Search Results (242)

This study investigates the environmental and energy performance of rice straw-based energy pathways in Egypt, combining life cycle assessment (LCA) with supply chain optimization to improve system efficiency. The analysis covers thirteen governorates producing over 4.45 million tons of rice straw annually. It examines the whole supply chain from paddy farming, straw collection, and transport to electricity generation and ash disposal. Total energy consumption was 11,287 TJ, dominated by farming (5673 TJ) and transport (5490 TJ). Greenhouse gas (GHG) emissions were estimated at 12,007.5 million kg CO₂-eq, with significant contributions from farming (5158 million), combustion (3630 million), and natural gas use (3039 million). Gross electricity output was 5525 GWh, yielding a net of 4973 GWh, equivalent to 1116.5 kWh per ton of straw. Scenario analysis highlighted that the optimized multi-hub system, prioritizing Cluster 1 in the Nile Delta, which contributes over 92% of straw production and 4607 GWh of net electricity, achieved a reduction of more than 25% in transport distances and an 18% decrease in diesel consumption and related emissions. Sensitivity analysis further indicated that delivered electricity and GHG intensity are more sensitive to conversion efficiency and transmission and distribution losses than to moderate changes in transport assumptions. In addition to environmental improvements, the optimized scenario indicates potential social co-benefits, including rural employment generation, additional income opportunities for farmers, and improved air quality associated with reduced open-field burning. These outcomes are presented as indicative qualitative insights. Findings confirm rice straw as a strategic, scalable, and sustainable energy resource aligned with Egypt’s Vision 2030 and the UN Sustainable Development Goals (SDGs). Full article

Gum arabic (GA)-based edible coatings enriched with natural bioactive compounds offer a promising strategy for reducing postharvest losses and maintaining fruit quality. This study evaluated the effectiveness of GA coatings supplemented with carvacrol or caffeine in preserving the physicochemical quality, antioxidant status, and marketability of ‘Murcott’ mandarins during cold storage (5 ± 1 °C, 90–95% RH) for 60 days followed by 4 days of shelf life. Fruits were treated with distilled water (control), GA (10%), GA + imazalil (2000 ppm), GA + carvacrol (200 ppm), and GA + caffeine (200 ppm). Key quality parameters, including weight loss, decay incidence, firmness, electrolyte leakage, malondialdehyde (MDA), total soluble solids, titratable acidity, ascorbic acid, total phenolics, total flavonoids, and antioxidant enzyme activities of catalase (CAT) and peroxidase (POX), were evaluated. The results demonstrated that GA-based coatings, particularly GA + carvacrol, significantly reduced weight loss and decay while maintaining firmness and visual quality compared to the control. Coated fruits exhibited lower electrolyte leakage and MDA levels, indicating improved membrane integrity and reduced lipid peroxidation. In addition, the treatments enhanced antioxidant capacity, as reflected by increased phenolic and flavonoid contents and higher CAT and POX activities. Multivariate analysis further confirmed the strong association between coating treatments and improved quality attributes. In conclusion, GA coatings enriched with carvacrol or caffeine effectively improved postharvest quality and extended the shelf life of ‘Murcott’ mandarins, highlighting their potential as safe and eco-friendly alternatives to conventional postharvest treatments. Full article

This paper presents an analysis of the exhaust gas concentration of a compression ignition engine powered by diesel fuel and rapeseed oil under dynamic conditions. The measurement cycle consisted of a 100 s segment of the WLTC cycle. An attempt was then made to estimate the exhaust gas concentration using predictive algorithms based on parameters recorded using the OBD-II diagnostic interface. The model was validated based on previously unobserved measurements of the measurement cycle, and the procedure was repeated several times with random parameter changes. Due to the dynamic nature of the combustion process (taking into account its non-linearity and inertia), a delayed feature design was used. A consistent time horizon of input information was selected for the tabular and sequential models used. The results obtained indicated that Gradient-Boosted Regression Trees class algorithms achieved the highest quality of fit and were characterised by the greatest stability. Full article

