Graphite, primarily composed of carbon, is a valuable industrial material renowned for its exceptional thermal conductivity, high melting point, and resistance to thermal shock and corrosion. It exists in two forms: natural graphite, a mineral, and synthetic graphite, produced from coal and oil. As the costs of these materials rise and their industrial uses expand, researchers are exploring sustainable ways to produce graphite. This study assessed the possibility of making graphite from poplar wood, waste tires, and wheat straw through pyrolysis at temperatures from 500 to 800 degrees C. Results showed that higher temperatures resulted in lower bio-char yields, with the best efficiency at 500 degrees C due to increased bio-char breakdown. Elemental analysis revealed that the carbon content increased while the levels of hydrogen, nitrogen, and oxygen decreased as the temperature rose. FT-IR analysis detected both aromatic and aliphatic compounds, with a higher ratio of aromatics at higher temperatures, indicating dehydrogenation. The specific surface area of bio-char samples was highest at increased pyrolysis temperatures and varied among the materials. XRD analysis confirmed that the crystalline structure of graphite improved with rising temperature, while SEM images showed better porosity and surface area. TGA analysis revealed that all samples experienced less weight loss and greater thermal stability at higher temperatures.
In this work, a tannic acid-coated CoCr2O4-decorated silica composite (TA-CoCr2O4@SiO2) was synthesized via sequential CoCr2O4 formation/deposition on SiO2 followed by tannic acid (TA) functional coating, aiming to enhance adsorption of methylene blue (MB) from water. The composite exhibited a mesoporous texture with a specific surface area of 132.9 m2/g and a total pore volume of 0.739 cm3/g, facilitating dye transport to active sites. Batch adsorption experiments indicated rapid MB uptake, achieving high removal efficiency within 180 min under near-neutral conditions. Kinetic data were best described by the pseudo-second-order model (R2 = 0.94), while equilibrium was well fitted by the Langmuir isotherm (R2 = 0.97) with a maximum adsorption capacity (Qmax) of 21.1 mg/g. Thermodynamic analysis yielded Delta H degrees = -26.42 kJ/mol and Delta S degrees = -103.9 J/molK, indicating an exothermic and more ordered adsorption process, consistent with mixed noncovalent interactions (electrostatic attraction, hydrogen bonding, and pi-pi interactions) promoted by the TA/oxide surface chemistry. Regeneration results (adsorption-desorption cycling) provide initial evidence of partial capacity recovery, supporting the potential for reuse-oriented operation. Overall, TA-CoCr2O4@SiO2 offers a simple, functionalized composite platform for MB removal and provides a basis for further evaluation under realistic water matrices.
The environmental challenges posed by fossil fuels and climate change increasingly acknowledge biomass gasification as a vital renewable energy alternative. This study assesses the environmental and economic implications of syngas production from the gasification of wheat straw using a fixed-bed downdraft gasifier. A gate-to-gate life cycle assessment was performed using GaBi software and the ReCiPe 2016 method, with a functional unit of one ton of wheat straw. Midpoint results of the current study, with grid mix power, showed a climate change (GWP) of 2.21E+07 kg CO2 eq., a photochemical ozone formation, ecosystems (POFP, E) of 5.40E+05 kg NOx eq., and a fossil depletion (FDP) of 5.62E+06 kg oil eq. At the same time, the proposed photovoltaic scenario showed GWP as 1.99E+06 kg CO2 eq, POFP as 7.02E+04 kg NOx eq, and FDP as 1.35E+06 kg oil eq. Transitioning to a photovoltaic energy scenario results in reductions of 91% in GWP, 87% in POFP, and 76% in FDP, respectively. Dominant substance analysis indicates that electricity is the key contributor to several midpoint impact categories in the current study. Sensitivity analysis revealed that electricity, compressed air, and steam were critical inputs among the major determinants of environmental performance. The economic assessment indicates financial feasibility, including a 2.2 years payback period, USD 16.68 million net present value for 20 years lifespan, and 45% initial rate of return. The findings help achieve Sustainable Development Goals 7, 12, and 13 and validate the circular economy concept.
