o diagnóstico, identificando e classificando as falhas. A motivação da aplicação desta metodologia é o processo de fiscalização de estruturas, a fim de identificar e caracterizar as falhas, bem como tomar decisões visando evitar acidentes ou desastres. Os resultados demonstram a robustez e precisão da metodologia para a aplicação proposta.
... An interesting parallel can be drawn between Eq. (4) and the integral law for feedback controllers [18]: following the analogy, the Power-to-Steel accumulation within the storage tank can be viewed as the error term that such a control law aims to nullify, at all times, as far as possible. Anyway, since the profiles of industrial variables (especially those concerning renewable energy sources: e.g., solar and wind power generation) usually show a temporal discretization (as they are mostly collected hourly), Eq. (4) is rewritten and adapted to the typical discrete formulation of such problems (from now on, in fact, only discrete formulae will be considered): ...
... Furthermore, higher computational ease aside, such a technique was preferred over numerical integration methods based on interpolating functions (such as Simpson's rule) as it reconstructs the discrete-time values as a piecewise-constant continuous profile (i.e. a procedure that, by its nature, never generates values outside the range of the discrete-time ones). On the other hand, methods based on n-order polynomial interpolations (e.g., Simpson: n = 2) might introduce non-conservative excursions beyond the range of the discretetime values of the dataset they aim to integrate [18,19]. Then, the resultingṁ OUT (t) should be checked to guarantee that it complies with the aforementioned (a), (b), and (c) cases. ...
This paper introduces a general criterion for the optimal design and operation of hydrogen storage tanks. Specifically, the proposed procedure identifies the optimal delivery schedule that minimizes the capacity of material storage systems. Indeed, many manufacturing processes need some buffer storage to administer mass flows appropriately according to the operating needs (one class above all: Power-to-X processes) and have one of their highest expenditures right in those tanks when proving not sufficiently flexible. Hence, the novelty of the proposed method lies in a rigorous mathematical formulation that converts arbitrarily fluctuating inlet streams into optimally fluctuating outlet streams that minimize the storage volume and comply with different operating requirements. The criterion is validated by considering the techno-economic assessment of a chemical plant featuring a dedicated green hydrogen production facility that feeds the process. Specifically, the required capacity of the “Flexible” hydrogen buffer storage, which connects the green hydrogen generation system to the conversion process, significantly decreases by 91.31%–99.31% (depending on the flexibility ranges enabled by the downstream conversion process) compared to the “Rigid” storage alternative based on a constant outlet mass flow withdrawal, coinciding with the hydrogen consumption rate at nominal operating conditions. Correspondingly, the resulting levelized cost of hydrogen benefits accordingly, ranging from 4.19 to 6.03 USD/kg (California, 2023).
... El primero es el bien conocido caso monovariable de la identificación de la altura de un tanque h en función del caudal de entrada qi [15]. Como es sabido, la altura se mueve con la raíz cuadrada del caudal de salida (que depende del caudal de entrada), y es por lo tanto un proceso moderadamente no lineal [16], tanto en su ganancia como en su constante de tiempo. ...
... Para el caso SISO del tanque solo es necesario considerar las funciones de evaluación referidas a los errores y estándares de calidad (6a), (15) y (16). Se han usado datos de un ensayo en el cual, a partir de distintos caudales de entrada, se exploran distintas alturas con un máximo de 100cm. ...
Se presenta una metodología basada en optimización multiobjetivo para la identificación de modelos lineales con parámetros variantes tipo entrada-salida. Entre los múltiples criterios de calidad contemplados, se incluyen los tradicionales de minimización de errores, "blancura" e independencia de residuos, más otros índices diseñados especialmente para esta aplicación. La validez del procedimiento se verifica en dos ejemplos de identificación por medio de optimización multiobjetivo de procesos no lineales. Palabras claves-Modelos entrada-salida, sistemas multivariables, modelos lineales con parámetros variantes, optimización multiobjetivo.
... In chemical engineering, process variables vary with time and one of the examples is the transesterification process. The external intervention is the process control needed to ascertain the satisfaction of operational requirements such as safety, production specifications, environmental regulations, operational constraints, and economics of a process [6]. Since the structure of the biodiesel reactive distillation process is difficult, it is important to maximize mass and energy of the raw materials, hence, an appropriate control system for the process must be developed. ...
... PID Tuning Parameters[6,10] ...