Deep shale gas reservoirs in the southern Sichuan Basin (Weiyuan area) exhibit strong heterogeneity and complex pore-fracture networks. Traditional reservoir evaluation methods struggle to accurately capture their microscale pore characteristics and fracability, thereby restricting efficient development and precise sweet spot prediction. Therefore, integrating digital core technology with geological analysis is essential to systematically quantify key reservoir parameters, including microscale pore structure, mineral composition, and brittleness characteristics. To clarify the controlling factors of high-quality deep shale gas reservoirs in the Weiyuan area and assess their exploration and development potential, we performed digital core analysis at micron to nanometer scales. Three-dimensional digital core models of representative deep shale gas wells were constructed. Integrating mineral composition, geochemical characteristics, and pore space features, we discuss the geological conditions for deep shale gas accumulation and the fracability of horizontal wells, and we delineate favorable shale reservoir zones. The results show that digital core technology enables quantitative and visual characterization of each sublayer of the Longmaxi Formation shale reservoir, including mineral types, laminae types, pore-throat structures, and organic matter distribution. From the Long 11-1 sublayer to the Long 11-4 sublayer, the pore-throat radius, total pore volume, total throat volume, connected pore-throat percentage, and coordination number all gradually decrease. In the eastern Weiyuan area, the siliceous components in deep shale gas reservoirs at the base of the Longmaxi Formation are primarily of both biogenic and terrigenous origin. Due to local variations in the sedimentary environment, terrigenous input contributes significantly to the total siliceous content in this region. Although the Long 11-1 sublayer of the Longmaxi Formation is lithologically classified as mud shale, its particle size and mineral composition more closely resemble those of clayey siltstone or argillaceous sandstone, suggesting considerable potential for reservoir space development. Typical wells in the eastern Weiyuan area exhibit distinct lithological characteristics, including coarser grain sizes, stronger hydrodynamic conditions during deposition, and abundant terrigenous clastic supply. The rigid framework formed by silt- to sand-sized particles effectively mitigates compaction, thereby facilitating the preservation of intergranular pores and microfractures. High organic matter abundance, appropriate thermal maturity, and a considerable thickness of high-quality shale ensured sufficient hydrocarbon supply. The main types of natural fractures are intergranular and grain-edge fractures formed by differences in sedimentary grain size, and bedding-parallel fractures generated by hydrocarbon generation overpressure. Based on reservoir mineral composition, pore characteristics, areal porosity, and pore size distribution identified via digital core analysis, the bottom 0–3 m of the Long 11-1 sublayer is determined to be the optimal target interval. By delineating the microscopic characteristics of the shale reservoir and predicting rock mechanical parameters, a fracability evaluation index was established from digital core simulations. This guides the selection of target layers in deep shale gas reservoirs and optimizes hydraulic fracturing design. Full article

In this study, a three-dimensional simulation of a walking-beam reheating furnace was developed to improve the steel slab reheating process and reduce surface oxidation kinetics using computational fluid dynamics (CFD). Combustion, heat transfer, fluid dynamics, and chemical reaction models were integrated into the numerical framework of this study. In addition, dynamic mesh remeshing was coupled through user-defined functions (UDFs), enabling the simultaneous simulation of slab movement and evolution of the involved transport phenomena. Turbulence was modeled with the realizable k-ε formulation, combustion with the Eddy Dissipation model, and radiation with the P-1 model coupled with WSGGM to include CO₂ and H₂O gas radiation. Scale formation was modeled using customized functions based on Arrhenius-type kinetics and Wagner’s oxidation model, evaluating its growth as a function of time, temperature, and furnace atmosphere. The predicted thermal evolution inside the furnace was validated using industrial data, yielding an average deviation of 5%. Furthermore, the proposed operating conditions led to an average slab temperature of 1289.77 °C at the exit of the homogenization zone, which was 16 °C higher than that under the current operation but still within the target range (1250 ± 50 °C). The reduction in combustion air decreased energy losses and improved product quality, lowering the molar oxygen content in the furnace atmosphere from 4.9 × 10² mol to 6.7 × 10¹ mol. Additionally, annual savings of 4,793,472 kg of natural gas and 13,884 tons of steel were estimated owing to reduced oxidation losses. The proposed air–fuel adjustment led to estimated annual energy savings (equivalent to 4,793,472 kg of natural gas) and a reduction in material loss due to oxidation from 4.5% to 3.75% (an absolute reduction of 0.75 percentage points; relative reduction ≈ 16.7%), which has a significant industrial impact on metal conservation and descaling cost reduction. Although there are CFD studies on plate overheating and scale growth separately, this work presents three main contributions: (1) the integration, within a single numerical framework, of combustion, radiation, species transport, oxidation kinetics, and actual plate movement using a dynamic mesh; (2) validation against continuous industrial records (16 thermocouples) and quantification of operational benefits such as fuel savings and reduced material loss; and (3) a comparative analysis between actual and optimized conditions, which standardize the air–methane ratio. Full article

