Applying Artificial Neural Network in Prediction behavior of alkylation of m-Cresol with isopropanol process and yield optimization by Bee Colony algorithm

Karami, Hamid; Soltanali, Saeed; Tayyebi, Shokoufe

Applying Artificial Neural Network in Prediction behavior of alkylation of m-Cresol with isopropanol process and yield optimization by Bee Colony algorithm

Document Type : Original Research

Authors

H. karami ¹

S. Soltanali ²

S. Tayyebi ³

¹ Department of Chemical and Petroleum Engineering, Sharif University of Technology

² Catalysis Technologies Development Division, Research Institute of Petroleum Industry

³ Refining Technologies Development Division, Research Institute of Petroleum Industry

Abstract

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 300^oC. 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.

Keywords

artificial neural network

Bee Colony Algorithm

Optimization

Alkylation of m‐Cresol

Thymol

Subjects

nano-catalyst

[1] Shapiro S., The Inhibitory Action of Fatty Acids on Oral Bacteria, Oral Microbiology and Immunology, 11(5), pp.350-355, 1996.
[2] Didry N P., Dubreuil L. and Pinkas M., Antibacterial Activity of Thymol, Carvacrol and Cinnamaldehyde Alone or in Combination, Die Pharmazie, 48(4), pp.301-304, 1993.
[3] Teissedre Pl. and Waterhouse Al., Inhibition of Oxidation of Human Low-Density Lipoproteins by Phenolic Substances in Different Essential Oils Varieties, Journal of Agricultural and Food Chemistry, 48(9), pp.3801-3805, 2000.
[4] Biedermann W., Koller H. and Wedemeyer K., Process for preparing thymol, United States patent US 4,086,283, 1978.
[5] Yadav GD., Pathre GS., Novel mesoporous solid superacidic catalysts: activity and selectivity in the synthesis of thymol by isopropylation of m-cresol with 2-propanol over UDCaT-4,-5, and-6. J Phys Chem A;109:11080–8;2005.
[6] Malkar RS, Yadav GD. Selectivity engineering in synthesis of thymol using sulfated ZrO2–TiO2. Ind Eng Chem Res;56:8437–47;2017.
[7] Shelokar P., Kulkarni A., Jayaraman VK., Siarry P., Metaheuristics in process engineering: a historical perspective. Appl. Metaheuristics Process Eng., Springer, p. 1–38; 2014.
[8] Valadi J., Siarry P., Applications of metaheuristics in process engineering. Springer; 2014.
[9] Ganesan T., Vasant P., Elamvazuthi I., Advances in metaheuristics: applications in engineering systems. 1st Editio. Boca Raton: CRC Press; 2016.
[10] Sakthivel G., Prediction of Ci Engine Performance, Emission and Combustion Characteristics Using Fish Oil as a Biodiesel at Different Injection Timing Using Fuzzy Logic, Fuel 183, pp.214-229, 2016.
[11] Petit J., Zupan J., Leherte L., Vercauteren DP., Application of a Kohonen neural network to the analysis of data regarding the alkylation of toluene with methanol catalyzed by ZSM-5 type zeolites. Comput Chem 26, pp.557–72, 2002.
[12] Sun XY, Xiang SG. Product Distributions of Benzene Alkylation with Propylene Estimation Using Artificial Neural Network (ANN). Adv. Mater. Res., vol. 772, Trans Tech Publ, p.p 227–32, 2013.
[13] Mahmoudian F, Moghaddam AH, Davachi SM. Genetic‐based multi‐objective optimization of alkylation process by a hybrid model of statistical and artificial intelligence approaches. Can J Chem Eng 2021.
[14] Afreen G, Pathak S, Upadhyayula S. Gas phase alkylation of biomass-derived m-cresol with iso-propanol over zinc modified HY zeolite: Elucidating reaction mechanism and kinetics including deactivation. Chem Eng J, 400:125824, 2020.
[15] Ncanana ZS, Pullabhotla VSRR. Oxidative degradation of m-cresol using ozone in the presence of pure γ-Al2O3, SiO2 and V2O5 catalysts. J Environ Chem Eng 7,103072, 2019.
[16] Shahhosseini S, Vakili S. Optimization of styrene reactor using tabu search and genetic algorithm methods. Int J Chem React Eng , 9 , 2012.