The control of the transesterification process was conducted using MATLAB® codes as well as its Simulink environment. To realize the aim of the study, the dynamics data of methyl-oleate (BIODIESEL), decanter duty (manipulated variable) and reflux ratio (selected disturbance variable) were elicited from the Aspen Plus®. dynamic simulation of the formulated process model and this was used to get the first-order-plus-dead-time transfer function relation between methyl-oleate, decanter duty and reflux ratio with the help of MATLAB®. Open loop simulation was achieved by introducing steps to the input variables (reboiler duty and reflux ratio). The feed oil (Trolein) (98.3%) was converted into methyl-oleate and the final composition of the exit streams was 72.9% methyl-oleate, 1.7% triolein, 24.5% glycerol, 1.3% purge methanol. It was observed that a net duty of 5kW is required to achieve this production after 6000 mins at 100 °C. The controller was successfully tuned by Zeigler-Nichols (ZN) and Cohen-Coon (CC) techniques to conduct the disturbance rejection of the process. The performance of the CC tuning and ZN adjusting techniques in the disturbance rejection control simulation had Integral Square Error (ISE) and Integral Absolute Error (IAE) values of 1.269/ 4.09 and 1.126/3.909, respectively. It was noticed that the performance of the CC tuning technique was better than that of the ZN tuning technique in the disturbance rejection control simulation due to its lower ISE and IAE values. This study suggested that the reactive distillation process could be effectively operated to act as required using PID control to produce clean methyl-oleate.
... As pointed out previously, although different approaches for the automated synthesis of overall process control systems have achieved some success on smaller problems, there are no widely accepted methods to achieve the systematic synthesis of an overall process control system, especially one that could be practised at the undergraduate level. Stephanopoulos (1983Stephanopoulos ( , 1984 suggested a simple, yet practical, modular approach to the synthesis of control systems for overall processes. In this approach, the process is divided into a collection of operations or units. ...
... The approach needs to be developed further to consider these more complex interactions. The approach suggested here has much in common with the modular approach of Stephanopoulos (1983Stephanopoulos ( , 1984 and includes the inventory control considerations of Aske and Skogestad (2009). However, it extends this to take into account of both the overall purpose of the flowsheet and the impact of recycles on control degrees of freedom. ...
The synthesis of control systems for overall processes remains a challenge. Whilst a number of automated approaches have been explored academically, in practice systems are mainly synthesised using experience and intuition in conjunction with dynamic simulation software. An approach is presented in this paper for the systematic synthesis of overall process control systems which is simple, practical and appropriate for use at the undergraduate teaching level. Many potential problems can be identified and avoided in the synthesis of process control systems by ensuring that the control system can maintain a steady-state control of the overall mass, the mass of the individual components and, where necessary, multiple phases. Processes with recycles are vulnerable to process control problems when the overall mass balance of the system containing the recycle is not correctly managed. This can be overcome by self-regulation (perhaps involving process design changes) or by changing the structure of the control system. Achieving and maintaining the material balance are the main purposes of the majority of controllers in most chemical processes. Without robust delivery of material balance control, higher level control objectives (such as quality controls) are unlikely to be met reliably. This paper presents a step-wise approach to the synthesis of overall process control systems that avoids many potential problems by identifying which process parameters can be manipulated to achieve the intended process mass balance and designing the control structure accordingly.
... In the context of chemical engineering, this indicates a high level of system stiffness; mathematically, this scenario is designated as an ill-conditioned system. This implies that small perturbations or variations in the input of the mathematical dynamic model (in this case, dimensionless time) lead to significant and large changes in the outputs of the mathematical model [49,50]. In our dynamic system, these outputs correspond to the dependent variables, specifically the five state variables: initiator and monomer conversions, free radical and polymer concentrations (all dimensionless), and the average molecular weight of weight (M w ) [1,51]. ...
Citation: Velázquez-León, L.-F.; Rivera-Toledo, M.; Fernández-Anaya, G. Analytical Solutions and Stability Analysis of a Fractional-Order Open-Loop CSTR Model for PMMA Polymerization. Processes 2025, 13, 793. Abstract: This study examines the asymptotic stability of a continuous stirred tank reactor (CSTR) used for poly(methyl methacrylate) (PMMA) polymerisation, utilizing nonlinear fractional-order mathematical models. By applying Taylor series and Laplace transform techniques analytically and incorporating real plant data, we focus exclusively on the chemical reaction effects in the kinetic constants, disregarding mass transport phenomena. Our results confirm that fractional derivatives significantly enhance the stability and performance of dynamic models compared to traditional integer-order approaches. Specifically, we analyze the stability of a linearized fractional-order system at steady state, demonstrating that the system maintains asymptotic stability within feasible operational limits. Variations in the fractional order reveal distinct impacts on stability regions and system performance, with optimal values leading to improved monomer conversion, polymer concentration, and weight-average molecular weight. Comparative analyses between fractional-and integer-order models show that fractional-order operators broaden stability regions and enable precise tuning of process variables. These findings underscore the efficiency gains achievable through fractional differential equations in polymerisation reactors, positioning fractional calculus as a powerful tool for optimizing CSTR-based polymer production.
Acknowledgments: This paper has been dedicated to the memory of Luis Felipe Velázquez-León. He passed away unexpectedly on 31 December 2023.
... One standard approach to establish, and check, the validity of such networks are knockout experiments: some reaction is obstructed, via the knockout of its catalyzing enzyme, and the response of the network is measured, e.g., in terms of concentration changes of metabolites. The large area of metabolic control studies how reaction rates steer the network to desired behaviour, or switch between different tasks; see for example [HS96,Fel92,Ste84] and the references there. ...
