Optimizing Activated Carbon Production from Waste Cashew Nut Shell with Zinc Chloride: A Box-Behnken Design and Group Method of Data Handling (GMDH) Application

Abstract

In this study, Response surface methodology (RSM) and innovative Group Method of Data Handling (GMDH) approaches are applied to investigate the optimal process conditions of zinc chloride activated cashew nut production process. The effects of activation conditions (i.e. activation temperature, activation time, and impregnation ratio) on the achievable BET surface areas were studied with the aid of Box Behnken Design (BBD) and GMDH. Comparative analyses of RSM and GMDH-type neural models were further researched. During the process, the polynomial model equations developed were modified and fine-tuned to predict the highest BET surface area(s) using regression analysis and GMDH multi-layered iterative algorithm (MIA). Analysis of Variance (ANOVA) revealed that the significant factor(s) were impregnation ratio, impregnation ratio product, and the 2-way interactions (activation temperature and impregnation ratio) for ZnCl2 activated cashew nut shell. The best activation conditions for producing highest BET surface area of 504 m2.g-1 was activation temperature (873K), activation time (60 min), and impregnation ratio (1.50).The proposed GMDH-type BET model was ascertained to be the best model with average correlation coefficient (R) and root mean square error (RMSE) of 0.925 and 32.0 respectively. Sensitivity analysis conducted for GMDH-type neural network also revealed that the activation temperature and activation time with sensitivity values of 90.6% and 74.1% respectively were the most influential parameters in the basic (ZnCl2) activation process. The results of this study show that RSM and GMDH-type neural network could be applied as effective analytical tools for optimizing the ZCNS (zinc chloride-activated cashew nut shells) manufacturing process.