As the population increases, energy demands continue to rise rapidly. In order to satisfy this increasing energy demand, biogas offers a potential alternative. Biogas is economically viable to be produced through anaerobic digestion (AD) from various biomass feedstocks that are readily available in Malaysia, such as food waste (FW), palm oil mill effluent (POME), garden waste (GW), landfill, sewage sludge (SS) and animal manure. This paper aims to determine the potential feedstocks for biogas production via AD based on their characteristics, methane yield, kinetic studies and economic analysis. POME and FW show the highest methane yield with biogas yields up to 0.50 L/g VS while the lowest is 0.12 L/g VS by landfill leachate. Kinetic study shows that modified Gompertz model fits most of the feedstock with R 2 up to 1 indicating that this model can be used for estimating treatment efficiencies of full-scale reactors and performing scale-up analysis. The economic analysis shows that POME has the shortest payback period (PBP), highest internal rate of return (IRR) and net present value (NPV). However, it has already been well explored, with 93% of biogas plants in Malaysia using POME as feedstock. The FW generation rate in Malaysia is approximately 15,000 tonnes per day, at the same time FW as the second place shows potential to have a PBP of 5.4 years and 13.3% IRR, which is close to the results achieved with POME. This makes FW suitable to be used as the feedstock for biogas production.
Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research compares the efficacies of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models and evaluates their capability in estimating the adsorption efficiency of biochar for the removal of Cu (II) ions based on 480 experimental sets obtained in a laboratory batch study. The effects of operational parameters such as contact time, operating temperature, biochar dosage, and initial Cu (II) ion concentration on removing Cu (II) ions were investigated. Eleven different training algorithms in ANN and 8 different membership functions in ANFIS were compared statistically and evaluated in terms of estimation errors, which are root mean squared error (RMSE), mean absolute error (MAE), and accuracy. The effects of number of hidden neuron in ANN model and fuzzy set combination in ANFIS were studied. In this study, ANFIS model with Gaussian membership function and fuzzy set combination of [4 5 2 3] was found to be the best method, with accuracy of 90.24% and 87.06% for training and testing dataset, respectively. Contribution of this study is that ANN, ANFIS, and MLR modeling techniques were used for the first time to study the adsorption of Cu (II) ions from aqueous solutions using rambutan peel biochar.