We present a systematic mathematical analysis of the qualitative steady-state response to rate perturbations in large classes of reaction networks. This includes multimolecular reactions and allows for catalysis, enzymatic reactions, multiple reaction products, nonmonotone rate functions, and non-closed autonomous systems. Our structural sensitivity analysis is based on the stoichiometry of the reaction network, only. It does not require numerical data on reaction rates. Instead, we impose mild and generic nondegeneracy conditions of algebraic type. From the structural data, only, we derive which steady-state concentrations are sensitive to, and hence influenced by, changes of any particular reaction rate - and which are not. We also establish transitivity properties for influences involving rate perturbations. This allows us to derive an influence graph which globally summarizes the influence pattern of the given network. The influence graph allows the computational, but meaningful, automatic identification of functional subunits in general networks, which hierarchically influence each other. We illustrate our results for several variants of the glycolytic citric acid cycle. Biological applications include enzyme knockout experiments, and metabolic control.
... We begin by introducing the basic concepts of advanced process control [1,2,3,4]. Process control and monitoring are pivotal elements in ensuring the efficiency and safety of both chemical [42] and bioprocesses [25,43]. This study specifically zeroes in on the intricacies and challenges associated with polymer processes, underscoring the critical importance of advanced process management strategies in this domain. ...
This chapter presents an in-depth exploration of model-predictive control (MPC) or advanced process control (APC) techniques in the optimization of polyolefin manufacturing processes. Drawing on the foundational motivations outlined in previous discussions, it highlights the pivotal role of APC in enhancing industrial efficiency and innovation. Through a comprehensive introduction to the basic concepts and tools of APC, including definitions of manipulated variables (MV), feedforward/disturbance variables (FF/DV), controlled variables (CV), and the intricacies of multivariable dynamic models, this work delineates the advantages of APC over traditional proportional-integral-derivative (PID) control systems. It further elucidates the mechanisms through which APC achieves its benefits, such as model CV prediction, economic optimization, and dynamic control execution, leveraging Aspen DMCplus and DMC3 control structures for illustration.
The chapter provides a detailed walkthrough of developing a dynamic matrix controller model for a copolymerization process utilizing Aspen DMC3 Builder, transitioning to the formulation and control of nonlinear processes. It addresses the challenges inherent in constructing nonlinear dynamic models for polymerization process control, introduces the Wiener model for nonlinear processes, and discusses the state-space, bounded derivative network (SS-BDN) for nonlinear controller modeling. A hands-on workshop for the development of a nonlinear model-predictive control (NMPC) of a polypropylene process is presented, culminating in an overview of recent advancements in MPC with embedded AI technologies.
Serving both as a primer for newcomers and a sophisticated reference for experienced practitioners and scholars, this work underscores the transformative potential of APC integrated with AI in the polyolefin production sphere. It advocates for the systematic adoption of these advanced control strategies to realize significant improvements in process efficiency, optimization, and innovation within the chemical processing industries.
This is a preprint version of a chapter from our book - Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing. Please cite the original work if referenced [26,35]
... We begin by introducing the basic concepts of advanced process control [1,2,3,4]. Process control and monitoring are pivotal elements in ensuring the efficiency and safety of both chemical [42] and bioprocesses [25,43]. This study specifically zeroes in on the intricacies and challenges associated with polymer processes, underscoring the critical importance of advanced process management strategies in this domain. ...
This chapter presents an in-depth exploration of model-predictive control (MPC) or advanced process control (APC) techniques in the optimization of polyolefin manufacturing processes. Drawing on the foundational motivations outlined in previous discussions, it highlights the pivotal role of APC in enhancing industrial efficiency and innovation. Through a comprehensive introduction to the basic concepts and tools of APC, including definitions of manipulated variables (MV), feedforward/disturbance variables (FF/DV), controlled variables (CV), and the intricacies of multivariable dynamic models, this work delineates the advantages of APC over traditional proportional-integral-derivative (PID) control systems. It further elucidates the mechanisms through which APC achieves its benefits, such as model CV prediction, economic optimization, and dynamic control execution, leveraging Aspen DMCplus and DMC3 control structures for illustration. The chapter provides a detailed walkthrough of developing a dynamic matrix controller model for a copolymerization process utilizing Aspen DMC3 Builder, transitioning to the formulation and control of nonlinear processes. It addresses the challenges inherent in constructing nonlinear dynamic models for polymerization process control, introduces the Wiener model for nonlinear processes, and discusses the state-space, bounded derivative network (SS-BDN) for nonlinear controller modeling. A hands-on workshop for the development of a nonlinear model-predictive control (NMPC) of a polypropylene process is presented, culminating in an overview of recent advancements in MPC with embedded AI technologies. Serving both as a primer for newcomers and a sophisticated reference for experienced practitioners and scholars, this work underscores the transformative potential of APC integrated with AI in the polyolefin production sphere. It advocates for the systematic adoption of these advanced control strategies to realize significant improvements in process efficiency, optimization, and innovation within the chemical processing industries. This is a preprint version of a chapter from our book - Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing. Please cite the original work if referenced [26,35]
... In the selection of the of the PI controllers parameters, care was taken to provide a common method for each of the compared sequences. A tuning procedure that involved the minimization of the integral of the absolute value (IAE) for each loop of each scheme was used (Stephanopoulos, 1984). Therefore, for each loop, an initial value of the proportional gain was established and a search over the values of the integral reset time was conducted until the local optimum value of the IAE was obtained ...
