A nitrogen-doped reduced graphene oxide/carbon felt (N-rGO/CF) composite bioanode was fabricated by a facile single-step hydrothermal method for application in dual-chamber microbial fuel cells (MFCs). Urea served as the combined nitrogen precursor and mild reductant during simultaneous graphene oxide reduction and nitrogen incorporation at 180 degrees C for 12 h. the resulting N-rGO/CF electrode possessed a specific surface area of 87.4 m2 g-1, a water contact angle of 19.3 degrees, and an N-to-C atomic ratio of 5.7%, confirming successful nitrogen doping and marked hydrophilicity enhancement. Electrochemical characterisation by cyclic voltammetry and electrochemical impedance spectroscopy revealed a charge transfer resistance of 14.2 Omega, representing an 83.1% reduction relative to bare CF (84.1 Omega). When operated with a synthetic domestic wastewater inoculum enriched with Geobacter-dominated exoelectrogens, the N-rGO/CF-MFCs delivered a maximum power density of 2418 mW m-2 and an open-circuit voltage of 0.714 V, outperforming bare CF (618 mW m-2) and non-doped rGO/CF (1342 mW m-2) controls by 291% and 80%, respectively. Chemical oxygen demand removal reached 89.4 +/- 1.8% after 120 h of batch operation, with a coulombic efficiency of 34.7%. 16S rRNA amplicon sequencing confirmed selective enrichment of Geobacter (47.3%) and Desulfovibrio (18.2%) on the N-rGO/CF surface. These findings demonstrate that single-step nitrogen doping of graphene on three-dimensional carbon felts constitutes a scalable and cost-effective strategy to simultaneously accelerate biofilm formation, reduce interfacial resistance, and improve organic removal in bioelectrochemical systems targeting wastewater valorisation

This paper presents an empirical machine learning study proposing the MCS-Ensemble framework for Type 2 diabetes risk prediction. Type 2 diabetes is one of the most widespread chronic diseases globally, creating major healthcare and economic burdens. Early and accurate risk assessment is essential for enabling preventive interventions that can delay or prevent disease progression and reduce long-term complications. Traditional diabetes risk scoring methods rely on limited questionnaire-based information and fail to capture the complex interactions among metabolic, lifestyle, genetic, and environmental factors. The proposed framework integrates XGBoost, Random Forest, Multilayer Perceptron, and Support Vector Machine models within a stacked ensemble architecture. Imbalance handling and feature selection techniques are applied to improve predictive performance, while Monte Carlo simulation is incorporated to estimate prediction uncertainty and generate probabilistic risk ranges. Experimental evaluation on the public PIMA Indian Diabetes Dataset and a harmonised NHS-derived clinical subset indicates that the proposed framework achieves competitive predictive accuracy, sensitivity, specificity, and overall classification performance relative to the baseline classifiers evaluated; these results are subject to the limitation that fully independent external validation was not performed in the present study. The probabilistic analysis further supports more reliable clinical decision-making for borderline-risk individuals. The study highlights the effectiveness of ensemble learning and uncertainty-aware prediction for precision healthcare applications. Future research directions include privacy-preserving diabetes modelling, wearable sensor integration, explainable AI, and causal inference–driven risk assessment systems.

In The use of artificial intelligence (AI) in industrial control systems has evolved from predictive analytics to prescriptive decision support, but its adoption within high-risk petrochemical environments is currently limited by issues relating to reliability, transparency, and operator trust. We examine the practicability of TAI for refinery control, leveraging deep insight from real-world operations at the North Refineries Company in Baiji, Iraq - a challenging environment facing extreme winter weather with deteriorating distributed control systems and seriously varying crude oil properties. Several AI methods have been tested in a live operational environment. Long Short-Term Memory (LSTM) networks are used for early failing prediction of pumps, ANN For abnormal temperature gradient detection in crude distillation columns and Autoencoder models used for weak chemical anomaly recognition in gas chromatography data. The adoption of such models had led to measurable decrease in unplanned outages, lower maintenance costs and higher product quality. Despite these improvements, the work also sheds light on a fundamental limitation of legacy black box AI models: their inability to provide explanations for model warnings in safety-critical applications. Assumptions of root causes (sensor drift, feedstock variation or hardware degradation) require interpretability and human-in-the-loop. The paper suggests a four-pillar framework for trustworthy AI in refinery control to address this need: (i) hybrid AI-PID control architectures, (ii) explainable AI for all safety-critical decisions, (iii) continuous dual-sensor validation to overcome drift and bias in sensor data, and (iv) human-in-the-loop as the ultimate decision authority. By combining AI with engineering judgment, instead of eliminating it, we show how a practically feasible path to trustworthy, interpretable and operationally safe AI can be constructed in complex industrial settings.

