PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

The semi-batch esterification of propionic anhydride (PA) with 2-butanol (BT) in the presence of catalyst can be optimised using an optimal control strategy, which utilises the reactor temperature (TR) and feed (FR) flowrate. However, the opposing objective functions, which are maximum conversion (XM) and minimum process time (tf) in the autocatalytic esterification process, could complicate the optimisation strategy. Simultaneous optimisation of various objectives results in a multi-objective optimal control (MOOC) problem with numerous solutions known as non-dominated (ND) points. In this paper, control vector parameterisation (CVP) and hybrid strategy (HS) are utilised to form Pareto Front (PF) for two opposite targets, which are first to increase XM and secondly to reduce tf. Each ND point comprises variant optimal dynamic tracks of TR and FR, which results in various targets of XM and tf. These solutions provide numerous options for evaluating trade-offs and deciding on the most efficient operating strategy. It is found that the ND point in zone II can be selected as the trade-off of the optimal TR and FR in this study.

• Azmi, A., Sata, S. A., Rohman, F. S., & Aziz, N. (2020). Optimization studies of low-density polyethylene process: Effect of different interval numbers. Chemical Product and Process Modeling, 15(4), Article 20190125. https://doi.org/10.1515/cppm-2019-0125

• Azmi, A., Sata, S. A., Rohman, F. S., & Aziz, N. (2021). Dynamic optimization of low-density polyethylene production in tubular reactor under thermal safety constraint. Chemical Industry and Chemical Engineering Quarterly, 27(1), 85-97. https://doi.org/10.2298/CICEQ190108027A

• Balsa-Canto, E., Henriques, D., Gábor, A., & Banga, J. R. (2016). AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology. Bioinformatics, 32(21), 3357-3359. https://doi.org/10.1093/bioinformatics/btw411

• Banga, J. R., Balsa-Canto, E., Moles, C. G., & Alonso, A. A. (2005). Dynamic optimization of bioprocesses: Efficient and robust numerical strategies. Journal of Biotechnology, 117(4), 407-419. https://doi.org/10.1016/j.jbiotec.2005.02.013

• De, R., Bhartiya, S., & Shastri, Y. (2019). Multi-objective optimization of integrated biodiesel production and separation system. Fuel, 243, 519-532. https://doi.org/10.1016/j.fuel.2019.01.132

• Faust, J. M., Hamzehlou, S., Leiza, J. R., Asua, J. M., Mhamdi, A., & Mitsos, A. (2019). Dynamic optimization of a two-stage emulsion polymerization to obtain desired particle morphologies. Chemical Engineering Journal, 359, 1035-1045. https://doi.org/10.1016/j.cej.2018.11.081

• Maiti, S. K., Lantz, A. E., Bhushan, M., & Wangikar, P. P. (2011). Multi-objective optimization of glycopeptide antibiotic production in batch and fed batch processes. Bioresource Technology, 102(13), 6951-6958. https://doi.org/10.1016/j.biortech.2011.03.095

• Rohman, F. S., & Aziz, N. (2020). Dynamic multi-objective optimization of autocatalytic esterification in semi batch by using control vector parameterization (CVP) and non-dominated sorting genetic algorithm (NSGA-II). IOP Conference Series: Materials Science and Engineering, 778, Article 012081. https://doi.org/10.1088/1757-899X/778/1/012081

• Rohman, F. S., Sata, S. A., & Aziz, N. (2016). Online dynamic optimization strategy for handling disturbance in semi batch autocatalytic esterification process: Application of hybrid optimizer and simple re-optimization activator. Advanced Science Letters, 22(10), 2729-2733. https://doi.org/10.1166/asl.2016.7021

• Rohman, F. S., Sata, S. A., Othman, M. R., & Aziz, N. (2021a). Dynamic optimization of autocatalytic esterification in a semi-batch reactor. Chemical Engineering & Technology, 44(4), 648-660. https://doi.org/10.1002/ceat.202000308

• Rohman, F. S., Sata, S. A., Othman, M. R., & Aziz, N. (2021b). Optimizing autocatalysis with uncertainty by derivative-free estimators. Optimal Control Applications and Methods, 42(1), 180-194. https://doi.org/10.1002/oca.2668

• Storn, R., & Price, K. (1997). Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341-359. https://doi.org/10.1023/A:1008202821328

• Ubrich O. (2000). Improving safety and productivity of isothermal semi batch reactor by modulating feed rate (Doctoral dissertation). Swiss Federal Institute of Technology Lausanne, Switzerland.

• Vassiliadis, V. S., Sargent, R. W., & Pantelides, C. C. (1994). Solution of a class of multistage dynamic optimization problems. 1. Problems without path constraints. Industrial & Engineering Chemistry Research, 33(9), 2111-2122. https://doi.org/10.1021/ie00033a014

• Zaldivar, J. M., Hernandez, H., Molga, E., Galvan, I. M., & Panetsos, F. (1993). The use of neural networks for the identification of kinetic functions of complex reactions. In Proceedings of the third European symposium on computer aided process engineering, ESCAPE (Vol. 3). Oxford Press.

• Zulkeflee, S. A., Rohman, F. S., Sata, S. A., & Aziz, N. (2021). Autoregressive exogenous input modelling for lipase catalysed esterification process. Mathematics and Computers in Simulation, 182, 325-339. https://doi.org/10.1016/j.matcom.2020.11.006