Sotol bagasse (SB) is a byproduct generated during the fermentation of the sotol plant (Dasylirium sp.) for the production of a traditional alcoholic beverage in Mexico. In this study, a novel ethanol purification distillation sequence is proposed, utilizing ethanol produced from SB treatment as described by González-Chavez et al. (2022). Prior to purification, the ethanol-water mixtures exhibited ratios of 0.054 and 0.935. The proposed distillation sequence comprises three columns: i) depletion, ii) extraction (employing ethylene glycol as the solvent agent), and iii) solvent recovery. To assess energy consumption and controllability, three solvent:feed ratios (1.5:1, 2:1, 2.5:1) were examined, taking into account the feed to the second column. The lowest energy consumption was observed at 31 × 103 kW, corresponding to the 1.5:1 ratio. Optimal controllability was achieved at a solvent:feed ratio of 2.5:1, as evidenced by the lowest Integral Absolute Error (IAE) value of 3.3 × 10–3 and the highest proportional gain (Kc) value of 250. In summary, we leveraged previously reported experimental data to design a new distillation sequence with potential applications in the ethanol purification derived from SB fermentation. The outcomes of this investigation underscore the importance of promoting circular economy practices, particularly in the northern region of Mexico, where significant quantities of agrowastes are generated.
... Once well-developed, the model can be statistically validated, and the results will represent the unit's behavior. At this point, various operational conditions can be tested, as well as control methodologies, optimization, etc., in a quick and practical manner (Stephanopoulos, 1984;Roffel and Betlem, 2006;Edgar et al., 2001). Various software programs are used for dynamic process analysis, from general-purpose programming languages such as VBA, MATLAB, Fortran, etc. (Finlayson, 2006;Attaway, 2009) to simulation platforms like Aspen Plus Dynamics AspenTech and DynSim AVEVA. ...
Purpose: The project aims to leverage Business Intelligence integrated with dynamic simulations in the context of Industry 4.0, focusing on the implementation of a system for the dynamic analysis of a chemical reaction process. The system is designed to optimize data analysis and decision-making processes in large-scale industrial settings. Theoretical Framework: In the evolving landscape of Industry 4.0, the effective management of industrial data and processes is paramount. Traditional methodologies often overlook the potential of integrating Business Intelligence with dynamic simulations. This project proposes a novel approach, combining these elements to enhance process oversight and decision-making efficacy. Method: Utilizing PI System AVEVA/OSIsoft for structured data organization, the project implements a Business Intelligence framework applied to the dynamic simulation of a chemical reaction process. This method focuses on creating a harmonious integration of data analysis tools with real-time process simulations, aiming to improve operational efficiency and adaptability. Results and Conclusion: The implementation of this integrated system in a simulated industrial environment demonstrated a notable improvement in process analysis and decision-making efficiency. This indicates a significant advancement in the application of Business Intelligence in industrial operations, particularly in dynamic process simulations. Research Implications: This study underscores the importance of advanced data analysis techniques in modern industrial operations. The results suggest a substantial shift towards more data-driven, efficient, and adaptable process management strategies in Industry 4.0. Originality/Value: This research highlights the innovative application of Business Intelligence in conjunction with dynamic simulations for industrial processes. It showcases a pioneering approach in enhancing data analysis and decision-making capabilities in the context of Industry 4.0.
... Entre elas tem-se, de acordo com Stephanopoulos (1984): ...
A secagem é um processo historicamente empregado na conservação de alimentos. Vários métodos são utilizados desde então, com diversas configurações de secadores. Porém, o custo da operação, o impacto ambiental e a qualidade dos produtos ainda precisam se alinhar às exigências do mercado e sociedade atuais. Neste artigo, são apresentadas as técnicas mais usuais de secagem e os estudos de destaque sobre a automatização desses processos, que podem colaborar com a manutenção das especificações requeridas para determinado produto, com foco em estratégias de controle baseado em modelo interno, controle com lógica difusa e redes neurais, as quais foram identificadas como promissoras.
... Based on this, the lumped parameters dynamic modeling is a typical strategy in process system engineering that follows the principles of mass and energy conservation with the inclusion of the accumulative term. This topic is deeply presented by Himmelblau and Bischoff [104], Douglas [105], Ramirez [106], Ingham et al. [107], and Hangos and Cameron [108],it is the basis of process control cause as presented by Stephanopoulos [109], Luyben [110], Bequette [111], and Seborg et al. [112]. Particularly, the accumulative term corresponds to the dynamic change of the variables and relates to the geometrical aspects of the equipment. ...
