Search Results (81)

Arsenic contamination demands innovative, sustainable remediation. This study presents a fuzzy approach for synthesizing a magnetic biochar nanocomposite from pecan shell agricultural waste for efficient arsenic removal. Using a Multi-Input Fuzzy Rules Emulated Network (MiFREN), a systematic investigation of the synthesis process revealed that precursor type (biochar), Fe:precursor ratio (1:1), and iron salt type were the most significant parameters governing material crystallinity and adsorption performance, while particle size and N₂ atmosphere had a minimal effect. The MiFREN-identified optimal material, the magnetic biochar composite (FS7), achieved > 90% arsenic removal, outperforming the least efficient sample by 50.61%. Kinetic analysis confirmed chemisorption on a heterogeneous surface (q_e = 12.74 mg/g). Regeneration studies using 0.1 M NaOH demonstrated high stability, with FS7 retaining > 70% removal capacity over six cycles. Desorption occurs via ion exchange and electrostatic repulsion, with post-use analysis confirming structural integrity and resistance to oxidation. Application to real groundwater from the La Laguna region proved highly effective; FS7 maintained selectivity despite competing ions like ${Na}^{+}$, ${Cl}^{-},$ and ${SO}_4^{2-}$. By integrating AI-driven optimization with reusability and real contaminated water, this research establishes a scalable framework for transforming agricultural waste into a high-performance adsorbent, supporting global Clean Water and Sanitation goals. Full article

Growing cooling demand and environmental concerns motivate research into alternative technologies capable of converting low-grade heat into useful cooling. This study proposes a regression-assisted multi-objective optimisation framework using the Ant Lion Optimiser and its multi-objective variant to jointly maximise the coefficient of performance (COP), cooling capacity ($Q_{cc}$) and waste-heat recovery efficiency (${\eta }_e$). Pareto-optimal solutions exhibit a one-dimensional ridge in which ${\eta }_e$ declines, and COP and $Q_{cc}$ increase simultaneously. Within the explored bounds, non-dominated ranges span COP = 0.674–0.716, $Q_{cc}=$ 18.3–27.5 kW and ${\eta }_e=$ 0.118–0.127, with a practical compromise near COP ≈ 0.695, $Q_{cc}$ ≈ 24 kW and ${\eta }_{e }\approx$0.122–0.123. Compared to the typical reported COP band for single-stage silica-gel/water ADCs, the practical compromise solution (COP ≈ 0.695) offers a conservative COP improvement of approximately 16% when benchmarked against COP = 0.6, while the compromise $Q_{cc}$ ($Q_{cc}$ ≈ 24 kW) represents a conservative increase of approximately 20% relative to the upper product-class reference (20 kW). A one-at-a-time sensitivity analysis with re-optimisation identifies the hot- and chilled-water inlet temperatures and exchanger conductance as the dominant decision variables and maps diminishing-return regions. This framework can effectively utilise low-grade heat in future low-carbon buildings and processes, supporting the configuration of ADC systems. Full article

Amid intensifying global climate change and accelerating urbanization, maintaining a balance between carbon emission reduction has become essential for achieving sustainable development. This research investigates the spatiotemporal evolution and driving mechanisms of carbon sequestration services in the ecological development zone of northern Guangdong, China. By integrating Big Geodata technology with the InVEST model, the study quantitatively evaluates both the supply and demand dimensions of carbon sequestration services using land-use, nighttime light, and socioeconomic data. Carbon storage capacities were estimated for different land-use types (including cropland, forest, grassland, water body, built-up land, and undeveloped land), while carbon emissions were spatially distributed based on nighttime light intensity, providing a holistic perspective on the regional carbon budget. The findings indicate significant spatial heterogeneity: the western region exhibits an average carbon sequestration capacity approximately 20% higher than the eastern region, due to extensive forest and grassland coverage, whereas urban areas exhibit higher carbon demand coupled with insufficient supply. Through an analysis of land-use transfer matrices and contribution assessment, land-use transformations, particularly the conversion of ecological land to urban built-up areas, were quantitatively identified as the primary factor disrupting the regional carbon balance. This study proposes actionable territorial spatial planning strategies, such as prioritizing ecological conservation in high-carbon-supply areas and promoting low-carbon urban renewal in high-demand zones, directly derived from the spatial mismatch patterns revealed by the InVEST model outputs. These insights contribute significantly to regional sustainable development practices and global climate governance. Full article

