This review explores recent advancements in hydrogen gas (H₂) safety through the lens of artificial intelligence (AI) techniques. As hydrogen gains prominence as a clean energy source, ensuring its safe handling becomes paramount. The paper critically evaluates the implementation of AI methodologies, including artificial neural networks (ANN), machine learning algorithms, computer vision (CV), and data fusion techniques, in enhancing hydrogen safety measures. By examining the integration of wireless sensor networks and AI for real-time monitoring and leveraging CV for interpreting visual indicators related to hydrogen leakage issues, this review highlights the transformative potential of AI in revolutionizing safety frameworks. Moreover, it addresses key challenges such as the scarcity of standardized datasets, the optimization of AI models for diverse environmental conditions, etc., while also identifying opportunities for further research and development. This review foresees faster response times, reduced false alarms, and overall improved safety for hydrogen-related applications. This paper serves as a valuable resource for researchers, engineers, and practitioners seeking to leverage state-of-the-art AI technologies for enhanced hydrogen safety systems.

Globally million hectares of land annually is getting contaminated by heavy metalloids like As, Cd, Cr, Hg, Pb, Co, Cu, Ni, Zn, and Se, with current concentrations in soil above geo-baseline or regulatory standards. The heavy metals are highly toxic, mobile, and persistent and hence require immediate and effective mitigation. There are many available remediation techniques like surface capping, encapsulation, landfilling, soil flushing, soil washing, electrokinetic extraction, stabilization, solidification, vitrification, phytoremediation, and bioremediation which have been evolved to clean up heavy metal-contaminated sites. Nevertheless, all of the technologies have some applicability and limitations making the soil remediation initiative unsustainable. Among the available technologies, electrokinetic remediation (EKR) has been comparatively recognized to mitigate contaminated sites via both in-situ and ex-situ approaches due to its efficiency, suitability for use in low permeability soil, and requirement of low potential gradient. The work critically analyzes the EKR concerning techno, economic, and sustainability aspect for evaluating its application on various substrates and environmental conditions. The current soil contamination status in India is presented and the application of EKR for the heavy metal remediation from soil has been evaluated. The present work summaries a comprehensive and exhaustive review on EKR technology proving its effectiveness for a country like India where the huge amount of waste generated could not be treated due to lack of infrastructure, technology, and economic constraints.

The growing imperative to mitigate climate change and accelerate the shift toward energy sustainability has called for a critical evaluation of heat and electricity generation methods. This article presents a comparative life cycle assessment (LCA) of solar and biogas energy systems on a common basis of 1 kWh of useful energy using SimaPro, the ReCiPe 2016 methodology (both midpoint and endpoint indicators), and cumulative energy demand (CED) analysis. This study is the first to evaluate co-located solar PV, solar thermal compound parabolic concentrator (CPC) and biogas combined heat and power (CHP) systems with in situ data collected under identical climatic and operational conditions. The project costs yield levelized costs of electricity (LCOE) of INR 2.4/kWh for PV, 3.3/kWh for the solar thermal dish and 4.1/kWh for biogas. However, the collaborated findings indicate that neither solar-based systems nor biogas technology uniformly outperform the others; rather, their effectiveness hinges on contextual factors, including resource availability and local policy incentives. These insights will prove critical for policymakers, industry stakeholders, and local communities seeking to develop effective, context-sensitive strategies for sustainable energy deployment, emissions reduction, and robust resource management.

Comparative analysis of hybridized microbial fuel cell (MFC) with constructed wetland systems (CW) as continuous and batch flow in with/without plant setups has been done. The sewage-generated WW was spiked with reverse osmosis (RO) concentrate [1:1] and was used as the substrate. The effects of RO-induced salinity and efficiency of wastewater (WW) biomass conversion to energy have been analyzed. The analysis was based on the removal of chemical oxygen demand (COD), total suspended solids (TSS), total dissolved solids (TDS), voltage generation, and energy recovery (NERs) to find out the performance efficiency of the four systems. Systems consisted of two batch setups with the downflow regime, batch setup with plant (BWP) and batch setup without plant (BWOP) and two continuous setups with the upflow regime, continuous setup with plant (CWP) and continuous setup without plant (CWOP). The maximum COD removal and energy recoveries in BWP, BWOP, CWP, and CWOP were 91.66%, 86.45%, 90.62%, and 83.33% and 6.20, 1.98, 0.98, and 0.72 W·h/kgCOD, respectively. The incorporation of RO concentrate positively influenced the performance of CW-MFCs by its induced salinity specifically in presence of Canna indica, thus increasing the energy output as indicated by the voltage generation and energy recoveries.