This research article presents an experimental investigation of a two-stage scroll compressor using R32 refrigerant, focusing on the effects of vapor injection in heat pump applications. The study highlights the performance improvements achieved through vapor injection, with particular emphasis on operation at low ambient temperatures. Key performance indicators—heating capacity, coefficient of performance (COP), and discharge temperature—were selected to evaluate system efficiency. Notably, at an ambient temperature of − 20 °C, the vapor-injected system exhibited a 17.01

Modern power systems move toward high shares of wind and solar, a transition that demands storage capable of managing multi-hour balancing, fast frequency services, and long-duration backup with strong safety and reliability. This review assembles a cross-cutting view of lithium-ion, sodium-ion, supercapacitors, redox-flow batteries, hydrogen-based chains, and thermal storage, grounded in shared electrochemical and thermo-fluid models. The work introduces compact expressions for Nernst potentials, Butler-Volmer kinetics, heat generation, degradation surrogates, and state-of-charge tracking, then connects them to grid services, control strategies, and levelised cost of storage. Quantitative ranges are reported for energy density, power capability, cycle life, round-trip efficiency, and capacity fade, alongside failure modes such as thermal runaway, gas formation, electrode dissolution, membrane crossover, and tank stratification. The review further compares indicative levelised cost bands across technologies and highlights where sodium-ion, redox-flow, hydrogen chains, and thermal reservoirs can complement lithium-ion portfolios in hybrid plants. Emphasis on explicit models supports transparent comparison of storage options for planners, regulators, and system operators.

This review evaluates solar- and wind-powered green hydrogen pathways for decarbonizing energy systems, focusing on system design, electrolyser operation, storage, economics, environmental footprints, and policy alignment. A structured evidence map compiles input ranges for renewable generation, batteries, electrolysers, and storage and reconciles reported cost outcomes across case studies. Electrolyser efficiency spans 60–80%, water demand spans 9–15 L kgH₂⁻1, and salt-cavern storage costs fall less than $1 kWh⁻1. Hybrid photovoltaic–wind systems with battery buffering report curtailed renewable energy less than 15% in high-utilization designs. Across reviewed studies, the levelized cost of hydrogen commonly ranges from $3 to $13 kg⁻1 under present-day capital costs, financing, and capacity factors. Policy instruments such as tax credits, carbon contracts for difference, and concessional finance are reported to reduce costs by up to $3 kg⁻1 when paired with certification and procurement commitments. An integrated 2030–2040 roadmap aligns technical targets (electrolyser CAPEX 400–600 $ kW⁻1, lifetime more than 80 000 h) with phased policy milestones to support a cost pathway toward less than $2.0 kg⁻1 by 2040 while managing land and water trade-offs.

