This review discusses how Machine Learning has been applied to predict the quality of biomass briquettes produced from agricultural and municipal solid organic waste, which are crucial for advancing green and low-carbon energy solutions. Traditional methods of assessment of briquette quality involve destructive laboratory experiments, do not favor sample reuse, are time-consuming, and labor-intensive, posing barriers to efficient production. This paper reviews literature on various Machine Learning models applied for predicting and optimizing briquette quality parameters, including combustion, physical, and emission properties. Several Machine Learning models have shown promising results in predicting and optimizing these key parameters for example, a Random Forest model with R² of 0.9936 in deformation energy prediction and Artificial Neural Networks with R² of 0.8936 in the prediction of impact resistance. By enhancing the accuracy and efficiency of briquette quality predictions, Machine Learning algorithms contribute to the development of high-quality biomass briquettes, thereby creating sustainable and low-carbon energy systems. This review points to critical literature gaps regarding model generalizability across diverse biomass feedstocks and integration of broader quality parameters. Addressing these gaps will advance AI-based solutions, promote greener energy practices, and support sustainable development. The findings are intended to aid researchers, industry professionals, and policymakers in advancing the production of high-quality biomass briquettes for cleaner energy and sustainable development.

The escalating demand for clean and sustainable energy sources has propelled hydrogen to the forefront of alternative fuel research. Microbial biomass conversion, a bio-based process utilizing microorganisms to convert organic matter into hydrogen, presents a promising avenue for achieving this goal. This review provides a comprehensive overview of possible microbial biomass conversion methods, including both light-dependent and light-independent methods, and compares their hydrogen production rates (HPRs). Light-dependent methods such as photo-fermentation offer HPRs exceeding 3 m³/dm³, suggesting highly efficient hydrogen generation possibilities. However, most rely on indirect processes or specific light conditions, potentially hindering H₂ production. Dark fermentation (DF) demonstrates significantly higher HPRs, up to 12 m³/d/m³, with no light requirements, making it a strong contender for large-scale production. Microbial electrolysis cells (MECs) show even greater HPRs of up to 72 m³/d/m³, competing favorably in hydrogen generation feasibility. Despite promising advancements, challenges remain in scaling up these processes for commercial viability. While current research achieves high HPRs, reactor volumes are typically below 1 L. This review explores opportunities and challenges associated with scaling up, particularly focusing on integrating DF and MECs. Combining these methods holds promise for enhancing stability and achieving efficient energy recovery.

Currently, more and more industrial carbon emissions lead to a significant increase in greenhouse gases, which has a significant impact on global climate change. Therefore, the storage and reuse of carbon dioxide is an important issue in modern society. In this paper, calcium based CO₂ absorbent was prepared from converter slag by acetic acid extraction and modification of steel slag. The study investigated the effects of parameters in indirect acetic acid leaching, including acetic acid concentration, leaching time, solid-to-liquid ratio, and temperature, on the elemental content in the adsorbent. It also compared the cyclic adsorbent stability of calcium-based adsorbents with commercial calcium oxide. The results indicated that the optimal technical parameters were: acetic acid concentration 1 mol/L, leaching time 40 min, solid-liquid ratio of 1:10, leaching temperature of 40°C, achieving an extraction rate of 88.05% for calcium elements. Its initial CO₂ adsorbent capacity is 0.51 g_CO2/g_adsorbent, and the CO₂ adsorbent capacity after 20 cycles is 0.202 g_CO2/g_adsorbent, and the inactivation rate is 60.39%. Compared with AR CaO, the adsorbent has more ideal CO₂ capture ability.

Bioelectrochemical systems (BES) offer promising solutions for sustainable energy production and wastewater treatment. However, their complex biological and electrochemical dynamics pose significant challenges for traditional modeling approaches. This review explores the recent advancements in applying artificial intelligence (AI) techniques to enhance the performance and scalability of BES technologies. We detailed the roles of machine learning (ML) algorithms, such as artificial neural networks (ANNs), support vector regression (SVR), and random forest regression (RFR), in predicting critical BES performance metrics. Additionally, we discussed metaheuristic optimization techniques that have improved system design and operational parameters, yielding significant gains in energy recovery and stability. The integration of real-time monitoring and adaptive control systems, powered by AI, is highlighted for its potential to dynamically adjust BES operations in response to fluctuating environmental conditions. Despite these advancements, challenges remain, particularly in data standardization and modeling biological complexity within BES. We outline current limitations and future directions, emphasizing the need for robust datasets, standardized methodologies, and advanced AI frameworks to further unlock the potential of AI-driven BES systems in achieving sustainable bioenergy solutions.

To investigate the warming effect of rice straw ash (RSA) cement mortar facing on sunning water pools, this study focuses on a sunning water pool with a 5% substitution rate of RSA in its cement mortar facing. A temperature control test was conducted to compare it with a conventional cement mortar-faced sunning water pool. Additionally, finite element software was employed to create models for both the RSA and conventional cement mortar-faced sunning water pools, facilitating an analysis of the variations in water temperature within these systems.The results indicate that the RSA cement mortar facing can enhance the daily average water temperature of the sunning water pools by 0.1-0.6°C compared to those featuring conventional cement mortar facing. Simulation data reveal that the water temperature in the sunning water pool utilizing RSA cement mortar facing is approximately 0.46°C higher than that observed in its counterpart with standard cement mortar facing. The trends identified through theoretical calculations, experimental data, and simulation results are largely consistent, suggesting that RSA cement mortar facing effectively improves the thermal performance of sunning water pools.These findings provide valuable theoretical support for implementing RSA cement mortar in agricultural facilities.

Establishing the quantitative relationships between heavy metals and mineral phases in coal gangue is essential for its comprehensive landfill and refined utilization. In this study, the Guandi coal gangue was subjected to a stepwise dissociation method using seven concentration gradients (0.1, 1.0, 4.0, 6.0, 8.0, 10.0, 12.0 mol/L) of aqua regia and hydrofluoric acid. Combined with the Rietveld refinement method, inverse matrix calculations of residual fractions of mineral phases and dissociation degrees of heavy metals after dissociation, the quantitative relationships between Pb, As, Zn, Cr and the mineral phases were determined. The results show that kaolinite, quartz, pyrite, and the amorphous phase are the primary host phases for Pb, As, Zn, and Cr, with their contents in crystalline phases ranging from 71.36% to 87.68%. Validation via the standard addition method demonstrates that the relative standard deviation of the stepwise dissociation for Pb, As, Zn, and Cr is ≤7.23%, with spike recovery rates ranging from 85.43% to 112.85%, indicating favorable test results. Sequential chemical leaching demonstrates that heavy metals are mainly distributed in stable aluminosilicate-bound state and carbonate or oxide-bound state. The toxicity characteristic leaching procedure test indicated that Cr exhibited high toxicity and thus required long-term monitoring. The results of this study provide important theoretical guidance for the comprehensive landfilling and resource utilization of Guandi coal gangue, and the established analytical method can be extended to studies on quantitative relationships between heavy metals and mineral phases in other tailings.