Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor

Kamesh, Reddi; Rani, K. Yamuna

doi:10.1016/j.isatra.2016.05.016

Cited by 7 publications

(4 citation statements)

References 34 publications

(53 reference statements)

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“…In order to demonstrate the usefulness of the proposed approach to a MIMO system, an SBR, where hexyl monoester of maleic acid is formed by the reaction of maleic anhydride with hexanol, is considered. [26] The system is described as…”

Section: Sbr Without Coolant Dynamicsmentioning

confidence: 99%

“…The results of multivariable GMC serve as a target for comparison of efficacy of the proposed multivariable ANNGMC. Optimization studies for this system have been reported [26,27] to maximize the conversion at the fixed final time, where the optimal set point trajectories have been reported for both the output variables, and optimal input profiles have also been reported for the nominal operating conditions. Two different control vector parametrizations (polynomial and piecewise constant) had been considered, and two different models (first-principles model and a parametrized ANN model) had been employed for optimizing this system.…”

Section: Sbr Without Coolant Dynamicsmentioning

confidence: 99%

“…The controller formulations of SBR1 for multivariable ANNGMC and multivariable GMC are represented by Eqns (25) and (26), respectively. The tuning parameters for both the controllers are reported in Table 2.…”

Section: Sbr Without Coolant Dynamicsmentioning

confidence: 99%

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Application of artificial neural network‐based generic model control to multivariable processes

Kamesh

Rani

2017

Asia-Pacific J Chem Eng

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The present study focuses on the application of a purely data-driven artificial neural network (ANN)-based generic model controller (GMC) for multiple-input and multiple-output systems with relative degree one or more. The approach is based on development of an ANN model relating the time-derivatives of the outputs of the order equal to their relative degree with the past measured values of input and output variables and formulation of a constrained optimization problem to satisfy GMC performance objectives. Two variants of a multivariable semi-batch reactor (SBR) control problem for esterification reaction of maleic anhydride with hexanol to form hexylmonoester of maleic acid are considered without and with consideration of coolant dynamics (SBR1 and SBR2, respectively). Hexanol concentration and reactor temperature are considered as the outputs, and the coolant and feed flow rates are considered as the manipulated inputs. The present study illustrates that the proposed multivariable ANN-based GMC exhibits setpoint tracking and disturbance rejection performance similar to the exact model-based GMC for both the reactors, and the ANN-based GMC input profiles are smoother and closer to the nominal input profiles compared with GMC controller, although no process information is used other than the output measurements in the proposed approach.

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Section: Sbr Without Coolant Dynamicsmentioning

confidence: 99%

Section: Sbr Without Coolant Dynamicsmentioning

confidence: 99%

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Application of artificial neural network‐based generic model control to multivariable processes

Kamesh

Rani

2017

Asia-Pacific J Chem Eng

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“…There are two major approaches to the use of Fuzzy Inference System (FIS), namely Mamdani [1] and Takagi-Sugeno (TS) [2]. They have found many applications for steady state or dynamic modeling and control purposes in chemical as well as biological processes [3][4][5][6][7][8][9] as alternative approaches to the conventional mechanistic modeling approach.…”

Section: Introductionmentioning

confidence: 99%

Development of Genetically tuned Fuzzy dynamic model for nonlinear dynamical systems: Application on reaction section of Tennessee Eastman process

Ahmadi¹,

Royaee²,

Tayyebi³

et al. 2018

Scientia Iranica

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This work presents a new GA-fuzzy method to model dynamic behavior of a process, based on recurrent fuzzy modeling through Mamdani approach, whose inference system is optimized by genetic algorithms. By using the Mamdani approach, the proposed method surmounts the need to solve various types of mathematical equations governing the dynamic behavior of the process. The proposed method consists of two steps: i) constructing a startup version of the model and ii) optimizing the shape of membership functions of the fuzzy sets corresponding to the variables existing in the fuzzy model along with the production rules constituting the inference such that the obtained fuzzy model can predict the dynamic behavior of the process fairly accurately. The proposed method is used to predict the dynamic behavior of the reaction section of the Tennessee Eastman (TE) benchmark. The overall accuracy of the obtained results is shown in comparison with their corresponding counterparts in TE benchmark. The Mean Absolute Percentage Error (MAPE) of the key process variables, which are temperature, pressure, and level of the reactor, and the reactor cooling water outlet temperature are calculated 1.17%, 0.38%, 1.5%, and 1.57%, respectively, showing high prediction capability of the proposed method.

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Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Dutta

Upreti

2021

Can J Chem Eng

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In the last three decades, artificial intelligence (AI) has been increasingly and vigorously utilized for process control in chemical, biochemical, and biomedical engineering. These disciplines frequently involve fairly sophisticated processes under risks of operational upsets, and thus have an ever increasing demand of superior control strategies. As the deployment of AI in process control pushes the limits in this regard, the research advances, which are multitudinous and varied, need to be assimilated and organized to help promote further utilization and progress in the field. To that end, we examine more than 280 relevant research publications, and systematically collate the information. The AI‐based technologies are classified, and their over‐arching control paradigm is presented. Common AI‐based control technologies are then presented, which are based on expert systems, fuzzy logic, artificial neural networks, nature‐inspired algorithms, and hybrid approaches. Their working principles, types, and implementations are summarized along with advantages, limitations, and comparisons, if available. Important applications in the above disciplines are included with the help of tables that capture important details. A discussion is also provided on advanced and newly emerging AI‐based control technologies with pertinent applications. Overall trends are analyzed, and future prospects are identified based on the survey.

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Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor

Cited by 7 publications

References 34 publications

Application of artificial neural network‐based generic model control to multivariable processes

Application of artificial neural network‐based generic model control to multivariable processes

Development of Genetically tuned Fuzzy dynamic model for nonlinear dynamical systems: Application on reaction section of Tennessee Eastman process

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

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