Agricultural electrification is a transformative approach to enhancing sustainability, efficiency, and resilience amid rising global food demand and environmental pressures. This review explores recent advancements in electric tractors, solar-powered irrigation systems, and the integration of renewable energy sources into agricultural practices. Life cycle assessments demonstrate substantial reductions in greenhouse gas emissions and air pollution, although concerns remain regarding the sustainability of battery materials. Despite the clear environmental and economic advantages, high initial costs, limited infrastructure, and technological constraints continue to hinder widespread adoption. The paper concludes that a sustainable transition depends on targeted policy measures, including financial incentives to lower upfront costs, investment in rural charging infrastructure, and R&D support for next-generation battery technologies. Addressing these challenges can position electrification as a key strategy in promoting climate-resilient and energy-efficient agriculture.
The Wadi El-Rayan depression, a protected area in Egypt's Western Desert southwest of Cairo, contains two man-made lakes that serve as reservoirs for agricultural wastewater while supporting fisheries and irrigation. This study assessed their water quality, including heavy metals and microbiological parameters, using the Water quality index (WQI) and heavy metal pollution indices to evaluate suitability for aquatic life. Microbiological analysis found that total coliform, fecal coliform, and fecal streptococci levels at most sites were within acceptable limits for aquatic organisms. The Upper Lake demonstrated fair water quality (WQI = 52-78), while the Lower Lake was of medium quality (WQI = 50-66) and exhibited higher total dissolved solids. Heavy metal pollution was also more pronounced in the Lower Lake. The primary threat to the Upper Lake is effluent from the El-Wadi Drain, which degrades water quality through intensive agricultural discharge. The Lower Lake faces additional ecological stress from a shrinking surface area due to agricultural encroachment and sand dunes. To prevent further degradation, immediate actions, such as optimizing water allocation and establishing a robust water quality monitoring program for the lakes and the drain, are critical. This study underscores the potential for effective water resource conservation and calls for an integrated effort among policymakers, industries, and communities to ensure a sustainable water future.
Oil-dependent economies are encountering substantial environmental challenges stemming from the extraction, processing, and combustion of oil. These economies have challenges in diversifying their energy portfolio owing to their reliance on oil revenue. This study examines data from the five foremost oil-producing countries from 1990 to 2020 to assess the influence of green energy, technological innovation, oil production, and primary energy consumption on environmental quality. The Panel ARDL method is used to evaluate the impacts in both the long-run and short-run. Furthermore, robustness checks are performed using the panel quantile and panel least squares methodologies. The findings demonstrate that the transition to green energy significantly decreases emissions in the short-run; however, this impact becomes negligible in the long term owing to systematic inertia in oil-dependent economies. Technological innovation also contributes to substantial decarbonization. The utilization of primary energy sources continually elevates carbon emissions in both the long and short term. These findings suggest that oil-producing economies need to invest in green energy for immediate benefits. Additionally, policymakers should prioritize technological innovation to mitigate the effects of fossil fuel reliance. Overall, this study provides empirical evidence to help rebalance the energy portfolio in oil-reliant economies, aligning their growth targets with environmental goals.
This article presents a comprehensive review of recent research on renewable-driven multigeneration systems using integrated 3E (Energy, Exergy, Environment) and Life Cycle Assessment (LCA) frameworks. The proposed integration provides a comprehensive understanding of a system's performance, identifying potential hotspots, optimizing resource utilization, and ultimately driving the development of more environmentally friendly energy solutions. The manuscript reviews the system's energy potentials (both inputs and outputs) with an emphasis on total energy efficiency and identifying areas of energy loss. The exergy analysis evaluates energy quality by measuring thermodynamic losses and irreversibility. A cost–benefit analysis is also performed to analyze the economic sustainability of the multigenerational system (MGS). The LCA provides environmental impact indicators. The innovative multigeneration systems are intended to generate many useful outputs (power, heat, hydrogen, and cooling) from renewable resources like solar or biomass. The study provides data for making educated decisions on system design, technology selection, and process optimization that fit with environmental goals and economic feasibility. The MGS improves energy and exergy efficiency by manufacturing many products at the same time, resulting in less waste and greater utilization of primary resources. Using renewable resources and producing diverse products significantly reduces carbon footprints and greenhouse gas emissions, encouraging environmental conservation and sustainability. Instead of proposing a new optimization strategy, this review synthesizes existing multi-objective optimization approaches applied to renewable-driven multigeneration systems and critically discusses the trade-offs among energy, exergy, economic, and environmental objectives within integrated 3E–LCA frameworks.
