<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this research work, neural network based single loop and cascaded control strategies, based on Feed Forward Neural Network trained with Back Propagation (FBPNN) algorithm is carried out to control the product composition of reactive distillation. The FBPNN is modified using the steepest descent method. This modification is suggested for optimization of error function. The weights connecting the input and hidden layer, hidden and output layer is optimized using steepest descent method which causes minimization of mean square error and hence improves the response of the system. FBPNN, as the inferential soft sensor is used for composition estimation of reactive distillation using temperature as a secondary process variable. The optimized temperature profile of the reactive distillation is selected as input to the neural network. Reboiler heat duty is selected as a manipulating variable in case of single loop control strategy while the bottom stage temperature T9 is selected as a manipulating variable for cascaded control strategy. It has been observed that modified FBPNN gives minimum mean square error. It has also been observed from the results that cascaded control structure gives improved dynamic response as compared to the single loop control strategy.</p></abstract>

In this research work, neural network based single loop and cascaded control strategies, based on Feed Forward Neural Network trained with Back Propagation (FBPNN) algorithm is carried out to control the product composition of reactive distillation. The FBPNN is modified using the steepest descent method. This modification is suggested for optimization of error function. The weights connecting the input and hidden layer, hidden and output layer is optimized using steepest descent method which causes minimization of mean square error and hence improves the response of the system. FBPNN, as the inferential soft sensor is used for composition estimation of reactive distillation using temperature as a secondary process variable. The optimized temperature profile of the reactive distillation is selected as input to the neural network. Reboiler heat duty is selected as a manipulating variable in case of single loop control strategy while the bottom stage temperature T9 is selected as a manipulating variable for cascaded control strategy. It has been observed that modified FBPNN gives minimum mean square error. It has also been observed from the results that cascaded control structure gives improved dynamic response as compared to the single loop control strategy.

Modified neural network based cascaded control for product composition of reactive distillation

Madhav Institute of Technology & Science, Gwalior-474005, India

SGB Amravati University, Amravati-444062, India

Polish Journal of Chemical Technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

In this research work, neural network based single loop and cascaded control strategies, based on Feed Forward Neural Network trained with Back Propagation...

Modified neural network based cascaded control for product composition of reactive distillation

Data publikacji: 30 cze 2016

Zakres stron: 111 - 121

DOI: https://doi.org/10.1515/pjct-2016-0037

Słowa kluczowe
composition, inferential control, neural network, reactive distillation, soft sensor

© 2016 Vandana Sakhre et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Modified neural network based cascaded control for product composition of reactive distillation

Data publikacji: 30 cze 2016

Zakres stron: 111 - 121

DOI: https://doi.org/10.1515/pjct-2016-0037

Słowa kluczowecomposition, inferential control, neural network, reactive distillation, soft sensor

© 2016 Vandana Sakhre et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
composition, inferential control, neural network, reactive distillation, soft sensor