Control of polystyrene batch reactors using neural network based model predictive control (NNMPC): An experimental investigation

Hosen, Mohammad Anwar; Hussain, Mohd Azlan; Mjalli, Farouq S.

doi:10.1016/j.conengprac.2011.01.007

Cited by 115 publications

(61 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…(16)). The objective function of the NNMPC strategy can be written in the form as follows: (16) subject to the optimal feed forward neural network model , NN2…”

Section: Nn-based Model Predictive Control (Nnmpc)mentioning

confidence: 99%

“…In this way, neural networks offer alternative nonlinear models for implementing MPC in such as systems [10][11][12][13]. The applications of neural networks for chemical process modeling and MPC have also been investigated for SISO systems and iterative multistep neural network predictions in MPC based control for MIMO chemical processes [14][15][16][17][18][19]. Production of a uniform and reproducible CSD is a main difficulty in batch crystallization [20,21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

Kittisupakorn¹,

Somsong²,

Hussain³

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract. A main difficult task in batch crystallization is to control the size distribution of crystal products. Complexity and highly nonlinear dynamic behavior directly affect to model-based control strategies which heavily depend on the rigorous knowledge of crystallization. In this work, neural network-based model predictive control and inverse neural network control strategies are proposed and integrated with an optimization based on neural network-based hybrid model to control temperatures of a purified terephthalic acid batch crystallizer. A neural network-based hybrid model of the batch crystallizer is developed to provide nonlinear dynamic responses used in optimization algorithm for finding an optimal temperature profile related to the quality of a crystal product. Then, the obtained optimal profile is used as set points of the proposed control strategies for improving the crystal product quality. The performances and robustness of the proposed controllers are evaluated in several cases such as for set point tracking and plant/model mismatches. Simulation results show that the neural network-based model predictive control gives the best control performance among the inverse neural network control and a conventional PID controller in all cases.

show abstract

“…(16)). The objective function of the NNMPC strategy can be written in the form as follows: (16) subject to the optimal feed forward neural network model , NN2…”

Section: Nn-based Model Predictive Control (Nnmpc)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

Kittisupakorn¹,

Somsong²,

Hussain³

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this context, the models based on ANNs are presented as a powerful tool for modeling static and dynamic systems, with large nonlinearities and high dead-time, mainly due to two fundamental qualities: rapid adaptability and intrinsic approach (De Souza Jr et al, 1996;Krothapally and Palanki, 1997;Yu and Yu, 2003;Zhang, 2003;Ng and Hussain, 2004;Hosen et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Deviations that occur in the system, which are not predicted in the model, cause various control problems, such as high overshoot, offset, and others. In this case, adaptive techniques can be used to adjust the weights of the network and prevent such deviations from occurring (Zeybec et al, 2003;Ng and Hussain;2004;Marcolla et al, 2009, Hosen et al, 2011.…”

Section: Introductionmentioning

confidence: 99%

Predictive Control of a Batch Polymerization System Using a Feedforward Neural Network With Online Adaptation by Genetic Algorithm

Cancelier

Claumann

Bolzan

et al. 2016

Braz. J. Chem. Eng.

View full text Add to dashboard Cite

-This study used a predictive controller based on an empirical nonlinear model comprising a threelayer feedforward neural network for temperature control of the suspension polymerization process. In addition to the offline training technique, an algorithm was also analyzed for online adaptation of its parameters. For the offline training, the network was statically trained and the genetic algorithm technique was used in combination with the least squares method. For online training, the network was trained on a recurring basis and only the technique of genetic algorithms was used. In this case, only the weights and bias of the output layer neuron were modified, starting from the parameters obtained from the offline training. From the experimental results obtained in a pilot plant, a good performance was observed for the proposed control system, with superior performance for the control algorithm with online adaptation of the model, particularly with respect to the presence of off-set for the case of the fixed parameters model.

show abstract

“…In particular, the recurrent neural models of perceptron type with one hidden layer [16,18] are successfully used for approximation of numerous dynamic systems, e.g. a polystyrene batch chemical reactor [19], an ethylene-ethane distillation column and a polymerisation reactor [1], a neutralisation reactor [20], a fluid catalytic cracking unit [21].…”

mentioning

confidence: 99%

Pruning of recurrent neural models: an optimal brain damage approach

Chaber

Ławryńczuk

2018

Nonlinear Dyn

View full text Add to dashboard Cite

This paper considers the problem of pruning recurrent neural models of perceptron type with one hidden layer which may be used for modelling of dynamic system. In order to reduce the number of model parameters (i.e. the number of weights), the Optimal Brain Damage (OBD) pruning algorithm is adopted for the recurrent neural models. Efficiency of the OBD algorithm is demonstrated for pruning neural models of a neutralisation reactor benchmark process. For the considered neutralisation system, the OBD algorithm makes it possible to reduce as many as 60% of model parameters and reduce the validation error by some 30% when compared to the full (unpruned) models.

show abstract

Control of polystyrene batch reactors using neural network based model predictive control (NNMPC): An experimental investigation

Cited by 115 publications

References 48 publications

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

Predictive Control of a Batch Polymerization System Using a Feedforward Neural Network With Online Adaptation by Genetic Algorithm

Pruning of recurrent neural models: an optimal brain damage approach

Contact Info

Product

Resources

About