Evaporators are one of the most important equipment in the food process industries such as sugar, fruit juices, dairy products, edible oils, tomato paste, and coffee. They need a lot of energy in the form of steam from boiler and it is necessary to minimize their energy consumption. One of the best strategies for this purpose is the design and application of multiple-effect evaporators (MEEs), in which the vapor from one stage (effect) is the heating medium for the next stage. There are various configurations and designs for MEEs and they can also be equipped with vapor compression systems and steam ejectors to further reduce the energy consumption and increase their economic efficiency. This article is covering the fundamentals, design, simulation, control, and application of MEEs in various food industries for the first time with discussing recent advances in this field.
Graphical Abstract
... An alternative form for the PI and PID control algorithms is the so-called velocity form. In this form, one does not compute the actual value of the controller output signal at the nth sampling instant, but its change from the preceding period (11). ...
In this paper a nonlinear adaptive control method is presented for a pH process, which is difficult to control due to the nonlinear and uncertainties. A theoretical and experimental investigation was conducted of the dynamic behavior of neutralization process in a continuous stirred tank reactor (CSTR). The process control was implemented using different control strategies, velocity form of PI control and nonlinear adaptive control. Through simulation studies it has been shown that the estimated parameters are in good agreement with the actual values and that the proposed adaptive controller has excellent tracking and regulation performance.
... Process control refers to the use of control systems to regulate, control and optimize the operations of a manufacturing process [1]. The key element of a process control system is the controller, which plays a critical role in ensuring that the process operates within a desired set of parameters. ...
Advances in artificial neural networks (ANN), specifically deep learning (DL), have widened the application domain of process control. DL algorithms and models have become quite common these days. The training algorithm is the most important part of an ANN that affects the performance of the controller. Training algorithms optimize the weights and biases of the ANN according to the input-output patterns. In this paper, the performance of different training algorithms was evaluated, analysed, and compared in a feed-forward backpropagation architecture. The training algorithms were simulated on MATLAB R2021b with license number 1075356. Training data were generated using two benchmark problems of the process control system. The performance, gradient, training error, validation error, testing error, and regression of the different training algorithms were obtained and analysed. The data shows that the Levenberg-Marquardt (LM) algorithm produced the best validation performance with a value of 2.669*10 ⁻¹⁴ at 2000 epochs, while ‘traingd’ and ‘traingdm’ algorithms did not improve beyond their initial values. The LM algorithm tends to produce better results than other algorithms. These results indicate that the LM backpropagation best suits these types of benchmark problems. The results also suggest that the choice of training algorithm can significantly impact the performance of a neural network.
... The distributed delays that may be used to describe the response of a system to a perturbation is known as the impulse response (IR) of the system under investigation. IRs can be readily analyzed with a large set of tools developed in the context of systems theory (Stephanopoulos, 1984;Coughanowr, 1991;Seborg et al., 2004). In the context of thermoacoustic combustion instabilities, IRs were first used by Polifke et al. (2001c), who determined the unit impulse response of a heat source in a thermoacoustic system as an intermediate step of system identification, with the overall goal of determining the frequency response function from CFD time series data with broad-band frequency content. ...
... 978-1-6654-6310-2/23/$31.00 ©2023 IEEE II. TEMPERATURE CONTROL Accurate temperature control is a great challenge, that is why some industrial applications requires better then ±1 • C temperature accuracy [11] using appropriate material and equipment. ...
This paper reports the design of real-time acquisition and temperature control system using LabVIEW (Laboratory Virtual Instrument Engineering Workbench) which communicate with PC through USB interface. The solution is particularly suitable for educators and students interested in developing lab experiments to evaluate and control the temperature variation using portable and low-cost system. The proposed solution is validated through an experimental prototype using PIC 18F4550 microcontroller. The temperature effect on power BJT (Bipolar Junction Transistor) is demonstrated by measuring the characteristics of the transistor 2N6486 using Tektronix-370B curve tracer under different degrees of temperature. The value of temperature inside an insulated designed box wrapping the transistor, sensor and actuators is controlled by the PC. The developed system for data acquisition and command of temperature with an accuracy of 1 0 C can be used for many industrial applications.
... The basic principles of combustion control have remained very similar to those applied in earlier times in coal-fired power plants, although the hardware has evolved considerably. Typical control in WtE combustion plants is still largely based on proportional-integraldifferential (PID) control theory, which is well established and long-proven in process industries [63]. However, PID control schemes are, in general, strongly reliant on feedback loops, which introduce delays in responsive actions of the control. ...
Waste-to-energy processes remain essential to ensure the safe and irreversible removal of materials and substances that are (or have become) unsuitable for reuse or recycling, and hence, to keep intended cycles of materials in the circular economy clean. In this paper, the behavior of inorganic compounds in waste-to-energy combustion processes are discussed from a multi-disciplinary perspective, against a background of ever tightening emission limits and targets of increasing energy efficiency and materials recovery. This leads to the observation that, due to the typical complexity of thermally treated waste, the intelligence of combustion control systems used in state-of-the-art waste-to-energy plants needs to be expanded to better control the behavior of inorganic compounds that typically end up in waste furnaces. This paper further explains how this goal can be achieved by developing (experimentally validated) predictive numerical models that are engineering-based and/or data-driven. Additionally, the significant economic potential of advanced thermochemical intelligence towards inorganic compounds in waste-to-energy combustion control systems is estimated on the basis of typical operational figures.
