Volume 5, Issue 4 (2021)
IQBQ 2021, 5(4): 69-78 |
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karami H, Soltanali S, Tayyebi S. Applying Artificial Neural Network in Prediction behavior of alkylation of m-Cresol with isopropanol process and yield optimization by Bee Colony algorithm. IQBQ 2021; 5 (4) : 5
URL: http://arcpe.modares.ac.ir/article-38-53258-en.html
URL: http://arcpe.modares.ac.ir/article-38-53258-en.html
1- Department of Chemical and Petroleum Engineering, Sharif University of Technology
2- Catalysis Technologies Development Division, Research Institute of Petroleum Industry , sssoltan@gmail.com
3- Refining Technologies Development Division, Research Institute of Petroleum Industry
2- Catalysis Technologies Development Division, Research Institute of Petroleum Industry , sssoltan@gmail.com
3- Refining Technologies Development Division, Research Institute of Petroleum Industry
Abstract: (1331 Views)
Research subject: In recent decades, hybrid optimizations methods based on natural phenomenon have placed special position according to their capabilities in finding optimal solutions without expensive computational loads and disassociation on choosing initial points. Artificial Neural Network is used as one of the powerful tools of Artificial Intelligence for process simulation. The employment of the neural network in the modeling of m-Cresol alkylation process of with isopropanol as well as meta-heuristic methods in obtaining the optimal conditions for the catalyst and the reaction can prepare an effective step towards a high efficiency process.
Research approach: In the present study, the artificial neural network is applied to model alkylation of m‐Cresol with isopropanol process. In addition, the bee colony is employed in order to optimize the process yield. To verify its performance, the proposed method is used in prediction of the m‐Cresol conversion and Thymol selectivity of the alkylation process with isopropanol 120 data. In this process, the input variables are Weight Hourly Space Velocity (WHSV), pressure and temperature; m-cresol conversion and thymol selectivity are considered as the output variables of the neural network. Five hidden neurons are considered for the proposed neural network. 120 data is used to train the neural network. The meta-heuristic approach based on bee colony (BC) is applied to maximize the yield of the process.
Main results: The results confirm that the proposed method develops the accurate model with an R2 value of greater than 97.5%. The maximum yield is obtained 28.9% by bee colony algorithm with adjustable variables that are WHSV of 0.062 hr-1, the pressure of 1.5 bar and the temperature of 300oC. In addition, in order to achieve the better performance of the optimization algorithm, the appropriate values of acceleration coefficient and population size are chosen 100 and 10 during the trial-and-error phase.
Research approach: In the present study, the artificial neural network is applied to model alkylation of m‐Cresol with isopropanol process. In addition, the bee colony is employed in order to optimize the process yield. To verify its performance, the proposed method is used in prediction of the m‐Cresol conversion and Thymol selectivity of the alkylation process with isopropanol 120 data. In this process, the input variables are Weight Hourly Space Velocity (WHSV), pressure and temperature; m-cresol conversion and thymol selectivity are considered as the output variables of the neural network. Five hidden neurons are considered for the proposed neural network. 120 data is used to train the neural network. The meta-heuristic approach based on bee colony (BC) is applied to maximize the yield of the process.
Main results: The results confirm that the proposed method develops the accurate model with an R2 value of greater than 97.5%. The maximum yield is obtained 28.9% by bee colony algorithm with adjustable variables that are WHSV of 0.062 hr-1, the pressure of 1.5 bar and the temperature of 300oC. In addition, in order to achieve the better performance of the optimization algorithm, the appropriate values of acceleration coefficient and population size are chosen 100 and 10 during the trial-and-error phase.
Article number: 5
Keywords: Artificial Neural Network, Bee Colony Algorithm, Optimization, Alkylation of m‐Cresol, Thymol
Article Type: Original Research |
Subject:
nano-catalyst
Received: 2021/06/12 | Accepted: 2021/10/17 | Published: 2022/04/25
Received: 2021/06/12 | Accepted: 2021/10/17 | Published: 2022/04/25
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