This study synthesizes the rapidly expanding evidence base on machine learning that links renewable variability to green-hydrogen production optimisation and proposes an integrated PM–CFL–OPT architecture that embeds uncertainty handling, safety constraints, and interpretability across the decision chain. The architecture represents a synthesis-derived and testable functional hypothesis rather than a quantitatively validated superior pipeline. Because the included studies use heterogeneous datasets, horizons, system boundaries and performance measures—and only three studies are primarily classified as CFL—the present corpus cannot support a pooled estimate of the framework’s incremental performance over standalone alternatives. A PRISMA-aligned, feedback-loop workflow was executed across Scopus, Web of Science, Google Scholar, ScienceDirect, Taylor & Francis, and SpringerLink, yielding 244 records; 48 duplicates were removed, and 129 critical studies were retained for synthesis. Bibliometric profiling revealed a transition from early exploratory contributions during 2016–2019 to a marked acceleration after 2022, with publication output peaking in 2025 and expanding geographically across established research economies and emerging regions. A systems-map synthesis organises the literature using an Energy/System Vector–Machine-Learning Method–Application/Decision Domain (E–M–A) framework and overlays it with three functional roles: predictive modelling (PM), clustering and feature learning (CFL), and optimisation and control (OPT). E–M–A locates each study by physical context, computational method and decision task, whereas PM–CFL–OPT identifies how ML contributes to the forecast–representation–action chain. Persistent gaps include limited transferability across climates and grids, weak decision-focused learning pipelines, and under-specified safety cases for real-time and market participation. The review concludes with a deployment-oriented roadmap for climate-robust, privacy-preserving, and auditable ML that can scale hydrogen-integrated renewable operations. Findings clarify maturity, trust, and transferability bottlenecks for deployment.
Molecular docking is relatively underutilized in environmental microbiology, particularly, in the discourse of anaerobic digestion (AD). Multiple studies have confirmed that pre-treatment-produced inhibitors contribute to reduction in methane production. However, till today, limited studies offering molecular level mechanistic evidence against several enzymes that catalyses the pathways of AD demonstrating how these inhibitors interrupt the activity of methanogenic enzymes. Hence, the present study employed molecular docking, a computational chemistry approach, to elucidate enzyme-inhibitor interactions at the molecular level. In this study, nine (9) inhibitors (furfural, 5-hydroxymethylfurfural, vanillin, syringaldehyde, pyridine, pyrrole, pyrazine, levulinic acid and formic acid) were sourced from PubChem database, while four critical enzymes involved in the methanogenic stage were retrieved from Protein Data Bank (PDB) methyl-coenzyme M reductase (MCR), carbon monoxide dehydrogenase (CODH), heterodisulfide reductase (Hdr) and coenzyme M methyltransferase (MtaA). The study found that, among the nine inhibitors investigated, four (5-hydroxymethylfurfural, syringaldehyde, furfural and vanillin) of them exhibited strong enzyme-ligand interactions within the binding pockets of the target enzymes. Overall, the observed interactions between the studied inhibitors and enzymes were dominated by 25 hydrogen bond interactions, with two π–π stacking interactions contributing to ligand stabilization. MD simulations revealed that Hdr complexes maintained superior ligand occupancy and structural stability over the full 100 ns trajectory, while MCR complexes showed more transient inhibitor occupancy with periodic dissociation events. These findings provide molecular-level evidence of how pre-treatment-derived inhibitors interfere with the activity of methanogenic enzymes, contributing to reduced biomethane production. Overall, this research enhances understanding of the critical balance between improving substrate biodegradability through pre-treatment and minimizing the generation of inhibitory compounds.