This study presents the synthesis of an adsorbent material for the removal of brilliant green (BG) dye from aquatic systems using a composite (hereinafter, CTO/PC-HA) composed of chitosan and chemically modified pineapple (Ananas comosus) crown biomass via nitric acid (HNO3). Response surface methodology (RSM) was applied to examine the impact of three variables on BG dye adsorption: A: CTO/PC-HA dosage (0.02–0.08 g), B: pH (4–10), and C: duration (10–40 min). The key parameters for BG dye removal were optimized using a desirability function approach, yielding optimal conditions (CTO/PC-HA dosage = 0.055 g, solution pH = 9.7, and contact time = 39.3 min). A maximum dye removal efficiency of 87.75

The present study created a novel biocomposite of chitosan and acid-modified lignocellulosic biomass derived from pumpkin (Cucurbita moschata) peel for the efficient removal of methylene blue (MB) dye. The physicochemical characteristics of chitosan-based composite (hereinafter, CTN/PP-HN) were examined using techniques including pHpzc, XRD, CHNO, FTIR, BET, and FESEM-EDX. The Box-Behnken design (BBD) numerical desirability function was employed to optimize the main adsorption factors of CTN/PP-HN dosage (0.01–0.09 g), pH (4–10), and duration (10–40). A desire function technique was used to identify the ideal circumstances for MB dye removal (85.88

This study presents the synthesis of a multifunctional polymer nanocomposite (hereinafter, chitosan-4-hydroxybenzaldehyde/CuO [CHI-4HBA/CuO]) based on 4-hydroxybenzaldehyde (4HBA)-modified chitosan Schiff base and CuO nanoparticles. The CHI-4HBA/CuO nanocomposite was characterized using field emission scanning electron microscopy with energy-dispersive x-ray spectroscopy (FESEM-EDX), Brunauer-Emmett-Teller (BET) surface area analysis, differential scanning calorimetry-thermogravimetric analysis (DSC-TGA), x-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR). The adsorption properties of CHI-4HBA/CuO were investigated through the removal of organic dye (eosin Y, EOY). Box-Behnken design (BBD) was used to optimize the adsorption of EOY. The CHI-4HBA/CuO demonstrated a BET surface area of 24.43 m2/g and a total pore volume of 0.02135 cm3/g. The optimal adsorption conditions (0.075 g dose, pH similar to 4, 39 min) yielded 91.43% EOY removal efficiency. The CHI-4HBA/CuO demonstrated an adsorption capacity of 250.43 mg/g.

This study introduces a novel approach to sustainable fuel production through the integrated application of plasma gasification, Fischer-Tropsch (FT) synthesis, and solar-powered electrolysis, focusing on the covalorization of medical waste (MW) and biomass waste (BMW). Addressing critical challenges related to waste accumulation and energy security, the system transforms complex waste streams into synthetic e-fuels by optimizing syngas composition and hydrogen integration. In the case study of Iraq where over 4.12 million tonnes of solid waste were landfilled in 2023, including substantial medical and municipal waste this approach offers a viable solution for resource recovery. Plasma gasification converts MW and BMW into syngas, which is refined and processed for CO2 capture using an MDEA-based absorption system. Solar-derived hydrogen, produced through a PEM electrolyzer, is integrated to achieve the optimal H2/CO ratio for FT synthesis. System modeling was conducted in Aspen Plus and MATLAB, with a Genetic Algorithm (GA) employed to optimize parameters for hydrogen yield enhancement. Four biomass-to-medical waste blending ratios (0.2 to 0.8) were tested. The highest-performing scenario (0.8 ratio) achieved a hydrogen mole fraction of 45.78 %, a syngas flowrate of 8,670 Nm3/h, hydrogen production of 1,300 kg/h, and a peak FT conversion efficiency of 55.8 %. Liquid fuel yield reached 1,360 kg/h, with diesel comprising the dominant product at 519 kg/h and 22,317 MJ/h of energy output. Hydrogen utilization efficiency increased to 87.5 %, and energy cost for hydrogen electrolysis decreased to 54.2 MJ/kg. Economic evaluation revealed strong financial viability at scale, with the highest scenario yielding a net present value (NPV) of $43.71 million, return on investment (ROI) of 16.48 %, and a payback period reduced to 14.93 years. Environmental analysis showed significant reductions in CO2 emissions (down to 19.3 %) and improvements in carbon-to-fuel efficiency (up to 71.2 %), with stable solid residue losses.

