Optimization and performance prediction of carbon dioxide adsorption on chitosan/activated carbon/epichlorohydrin composite materials using Box–Behnken design and artificial neural network approaches - Loryuenyong, Vorrada; Nakhlo, Worranuch; Srikaenkaew, Praifha; Yaidee, Panpassa; Eiad-Ua, Apiluck; Buasri, Achanai (My University, 2025)
Spent coffee grounds (SCGs) can be used as biomass to synthesize activated carbon (AC) through physical carbonization and chemical activation. Epichlorohydrin (EP) was used to create the chitosan (CS) and AC biopolymer composites via emulsion crosslinking. The main goal of this research is to boost the efficiency of CS/AC/EP composite materials for carbon dioxide (CO2) capture by adsorption. The impact of CS content, AC concentration, and EP quantity on CO2 removal was studied applying the Box–Behnken design (BBD)-based response surface methodology (RSM) and artificial neural network (ANN)-based artificial intelligence (AI) models. The conditions for the adsorption process were optimized to forecast the maximum CO2 adsorption utilizing BBD and ANN approaches. Optimal process parameters of 15.11 g CS content, 38.95 %w/w AC concentration, and 7.16 g EP quantity resulted in a CO2 adsorbed of approximately 7.62 cm3/g. The coefficient of determination (R2) for the BBD model was 0.9995, while the correlation coefficient (R) for the ANN model was 0.9992. The CO2 adsorption efficiency of adsorbents is enhanced by increasing the amounts of AC and EP. This study provides a technique for predicting and improving CO2 capture through the development of porous polymer composite beads (CBs) with a high CO2 adsorption capacity.
Keywords: Activated carbon; Adsorption of carbon dioxide; Artificial intelligence; Biopolymer composites and sustainability; Carbon capture; Machine learning; Product innovation; Reducing greenhouse gas emissions; Response surface methodology