Modeling of Continuous Stirred Tank Reactor based on Artificial Neural Network
This paper presents the dynamic model identification algorithm of the continuous stirred tank reactor (CSTR) using a multi-layer perceptron (MLP) neural network topology. The neural network approach for (CSTR) dynamic modeling is trained by using a particle swarm optimization (PSO) technique as a s...
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| Format: | Article |
| Language: | English |
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Al-Nahrain Journal for Engineering Sciences
2017-05-01
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| Series: | مجلة النهرين للعلوم الهندسية |
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| Online Access: | https://nahje.com/index.php/main/article/view/178 |
| Summary: | This paper presents the dynamic model identification algorithm of the continuous stirred tank reactor (CSTR) using a multi-layer perceptron (MLP) neural network topology. The neural network approach for (CSTR) dynamic modeling is trained by using a particle swarm optimization (PSO) technique as a simple and fast training unsupervised algorithm. Polywog wavelet activation function is used in the structure of MLP neural network. The identification algorithm given in this paper has been proved to be reasonable and precise via Matlab simulation results in terms of fast, stable and minimum number of fitness evaluation for the CSTR modeling.
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| ISSN: | 2521-9154 2521-9162 |