This study develops an integrated evaluation framework that combines Life Cycle Assessment (LCA) with economic feasibility analysis to support the selection of waste management technologies aligned with circular economy principles at a community-based solid waste processing facility in Cebongan Village, Salatiga City, Indonesia. Primary data on waste generation showed that organic waste accounted for approximately 78% of total waste by weight, while plastics represented about 54% of total waste by volume. These findings indicate that organic waste is the dominant component of local waste generation, supporting the application of biological treatment systems. The LCA results show that windrow composting has the lowest environmental impact across most categories, including eutrophication (0.00014 kg PO₄ eq), acidification (0.00023 kg SO₂ eq), marine aquatic toxicity (0.033 kg 1,4-DCB eq), and ozone depletion (2.07 × 10⁻⁸ kg CFC-11 eq) per kilogram of waste processed. The shredding process was identified as the main environmental hotspot across all technologies. The LCA further indicates that environmental hotspots are mainly associated with greenhouse gas emissions, energy consumption, and eutrophication impacts. The economic assessment showed that recovered products, including compost, digestate, animal feed, and biogas, can generate sufficient revenue to support operational costs, with an estimated payback period (PBP) of 3–5 years. The proposed system could reduce waste volume by up to 75.9%, thereby extending landfill lifespan and supporting local sustainability and circular economy objectives. These findings provide practical guidance for selecting environmentally and economically suitable biological waste treatment systems in community-based waste management settings.
The crystallographic biography of silver nanoparticles (Ag NPs) connecting green methodologies, microstructural face-centered cubic (FCC) phase evolution. The structure of Ag NPs depend functional applications explores a unique, simple pathway as the main focus of this study. A thorough examination of advanced X-ray diffraction (XRD) techniques is provided, encompassing Rietveld refinement for lattice parameters, various identical models to quantify crystallite size and lattice strain. Beyond the predominant FCC structure, formation mechanisms of metastable phases are also discussed. The review finds correlations between specific crystallographic parameters of Ag NPs derived from green synthesis, functional performance in biomedical and catalytic applications. The green route components control crystal growth by dominating the nucleation kinetics as well as the surface charge to govern grain growth, explored by transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). Finally, current standardization gaps and life cycle assessment (LCA) challenges are addressed to guide the future rational and sustainable design of phase-controlled Ag NPs.
This study proposes an integrated system consisting of steam sterilization, a vapor-injection heat pump, and a drying unit for the treatment of infectious medical waste (IMW) and the production of refuse-derived fuel type 3 (RDF-3). The system combines waste sterilization, moisture removal, air quality improvement, and energy recovery to improve both environmental and economic performance. Thermal simulation, prototype development, and experimental validation were conducted to evaluate system performance. The results indicate that the system can process IMW at a rate of 240 kg/d, producing 85.62 kg/d of RDF-3 with a significant reduction in moisture content from approximately 85% to below 10%. A drying process with a heating requirement of 20 kW is effectively supplied by a high-temperature vapor-injection heat pump with a condenser capacity of 20.76 kW at a hot water temperature of 90 °C. The system achieves a heating coefficient of performance (COP) of 2.39 kWth/kWe, demonstrating efficient energy utilization. Environmental analysis shows that the proposed system can reduce greenhouse gas emissions by 297.49 t CO₂-eq/y (68.11%) compared to the conventional landfill-based scenario. Furthermore, the implementation of a vapor-injection heat pump for condensing moisture from the exhaust air of the steam sterilization process contributes to improved air quality, ensuring compliance with international standards, particularly for total volatile organic compounds (TVOCs) at 0.3 ppm and volatile organic compounds (VOCs), specifically acrolein, at 0.18 μg/m3. From an economic perspective, the system requires a total investment of 27,560 USD and generates a net annual income of 3570 USD. The levelized cost of IMW treatment is reduced to 0.0418 USD/kg, significantly lower than that of the conventional system (0.6445 USD/kg). The payback period and internal rate of return are 7.72 years and 11.48%, respectively. Overall, the proposed system demonstrates strong potential for sustainable IMW management by integrating sterilization, drying, air quality improvement, and energy recovery into a single process, offering significant energy savings, emission reductions, and economic benefits.
