Search Results (174)

Structural design codes provide safe procedures for verifying steel–concrete composite beams but do not directly guide engineers toward cost-effective configurations. This study aims to develop and evaluate a surrogate-assisted framework that combines a Multilayer Perceptron neural network with Bayesian Optimization for the preliminary flexural-resistance and material-cost optimization of simply supported steel–concrete composite beams designed according to the Brazilian code NBR 8800. A dataset containing 20,000 beam configurations and 15 input variables was generated using a Python-based analytical routine that implements the NBR 8800 provisions for the positive bending resistance of composite beams. The generated dataset was used to train a Multilayer Perceptron neural network to predict the design bending resistance. The trained surrogate model was then integrated with Bayesian Optimization to search a discrete design space comprising commercial steel profiles, concrete slab thicknesses, shear connector quantities, and connector diameters. The selected neural network architecture achieved validation MAE and RMSE values of 2.128 kN·m and 3.013 kN·m, respectively, with an $R^2$ of 0.9999. In ten benchmark scenarios, the BO–MLP framework identified candidate solutions using only 60 objective-function evaluations. This corresponds to 3% of the evaluation budget adopted for GA and PSO and approximately 0.057% of the configurations examined by exhaustive search. Despite this limited sampling budget, the resulting candidate solutions presented an average optimality gap of approximately 12.1% relative to the global reference. In computational terms, GA and PSO required approximately 4.1 and 4.9 times the execution time of BO–MLP, respectively, while exhaustive search required approximately 18.4 times the execution time. Overall, the proposed framework offers a computationally efficient means of exploring discrete composite-beam configurations and identifying cost-competitive candidate solutions. Direct NBR 8800 verification showed that seven of the ten selected candidates satisfied the resistance requirement, while three presented resistance-to-demand ratios slightly below unity. Therefore, the framework should be used as a preliminary screening tool, with the selected configurations subsequently verified using the complete code-based procedure. Within the restricted structural domain investigated, the framework can support preliminary decisions related to positive bending resistance and material cost. Full article

To reveal the chemical composition and concentration distribution, spatial distribution characteristics, source composition, and health impacts of volatile organic compounds (VOCs) in the two multi-industry industrial parks in Tianjin, 57 VOC species were determined by using SUMMA canister sampling with preconcentration gas chromatography/mass spectrometry. Based on these measurements, the Positive Matrix Factorization (PMF) model was used for source analysis to achieve quantitative identification and contribution analysis of different source factors. The health risks of VOC concentrations were analyzed based on the assessment framework recommended by the United States Environmental Protection Agency (USEPA). The results showed that the average concentrations of TVOCs in A-1 (Industrial Park and Cluster A, 5 m), A-2 (Industrial Park and Cluster A, 10 m), B-1 (Industrial Park and Cluster B, 5 m), and B-2 (Industrial Park and Cluster B, 10 m) were 59.72 ppbv, 45.35 ppbv, 32.44 ppbv, and 21.65 ppbv, respectively. TVOC concentrations were higher at A-1 and B-1 than at A-2 and B-2, and were generally higher at site A than at site B. The VOC components were mainly alkanes (53.5%–71.4%), followed by aromatic hydrocarbons (15.6%–36.2%), alkenes (4.6%–9.9%), and alkynes (3.1%–7.6%). The proportion of aromatic hydrocarbons in A was higher (22.13% and 36.22%), while alkanes dominated absolutely in B (71.4% and 66.9%). n-Hexane was the key species driving the spatial differences (a typical source of VOCs in solvent usage). The concentration in A-1 was 3.1 times that of A-2, and B-1 was 3.3 times that of B-2. It was mainly controlled by local solvent unorganized emissions. The differences in species between the two points at site A for toluene, xylene, etc., were relatively gentle, while at site B, the concentration of the same aromatic hydrocarbons at B-1 was significantly higher than that at B-2, presenting a clearer spatial characteristic, indicating differences in the spatial distribution of organic solvent-related industrial activities in different sites. The source analysis showed that A-1 was dominated by solvent usage (38.64%) and natural gas/LPG sources (36.96%); A-2 by vehicle exhaust (40.20%) and natural gas/LPG sources (36.15%); B-1 by cleaning-agent usage (38.81%) and fuel combustion (30.33%); and B-2 by plastic and rubber production (36.90%) and fuel combustion (31.84%). The health risk assessment showed that benzene, n-hexane, and xylene dominated the non-carcinogenic risks, but the overall non-carcinogenic (HI < 1) and carcinogenic (CR < 1 × 10⁻⁶) risks were both below the threshold. This study reveals inter-site differences in VOC concentrations, compositions, and source contributions across two mixed industrial parks, providing a basis for site-specific VOC control priorities, source-targeted monitoring strategies, and risk management in mixed industrial areas. Full article

