OPTIMIZATION OF CHEMICAL SYNTHESIS OUTPUT WITH TOPSIS

The present study focuses on a new application of a decision-making process using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for the optimization of the chemical synthesis output. This investigation is important as many chemical reactions have been performed in labs without any analysis of their optimization. The factors that affect the chemical synthesis output such as catalyst, nanosensor network, and temperature have been considered in this study. Moreover, labor that corresponds to the prices of chemicals that are used in chemical reactions has also been considered. Different chemical synthesis procedures with or without these factors have been analyzed in the current study. In the first series of analyses, the same weight values were considered for all criteria, whereas in the second series of analyses, the weight values for the nanosensor network and labor were more than those of catalyst and temperature. The obtained results showed that the consideration of profit criteria and cost criteria and equal or different weights for the candidates could affect the output of TOPSIS. Therefore, the prediction of the chemical synthesis output using this algorithm for three different conditions for performing chemical reactions. Moreover, it was shown that different considerations of these conditions could help optimize the reactions. In the first series of analysis, the second candidate was ranked in the first position, whereas the third candidate and the first candidates were positioned in the second and third positions, respectively. The ranking of candidates was different in the second series of analysis as the first, second, and third candidates were ranked in the first, second, and third positions, respectively. The results of this investigation can be used for the optimization of chemical reactions and lab procedures.

[1] Bartlewicz, O. et al. Heterogeneous catalysis with the participation of ionic liquids, Catalysts, 10(11), 1227, 2020. https://doi.org/10.3390/catal10111227.

[2] Chen, W. et al. Mesoporous cobalt-iron-organic frameworks: plasma-enhanced oxygen evolution electrocatalyst, Journal of Materials Chemistry A, 7(7), 2019. https://doi.org/10.1039/C8TA10952D.

[3] Vogiatzis, K.D. et al. Computational approach to molecular catalysis by 3d transition metals: Challenges and opportunities, 119, 4, 2453-2523, 2019. https://doi.org/10.1021/acs.chemrev.8b00361.

[4] Zarepour, E. et al. Nano sensor networks for tailored operation of highly efficient gas-to-liquid fuels catalysts, The University of New South Wales, Technical Report, UNSW-CSE-TR-201318, 2013.

[5] Chodosh, D.F. et al. Automated chemical synthesis. Part 3: Temperature control systems, Journal of Automatic Chemistry, 5(2), 103-107, 1983.

[6] Synthesis and manufacturing: Creating and exploiting new substances and new transformations in Beyond the Molecular Frontier: Challenges for Chemistry and Chemical Engineering, Washington: National Academies Press (US); 2003.

[7] Levin, I. et al. Merging enzymatic and synthetic chemistry with computational synthesis planning, Nature Communications, 13, 7747, 2022. https://doi.org/10.1038/s41467-022-35422-y.

[8] Huang, W.F. An improved TOPSIS method for material selection model, Advanced Materials Research, 951, 120-123, 2014. https://doi.org/10.4028/www.scientific.net/AMR.951.120.

[9] Ma, F. et al. A comprehensive MCDM model combining TOPSIS and IEM for sustainable material selection considering life cycle assessment method, IEEE Access, 99, 1-1, 2018. https://doi.org/10.1109/ACCESS.2018.2875038.

[10] Javanbakht, T. Optimization of Cdx transcription factors characteristics, Journal of Engineering Sciences, 10(2), E1-E7, 2023. https://doi.org/10.21272/jes.2023.10(2).e1.

[11] Zakeri, S. et al. A decision analysis model for material selection using simple ranking process, Scientific Reports, 8631, 2023. https://doi.org/10.1038/s41598-023-35405-z.

[12] Javanbakht, T., Chakravorty, S. Prediction of human behavior with TOPSIS, Journal of Fuzzy Extension and Applications, 3 (2), 109-125, 2022.

[13] Javanbakht, T., Chakravorty, S. Optimization of machine learning algorithms for proteomic analysis using topsis, Journal of Engineering Sciences, 9 (2), E7-E12, 2022.

[14] Amrabadi, T. et al. Application of TOPSIS algorithm in describing bacterial cellulose-based composite hydrogel performance in incorporating methylene blue as a model drug, Scientific Reports, 13, 2755, 2023.

[15] Javanbakht, T. Analysis of nanoparticles characteristics with TOPSIS for their manufacture optimization, Journal of Engineering Sciences, 9 (2), C1-C8, 2022.

[16] Javanbakht, T. Optimization of physical instruments' characteristics with TOPSIS, Ukrainian Journal of Mechanical Engineering and Materials Science, 8 (3), 1-9, 2022.

[17] Jha, K. et al. Application of modified TOPSIS technique in deciding optimal combination for bio-degradable composite, Vacuum, 157, 259-267, 2018.

[18] Sanjay, M.R. et al. TOPSIS method for selection of best composite laminate, in Modelling of Damage Processes in Biocomposites, Fibre-Reinforced Composites and Hybrid Composites, Woodhead Publishing Series in Composites Science and Engineering, 199-209, 2019.

