A potential slow-release fertilizer based on biogas residue biochar: Nutrient release patterns and synergistic mechanism for improving soil fertility.

The large yield of anaerobic digestates and the suboptimal efficacy of nutrient slow-release severely limit its practical application. To address these issues, a new biochar based fertilizer (MAP@BRC) was developed using biogas residue biochar (BRC) to recover nitrogen and phosphorus from biogas slurry. The nutrient release patterns of MAP@BRC and mechanisms for enhancing soil fertility were studied, and it demonstrated excellent performance, with 59% total nitrogen and 50% total phosphorus nutrient release rates within 28 days. This was attributed to the coupling of the mechanism involving the dissolution of struvite skeletons and the release of biochar pores. Pot experiments showed that crop yield and water productivity were doubled in the MAP@BRC group compared with unfertilized planting. The application of MAP@BRC also improved soil nutrient levels, reduced soil acidification, increased microbial populations, and decreased soil heavy metal pollution risk. The key factors that contributed to the improvement in soil fertility by MAP@BRC were an increase in available nitrogen and the optimization of pH levels in the soil. Overall, MAP@BRC is a safe, slow-release fertilizer that exhibits biochar-fertilizer interactions and synergistic effects. This slow-release fertilizer was prepared by treating a phosphorus-rich biogas slurry with a nitrogen-rich biogas slurry, and it simultaneously addresses problems associated with livestock waste treatment and provides a promising strategy to promote zero-waste agriculture.

Effective waste management through Co-pyrolysis of EFB and tire waste: Mechanistic and synergism analysis.

Driven by the need for solutions to address the global issue of waste accumulation from human activities and industries, this study investigates the thermal behaviors of empty fruit bunch (EFB), tyre waste (TW), and their blends during co-pyrolysis, exploring a potential method to convert waste into useable products. The kinetics mechanism and thermodynamics properties of EFB and TW co-pyrolysis were analysed through thermogravimetric analysis (TGA). The rate of mass loss for the blend of EFB:TW at a 1:3 mass ratio shows an increase of around 20% due to synergism. However, the blend's average activation energy is higher (298.64 kJ/mol) when compared with single feedstock pyrolysis (EFB = 257.29 kJ/mol and TW = 252.92 kJ/mol). The combination of EFB:TW at a 3:1 ratio does not result in synergistic effects on mass loss. However, a lower activation energy is reported, indicating the decomposition process can be initiated at a lower energy requirement. The reaction model that best describes the pyrolysis of EFB, TW and their blends can be categorised into the diffusion and power model categories. An equal mixture of EFB and TW was the preferred combination for co-management because of the synergistic effect, which significantly impacts the co-pyrolysis process. The mass loss rate experiences an inhibitive effect at an earlier stage (320 °C), followed by a promotional impact at the later stage (380 °C). The activation energy needed for a balanced mixture is the least compared to all tested feedstocks, even lower than the pyrolysis of a single feedstock. The study revealed the potential for increasing decomposition rates using lower energy input through the co-pyrolysis of both feedstocks. These findings evidenced that co-pyrolysis is a promising waste management and valorisation pathway to deal with overwhelming waste accumulation. Future works can be conducted at a larger scale to affirm the feasibility of EFB and TW co-management.

Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction.

Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that well-performing ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no one-size-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.