As Artificial intelligence AI is being infiltrated into the industrial control systems in large numbers, it can be noticed that the AIs have performed, potentially, a wide range of advantages within such a setting to make the operations more efficient and the models more adaptable to the changes than the fault-tolerance. The reliability of control strategies on AI, however, can hardly be ensured without some effort, especially regarding oil refineries, with complex equipment, operating (volatile infrastructure, frequent power outage, communication latency and sensor malfunction) conditions and in the absence of ground-truth validation information. These limitations pose a grave challenge to the stability and dependability of the data-driven control strategies to the usual industrial applications.To overcome these problems in this paper, we are proposing an MRAI (Multi-dimensional Reliability Assessment Index) in order to estimate the reliability of the AI-controlled system when the infrastructure is unstable and to offer a comparative benchmark that is more comprehensible. The developed index, considers four main and interdependent variables that will be applied to ensure a reliable implementation of industrial AI, i.e., data quality, model robustness, interpretability and response time. All the four dimensions provide a full reliability analysis that is fit to be utilized in the real-world refining plant that can be reduced to one comprehendible measure.The sensitivity analysis conducted on a real operating data of the Iraqi refinery control systems has proven that the MRAI formulated in this paper is practical and effective. Every form of damage or disruption of infrastructure is inbuilt into those models such as simulation games of operational uncertainties. Findings indicate that the developed index exhibited monotonic, constant and interpretable under different levels of uncertainty and it exhibited low intra-trial variability that validates its strength and validity.

Third-world countries are suffering from an economic crisis that has caused an apparent deficiency in supplying electric power to people, and this has prompted researchers to find alternatives to providing hot water to homes, especially in Iraq. In this article, five innovative designs of solar storage collectors presented by Iraqi researchers as an alternative to water conservation tanks commonly used in homes in developing countries and Iraq will be compared. It is also noted that the triangular storage collector recorded the peak value of average water temperature among other designs. The storage water temperature of the triangle solar collector is about 45.5 °C. The outcomes disclosed that the maximum recorded temperature was in a triangular storage collector with a front area to storage volume ratio of 8.48 m2/m3. The cylindrical storage collector, which has a 5.67 m2/m3 front area to storage volume ratio, recorded the lowest temperature. Thus, area density is a crucial factor to consider when designing these solar storage systems.

Fast charging elevates lithium-ion module temperature and spatial nonuniformity through resistive and polarization heat generation. This study develops a radially percolated composite panel using an aluminum nitride skeletal framework infiltrated with phase change material and bonds a 2 mm panel on both broad faces of a 4-cell prismatic module. Microstructure confirms radially oriented channels and continuous filling through thickness. Thermophysical testing shows in-plane conductivity rises from 0.32 W m- 1 K- 1 for neat phase change material to 6.40 W m- 1 K- 1 for the composite while latent heat remains 162 J g- 1 compared with 188 J g- 1 for neat matrix and melting stays within 44-48 degrees C. Module validation at 25 degrees C under 3C charging reduces peak surface temperature from 61.8 degrees C to 47.9 degrees C, corresponding to 22.5% reduction and reduces peak cell-to-cell temperature difference from 9.6 degrees C to 2.8 degrees C, which corresponds to a 70.8% reduction. Outer face heat flux peak decreases from 2900 W m- 2 to 1700 W m- 2 consistent with 41% reduction. A physics informed reduced order model matches measured temperatures and enables constrained control that cuts time over 48 degrees C by 72% while keeping cooling power under 120 W.