Decarbonizing the global energy system requires clean fuel pathways that are low carbon at the point of use and sustainable throughout their lifecycles. This review compares green hydrogen (H 2 ), green ammonia (NH 3 ), and synthetic electrofuels (e-fuels). It focuses on integrating advanced monitoring technologies and standardized life-cycle assessment (LCA) frameworks. We critically examine contemporary monitoring techniques, including Raman spectroscopy, tunable diode laser absorption spectroscopy (TDLAS), gas chromatography–mass spectrometry (GC–MS), fiber-optic sensing, and AI-enabled digital twins with SCADA systems. Their effectiveness is assessed for leak detection, fuel quality, emissions quantification, and operational safety across production, storage, transport, and end-use phases. A synthesized cradle-to-grave and well-to-wheel LCA, consistent with International Organization for Standardization (ISO) 14040 and ISO 14044 standards, quantifies environmental performance and shows key sources of variability among the three energy carriers. The literature shows greenhouse gas (GHG) emission reduction potentials from about 70% to 98%, depending on electricity carbon intensity, production pathways, carbon dioxide (CO 2 ) sourcing, and system boundary definitions. H 2 offers the greatest decarbonization potential for industrial and grid-scale applications. NH 3 is useful for long-distance transport and seasonal energy storage. E-fuels, though less energy-efficient, help facilitate near-term adoption in hard-to-electrify sectors like aviation and maritime transport. Combining operational monitoring data with life-cycle carbon accounting enables transparent, certification-ready sustainability governance that aligns with United Nations Sustainable Development Goals 7, 9, and 13.
This study presents a satellite-based analytical framework that integrates Nitrogen Dioxide (NO2) observations from Sentinel-5P/Tropospheric Monitoring Instrument (TROPOMI) with meteorological variables (temperature and humidity) from Moderate Resolution Imaging Spectroradiometer (MODIS) and wind speed data from Gridded Surface Meteorological Dataset (GRIDMET) to assess air pollution dispersion near bio refinery sites. The framework uses Long Short-Term Memory (LSTM) neural networks enhanced with Time2Vec temporal encoding to forecast NO2 concentrations across three Saudi Arabian cities-Al Riyadh, Al Jubail, and Najran-using daily measurements collected between August 2018 and April 2023. Data preprocessing includes cloud masking through Google Earth Engine (GEE) quality-control filters, kriging-based interpolation for meteorological variable alignment, and min-max normalization. The Time2Vec-LSTM model achieves a mean absolute error of 41.57 mu g/m3 for Al Riyadh, with a Root Mean Square Error (RMSE) of 54.44 mu g/m3 and a Mean Absolute Percentage Error (MAPE) of 33.07%, representing a 2.8% improvement over the baseline LSTM. Spatiotemporal analysis indicates distinct seasonal variations, with peak NO2 concentrations during winter (attributed to increased heating emissions) and minimum levels during summer (due to enhanced atmospheric dispersion). Overall, the proposed framework addresses monitoring gaps in regions with limited ground-based air quality networks and offers valuable decision-support capabilities for environmental policy development and sustainable urban planning.
Fuel and design parameters are the two major parameters for controlling the emission characteristics of a Partially Premixed Charge Compression Ignition (PPCCI) engine and much works were not focussed to investigate their combined influence by simultaneously varying these parameters which require an extensive experimental work. This unique work is aimed to develop and test an integrated predictive PPCCI engine model to reduce its NOx emissions while minimizing adverse effects on smoke emissions and engine performance. MATLAB R2022 software was used by recalibrating a conventional compression ignition (CI) engine in the existing MATLAB simulation framework. Optimal factor levels were obtained through Response Surface Methodology (RSM) and Multi-Response Signal-to-Noise Ratio (MRSN) optimization techniques and compared. In meeting the objective of this work, main injection fuel is the most influencing factor followed by main fuel injection timing and these two factors were collectively contributed around 88.5% as the influencing factors. Fuel-related factors had a greater cumulative influence (43%) than design-related factors (39.5%). When comparing the optimized results of the developed model with a conventional CI engine parameters, MRSN optimization technique resulted in a marginal decrease in NOx emission with an increase in smoke emission and decrease in brake thermal efficiency (BTE) and RSM has achieved a simultaneous reduction in NOx (17.7%) and smoke emissions (9.2%) along with a marginal (3%) increase in engine performance which shows that RSM was more effective than MRSN in meeting the objective of this work.
