The Use of Factorial Design to Improve a Harmony Search Algorithm to Synthesize Heat-Integrated Distillation Sequences

Abstract

Optimization problems often involve a large number of design variables, and the exact influence of each of these variables upon the objective function can become rather complex; there may exist local optima for the objective function, but for the typical heat-integrated distillation sequence, the matter of interest is solely the global optimum. Therefore, it is necessary to create a stochastic algorithm method which can synthesize distillation systems with multiple components. The encoding process employs and integer number series which allows the system flow sheet structure to be portrayed and then managed. Within this portrayal, the broad synthesis problem takes the form of an implicit MILP (mixed-integer linear programming) problem. This study considers the attributes of six well-known optimization algorithms: Harmony Search algorithm (HS), Artificial Bee Colony (ABC), Bat Algorithm (BA), Crow Search Optimization (CSO), Grew Wolf Optimization (GWO) and Monarch Butterfly Optimization (MBO). The optimal variables which influence the harmony search algorithm can be determined through full factorial design analysis. These variables can then be employed in the search to discover the optimal heat-integrated distillation sequence. The study investigates the attributes of the optimal configuration solution, in terms of harmony size (HS), required number of iterations, harmony memory considering the rate (HMCR), and pitch adjustment rate (PAR). The study then adopts the HS algorithm which is duly improved in order to address the problem. In comparison with alternative techniques, HS is more effective and more robust than other approaches.