[19] Dukkipati, B.N. et al. TOPSIS ranking of epoxy hybrid composites, IJESC, 15244, 2017.

[20] Javanbakht, T., Laurent, S., Stanicki, D., Frenette, M. (2020). Correlation between physicochemical properties of superparamagnetic iron oxide nanoparticles and their reactivity with hydrogen peroxide, Canadian Journal of Chemistry, Vol. 98(10), pp. 601–608. https://doi.org/10.1139/cjc-2020-0087.

[21] Javanbakht, T., Laurent, S., Stanicki, D., David, E. Related physicochemical, rheological, and dielectric properties of nanocomposites of superparamagnetic iron oxide nanoparticles with polyethyleneglycol, Journal of Applied Polymer Science, 136:48280-48290, 2019. https://doi.org/10.1002/app.48280.

[22] Javanbakht, T., Sokolowski, W. Thiol-ene/acrylate systems for biomedical shape-memory polymers, Shape Memory Polymers for Biomedical Applications, 157-166, 2015. https://doi.org/10.1016/B978-0-85709-698-2.00008-8.

[23] Javanbakht, T., Hadian, H., Wilkinson, K.J. Comparative study of physicochemical properties and antibiofilm activity of graphene oxide nanoribbons, Journal of Engineering Sciences, 7(1):C1-C8, 2020. https://doi.org/10.21272/jes.2020.7(1).c1.

[24] Soares, S. et al. Nanomedicine: Principles, properties, and regulatory issues, Frontiers, 6, 2018.

[25] Singh, J. Electronic and optoelectronic properties of semiconductor structures, Cambridge University Press, 2003.

[26] Javanbakht, T., Ghane-Motlagh, B., Sawan, M. Comparative study of antibiofilm activity and physicochemical properties of microelectrode arrays, Microelectronic Engineering, 229:111305, 2020. https://doi.org/10.1016/j.mee.2020.111305.

[27] Javanbakht, T., David, E. Rheological and physical properties of a nanocomposite of graphene oxide nanoribbons with polyvinyl alcohol, Journal of Thermoplastic Composite Materials, 0892705720912767, 2020. https://doi.org/10.1177/0892705720912767.

[28] Murugan, S.S. Mechanical properties of materials: Definition, testing and application, International Journal of Modern Studies in Mechanical Engineering, 6, 2, 28-38, 2020.

[29] Djavanbakht, T., Carrier, V., André, J.M., Barchewitz, R, Troussel, P. Effets d'un chauffage thermique sur les performances de miroirs multicouches Mo/Si, Mo/C et Ni/C pour le rayonnement X mou, Journal de Physique IV, France, 10:281-287, 2000. https://doi.org/10.1051/jp4:20001031.

[30] Nagadeepan, A. et al. Advanced optimization of surface characteristics and material removal rate for biocompatible Ti6Al4V using WEDM process with BBD and NSGA II, Materials, 16(14), 4915, 2023.

[31] Wierzbanowski, K. et al. Optimization of material properties using genetic algorithms, Materials Science Forum, 652, 1-6, 2010.

[32] Gan, N., Wang, Q. Topology optimization of multiphase materials with dynamic and static characteristics by BESO method, Advances in Engineering Software, 151, 102928, 2021.

[33] Zhang, Y. et al. Bayesian optimization for materials design with mixed quantitative and qualitative variables, Scientific Reports, 4924, 2020.

[34] Deshwal, A. et al. Bayesian optimization of nanoporous materials, Molecular Systems Design and Engineering, 6, 12, 1066-1086, 2021.

[35] Allen, A.A., Tkatchenko, A. Machine learning of material properties: Predictive and interpretable multilinear models, Science Advances, 8, 18, 2022.

[36] Tian, X.-L. et al. Machine learning-guided property prediction of energetic materials: Recent advances, challenges, and perspectives, Energetic Materials Frontiers, 3, 3, 177-186, 2022.

[37] Ward, L. et al. A general-purpose machine learning framework for predicting properties of inorganic materials, Computational Materials, 16028, 2016.

[38] Chibani, S., Coudert, F.-X. Machine learning approaches for the prediction of materials properties, APL Materials, 8(8), 080701, 2020.

[39] Vasicek, D. Artificial intelligence and machine learning: Practical aspects of overfitting and regularization, Information Services and Use, 39(4), 1-9, 2019.

[40] Ohlsson, C. Exploring the potential of machine learning, Thesis, 2017.

[41] Kim, P., MATLAB deep learning: With machine learning, neural networks and artificial intelligence, Apress, 2017.

[42] Morgan, D., Jacobs, R. Opportunities and challenges for machine learning in materials science, Annual Review of Materials Research, 50, 71-103, 2020.

[43] Haykin, S. Neural networks and learning machines, Prentice Hall, 2009.