Natural gas streams extracted from production wells often contain undesirable components such as water vapor, gas condensate, and solid particulates. These impurities reduce fuel quality and damage downstream equipment through corrosion, fouling, and foaming. This study presents the development and field-scale evaluation of a high-performance gas–liquid separator designed for the deep decontamination of natural gas. The proposed separator incorporates 30 suspended baffles arranged in three rows and an anti-foaming mesh to enhance phase separation and prevent liquid re-entrainment. Field experiments were conducted at the Somontepa gas field in Uzbekistan. Compared to the baseline industrial unit, the upgraded separator reduced gas condensate from 16.58 g/m³ to 0.725 g/m³, water from 4.84 g/m³ to 0.10 g/m³, and solid impurities from 1.20 g/m³ to 0.0058 g/m³. The foam height was lowered from 96.4 mm to 10.2 mm, and the average bubble diameter was reduced by over 60%. The design maintained low pressure drops and demonstrated stable operation under varying flow rates. Fractional analysis confirmed the quality of a recovered condensate suitable for downstream utilization. The proposed configuration offers substantial improvements in gas purification performance and economic efficiency. These results support the application of this separator design for high-contaminant natural gas streams in industrial gas processing facilities. Full article

The Mabi Block is located in the southern Qinshui Basin, representing an underexplored region with high-rank coal seams that host significant Coalbed Methane (CBM) potential. Despite extensive CBM development in the nearby Anze and Zheng Zhuang blocks, the geological and geophysical controls on Coalbed Methane enrichment in Mabi remain insufficiently constrained. This study integrates the core data (63 samples) of isothermal adsorption tests, well-logging data from (13 wells), and 3D seismic attributes to systematically evaluate the key controlling factors, such as burial depth, roof and floor lithology, and sealing capacity, in the horizons of the No.3# and No.15# coal seams. Lithology is characterized using natural gamma ray (GR), acoustic (AC), deep resistivity (RD), compensated neutron log (CNL), and seismic wave impedance inversion. Coal quality parameters, ash content, and the Langmuir volume (V_L) are correlated with gas content, and structural controls are mapped using curvature, fault interpretation, and burial depth analysis. The results show that thick mudstone and limestone roofs, moderate burial depth (1100–1350 m), synclinal structural lows, and thicker coal seams (6–9 m) collectively enhance methane preservation. The ash content (%) exhibits a moderate negative correlation with the Langmuir volume (R² = 0.4) and gas content. Structural curvature (syncline) and fault intensity strongly govern lateral sealing integrity, where anticline zones and faulted regions display notable degassing. This integrated assessment contributes to a refined CBM optimization model for the Mabi Block and guides targeted future drilling, reservoir evaluation, and production optimization. Full article

This study assesses the technical, operational, environmental, and economic feasibility of integrating alkaline water electrolysis (AEL) using on-site measured surplus electricity from two 20 MW natural-gas turbogenerators installed at a Central Processing Facility (CPF) in a Colombian oilfield. Unlike approaches based on modeled profiles, the analysis relies on more than 31,000 experimental records of gas consumption and active power, enabling an accurate characterization of the structural availability of energy surpluses under real operating conditions. A specialized industrial water treatment and purification company was consulted and provided with the physicochemical characterization results obtained from process water samples analyzed by an accredited laboratory. Based on these parameters, the technical supplier confirmed the feasibility of designing a multistage treatment train, including equalization, filtration, clarification, activated carbon, ultrafiltration, and reverse osmosis, capable of achieving final conductivities at or below 5 µS/cm. This water quality level is compatible with typical industrial alkaline electrolysis requirements and in line with technical specifications commonly aligned with ASTM and ISO standards for pressurized AEL systems. A strategic comparison between PEM and AEL technologies, supported by IFE/EFE matrices and sensitivity analyses, identified alkaline electrolysis as the optimal alternative under a stable electrical profile and capital expenditure constraints. Energy sizing for scenarios between 1.5 and 10 MW, assuming continuous 24 h operation and an average specific consumption of 50 kWh/kg H₂, yields productions between 0.5 and 3.5 t H₂/day, with electrical efficiencies above 70%. A 20-year financial analysis indicates a techno-economic threshold near 3 MW (NPV > 0; IRR > WACC), with optimal performance in the 6.5–10 MW range and payback periods between 2 and 4 years under internal valorization of the surplus electricity. From an environmental perspective, the produced hydrogen is classified as low-carbon rather than “green” due to its thermal origin; however, the integration improves the turbines’ operating regime and valorizes surplus electrical exergy that was previously unused, providing a replicable strategy for industrial assets with self-generation and treatable water availability. Full article

