The modeling and optimization of the substrate treatment process in biogas energy production from yam peel substrates via anaerobic digestion was the purpose of this work. Thermo-chemical pretreatment method was used in the substrate treatment. The instrumental analysis of the substrate was investigated using FTIR and SEM. Central composite design was used in the design of experiment and intelligent modeling of the substrate treatment in the biogas production via adaptive neuro-fuzzy inference systems (ANFIS), artificial neural network (ANN), and response surface methodology (RSM). The kinetics of the anaerobic digestion process was studied using five kinetic models. The result indicated that among the three process parameters investigated, temperature has the most significant effect on the substrate pretreatment. The instrumental analysis showed that the thermo-chemical modification of the substrate resulted in the transformation of its bond structure and the solubilization of the hemicelluloses molecules. ANFIS, ANN, and RSM models were efficient in modeling the anaerobic digestion process with correlation coefficient of 0.9997, 0.9997 and 0.9887, respectively showing good agreement between the experimental and predicted biogas yield. Further statistical error indices involving HYBRID (ANFIS=0.3180, ANN=0.3207, and RSM=11.157), RMSE (ANFIS=0.3359, ANN=0.8235, and RSM=4.969), and ARE (ANFIS=0.3068, ANN=0.3075, and RSM=1.5756) depicted the ANFIS as being marginally better than the ANN in simulating and modeling the anaerobic digestion. Optimization of the ANFIS model yielded a biogas volume of 356.24 ml at concentration, time, and temperature of 0.04 N, 60 s and 80 ?, respectively. The kinetics of the cumulative biogas production was described by the Modified Logistics, Transference and the Logistics models. The purification of the biogas by scrubbing gave about 92% methane gas. The sediments from the digestion with low C/N ratio could be used as manure and soil conditioner in agriculture.