... Chemical process has many phases such as filling or emptying the reactor, mixing, etc. (Stephanopoulos, 1984). The intelligent control system has an advantage of the ultimate degree of freedom in terms of self-learning, self-regulation, reasoning and decision making. ...
... An important problem in the process industries is to keep the liquid level at the wished setpoint of value for two connected each other tanks fed by the pump. Because of the interaction between the tanks, designing control is kind of defying and many persons have to try given big attempts [1]. The coupled water tank system is described as the SISO process [2] and is hard to control because of its basic nonlinearity and the appearance of the relation between variables input-output [3]. ...
The control of liquid level in tanks is a classic problem in process industries. Most of the liquid will be processed by chemical or mixing treatment in the tanks. Because of that, the liquid level in the tanks must be regulated, so that in order for this system to work as we want, it needs a control strategy. Therefore, this research will use a control strategy using fuzzy sugeno with a gain compensator based on pole placement for controlling level of tank 2 in the coupled water tank system with setpoint is 10 centimeters at time 0 seconds and 8 centimeters given at time 1000 seconds. Wherein, the gain compensator based on pole placement is used to make the output system robust to changes in setpoint with zero steady-state error and fuzzy sugeno for faster time response. The results show that using the fuzzy sugeno with a gain compensator based on pole placement can follow setpoint given with 0 centimeters of steady-state error, 0% for overshoot, 44,6538 seconds for rising time, 62,2688 seconds for settling time and can follow setpoint changes in 58,8662 seconds.
... In addition to his research accomplishments, George also authored a classic and widely used textbook on Process Control (Stephanopoulos, 1984). He is also among the very few academicians who have made substantial contributions to industrial practice. ...
... Classical results on safe and optimal control [18] of chemical reactions [40] are the conceptual foundation for industrial chemical processes. Formal methods for chemical reactors are well-studied [4,20,25,30,36], but even textbook cases [18] lack high fidelity models (e.g., nonlinear dynamics and wide ranges of parameter values). ...
Safety-critical chemical processes are well-studied in the formal methods literature, including hybrid systems models which combine discrete and continuous dynamics. This paper is the first to use a theorem-prover to verify hybrid chemical models: the KeYmaera X prover for differential dynamic logic. KeYmaera X provides parametric results that hold for a whole range of parameter values, non-linear physical dynamics, and a small trusted computing base.We tell a general story about KeYmaera X: recent advances in automated reasoning about safety and liveness for differential equations have enabled elegant proofs about reaction dynamics.KeywordsHybrid SystemsTheorem ProvingChemical Reactor
Process automation plays a crucial role in modern industrial control, ensuring efficiency, reliability, and safety. This study explores the implementation of a Distributed Control System (DCS) using Yokogawa Centum-VP, a widely adopted solution in the process industry. The laboratory experiment focuses on configuring and analyzing process control loops within the Centum-VP environment. The methodology includes system setup, control strategy development, parameter tuning, and real-time performance evaluation. Experimental results demonstrate
the effectiveness of the system in maintaining process stability under different operating conditions. The study highlights key advantages of Centum-VP, such as its scalability, redundancy, and advanced control features, while also addressing its limitations and potential improvements. These findings provide valuable insights for students and professionals in process automation and industrial control.
Model fitting of laboratory-generated experimental data is a foundational task in engineering, bridging theoretical models with real-world data to enhance predictive accuracy. This process is particularly valuable in batch dynamic experiments, where mechanistic models are often used to represent complex system behavior. Here, we propose a systematic algorithm tailored for the model fitting and parameter estimation of experimental data from batch laboratory experiments, rooted in a Process Systems Engineering framework. The paper provides an in-depth, step-by-step approach covering data collection, model selection, parameter estimation, and accuracy assessment, offering clear guidelines for experimentalists. To demonstrate the algorithm’s effectiveness, we apply it to a series of dynamic experiments on the pressure-constant cake filtration of calcium carbonate, where the pressure drop across the filter is varied as a key experimental factor. This example underscores the algorithm’s utility in enhancing the reliability and interpretability of model-based analyses in engineering.
A maximum sensitivity constrained modified Smith predictor consisting of two PD controllers is developed for integrating type continuously stirred tank reactors (CSTRs) model. This CSTR type model approximated as integrating first order plus time delay with and without inverse response processes is challenging to control as it contains integrating term, process time delay and a zero lying in the right half of s-plane. The inner and outer loop PD blocks are tuned to achieve the desired set-point tracking and load disturbance rejection responses using direct synthesis approach. The proposed control is free from integrating action and therefore, smooth setpoint tracking response with no overshoot is obtained which is highly desirable in the process industries. Two design parameters λ and τ are involved in the suggested control whose suitable values are recommended to achieve the desired robustness level by specifying the maximum sensitivities of the inner and outer loop transfer functions. Various illustrative models are considered to show the effectiveness and superiority of the proposed control structure. The system robustness towards process parameter perturbations and load disturbances are investigated in all the examples. Performance of the suggested modified Smith predictor is found superior as compared to the other recently published works.
