Implementation of an Inverse-Model-Based Control Strategy Using Neural Networks on a Partially Simulated Exothermic Reactor

Hussain, Mohd Azlan; Kershenbaum, L.S.

doi:10.1205/026387600527167

Cited by 46 publications

(13 citation statements)

References 16 publications

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“…As per our goal we used the open loop online data for training the inverse of the process, and then used it as the controller to implement the neural network model based DIC (Direct Inverse Control) strategy. When neural networks originally were proposed for controlling unknown non-linear systems, one of the first methods being reported was on training a network to act as the inverse of the system and use this as a controller [36]. Training in this case includes mapping of the neural network to the required model by optimization of the various neural network parameters.…”

Section: Neural Network Tool Implementationmentioning

confidence: 99%

Advanced control software framework for process control applications

Hossain¹,

Hussain²,

Omar³

2014

IJCIS

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Industries are now moving towards PC-based control from PLC (Programmable Logic Control) based control as the PC (personal computer) is easily available and capable of implementing various control strategies to improve productivity. Advances in both hardware and software technology are expediting this move. Absence of a user friendly, cost effective and easy to apply advanced control strategy based software is the motivation for this work. In this paper we reviewed issues of software application in process control systems. We present the development of an advance process control software tool and its application on a pH neutralization plant. Neural network model based DIC (Direct Inverse Control) strategy is implemented to control the process. The application and results shows the viability and robustness of the advanced control software and its superiority over the conventional PID (proportional integral derivative) controller.

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Section: Neural Network Tool Implementationmentioning

confidence: 99%

Advanced control software framework for process control applications

Hossain¹,

Hussain²,

Omar³

2014

IJCIS

Self Cite

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“…Artificial neural network is a "black-box" estimator where there is no attempt to interpret the model structure (Hussain, 1999;Hussain and Kershenbaum, 2000;Hussain et al, 2002). It is a universal function approximator that typically works much better in practical applications than most traditional and polynomial-based function approximations methods.…”

Section: Control Of a Batch Polymerization System Using Hybrid Neuralmentioning

confidence: 99%

Control of a Batch Polymerization System Using Hybrid Neural Network ‐ First Principle Model

2007

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On a étudié dans ce travail l'utilisation d'un réseau neuronal hybride avec des modèles basés sur les premiers principes dans le but de modéliser et contrôler un procédé de polymérisation discontinu. Au niveau méthodologique, on a d'abord mis au point des modèles anticipatifs à réseau neuronal hybride (HNN) et des modèles inverses à HNN pour le procédé, puis on a étudié la performance du modèle en stratégie de contrôle inverse directe et en stratégie de contrôle de modèle interne (IMC). À des fins de comparaison, la performance du contrôleur de réseau neuronal et de PID a été comparée au HNN proposé. Les résultats montrent que le HNN est capable de contrôler parfaitement en ce qui a trait aussi bien au suivi des points de consigne que du rejet des perturbations.

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“…A similar strategy was also utilized by Hussain and Kershenbaum [11] in order to simulate a chemical reaction. Having an accurate amount of the heat generated is important to obtain good and reliable experiment results.…”

Section: Heat Of Reaction Estimationmentioning

confidence: 99%

Temperature control of a pilot plant reactor system using a genetic algorithm model‐based control approach

Wahab

Hussain

Omar

2007

Asia-Pacific J Chem Eng

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The work described in this paper aims at exploring the use of an artificial intelligence technique, i.e. genetic algorithm (GA), for designing an optimal model-based controller to regulate the temperature of a reactor. GA is utilized to identify the best control action for the system by creating possible solutions and thereby to propose the correct control action to the reactor system. This value is then used as the set point for the closed loop control system of the heat exchanger. A continuous stirred tank reactor is chosen as a case study, where the controller is then tested with multiple set-point tracking and changes in its parameters. The GA model-based control (GAMBC) is then implemented experimentally to control the reactor temperature of a pilot plant, where an irreversible exothermic chemical reaction is simulated by using the calculated steam flow rate. The dynamic behavior of the pilot plant reactor during the online control studies is highlighted, and comparison with the conventional tuned proportional integral derivative (PID) is presented. It is found that both controllers are able to control the process with comparable performance.  2007 Curtin Figure 2. Monitoring and control software (left) and genetic algorithm model-based controller (right) developed for the PARS-EX reactor. This figure is available in colour online at www.apjChemEng.com.

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Implementation of an Inverse-Model-Based Control Strategy Using Neural Networks on a Partially Simulated Exothermic Reactor

Cited by 46 publications

References 16 publications

Advanced control software framework for process control applications

Advanced control software framework for process control applications

Control of a Batch Polymerization System Using Hybrid Neural Network ‐ First Principle Model

Temperature control of a pilot plant reactor system using a genetic algorithm model‐based control approach

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