Drying is a critical yet energy-intensive stage in agricultural processing. This research investigates the integration of a solar dryer with a micro-combined heat and power (MCHP) system for waste heat recovery, designed to regulate temperature and enhance energy efficiency for drying barberries. The findings revealed that at an air velocity of 1.5 m/s, total phenol content initially increased by 3% as the temperature rose to 60 degrees C, reaching a peak value of 84.6 mg GAE/g. However, further temperature increases (60 to 70 degrees C) resulted in a 6% reduction in phenol content. Anthocyanin levels were 26% and 53.7% higher at 50 degrees C than at 60 and 70 degrees C, respectively, at the same air velocity. Antioxidant activity was enhanced by 11% and 9% at 70 degrees C compared to 50 and 60 degrees C, respectively. Optimal drying conditions were identified at an inlet air temperature of 65 degrees C and a velocity of 1.5 m/s, yielding an energy efficiency of 21%, an exergy efficiency of 59%, a drying time of 143 minutes, a phenol content of 83 mgGAE/g, an anthocyanin content of 60 mg/g, and antioxidant activity (IC50 of 59 mu g/mL). These results establish the combined MCHP-solar dryer as a promising approach for sustainable drying.
In order to solve a major environmental concern, this analysis highlights the potential of synthetic biodegradable polymers for wastewater color removal. Because of their biodegradability, environmental compatibility, and adjustable qualities, the study intends to investigate the effectiveness of polymers such as polylactic acid (PLA), polyhydroxyalkanoates (PHA), poly ε-caprolactone (PCL), polybutylene succinate (PBS), and poly (lactic-co-glycolic acid) (PLGA). Techniques for synthesis and modification are described in depth, including copolymerization, ring-opening polymerization, polycondensation, biomimetic synthesis, and surface modifications such as chemical functionalization and nanostructuring. Adsorption mechanisms, including physical interactions, chemical interactions, and ion exchange, are discovered, highlighting the effectiveness of polymers for adsorbent with different dyes such as Azo, Anthraquinone, reaction, acid, and basic color. Research shows the effectiveness of these polymers by adsorbent kinetics and balanced model. Future research guidelines include the development of new polymers, integrated advanced materials and nanotechnology, optimizing adsorption processes, and promoting long-term methods. These trends are aimed at improving performance, selection, and environmental sustainability of general biodegradation polymers, providing a promising and environmentally friendly solution to pollute dyes in wastewater, finally protecting the environment and human health.
Tetracycline hydrochloride (TCH) is a common contaminant, which has a detrimental impact on the environment and ecological balance; the removal of TCH from wastewater is a crucial environmental concern. In this study, the CuO-loaded HNbWO6 nanosheet (CuO@HNbWO6-NS) was prepared using an exfoliation-flocculation method. A series of characterizations of the material demonstrated that there was a significant interaction between the loaded CuO and HNbWO6 nanosheet. The composite photocatalyst was employed in a photocatalytic degradation reaction of TCH, with a degradation efficiency of 74.7% achieved within 2 h. A potential photocatalytic mechanism was proposed based on electrochemical impedance and radical quenching experiments, and the radical contribution in this photodegradation system was in descending order: superoxide radical (O-2(-)) > photogenerated holes (h(+)) > hydroxyl radical (OH). The results showed that the semiconductor heterojunction architecture can effectively enhance the spatial separation efficiency of electrons and holes, thereby enhancing the semiconductor photocatalytic activity. The findings of this research provide a viable strategy for the treatment and remediation of water pollution.
