Performance metrics analysis of dynamic multi-objective optimization for energy consumption and productivity improvement in batch electrodialysis

Abstract

Empty fruit bunch (EFB) can be utilized to produce sugar via hydrochloric acid (HCl) hydrolysis where the HCl must be concentrated to retain the pure sugar. Electrodialysis (ED) is an effective method for acid-sugar separation and has to be optimized to ensure that the most economical operation can be achieved. However, ED batch optimization consists of conflicting multiple objectives that are required to be solved as a dynamic multi-objective optimization (MOO) problem. In the past, the dynamic optimization of acid-sugar ED separation was carried out by solving a single objective optimization problem in sequence but this approach could not be accomplished with different objective functions simultaneously. In this work, the results of the MOO which consist of a number of optimal solutions are configured as a Pareto Front (PF). The weighted sum, the ε-constraint, and the elitist non-dominated sorting genetic algorithm or NSGA-II approaches are applied to solve the conflicting objectives functions i.e. minimization of energy consumption and maximization of acid concentration These result in a set of several Pareto optimal solutions with two objectives: maximizing concentrated acid ranging from 1.89 M to 2.1 M is accompanied by minimizing energy consumption ranging from 630 Wh to 848 Wh. Each point of the Pareto solutions contains a different combination of optimal current density and flowrate trajectories which promotes a different amount of energy consumption and acid concentration. This set of solutions provides choices in assessing the tradeoffs and determining the most appropriate operating policy.