The fluidized catalytic cracking (FCC) unit is the main unit of the oil
refinery, where its objectives are upgraded heavy low-value petroleum streams
into higher value products, mainly gasoline and olefin. In this process the
catalyst is circulated back into the regenerator where coke is burned and the
catalyst is regenerated. FCC has many parameters to control; the most important
are temperature of the reactor, the level and flow rate of the catalyst. The
temperature of the reactor should be kept at (505°C) and the catalyst
temperature (655°C). The control strategy was developed and the transfer
functions were identified. From these transfer functions the characteristic
equations were calculated as well as the open- loop transfer functions. Routh,
direct substitution, Root – Locus and bode methods were used for tuning,
stability analysis and simulation response of the system with adjustable
parameter of Kc = 33.25 ,Ʈ i = 1.41 sec , Ʈ d = 0.211 sec the same were
obtained for the other loops . The systems simulation results were found to be
initially oscillatory and finally settled. From the response the following
parameters were determined for loop1 these are: beak time =2.07 sec , over
shoot = 3.71cm , settling time =376 sec and rise time of 0.211 sec , beak time
of 0.106 sec , over shoot of 12.2cm, decay ratio of 0.648 cm for loop 2 , loop 3
and 4 have typical result. It is concluded that the stability and tuning methods
give almost equal results showing that the average lead to more accurate results.
It is recommended that a ratio controller for the air, fuel mixture has to be
installed in the heat exchanger for heating the air to the regenerator. It is also
recommended that digital treatment of tuning and stability analysis have to be
made. As the conditions of the FCC are very dangerous for laborers and
engineers, it is recommended that SCADA system needs to be used.
The control strategy was developed in which a cascade control system was constructed where the level is taken as a master loop and the extract composition as a secondary loop. The transfer functions were identified from the literature and characteristic equations were determined. The ultimate gains and periods were obtained, using the average between direct substitution, Root locus and Bode methods. The ultimate gains and periods were inserted in Ziengler tuning table and the optimum adjustable parameters (K c ,τ i , τ d) were taken for P,PI&PID .These were inserted into each overall transfer functions and the controller that gave minimum overshoot was taken and introduced into either loop, the controller was the secondary loop is P controller and for the primary loop is also P controller. It's recommended that the controller system obtained in this process is to be made into digital computer system where each controller is a PLC.
The conventional proportional–integral–derivative (PID) controller is used in the majority of process industries. Although the most commonly used, the classic PID controller has several drawbacks like variable performance for the non-linear system, multivariable control design is not straightforward; the response is influenced by dead time, no constraints involvement, etc. The field of process control systems has grown quickly, and other controllers have been developed that try to overcome the weakness of PID controllers. Advances in artificial neural networks, specifically deep learning, have widened the application domain of process control systems. In this paper, a cascaded tank system, which is a benchmark problem, has been simulated. The input-output data of the plant has been generated and used to train a deep-learning controller using backpropagation. Several measures, such as time response, frequency response, and signal statics performance indices, are used to evaluate the outcomes of the proposed controller. The proposed model performs better on every assessment criterion than the traditional controller.
HCl üretim endüstrisinde atık su arıtma işleminde kullanılan pH nötralizasyonu prosesi sürekli frekans salınım yöntemi kullanılarak parametreleri ayarlanmış bir kontrol edici ile set noktasında kontrol edilmiştir. PID kontrol edici parametreleri için iki farklı PID ayarlama tekniği Ziegler Nichols ve Tyreus Luyben uygulanmış ve performansları karşılaştırılmıştır. Kimyasal prosesin doğrusal olmaması ve kararsızlığı nedeniyle, ayar noktasına ulaşmak için minimum oturma süresi, azaltılmış salınımlar, kısa yükselme süresi, ofseti ortadan kaldırma, minimum yüzde aşma, yüksek stabilite gürültü sinyalleri ve yük etkileri varlığında optimum ve istenen kontrol performansı elde edilmesi hedeflenmiştir. Elde edilen deneysel sonuçlardan, Oransal İntegral Türev (PID) denetleyicisinin farklı ayarlama yöntemleri için zaman alanı özellikleri hesaplanmıştır. Tyreus Luyben ayar formülünün Ziegler Nichols yöntemine kıyasla daha az yükselme zamanı ve pik tepe aşım yüzdesine sahip olduğu ve daha küçük oturma zamanı verdiğini göstermiştir.
In this paper, a method for controlling multivariable process is presented. The system under investigation is a two tank interacting process. A decoupler is designed in order to minimize the interaction effects. Then, the Model Reference Adaptive Controller is designed for the process with decoupler block. The tuning parameter is optimised using Genetic Algorithm. Optimised Model Reference Adaptive Controller was then compared with conventional controllers. The performance comparisons have been made in terms of rise time, settling time and performance criteria.
