Optimisation of Multiple Response Processes Using Different Modeling Techniques

Abstract

Purpose:  This article aims to compare the impact on process optimisation with multiple responses of two different mathematical modelling methods: Ordinary Least Squares Method (OLS) and Symbolic Regression Method (SR). Methodology/Approach:  Data from the literature were selected from the design of experiments for a process with multiple responses. Using these data, models were obtained that represented each response as a function of independent variables using the OLS and SR techniques. Then, the Desirability method was applied together with the Generalized Reduced Gradient (GRG) in order to obtain the process adjustment that would lead to the optimisation of the responses. Findings:  The findings illustrate that the SR modelling technique yields models with superior predictive capabilities when contrasted with the OLS technique. Throughout the optimisation process, it becomes evident that the adjustments in the process diverge, even though the desirability function's value exhibits negligible variation. Research Limitation/implication: This research considered only an SR algorithm and a process with two dependent variables and two independent variables. Originality/Value of paper: No works were found in the literature that reported the use of the Age-Layered Population Structure (ALPS) algorithm in modelling processes that contain multiple responses. Furthermore, no comparison of this method with the OLS method was available. Category: Research paper.