Integrating sustainability into distribution networks remains a critical challenge for organizations aiming to align with the United Nations Sustainable Development Goals (SDGs) while maintaining competitiveness in the global market. This challenge is especially pronounced in the additive manufacturing industry, where natural gas is the primary energy source. While previous studies have assessed life cycle impacts of methanol production, region-specific analyses evaluating environmental consequences across methanol supply chains are limited. This study addresses this gap by conducting a comprehensive life cycle assessment (LCA) of methanol production, focusing on Qatar as a case study. The analysis evaluates environmental impacts across all stages, from raw material acquisition to product synthesis, quantifying primary and secondary greenhouse gas (GHG) emissions, including carbon dioxide, methane, and nitrous oxide. Aspen HYSYS simulations, known for their engineering rigor, were employed. Results indicate that the Steam Reforming process contributes 81.9% of direct CO 2 -equivalent emissions, making it the leading source of the carbon footprint. The methanol synthesis phase accounts for 52.4% of Scope 2 emissions, primarily due to energy-intensive separation operations. Based on these findings, this research proposes a framework to inform sustainability strategies and policy development aligned with corporate environmental goals.
The world's energy consumption is hugely affected by residential and commercial buildings. The building sector accounts for almost 30%-40% of the world's total energy. To minimize this energy consumption and the impact of buildings on the climate, an essential solution is for buildings to attain the Net Zero Energy Buildings (NZEB) status. This research explains the influence of design and parametric variables (Building orientation, Roof Materials, External Walls Materials, shading, glazing, lighting, and Heating, Ventilation and Air Conditioning (HVAC) systems) on the cooling load of a residential building located in Peshawar, Pakistan (ASHRAE zone 2B) and to achieve the status of Net Zero Residential Building (NZRB). In addition, the rooftop Building Integrated Photovoltaic (BIPV) systems yielded an annual generation of 6727.68 kWh/year, offsetting a substantial portion of the annual demand of 6258.77 kWh/year and moving the building to a net-zero balance. Results indicate that building orientation is the most influential parameter, with a standardized regression coefficient of -0.74 (p = 0.00), confirming its dominant role in cooling demand. The optimized configuration reduced the annual cooling load from 2164.27 kWh/year to 1834.03 kWh/year, representing a 15.25% reduction. The study also determined that building orientation impacts 24.6% of the cooling load, regardless of other design variables, and the least impactful factor on the cooling load is external wall materials, which is 2.9% of the cooling load. This study contributes a region-specific NZRB optimization model that strengthens the literature and provides actionable insights for energy-efficient housing in Pakistan.
The start-up process of particle-based solar cavity receivers can require several hours due to their high operating temperature targets and significant thermal mass. This duration is not fixed but varies with location, time of day, and solar resource availability. Assuming a constant start-up time based only on steady-state operation overlooks the inherent variability of solar input, leading to significant uncertainty in system performance. This article investigates the influence of key geometric parameters on the dynamic start-up response of a cylindrical cavity particle receiver, considering both steady-state and transient operating conditions. A transient thermal model was developed to predict the time required to reach a target start-up temperature of 1000 degrees C and associated heating rates. Geometric variations included aperture diameter (6-9 m), cavity diameter (8-14 m), cavity length (14-20 m), lining thickness (50-200 mm), and surface absorptivity (0.6-0.9). Two solar input cases were analyzed: (i) constant Direct Normal Irradiance (DNI) values between 600 and 1000 W/m2, and (ii) real-time hourly DNI data. Results show that neglecting solar variability can cause both over- and under-prediction of start-up times by up to 80%, depending on geometry and irradiance. This highlights the critical need to incorporate transient solar resource data into start-up modeling for reliable design and operation of particle receivers.