Recent advancements in renewable energy harvesting system have triggered the photovoltaic research in different dimensions. It has enabled rapid development of systems for renewable energy production. To yield an effective production of solar energy, it has become mandatory to predict the solar cell performance virtually. Such prediction will lead to performance analysis before the commencement of solar cell production. Virtual performance analysis of solar cell will support better design, analysis and control of photovoltaic systems. By an appropriate choice of software tool, performance prediction of solar cells will minimize time and cost by virtual production analysis. The work presented in this paper provides successful production rate of solar cell. For analysis, 10,000 samples have been taken into virtual performance analysis, where 9337 successful samples have been yielded. Thus, electrical yield of 92% have been obtained using photo luminescent (PL) imaging. It is observe that analysis without photo luminescent (PL) imaging have electrical yield of 86.2%. The defect rate of solar cells has been minimized through virtual performance prediction. Around 5.8% of increase in electrical yield has been observed through PL imaging.
In a hybrid renewable energy system (RES), different types of energy sources are integrated for meeting the continuous power demand. To overcome the problem of intermittent availability of energy RES, suitable energy storage system is required. The energy storage as hydrogen gas is more efficient and suitable for long-term storage applications. Proton exchange membrane (PEM) electrolyzer is used to store the surplus amount of energy from RES in the form of hydrogen gas. In this paper, initially the mathematical modeling of PEM electrolyzer is developed to know the influence of various parameters on the performance. It is noted that the cell voltage and efficiency of the electrolyzer depend on its temperature. Based on this observation, temperature is taken as input parameter, and the system model is identified for PEM electrolyzer using system identification technique. The different black box approaches are used to identify the suitable system by comparing the response on the basis of percentage of fitness, cost function and final prediction error. It is found that Box–Jenkins method is appropriate for PEM electrolyzer with percentage fitness of 90.5%. To check the system stability, Lyapunov stability analysis is applied and it is found to be stable. For the stable system, conventional PID controller based on Ziegler–Nichols method is used to maintain constant cell voltage by varying the temperature of PEM electrolyzer. For the system with the PID controller, the rise time is 3.8 s, settling time is 75.3 s, peak time is 9.3 s and overshoot is 57.4%.
The use of microalgae in biotechnology is on the rise, as they have some advantages over other microorganisms, such as the use of light and carbon dioxide as substrates for their growth, returning oxygen and naturally synthesizing antioxidants, proteins, carbohydrates, carotenoids, among other products. However, the cultivation of microalgae still faces serious productivity limitations and metabolic directions due to light, or the photosynthetic active radiation (PAR). This study addresses the development of an automatic PAR controller based on Fuzzy logic, also contemplating an unprecedented functionality, the possibility of choosing the wavelength that will be emitted. Fuzzy logic easily allows new variables to be added to the controller. Our premise is the search for a control system that is robust for the current process and that at the same time is flexible to absorb new guidelines related to industry 4.0. The present study compared cultures without using the controller against cultures that used the PAR controller. Controlled cultures had higher biomass productivity compared to uncontrolled ~ 200%. Changing the wavelength from white to blue in fixed emitted PAR cultures showed an increase of 173% in protein production and activating the controller, an additional 23% increase was obtained. In addition, the PAR controller reduced energy consumption by three times.
Each government is looking for to provide the best services to establish efficiency and quality of performance. This goal could be accomplished by improving the service performance of entire sectors in society. The government of Syria has realized the importance of moving in the direction of information technology. Therefore, E-governance initiatives were launched in Syria as a part of overall country information technology in 20 s century. Each government sector has since upgraded the performance by having its websites and e-services application. However, there are gaps and loose connections exist among the sectors, which has accordingly tarnished the image of Syrian E-governance. This has led to significant questions about the requirement of modification and enhancement of such service. Hence, the purpose of this research is to investigate and explore the factors that drive the E-governance implementation and affect government performance as well as the government-citizen relationship in Syria.KeywordsE-governanceGovernment of SyriaGovernment-Citizen
Continuous Stirred tank reactor is a chemical reactor system which exhibits complex non-linear dynamic characteristics. The quality of final product is based on the design of the controller. The mathematical modeling of CSTR is designed based on first principle method. Conventional PID controllers Ziegler-Nichols, Tyreus-Luyben, Cohen-Coon and IMC based PID have been implemented and the performance analysis of different PID tuning methods is done. The performance of the PID controller is analyzed in MATLAB simulation.KeywordsCSTRPID controllerTuning
The design of proportional integral derivative (PID) controller based on internal model control (IMC) principle with a new form of filter is presented to realize the controller to achieve satisfy regulatory behavior, servo behavior, robustness and input constraints handling capacity. The technique is evaluated on the integrating process with time delay, incorporating a third order filter to realize the controller. To demonstrate the efficacy of IMC technique to tune PID-type controller, a laboratory benchtop liquid level process setup is considered for implementation. The outcomes of the experiments carried out with design of controller for maximum sensitivity for uniform robustness rank for comparison of the proposed technique with other techniques and incorporation of model mismatch of 20% contributed <1% variation in the performance indicators, rendering the adoption of the proposed technique.KeywordsIntegrating processLiquid level processIMCRobustnessPerformanceSensitivitySetpoint filterPIDFilterDisturbance rejection
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