Dust accumulation on photovoltaic (PV) modules is a major factor contributing to reduced power output, lower efficiency, and accelerated material degradation, particularly in arid and industrialized regions. This study presents a comprehensive review and analysis of the influence of dust deposition on PV performance, covering its optical, thermal, and electrical impacts. Findings from global literature indicate that dust-induced efficiency losses typically range from 10% to 70%, depending on particle characteristics, environmental conditions, and surface orientation. Experimental and modeled I–V and P–V characteristics further reveal significant declines in current and power output as soiling levels increase. Through an extensive literature assessment, this paper identifies Machine Learning (ML)-based approaches as emerging and highly effective techniques for dust detection and mitigation. Recent studies demonstrate the integration of image processing, drone-assisted monitoring, and convolutional neural networks (CNNs) to enable automated, real-time soiling assessment. These intelligent methods outperform conventional manual and time-based cleaning strategies in accuracy, scalability, and cost efficiency. By synthesizing current research trends, this review highlights the growing role of ML and data-driven technologies in enhancing PV system reliability, informing predictive maintenance, and supporting sustainable solar energy generation. Full article

Autonomous underwater cleaning in water pools requires reliable perception, efficient coverage path planning, and robust control. However, existing autonomous underwater vehicle (AUV) cleaning systems often suffer from fragmented software frameworks that limit end-to-end performance. To address these challenges, this paper proposes an integrated vision-based autonomous underwater cleaning system that combines global-camera AprilTag localization, YOLOv8-based dirt detection, and a multi-scale A* coverage path planning algorithm. The perception and planning modules run on a host computer system, while a NanoPi-based controller executes motion commands through a lightweight JSON-RPC protocol over Ethernet. This architecture ensures real-time coordination between visual sensing, planning, and hierarchical control. Experiments conducted in a simulated pool environment demonstrate that the proposed system achieves accurate localization, efficient planning, and reliable cleaning without blind spots. The results highlight the effectiveness of integrating vision, multi-scale planning, and lightweight embedded control for autonomous underwater cleaning tasks. Full article

Near-infrared (NIR) imaging plays a crucial role in precision agriculture; however, the high cost of multispectral sensors limits its widespread adoption. In this study, we generate synthetic NIR images (2592 × 1944 pixels) of pineapple crops from standard RGB drone imagery using the Pix2PixHD framework. The model was trained for 580 epochs, saving the first model after epoch 1 and then every 10 epochs thereafter. While models trained beyond epoch 460 achieved marginally higher metrics, they introduced visible artifacts. Model 410 was identified as the most effective, offering consistent quantitative performance while producing artifact-free results. Evaluation of Model 410 across 229 test images showed a mean SSIM of 0.6873, PSNR of 29.92, RMSE of 8.146, and PCC of 0.6565, indicating moderate to high structural similarity and reliable spectral accuracy of the synthetic NIR data. The proposed approach demonstrates that reliable NIR information can be obtained without expensive multispectral equipment, reducing costs and enhancing accessibility for farmers. By enabling advanced tasks such as vegetation segmentation and crop health monitoring, this work highlights the potential of deep learning–based image translation to support sustainable and data-driven agricultural practices. Future directions include extending the method to other crops, environmental conditions and real-time drone monitoring. Full article

The structural modification of tyre-derived activated carbon (TDAC) after chemical activation is not sufficiently recognised yet, especially regarding its crystallinity and porosity. This study examined the development of the crystal structure of TDAC by X-ray diffraction (XRD) analysis, concentrating on critical parameters like interplanar distance (d(002)), crystallite size (Lc), and crystalline percentage. Mixed tyres were pyrolysed at 550 °C to produce char and then chemically activated with KOH at different ratios and temperatures, thereafter undergoing structural characterisation. The results indicate that TDAC is mostly non-graphitizing, maintaining a disordered turbostratic structure even after activation. The widening of the (002) XRD peak and the lack of distinct (hkl) diffraction peaks validate its amorphous characteristics. Higher activation levels lead to an expanded surface area with decreasing crystallite size, signifying a shift towards higher disorder. This research examined the relationship among activation factors, porosity, and structural alterations, emphasising the compromise between crystallinity and surface area. Full article

Concentrated Solar Power (CSP) technology offers significant potential for stable and dispatchable renewable electricity generation through integration with thermal energy storage. However, adoption remains limited due to high capital costs, technical complexity, and market competition from photovoltaic (PV) systems. This review systematically synthesises recent literature on CSP and applies a hybrid SWOT–Analytic Hierarchy Process (AHP) methodology to quantitatively evaluate key internal and external factors influencing CSP deployment. The analysis identifies major strengths such as high-capacity factors and grid stability enabled by thermal storage, as well as weaknesses including high initial investment and site requirements. Opportunities stem from technological innovation, supportive policy frameworks, and potential for local job creation, while threats include rapid cost reductions in PV systems, water scarcity, and market and regulatory uncertainties. The integrated SWOT–AHP approach provides a robust decision-making framework and strategic insights for stakeholders seeking to promote CSP technology in diverse market contexts. The findings underscore the importance of tailored policy support and targeted investment to overcome barriers and realise CSP’s full potential within the renewable energy landscape. Full article

