ACS Omega, Год журнала: 2025, Номер 10(7), С. 6470 - 6501

Опубликована: Фев. 13, 2025

Energy plays a key role in the socioeconomic development of society, and most its global demand is provided by conventional resources (e.g., fossil fuels). Utilizing renewable energy significantly growing since it can meet while minimizing adverse impacts carbon emissions on climate change. Biomass an appealing option among emerging alternatives wind solar). Torrefaction mild pyrolysis process, this research aims to analyze torrefaction process lignocellulosic biomass. The methodology proposed involves employing hybrid models artificial neural network-particle swarm optimization (ANN-PSO), adaptive neuro-fuzzy inference system (ANFIS), coupled simulated annealing-least-squares support vector machine (CSA-LSSVM). In addition learning algorithms, correlation developed using gene expression programming (GEP) interrelate biomass properties, including moisture content, volatile matter, fixed carbon, ash, sample size, contents oxygen, hydrogen, nitrogen along with operating condition encompassing residence time, temperature, concentration CO2, O2, N2 solid yield as target variable. results reveal that CSA-LSSVM model has highest accuracy, statistical metrics coefficient determination (R2), mean square error (MSE), average absolute relative percentage (AARE%) are 0.98, 0.00082, 2.61%, respectively. parametric sensitivity analysis demonstrates content influential variables, temperature playing crucial findings be used assess similar torrefaction, providing required knowledge for modeling process. Hence, bioenergy industry optimal conditions, cost energy, lessen negative CO2 emission.

Язык: Английский