Background: Lycium barbarum L. is gaining significant interest as a medicinal and culinary raw material. The quality and aroma are significantly influenced by metabolite accumulation, which differs based on origins and drying methods. Methods: This study utilizes gas chromatography–mass spectrometry (GC-MS) to analyze the metabolic profiles of the ‘Ningqi’ No. 1 variety from three distinct origins employing two drying techniques (natural sun drying, NSD; hot-air drying, HAD). The samples include Zhongping, Ningxia, with HAD (1-1); Zhongning, Ningxia, with NSD (1-2); Wuwei, Gansu, with NSD (1-3); Nuomuhong, Qinghai, with NSD (1-4); and Nuomuhong, Qinghai, with HAD (1-5). Results: The study found that aldehydes, esters, ketones and alcohol are key secondary metabolites generated during NSD and HAD treatments of goji berry from various regions. Flavor analysis revealed the compound Ethanol, 2-phenoxy- (balsamic) was up accumulated in goji berry from Qinghai drying with NSD compared with HAD; goji berry drying with HAD collected from Ningxia compared with Qinghai; goji berry drying with NSD collected from Gansu compared with Ningxia; and goji berry drying with NSD collected from Qinghai compared with Ningxia. The compound 2-Thiophenemethanol (burnt) was up accumulated in goji berry drying with HAD collected from Ningxia compared with Qinghai. Further flavor analysis revealed that the compound Undecanal (floral) was up accumulated in goji berry drying with NSD collected from Qinghai compared with Ningxia and Gansu. 1H-Pyrrole-2-carboxaldehyde (burnt), 1-ethyl- (burnt) was up accumulated in goji berry drying with NSD collected from Qinghai compared with Gansu. KEGG enrichment analysis suggests that ‘Arginine and proline metabolism’ could be the primary metabolic pathway in the goji berry drying process. Conclusions: This study examined how origins and drying methods affected the metabolites and metabolic pathways of goji berries to elucidate the mechanisms impacting their quality and flavor. The findings provide important insights into the use of goji berries in functional foods and pharmaceuticals. Full article

Reducing biogas produced by solid waste landfills is a key solution in achieving climate neutrality goals, contributing to GHG emission reduction. This study aimed to investigate the opportunity to invest in a landfill biogas energy production plant when the quality of the biogas (methane concentration) is low. The research was conducted on three municipal solid waste landfills located in Bacău, Ilfov, and Brașov in Romania. Due to improper selective collection and recycling, the average methane content in these landfills is between 7 and 30%. The methodology used to conduct the research combined scientific and digital bibliographic sources, data processing and economic calculations using MS Excel, and the estimation of landfill gas emissions using LandGEM software. The analysis showed sales prices ranging between 155 and 450 [EUR/MWh]. However, the environmental analysis highlights that only the third landfill, with a methane concentration of over 30%, truly contributes to reducing emissions. Also, the use of high quantities of natural gas for energy production is incompatible with the European Union’s climate neutrality objectives. These results demonstrate the need for more efficient technologies or methods for producing and using biogas from waste before it reaches the landfill. Full article

