Bagasse is the major leftover material from the sugarcane industry, and it has significant untapped energy. Biogas production from bagasse is employed as eco-friendly energy but its intricate composition makes it resistant to degradation. This study endeavors to explore the impact of bokashi technology, a technique that applies effective microorganisms on the potential methane production from bagasse. According to findings, bagasse had the ability to produce biogas but applying bokashi technology to bagasse led to getting more methane production. The methane production from treated bagasse for one month via bokashi bran was 243.80 LCH₄/kg_VS compared to 106.84 LCH₄/kg_VS only from fresh bagasse which is often attributed to improved fibrous carbohydrates degradation by the pre-treatment process. The reduction of total solids and chemical oxygen demand were more with treated bagasse. Two-dimensional mathematical modeling (TDMM) and artificial neural network (ANN) were utilized to forecast the production of methane through the anaerobic co-digestion process. The main advantage of ANN model is its ability to be constructed and trained for any experiment, regardless of the availability of a pre-existing study or understanding of the underlying phenomena. On the other hand, existence of a mathematical model that accurately describes the behavior of the current experiment is a fundamental requirement for constructing the TDMM model. The TDMM model remains stable in each run, as it relies on the established mathematical equations. On the other hand, ANN model may exhibit variations in each run due to the random initialization of weights. Graphical abstract

This study investigates the combined effect of wheat straw particle size and mixing ratio on the anaerobic co-digestion (ACD) of waste activated sludge under mesophilic conditions. Ten batch digesters were tested with varying straw-to-sludge ratios (0–1.5%) and particle sizes (5 cm, 1 cm, and <2 mm). Fine straw particles (<2 mm) at 1.5% loading achieved the highest removal efficiencies for TS (43.55%), TVS (47.83%), and COD (51.52%), resulting in a 140% increase in biogas yield and methane content of 60.15%. The energy recovery reached 14.37 kWh/kg, almost double the control. The developed Recurrent Neural Network (RNN) model (3 layers, 13 neurons, 500 epochs) predicted biogas production with 99.8% accuracy, a root mean square error (RMSE) of 0.0038, a mean absolute error (MAE) of 0.0093, and an R² close to 1. These results confirm the potential of integrating agricultural residues into wastewater treatment for renewable energy recovery and emission reduction. This study uniquely integrates mechanical pretreatment of wheat straw with RNN-based modeling to enhance biogas generation and predictive accuracy. It establishes a dual-experimental AI framework for optimizing sludge–straw co-digestion systems. This approach provides a scalable, data-driven solution for sustainable waste-to-energy applications.

This study investigates the environmental and energy performance of rice straw-based energy pathways in Egypt, combining life cycle assessment (LCA) with supply chain optimization to improve system efficiency. The analysis covers thirteen governorates producing over 4.45 million tons of rice straw annually. It examines the whole supply chain from paddy farming, straw collection, and transport to electricity generation and ash disposal. Total energy consumption was 11,287 TJ, dominated by farming (5673 TJ) and transport (5490 TJ). Greenhouse gas (GHG) emissions were estimated at 12,007.5 million kg CO₂-eq, with significant contributions from farming (5158 million), combustion (3630 million), and natural gas use (3039 million). Gross electricity output was 5525 GWh, yielding a net of 4973 GWh, equivalent to 1116.5 kWh per ton of straw. Scenario analysis highlighted that the optimized multi-hub system, prioritizing Cluster 1 in the Nile Delta, which contributes over 92% of straw production and 4607 GWh of net electricity, achieved a reduction of more than 25% in transport distances and an 18% decrease in diesel consumption and related emissions. Sensitivity analysis further indicated that delivered electricity and GHG intensity are more sensitive to conversion efficiency and transmission and distribution losses than to moderate changes in transport assumptions. In addition to environmental improvements, the optimized scenario indicates potential social co-benefits, including rural employment generation, additional income opportunities for farmers, and improved air quality associated with reduced open-field burning. These outcomes are presented as indicative qualitative insights. Findings confirm rice straw as a strategic, scalable, and sustainable energy resource aligned with Egypt’s Vision 2030 and the UN Sustainable Development Goals (SDGs).