Dynamic Multi‐Objective Optimization of Autocatalytic Esterification in a Semi‐Batch Reactor

Abstract

The catalytic esterification of sec-butyl propionate in a semi-batch reactor is examined using dynamic, nonlinear programming-based optimization. To solve the conflicting bi-objective of minimizing the final time t f and maximizing the conversion X c , the e-constraint, weighted sum, and NSGA-II (non-dominated sorting genetic algorithm II) techniques were implemented. By computing performance measurements like hypervolume, pure diversity, and spacing, these multiobjective optimization techniques were compared to the characteristics of the Pareto solution. Each Pareto solution point comprises a unique combination of optimal feed flow rate and temperature trajectories. These solutions provide various options for evaluating trade-offs while establishing the best operating strategy.