Biogas production is one of the renewable energy alternatives to fossil fuel consumption amidst a global rise in energy demand. In this study, goat manure was used as the substrate in batch-process anaerobic digesters, and the inoculum used was obtained from an existing biogas plant. The aim was to develop an empirical biomethane potential (BMP) of goat manure, develop a predictive model, and establish optimum process parameters for its anaerobic digestion using a response surface methodology. Proximate analysis of the substrates and inoculum was carried out according to American Public Health Association standards. An empirical BMP study was conducted on the substrate using the anaerobic biodegradation, activity, and inhibition protocol, and the effects of the process parameters on biogas yield from anaerobic digestion were investigated according to the central composite design generated by Design Expert 11 software (Stat-Ease Inc.). The results showed that goat manure has a dry matter content of 205.60 ± 12.64 g/kg and organic matter of 688.78 ± 18.02 g/kg of dry matter. The BMP was 0.49 ± 0.002 LCH4/gVS. Maximum biogas production of 650 mbar was recorded at a hydraulic retention time (HRT) of 60 days and a substrate-to-inoculum (S/I) ratio of 10:1. The significant mathematical model was linear, with a p-value < 0.0001. The optimum biogas yield was obtained at a S/I ratio of 0.5 and an HRT of 33.09 days at room temperature of 29 oC. The biogas composition was 58.53% CH4 and 41.47% CO2. The study established goat manure as a good substrate for biogas production. It also established a mathematical model to predict process parameters for optimum yield.

Co‐pyrolysis of plastics is a simultaneous thermochemical conversion of a mixture of plastics to value‐added products. This study quantified air emissions from co‐pyrolysis of low‐density polyethylene (LDPE), polystyrene (PS), and polyethylene terephthalate (PET) at varying mixture compositions. The emissions were modelled using simplex lattice design (SLD) and artificial neural network (ANN). A mixture containing 100% LDPE, 0% PS, and 0% PET emits the highest quantities of hydrocarbons (HCs), carbon monoxide (CO), and nitrogen oxide (NO) with values of 7980.46, 1793.67, and 41.41 mg/m ³ , respectively, with no indication of NO ₂ , SO ₂ , H ₂ S, or CO ₂ emissions. Meanwhile, mixture containing 0% LDPE, 0% PS, and 100% PET emits the lowest quantities of HCs and CO, with values of 200.47 and 44.69 mg/m ³ , respectively. Mixtures containing 17% LDPE, 17% PS, and 66% PET; 17% LDPE, 66% PS, and 17% PET; and 0% LDPE, 50% PS, and 50% PET showed no emissions of NO. The study generated predictive models for the emissions of HC, CO, and NO and statistically established that using SLD, a special quartic model best describes HC and CO emissions, while a quadratic model best describes NO emissions. Although ANN shows good modelling of the emissions, the coefficients of estimate were lower than that of simplex lattice design.