Addresses off-grid potable-water gaps in arid regions with a bio-inspired hybrid aerogel that couples nocturnal atmospheric moisture adsorption with solar-driven interfacial desalination. Rice-husk biochar scaffolds loaded with CaCl2 and plasmonic nanoparticles are folded into origami panels; characterized physicochemical properties, dynamic vapor sorption across humidity and temperature ranges, photothermal evaporation, and autonomous day night cycling; conducted 0.25 m2 field deployments including arid region, Abha, Saudi Arabia sites producing 2.7 +/- 0.3 L day- 1 at about 35 % ambient humidity; energy analysis indicated 1.7 MJ kg H2O- 1 (0.47 kWh L- 1) regeneration; produced water met potable quality metrics; cradle to gate life cycle model reported global warming potential 17.4 kg CO2eq and cumulative energy demand 312 MJ per functional m2 with intensity 0.48 kg CO2eq m3 across assessed service life; cost model with sorbent fabrication $42 m- 2 and identified freeze drying throughput as key lever; concept reduces reliance on grid energy and external infrastructure for dispersed communities; limits include processing energy, material cost, and climatic dependence; integration pathways with modular 4 to 6 m2 units discussed, targeting off grid clinics and relief camps.

The cement industry is one of the largest contributors to global carbon emissions, accounting for a significant share of industrial greenhouse gases. In response to this challenge, researchers have increasingly explored the use of agro-industrial waste as a sustainable alternative to conventional cement-based materials. This study reviews and assesses the environmental potential of such waste-derived construction products, focusing on their ability to reduce emissions and conserve natural resources. Findings indicate that substituting traditional cement with eco-friendly, waste-based materials could achieve up to a 32% reduction in greenhouse gas emissions, thereby offering a tangible pathway toward decarbonization of the construction sector. Beyond emission reduction, these materials also address critical issues of resource scarcity by valorizing agricultural and industrial by-products that would otherwise contribute to waste streams. The integration of these innovative materials into mainstream construction practices represents a unique opportunity to simultaneously mitigate climate change, promote circular economy principles, and advance the development of environmentally responsible infrastructure.

A novel chitosan-vanillin Schiff base/multi-walled carbon nanotube (CTS-VAN/MWCNT) nanocomposite was developed in this study as an efficient adsorbent for removing the cationic dye methylene blue (MB) from aqueous solutions. Comprehensive characterization of the CTS-VAN/MWCNT material was carried out using field emission scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (FESEM-EDS), Brunauer-Emmett-Teller (BET) surface area analysis, X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR). The CTS-VAN/MWCNT nanocomposite demonstrated a BET specific surface area of 50.43 m2/g, a Langmuir specific surface area of 77.44 m2/g, a total pore volume of 0.2943 cm3/g, and an average pore diameter of 23.34 nm. Process optimization for MB adsorption was carried out using the Box-Behnken design (BBD), focusing on key variables such as adsorbent dosage, solution pH, and contact time. Adsorption equilibrium data were best described by the Freundlich isotherm model, while kinetic analysis indicated that the pseudo-second-order model most accurately captured the adsorption dynamics. The CTS-VAN/MWCNT nanocomposite achieved a maximum MB adsorption capacity of 209.58 mg/g. The proposed adsorption mechanism involves electrostatic interactions, n-pi bonding, hydrogen bonding, and pi-pi stacking. Overall, the results suggest that the CTS-VAN/MWCNT adsorbent is a promising multifunctional material for the treatment of complex industrial wastewater.

Seawater desalination demands membranes that couple high water throughput with tight salt rejection under gentle hydraulic conditions. This study reports electrostatic spray printing of dual charge covalent organic framework graphene active layers on porous supports for forward osmosis desalination of synthetic seawater. The printing route yields uniform films with thickness around 2.8 mu m, structural parameter has value 85 x 10-4 m, and mean surface pore size 0.86 mu m with BET area 112 m2 g- 1. Under 1 M NaCl draw and 3.5 wt% feed at 25 degrees C, the optimized membrane achieves water flux 78 +/- 2 L m- 2 h- 1 and reverse salt flux 0.8 +/- 0.1 g m- 2 h- 1, while graphene only and covalent organic framework only controls reach 42 and 25 L m- 2 h- 1 with 1.2 and 2.1 g m- 2 h- 1 respectively. A random forest model trained on 45 fabrication and operation runs attains R2 of 0.92 and root mean square error 3.2 L m- 2 h- 1, and Shapley analysis highlights applied voltage, flow rate, and print layer count, with an optimum around 130 layers.

