Probabilistic-based Multi-objective Optimization of Aromatic Extraction Process

Abstract

Aromatics extraction is a crucial step in the aromatics production process. Optimization of aromatics extraction process is of great significance for enhancing the overall efficiency of the aromatics unit system with minimizing process energy consumption. The purity and energy consumption of a product are fundamental metrics that need to be optimized simultaneously, which thus make it a multi-objective optimization problem. However, a careful analysis reveals that previous multi-objective optimization methods lack a clear perspective despite having algorithms. This article provides a procedure for maximizing product purity and minimizing process energy consumption during aromatic extraction by means of probabilistic multi-objective optimization (PMOO) together with regression so as to supply optimum parameters of aromatic extraction. PMOO is based on the viewpoint of systems theory, which adopts the method of probability theory to deal with the problem of simultaneous optimization of multiple objectives, and introduces the concept of "preferable probability", it establishes a methodology of probabilistic multi - objective optimization. The evaluated objectives of the candidate in the optimization task are preliminarily divided into two basic types, i.e., the beneficial type and the unbeneficial (cost) type, and the corresponding quantitative evaluation methods of partial preferable probabilities are formulated for these two types. Taking the overall optimization of the optimal problem as a system, the simultaneous optimization of multiple attributes is analyzed as the simultaneous occurrence of multiple events in probability theory. Therefore, the total preferable probability of each alternative candidate is the product of partial preferable probabilities of all possible attributes of the alternative candidate, which optimizes the system as a whole. Finally, all alternative candidates are sorted and optimized according to their values of the total preferable probabilities. Beside, in the optimization, the functional relationship between the total preferable probability and input variables is regressed to get the optimum status and corresponding parameters with reliable limited test data. This method opens up a new way to solve multi-objective problems and has broad application prospects.