The aim of this study is to investigate and demonstrate the role of correction functions and optimisation-based refinement algorithms in enhancing the performance of machine learning models, particularly in predictive anomaly detection tasks applied in industrial environments. The performance of machine learning models is highly dependent on the quality of data preprocessing, model architecture, and post-processing methodology. In many practical applications—particularly in time-series forecasting and anomaly detection—the conventional training pipeline alone is insufficient, because model uncertainty, structural bias and the handling of rare events require specialised post hoc calibration and refinement mechanisms. This study provides a systematic overview of the role of correction functions (e.g., Principal Component Analysis (PCA), Squared Prediction Error (SPE)/Q-statistics, Hotelling’s T², Bayesian calibration) and adaptive improvement algorithms (e.g., Genetic Algorithms (GA), Particle Swarm Optimisation (PSO), Simulated Annealing (SA), Gaussian Mixture Model (GMM) and ensemble-based techniques) in enhancing the performance of machine learning pipelines. The models were trained on a real industrial dataset compiled from power network analytics and harmonic-injection-based loading conditions. Model validation and equipment-level testing were performed using a large-scale harmonic measurement dataset collected over a five-year period. The reliability of the approach was confirmed by comparing predicted state transitions with actual fault occurrences, demonstrating its practical applicability and suitability for integration into predictive maintenance frameworks. The analysis demonstrates that correction functions introduce deterministic transformations in the data or error space, whereas improvement algorithms apply adaptive optimisation to fine-tune model parameters or decision boundaries. The combined use of these approaches significantly reduces overfitting, improves predictive accuracy and lowers false alarm rates. This work introduces the concept of an Organically Adaptive Predictive (OAP) ML model. The proposed model presents organic adaptivity, continuously adjusting its predictive behaviour in response to dynamic variations in network loading and harmonic spectrum composition. The introduced terminology characterises the organically emergent nature of the adaptive learning mechanism. Full article

Rice seed vigor is one of the critical factors determining rice yield and quality. Identifying substances related to seed vigor and rapidly assessing seed vigor by non-destructive methods are of great significance for increasing rice production. This study employed near-infrared diffuse reflectance spectroscopy (NIR-DRS) and transmission spectroscopy (NIR-TS) to evaluate the vigor of naturally aged rice seeds. The NIR-DRS failed to establish a reliable relationship between spectral data and seed vigor, proving ineffective in distinguishing seed vigor. After enhancing the spectral differences between viable and non-viable seeds, the NIR-TS successfully identified high-vigor and non-viable seeds, with a partial least squares discriminant analysis (PLS-DA) model achieving accuracy and germination rates of 84.52% and 88.57% on the test set, respectively. Furthermore, three algorithms, including interval partial least squares (iPLS), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS), were applied to extract characteristic spectral wavelengths associated with seed vigor. Among these, the CARS algorithm performed the best, identifying 38 characteristic wavelengths. Wavelength analysis indicated that rice seed vigor is primarily influenced by molecules such as starch, protein, moisture, and lipids. Using the characteristic wavelengths selected by the CARS algorithm, a PLS-DA prediction model for rice seed vigor was constructed, achieving high accuracy and germination rates of 90.47% and 95.38% on the test set, respectively. This study demonstrates that NIR-TS outperforms NIR-DRS in assessing rice seed vigor. Moreover, wavelength selection techniques can effectively identify characteristic spectral features related to seed vigor and significantly enhance the prediction accuracy of the model. Full article

