Multi-response optimization of charcoal briquettes process for green economy using a novel TOPSIS linear programming and genetic algorithms based on response surface methodology

Abstract

The production of charcoal briquettes was based on the concept of utilizing waste for sustainable and eco-friendly solutions within the green economy. This study introduces a new approach for optimizing important properties of charcoal briquettes. The approach aims to determine the appropriate operating conditions using a hybrid method. It integrates a novel TOPSIS linear programming with genetic algorithms based on response surface methodology. The experiment used an optimal mixture ratio of 25:10:6, including coconut shell, bagasse ash, and corncob. The results of the responses showed increased heating value and burning time by 5.13 % and 5.71 %, respectively, and moisture content decreased by 15.12 %. Furthermore, confirmation experiments within a 95 % confidence interval verified that the three responses obtained at the optimal operating conditions of process factors. The total cost of charcoal briquettes is 0.903 USD/kg and a payback period of 4.85 years. This illustrates that process improvement also aligns with the broader goals of promoting green and a circular economy.