Response surface methodology (RSM): An overview to analyze multivariate data

Abstract

In recent years, the fascinating range of Response surface methodology (RSM) applications has captured the interest of many researchers and engineers worldwide. RSM is entirely based on well-known regression principles and variance analysis principles that enable the user to improve, develop and optimize the process or product under study. An overview of the theoretical principles of RSM, the experimental strategy and its tools and components, along with the applications and pros and cons, are described in this paper. Some of the widely used experimental designs of RSM compared in terms of its characteristics and efficiency are included, which helps to point out the importance of design of experiments (DOE) in optimization using RSM. The live demonstrations of a few optimization examples using response surface methodology in different research manuscripts included in this paper also provide a better understanding of the characteristics of RSM in different scenarios.

Study on the Performance Optimization of Plant-Growing Ecological Concrete

“…Response Surface Methodology (RSM) is an experimental design approach that uses logical experiment point selection to obtain data from a smaller number of experimental groups. It employs a multivariate regression quadratic equation to fit response factors and values, optimizing design objectives through the regression equation [17,18]. Compared to other experimental design methods, the regression model established by RSM can effectively reflect the interaction of multiple factors, thus precisely and effectively predicting various indicators of the research objective.…”

Heavy metals rejection from water by CMC complexation- PVDF ultrafiltration system: Box-Behnken design optimization

“…In this regard, practical suitability is important to consider as it should ideally be operated using the highest output while incurring the least costs. Response surface methodology (RSM) is an important tool in this regard by which the in uence of various selected parameters can be obtained while the required output can be maximized by obtaining optimized conditions [30,31]. RSM methods have been applied using various designs such as central composite design, Doehlert matrix, neural networks, and Box-Behnken design [13,[32][33][34].…”

Maximizing biodiesel yield of a non-edible chinaberry seed oil via microwave assisted transesterification process using response surface methodology and artificial neural network techniques

“…A statistical technique for enhancing the process parameters of chemical reactions is called Response Surface Methodology (RSM) [ 15 ]. To create the best possible operating circumstances, RSM creates an appropriate experimental design model [ 16 ]. RSM provides a number of benefits, including the ability to simultaneously analyze many components and their interactions, minimize the number of tests required, and model intricate correlations between variables [ 17 ].…”