Study on the Simulation Control of Neural Network Algorithm in Thermally Coupled Distillation
Asian Journal of Research in Computer Science · pp. 53–64 · Published 9 Jul 2021
10.9734/ajrcos/2021/v10i330244Abstract
Thermally coupled distillation is a new energy-saving method, but the traditional thermally coupled distillation simulation calculation process is complicated, and the optimization method based on the traditional simulation process is difficult to obtain a good feasible solution. The neural network algorithm has the advantages of fast learning and can approach nonlinear functions arbitrarily. For the problems in complex process control systems, neural network control does not require cumbersome control structures or precise mathematical models. When training the network, only the input and output samples it needs are given, so that the dynamics of the system can be controlled. In this way, the dynamic performance of the system can be approximated. This method can effectively solve the mathematical model of the thermally coupled distillation process, and quickly obtain the solution of the optimized variables and the objective function. This article summarizes the research progress of artificial neural network and the optimization control of thermally coupled distillation and the application of neural network in thermally coupled distillation.
Cited by 11
Mengyu Wang, Zhu Zhu · Asian Journal of Chemical Sciences · 2022
Dian Jin, Jumei Xu · Asian Journal of Chemical Sciences · 2022
Jing Sun, Qi Tang · Journal of Engineering Research and Reports · 2021
Chunyu Wang, Lujie Wang · Journal of Engineering Research and Reports · 2023
Tuba Siraj Ansari, S. A. Taqvi · Periodica Polytechnica: Chemical Engineering · 2025
Lei Wang, Jiacong Kang · Asian Journal of Advanced Research and Reports · 2022
Bing-Qiang He, Yue Si · Asian Journal of Research in Computer Science · 2022
T. An, Ju-Mei Xu · Asian Journal of Advanced Research and Reports · 2022
Po Li, Zaoyong Lu · Asian Journal of Research in Computer Science · 2022
Jing Sun, Qi Tang · arXiv.org · 2021
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