The transition toward a circular economy requires innovative strategies for valorizing industrial by-products. This study investigates the potential of steelmaking furnace slag (SFS) as a low-cost adsorbent for the removal and recovery of nickel and copper ions from aqueous systems. The slag was characterized using XRF, XRD, SEM, FTIR, and thermal analyses, confirming the presence of reactive phases such as lime, periclase, and calcium silicates. Batch adsorption experiments revealed high sorption capacities (up to 147 mg·g⁻¹) and were best described by the Langmuir isotherm and pseudo-second-order kinetic model, indicating chemisorption as the rate-limiting step. FTIR and SEM analyses demonstrated the formation of nickel and copper hydroxide/oxide phases, confirming surface precipitation mechanisms. Subsequent thermal treatment produced NiO- and CuO-enriched oxide systems with photocatalytic and antibacterial potential, while hydrometallurgical recovery using ammonia solutions achieved desorption efficiencies of 90–97%. The results highlight the dual role of SFS as an efficient sorbent for wastewater pre-treatment and as a secondary source of valuable metals, contributing to sustainable materials management and circular economy goals. Full article

Carbon dioxide (CO₂), the primary anthropogenic greenhouse gas, drives significant and potentially irreversible impacts on ecosystems, biodiversity, and human health. Achieving the Paris Agreement target of limiting global warming to well below 2 °C, ideally 1.5 °C, requires rapid and substantial global emission reductions. While recent decades have seen advances in clean energy technologies, carbon capture, utilization, and storage (CCUS) remain essential for deep decarbonization. Despite proven technical readiness, large-scale carbon capture and storage (CCS) deployment has lagged initial targets. This review evaluates CCS technologies and their contributions to net-zero objectives, with emphasis on sector-specific applications. We found that, in the iron and steel industry, post-combustion CCS and oxy-combustion demonstrate potential to achieve the highest CO₂ capture efficiencies, whereas cement decarbonization is best supported by oxy-fuel combustion, calcium looping, and emerging direct capture methods. For petrochemical and refining operations, oxy-combustion, post-combustion, and chemical looping offer effective process integration and energy efficiency gains. Direct air capture (DAC) stands out for its siting flexibility, low land-use conflict, and ability to remove atmospheric CO₂, but it’s hindered by high costs (~$100–1000/t CO₂). Conversely, post-combustion capture is more cost-effective (~$47–76/t CO₂) and compatible with existing infrastructure. CCUS could deliver ~8% of required emission reductions for net-zero by 2050, equivalent to ~6 Gt CO₂ annually. Scaling deployment will require overcoming challenges through material innovations aided by artificial intelligence (AI) and machine learning, improving capture efficiency, integrating CCS with renewable hybrid systems, and establishing strong, coordinated policy frameworks. Full article

Electricity tariffs in emerging economies are often influenced by macroeconomic volatility and regulatory design, affecting both affordability and system stability. Understanding these interactions is crucial for anticipating price fluctuations and ensuring sustainable energy policy. This paper examines the influence of macroeconomic conditions on electricity tariff dynamics in Colombia by integrating econometric and machine learning approaches. Using monthly data from 2009 to 2024 and a set of 153 macroeconomic indicators condensed via principal component analysis (PCA), we assess the predictive performance of vector autoregressive (VAR), SARIMAX, and XGBoost models, as well as a hybrid VAR–XGBoost specification. Impulse-response analysis reveals that tariff components exhibit limited sensitivity to macroeconomic shocks, underscoring the buffering role of regulation and sector-specific drivers. However, forecasting exercises demonstrate that accuracy is highly component-specific: SARIMAX performs best for transmission and restrictions, and VAR dominates for distribution and losses, while the hybrid model outperforms for generation and commercialization. These findings highlight that although macroeconomic pass-through into tariffs is weak, hybrid approaches that combine structural econometric dynamics with nonlinear learning can deliver tangible forecasting gains. The study contributes to the literature on electricity pricing in emerging economies and offers practical insights for regulators and policymakers concerned with tariff predictability and energy affordability. Full article