This study investigates residential electricity and gas demand in Iraq using smart meter data from 15,000 households between 2019 and 2023. The primary objective is to analyze temporal energy consumption patterns, focusing on the impact of environmental, behavioral, and household-specific factors. The results show that electricity consumption peaks during the afternoon, particularly between 12:00 and 14:00, with average usage reaching 2.3 to 2.6 kWh, while gas consumption increases in the winter months, especially in the early morning hours, driven by heating and cooking activities. The analysis of weekend versus weekday consumption reveals a 6.4% increase in electricity and a 3.1% rise in gas usage on weekends, indicating shifts in behavioral energy usage patterns. The study finds that predicted values closely match observed data, with a deviation of only 5% for electricity and 7% for gas, showcasing high model accuracy. The concept of "variability" is clarified as fluctuations in demand across different times of the day, which were reduced in households with efficient appliances, as identified from household energy audits linked to smart meters. The dataset also provides insights into the use of energy-efficient appliances, collected from utility-linked surveys and integrated household registration data. The findings offer valuable insights into demand-side energy management and are particularly relevant to urban Iraqi households but may also inform demand forecasting in similar regions with comparable climates and energy use behaviors.

This article reviews hybrid solar pond technologies, which represent an advanced development in harnessing solar energy by integrating traditional ponds with auxiliary systems such as photovoltaic (PV) panels, phase change materials (PCMs), heat exchangers, and advanced thermal insulation. Unlike traditional ponds that rely solely on salinity gradients and suffer from poor thermal conductivity and seasonal efficiency fluctuations, hybrid systems have significantly improved thermal storage, energy conversion efficiency, and reduced losses. Studies indicate that these systems can achieve bottom temperatures of up to 90 °C, with energy and exergy efficiencies exceeding 50 %, and generate hybrid capacities of about 5 MW. Self-desalination units based on hybrid ponds have demonstrated exergy efficiencies of around 54 % with an annual output of approximately 2,381 m3 of freshwater, equivalent to 73 % of their operational capacity. These results confirm the ability of these systems to combine electricity production with thermal energy storage, thereby supporting water desalination processes. Recent developments highlight the integration of artificial intelligence technologies with high thermal conductivity materials to achieve adequate control and reduce thermal losses, thereby extending operational life. Comparisons confirm that hybrid solar ponds outperform traditional ones in terms of efficiency and reliability, providing scalable solutions for industrial heating, smart grids, and multi-generation systems. Researchers conclude that these ponds represent a promising and cost-effective solution for addressing global challenges in energy and freshwater, making them an essential pillar in the future renewable energy system.

Solar chimney–desalination systems offer a promising approach for simultaneous freshwater and energy production; however, reported performance varies considerably with system configuration, scale, climatic conditions, and evaluation methods. Four architectures were considered: solar chimney–solar still systems (SCSS), solar chimney–reverse osmosis systems (SCROT), photovoltaic-integrated solar chimney desalination systems (PVCDS), and atmospheric vortex engine desalination systems (AVEDS). Enhanced SCSS configurations typically achieved water productivity of 4.7–6.2 L/m2·day, while condenser and airflow modifications improved performance by approximately 8.8–9.1 %. Large-scale hybrid systems reported considerably higher water and energy outputs, although direct comparison is limited by differences in system scale, operating conditions, reporting units, and methodology. Strong site dependence was also observed, with annual electricity generation ranging from 347.6 to 676.2 MWh/year across different locations. SCROT provides high freshwater-production capacity but involves greater energy and brine-management requirements, whereas PVCDS improves combined energy–water utilization. AVEDS shows promising economic and scalability potential but requires further field validation. Numerical modeling and AI-based optimization also offer opportunities for performance enhancement, although experimental evidence remains limited. Overall, no single architecture is universally superior; system selection depends on climatic conditions, scale, water and energy demands, land availability, and economic constraints. Future research should prioritize standardized performance metrics, uncertainty analysis, pilot-scale validation, advanced thermal storage, and comprehensive techno-economic assessment.