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This study explores the potential of converting food waste (FW) in South Africa to biogas via anaerobic digestion (AD) using the Aspen Plus Simulator version 11. The simulation modeled the AD process, focusing on hydrolysis, acidogenesis, acetogenesis, and methanogenesis stages, with unconverted substrate recycling at the acetogenesis stage, employing a Non-Random Two-Liquid (NRTL) model for the polar mixture components. The FW used contained 49.86 % volatile solids (VS) of carbohydrates, 39.31 % VS of proteins, and 10.83 % VS of lipids. The results revealed that biogas yield at mesophilic temperature (37 °C) was 50 % higher than at thermophilic temperature (55 °C) with a residence time of 35 days, and methane composition was 45.98 % higher. Methane and carbon dioxide percentage compositions increased with hydraulic retention time (HRT), varying between 37.5–45.5 % and 34.3–39.9 % at mesophilic conditions. Economic analysis indicated a profitable outlook, with a payout period of 3 years, 5 months, and a 15-year positive revenue forecast. The project exhibited an internal rate of return (IRR) of 59.6629 %, suggesting strong economic viability and potential for investment. This study underscores the potential for optimized AD processes to enhance biogas production from FW in South Africa, presenting a viable, eco-friendly waste management solution.

In recent times, many investigators have delved into plastic waste (PW) research, both locally and internationally. Many of these studies have focused on problems related to land-based and marine-based PW management with its attendant impact on public health and the ecosystem. Hitherto, there have been little or no studies on forecasting PW quantities in developing countries (DCs). The key objective of this study is to provide a forecast on PW generation in the city of Johannesburg (CoJ), South Africa over the next three decades. The data used for the forecasting were historical data obtained from Statistics South Africa (StatsSA). For effective prediction and comparison, three-time series models were employed in this study. They include exponential smoothing (ETS), Artificial Neural Network (ANN), and the Gaussian Process Regression (GPR). The exponential kernel GPR model performed best on the overall plastic prediction with a determination coefficient (R2) of 0.96, however, on individual PW estimation, ANN was better with an overall R2 of 0.93. From the result, it is predicted that between 2021 and 2050, the total PW generated in CoJ is forecasted to be around 6.7 megatonnes with an average of 0.22 megatonnes/year. In addition, the estimated plastic composition is 17,910 tonnes PS per year; 13,433 tonnes PP per year; 59,440 tonnes HDPE per year; 4478 tonnes PVC per year; 85,074 tonnes PET per year; 34,590 tonnes LDPE per year and 8955 tonnes other PWs per year.