Composting chicken manure is a source of significant ammonia (NH₃) emissions, which, because of propagation, contributes to the eutrophication of the environment and decreases in air quality. Therefore, it is reasonable to use methods to limit its emission into the atmosphere. Biofiltration, using the metabolic activity of nitrifying and heterotrophic microorganisms capable of oxidizing ammonia, is an effective method to reduce ammonia emissions. In addition, the performance of the biofiltration process depends on operational parameters such as the humidity of the medium, the temperature, the contact time of the gas with the biofiltering medium, and the chemical composition and structure of the filter material. The aim of the study was to evaluate the effectiveness of biofilter fillings in reducing ammonia emissions from composting chicken manure along with the identification of factors allowing us to determine the proposed design solution as the most advantageous in terms of efficiency. Experiments on reducing odour emissions with biofiltration were carried out in two compact composting reactors, in which a compost mixture with a C:N ratio of 10:1 was used. The mixture was prepared in a ratio of 5:1 of chicken manure to the structuring material, with wheat straw used as the structuring material. Based on the results of the research on the course of the composting process, high values of ammonia concentration were recorded. Ammonia concentrations of 886 ppm (composter 1) and 811 ppm (composter 2) were recorded, which confirms the intensive nature of this gas emissions during the process of stabilizing the chicken manure. As part of the conducted research, the effectiveness of biofiltration in reducing ammonia emissions was evaluated by analysing the influence of the aeration intensity of the biofilter (20 dm³/h and 50 dm³/h), directly determining the time of contact of the gas with the bed (EBCT—Empty Bed Contact Time). Coconut-activated carbon was used as a filter bed, which was an effective carrier for the development of microorganisms responsible for the biological removal of ammonia from waste gases generated during composting. In addition, this material showed the ability to physically adsorb ammonia, thus supporting the process of its elimination. Each of the test stations has been equipped with a biofiltration installation. To determine the effectiveness of biological removal of ammonia and to assess the legitimacy of the use of selected strains of microorganisms in the process of biological removal of ammonia, the bed of one of the biofilters (biofilter 2) was inoculated with a strain of nitrifying bacteria. During the study, the high efficiency of ammonia removal because of biofiltration was noted in each of the configurations. In the case of an aeration intensity of 20 dm³/h, a reduction in emissions of 99% was achieved; with a higher aeration value, i.e., 50 dm³/h, the efficiency was 89%. These results indicate that the intensity of aeration has a significant impact on the efficiency of the biofiltration process. The analysis of a biofilter enriched with a strain of nitrifying bacteria requires long-term testing. This is important to reliably determine the effect of inoculation on the efficiency of the biological removal of ammonia in biofilters. It has been shown that optimizing these factors allows us to achieve a reduction in ammonia emissions of up to 90%, while minimizing the formation of unpleasant odours. The use of biofiltration in composting systems for organic waste of animal origin is an effective, sustainable solution that fits into the idea of sustainable development, combining the efficiency of air purification technology with environmental protection and the responsible management of resources. This study demonstrates that biofiltration using coconut-shell-activated carbon is an effective and economical method for reducing ammonia and odour emissions from composting chicken manure. The results provide valuable theoretical and practical information on emissions management in organic waste composting processes. Data from this study could be useful in developing strategies to minimize odour emissions, including from the agricultural sector. Full article

Terpinen-4-ol (TP4O) is a key monoterpene alcohol commonly used as a quality and authenticity marker in essential oils, cosmetics, herbal products, and pharmaceutical formulations. However, reliable and comparable quantification of TP4O across laboratories is challenged by variability in natural matrices and the limited availability of well-characterized, traceable reference materials. In this study, a high-purity certified reference material (CRM) of TP4O was developed and characterized by the National Institute of Metrology (Thailand). The material’s purity was determined using two independent and complementary approaches: a mass balance method (MB) method based on gas chromatography with flame ionization detection (GC-FID), Karl Fischer coulometric titration (KFT), and thermogravimetric analysis (TGA), and a quantitative ¹H NMR (qNMR) method employing DSS-d₆ as an internal standard. The purity values obtained using the MB (98.41 ± 0.09%) and qNMR (99.13 ± 0.94%) methods were statistically equivalent (p > 0.05). Based on the combined evaluation, a certified purity value of 98.77% with an expanded uncertainty of 3.05% (k = 2) was assigned. Homogeneity and short- and long-term stability assessments confirmed the suitability of the material for its intended use. This TP4O CRM provides an SI-traceable, high-purity reference to support calibration, method validation, and quality assurance in analytical applications involving essential oil components. Full article

Honey is a complex natural product with nutritional and therapeutic properties that depend on the diversity of its chemical composition, which includes volatile organic compounds (VOCs). VOCs in honey are key indicators of its botanical and geographical origin, as well as its quality and authenticity. This review provides a comprehensive overview of the analytical instruments and methods used for the identification and quantification of VOCs in different types of honey. Techniques such as headspace solid-phase microextraction (HS-SPME) are used for VOC extraction, and gas chromatography coupled with mass spectrometry (GC-MS) and electronic nose (e-nose) systems for honey analyses, as well as their advantages, limitations, and applications and challenges related to VOC analysis, such as for different types of honeys, their aroma profile, compound variability, and data interpretation, are also discussed. By summarizing recent advancements in analytical methodologies, this review provides an overview of the analysis of VOCs for authentication and research purposes in honey production and processing. Full article