The rising demand for cement and natural aggregates in construction has led to the depletion of renewable resources, highlighting the need for sustainable alternatives. Geopolymer-concrete (GPC), produced from industrial by-products like fly ash and ground granulated blast-furnace slag (GGBS), offers an eco-friendly substitute for traditional cement-based concrete. However, developing an effective GPC mix is complex due to the interdependence of factors influencing strength and durability. This study focuses on optimizing GPC mix designs using correlation analysis, scatter plots, multi-objective optimization, and predictive models such as support vector regression, decision tree, random forest, and genetic algorithm (GA). The correlation matrix results suggest the influence of GGBS and fly ash (FA) on compressive strength in GPC, with strong positive correlations of 0.96 and 0.97, respectively. Similarly, coarse aggregate exhibited a strong negative correlation of 0.97. Compressive strength of GPC was increased by the ideal dosages of NaOH, Na2SiO3, GGBS, and fly ash, whereas it was decreased by excessive alkalinity or material imbalances. To attain the maximum compressive strength of 47 MPa, multi-objective optimization determined the ideal ranges for GGBS (100-110 kg), fly ash (200 kg), and coarse aggregate (400 kg), highlighting the significance of exact material balance. These results were further validated using a regression model optimized by GA, which outperformed SVR, decision tree, and random forest models, achieving the best prediction accuracy with an R 2 of 0.97 and an RMSE of 1.27. The reliability of the model was further ensured through fivefold cross-validation, which helped prevent overfitting and confirmed the generalization capability of the GA-based model.
The carbonaceous composition of particulate matter (PM) directly influences its light-absorbing properties. To study the light absorption characteristics of PM emitted from biodiesel combustion, PM samples were collected from engines fueled with diesel, palm oil methyl ester (PME), waste cooking oil methyl ester (WME), and soybean oil methyl ester (SME). By integrating ultraviolet–visible spectrophotometry with carbonaceous component analysis, the mechanism through which organic carbon (OC) components influence the light absorption behavior of biodiesel-derived PM was investigated. Results show that PM from diesel combustion exhibits significantly higher light absorption intensity than that from biodiesel combustion, with the light absorption capacity of biodiesel-derived particles increasing as fuel iodine value rises. OC2 dominates OC in diesel-derived particles, whereas OC in biodiesel-derived particles is composed of OC2, OC3, and OC4, among which OC3 has the highest proportion. Furthermore, the mass absorption cross-section (MAC) of elemental carbon (EC) is greater in biodiesel combustion particles than in diesel combustion particles, and the magnitude of light absorption enhancement correlates positively with increasing OC/EC ratios. These findings highlight the critical role of brown carbon in enhancing light absorption in biodiesel-generated particulate matter.
This study reported the application of these soft computing techniques to predict the emissions and performance characteristics of a diesel engine fueled with cottonseed biodiesel under different operating conditions. Fuel injection timing ( o bTDC), fuel injection pressure (bar), biodiesel blend (%), and engine load (%) are the input parameters. The objective of this study was to obtain the finest output for parameters such as brake thermal efficiency (BTE), brake-specific energy consumption (BSEC), heat release rate (HRR), ignition delay (ID), unburnt hydrocarbons (HC), carbon monoxide (CO), and oxides of nitrogen (NOx). Cottonseed ethyl ester blends of B5, B10, B15, B20, and B25 are employed as fuels. The experiment was conducted using the response surface methodology (RSM) approach. RSM is the ideal combination for improving engine output. Analysis of variance (ANOVA) demonstrated that all of the created models were statistically relevant. Furthermore, three metrics (MSE, RMSE, and R 2 ) are investigated in depth to evaluate the efficacy of soft computing-based prediction models. In contrast, when it came to forecasting CI engine reactions, both prediction models performed well. Furthermore, it was observed that ANFIS yields more accurate forecast findings than ANN.
This study investigates the in situ epoxidation of a hybrid feedstock comprising waste cooking oil and oleic acid to enhance oxirane conversion for sustainable epoxide groups production. A key challenge with waste cooking oil is its low epoxidation efficiency when used alone. By blending it with oleic acid at a 1:1 weight ratio, a maximum relative conversion to oxirane (RCO) of 63.4% was achieved compared to 29.5% when using waste cooking oil alone accompanied by homogenous catalyst usage. The optimum reaction temperature was found at 70 degrees C. Side reactions were minimal within 30 min of reaction time, but ring opening became significant at a higher temperature (80 degrees C) and longer times. FTIR analysis confirmed successful epoxidation, where the disappearance of C=C stretching (similar to 1650 cm(-1)) and the appearance of epoxy ring vibrations (similar to 823 cm(-1)) were observed. Kinetic modeling of the system demonstrated high reaction rates with minimal side reactions, validating the approach's accuracy and robustness. This work presents a novel hybrid strategy to valorize waste cooking oil, offering a more efficient and sustainable route for bio-based epoxide production.