The global energy transition, characterized by the proliferation of intermittent renewables and the evolution of electricity markets, has positioned virtual power plants (VPPs) as crucial aggregators of distributed energy resources. However, their participation in competitive markets is fraught with multifaceted uncertainties stemming from price volatility, renewable generation intermittency, and unpredictable prosumer behavior, which necessitate sophisticated, risk-averse bidding strategies to ensure financial viability. This review provides a comprehensive analysis of the state-of-the-art in risk-averse bidding for VPPs. It first establishes a resource-centric taxonomy, categorizing VPPs into four primary archetypes: DER-driven, demand response-oriented, electric vehicle-integrated, and multi-energy systems. The paper then delivers a comparative assessment of different optimization techniques—from stochastic programming with conditional value-at-risk and robust optimization to emerging paradigms such as distributionally robust optimization, game theory, and artificial intelligence. It critically evaluates their application contexts and effectiveness in mitigating specific risks across diverse market types. Finally, the review synthesizes these insights to identify persistent challenges—including computational bottlenecks, data privacy, and a lack of standardization—and outlines a forward-looking research agenda. This agenda emphasizes the development of hybrid AI–physical models, interoperability standards, multi-domain risk modeling, and collaborative VPP ecosystems to advance the field towards a resilient and decarbonized energy future. Full article

Biogas from palm oil mill effluent (POME) is a promising fuel that has many advantages as an alternative fuel. The methane content in biogas derived from POME is up to 75% and can be used as an alternative fuel in an internal combustion engine. One of the technologies for utilizing biogas in compression ignition engines is the Diesel Dual-Fuel (DDF) technique due to the different characteristics of fuel and the impact on the environment due to significantly reducing emissions. This study aims to find the effect of biogas POME composition and energy ratio on the DDF engine’s performance and emissions. The simulations using AVL BOOST software were confirmed by experimental engine parameters. The modeling was conducted on the biogas energy ratio (20%, 40%, 60%, and 75% POME) and biogas POME composition (55% and 75% methane). The results showed that the fuel consumption of diesel fuel was reduced by up to 69%, and NO_x and soot emissions were reduced by up to 92% and 80%, respectively, with dual-fuel mode operation. Meanwhile, the value of brake mean effective pressure (BMEP) and efficiency was reduced by up to 18%, volumetric efficiency decreased by up to 4%, the increase in brake specific energy consumption (BSEC) was up to 23%, and brake specific fuel consumption (BSFC) was up to 155%. The optimum of the engine’s performance and emission was 40% biogas ratio with 75% methane content. Full article

Artificial neural networks (ANNs) offer considerable advantages in predicting evaporation (EVAP), particularly in handling nonlinear relationships and complex interactions among factors like soil surface temperature (SST) and wind speed (WS). In Al Medina, Saudi Arabia, the connections among WS, SST at 5 cm, SST at 10 cm, and EVAP have been modeled using an ANN. This study demonstrates the practical effectiveness and applicability of the approach in simulating complex nonlinear dynamics in real-life systems. The modeling process employs time series data for WS, SST at both 5 cm and 10 cm, and EVAP, gathered from January to December (2002–2010). Four ANNs labeled T1–T4 were developed and trained with the feedforward backpropagation (FFBP) algorithm using MATLAB routines, each featuring a distinct configuration. The networks were further refined through the enumeration technique, ultimately selecting the most efficient network for forecasting EVAP values. The results from the ANN model are compared with the actual measured EVAP values. The mean square error (MSE) values for the optimal network topology are 0.00343, 0.00394, 0.00309, and 0.00306 for T1, T2, T3, and T4, respectively. Full article

This study considered and compared silver, gold, and their combination of nanoparticles (AgNPs, AuNPs, and Au-AgNPs) with biocompatible material mesoporous silica SBA-15 as potential antibacterial agents. A facile, one-pot “green” methodology, utilizing L-histidine as a reducing agent and bridge between components, was employed to obtain Ag@SBA-15, Au@SBA-15, and Au-Ag@SBA-15 nanocomposites without the use of external additives. Various physicochemical tools (UV-Vis, TEM, SAED, FESEM, XPS, BET, XRD, and FTIR) presented SBA-15 as a good carrier for spherical AgNPs, AuNPs, and Au-AgNPs with average diameters of 8.5, 16, and 9 nm, respectively. Antibacterial evaluations of Escherichia coli and Staphylococcus aureus showed that only Ag@SBA-15, at a very low Ag concentration (1 ppm) during 2 h of contact, completely reduced the growth (99.99%) of both strains, while the Au@SBA-15 nanocomposite required higher concentrations (5 ppm) and time (4 h) to reduce 99.98% E. coli and 94.54% S. aureus. However, Au introduction in Ag@SBA-15 to form Au-Ag@SBA-15 negatively affected its antibacterial potential, lowering it due to the galvanic replacement reaction. Nevertheless, the rapid and effective combating of two bacteria at low NPs concentrations, through the synergistic effects of mesoporous silica and AgNPs or AuNPs, in Ag@SBA-15 and Au@SBA-15 nanocomposites, provides a potential substitute for existing bacterial disinfectants. Full article