Invention of a suitable controller for a non linear Chemical process

Rose, T. Pravin; Devadhas, G. Glan; Rex, S. Reeba

doi:10.1109/iccicct.2014.6993190

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…An earlier study by Syafiie et al [36] equally presented a macro-actions in model-free intelligent control while Zheng and Wang [37] suggested a model-free control based on neural networks. Although no direct comparison was made to determine which of the advanced/intelligent controllers provided greater performances in terms of pH monitoring and control, Rose et al [38] alleged that Fuzzy Logic controllers are, however, less demanding visà-vis its hardware requirements and other implementation tools. It is important to remember that the fuzzy logic control is based on the intelligent methodology of human thinking and decision making mechanisms which ultimately mimic the problem-solving capabilities of a more skillful operator [3].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Novel Self-adaptive Fuzzy Logic Controller for a pH Neutralization Process

Nsengiyumva¹,

Chen²

2018

Proceedings of the 2018 3rd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2018)

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This paper presents a self-adaptive fuzzy logic controller for a pH neutralization process which has been designed and implemented in real time process. The controller deals with the minimization of both the steady state error and time taken to reach steady state under varying operating conditions. The interesting fact about this control technique is that it operates a full-scale pH neutralization plant using the same equipment like that in real time full-scale industrial pH neutralization plant. The proposed self-adaptive technique updates the control parameters and the normalized universe of discourse of output fuzzy membership functions using varying scaling factors based on error and change of error values. A combination of both the coarse control and fine control was used for the overall control action. The speed of the response of the self-adaptive controller is handled by the coarse control technique while the amount of deviation of the measured values with respect to the setpoint under steady state is handled by the fine control technique. The performance of the envisaged selfadaptive scheme is tested for pH control in real time, and results are encouraging. MATLAB TM SIMULINK TM software is used for control, display as well as online communication with the DCS system through an OPC server configured on a PC. The controller is easily implementable and user-friendly and can, therefore, be adopted by process engineers. Lastly, the control technique envisaged herein can be implemented on preexisting control loops, hence alleviating financial constraints and great savings benefits of the controller.

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Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Novel Self-adaptive Fuzzy Logic Controller for a pH Neutralization Process

Nsengiyumva¹,

Chen²

2018

Proceedings of the 2018 3rd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2018)

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“…Since the necessity of maintaining the pH in a specific range value, many control strategies have been proposed. For this purpose, [1]- [3] designate a PID controller to control the pH value. An adaptive controller is developed in [4].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control for pH Neutralization Process based on SOMA Algorithm

Degachi¹,

Chagra²,

Ksouri³

2018

ijacsa

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Abstract-In this work, the pH neutralization process is described by a neural network Wiener (NNW) model. A nonlinear Model Predictive Control (NMPC) is established for the considered process. The main difficulty that can be encountered in NMPC is solving the optimization problem at each sampling time to determine an optimal solution in finite time. The aim of this paper is the use of global optimization method to solve the NMPC minimization problem. Therefore, we propose in this work, to use the Self Organizing Migrating Algorithm (SOMA) to solve the presented optimization problem. This algorithm proves its efficiency to determine the optimal control sequence with a lower computation time. Then the NMPC is compared to adaptive PID controller, where we propose to use the SOMA algorithm to formulate the PID in order to determine the optimal parameters of the PID. The performances of the two controllers based on the SOMA algorithm are tested on the pH neutralization process.

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Application of Advanced Process Control Technologies for Optimization of Polymers Production Processes

Yusupbekov

Abdurasulov²,

Adilov

et al. 2019

Advances in Intelligent Systems and Computing

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Invention of a suitable controller for a non linear Chemical process

Cited by 4 publications

References 11 publications

Design and Implementation of a Novel Self-adaptive Fuzzy Logic Controller for a pH Neutralization Process

Design and Implementation of a Novel Self-adaptive Fuzzy Logic Controller for a pH Neutralization Process

Nonlinear Model Predictive Control for pH Neutralization Process based on SOMA Algorithm

Application of Advanced Process Control Technologies for Optimization of Polymers Production Processes

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