The production of hot mix asphalt (HMA) is energy-intensive, resulting in carbon dioxide (CO₂) and greenhouse gas (GHG) emissions. The primary energy source (accounting for over 97%) and emissions source is the rotary drum employed for the drying and heating of the aggregates. Quantifying the CO₂ emissions associated with combustion is of crucial importance in order to facilitate a more profound comprehension of the environmental impacts of HMA production. The objectives of this study are to develop a methodological framework for the quantification of combustion-related carbon dioxide emissions in the context of asphalt production. The proposed framework investigates three complementary approaches: (i) an energy-balance-based thermal energy (TE) model, (ii) recordings of fuel consumption and (iii) direct measurement of exhaust-gas composition. By applying these methods in parallel and cross-comparing their results batch by batch, the framework enables reliable verification of actual CO₂ emissions from the module A3—production stage of asphalt manufacturing. In this stage, the predominant source of greenhouse gases is fuel combustion during aggregate drying and heating. A comprehensive set of data was collected from two HMA batch plants, each operating under distinct conditions. The parameters considered included fuel type, asphalt mixture type, asphalt production time, aggregate moisture content, mixing temperature, and production rate. The TE model demonstrated a robust linear correlation with measured energy consumption (R² = 0.97), and fuel-based CO₂ estimates exhibited minimal discrepancy compared to direct exhaust-gas measurements on average (mean difference 1.0%; t-test p = 0.674). However, systematic discrepancies were observed between the two plants (with overestimation of up to 20% at one plant (AP1) and underestimation of up to 12% at the other (AP2)). This demonstrates that energy-based CO₂ estimation methods require plant-specific calibration against direct measurement before they can be reliably applied in life cycle assessment (LCA) and environmental product declaration (EPD) practice. Measured CO₂ emission intensities ranged from 17.39 to 21.76 kg/t at AP1 and from 16.05 to 18.44 kg/t at AP2; the casing-losses factor of the TE model was calibrated to CL = 23% for the studied diesel-fired plants (mean deviation +0.4% from measured energy); and aggregate moisture content explained 74% of the variance in measured energy consumption (R² = 0.743). Full article

Reliable combustion is essential for the efficient and reliable operation of aging offshore gas motor compressors fueled by associated petroleum gas (APG). This study investigated the combined influence of fuel composition, scavenging-air pressure, and compressor loading on combustion stability under real industrial conditions. Operational data were obtained from four low-speed 10GKNAM gas motor compressors operating on APG with elevated heavy-hydrocarbon content. Fuel composition was determined by gas chromatography, while combustion stability was evaluated using exhaust-gas temperatures measured in all engine cylinders together with scavenging-air pressure and compressor loading. Two dimensionless diagnostic indicators, the Combustion Stability Index (CSI) and the Temperature Dispersion Coefficient (TDC), were developed using routinely monitored operating parameters, providing a practical combustion-diagnostic approach without additional instrumentation. Although the investigated fuel exhibited increased combustion reactivity, fuel composition did not account for the observed differences in combustion stability because all compressors operated on essentially identical APG. Instead, scavenging-air pressure was identified as the dominant operational factor, whereas higher compressor loading provided an additional stabilizing effect. Increased scavenging-air pressure reduced exhaust-gas temperature dispersion and improved combustion uniformity, whereas the influence of compressor loading became more pronounced under adequate scavenging conditions. The proposed methodology provides a practical basis for quantitative combustion assessment using existing industrial monitoring systems and may support condition-based maintenance, operational optimization, and future development of predictive combustion-diagnostic approaches for aging offshore gas motor compressors. Full article

Background: Severe Mycoplasma pneumoniae pneumonia (SMPP) presents with heterogeneous clinical complications and imaging manifestations, including pleural effusion (PE), bronchiolitis obliterans (BO), and distinct imaging/bronchoscopic phenotypes. The precise systemic immune subpopulation dynamics underlying these distinct phenotypes remain poorly defined. Methods: We conducted flow cytometric profiling of peripheral blood mononuclear cells (PBMCs) from pediatric patients to compare mild MPP (MMPP-N) and severe MPP subtypes. SMPP patients were stratified into subgroups including those with BO (SMPP&BO, n = 3), PE (SMPP&PE), and three imaging/bronchoscopic phenotypes: Group A (SMPP-GA, mucous plugs), Group B (SMPP-GB, mucosal necrosis), and Group C (SMPP-GC, diffuse bronchiolitis). Composite immune indices and multi-marker discriminant analysis were constructed for secondary analysis. Results: Comprehensive high-dimensional profiling suggested that a fundamental innate-adaptive immune rebalancing may represent a key pathophysiological axis in severe disease. Patients with PE exhibited massive CD16⁻ monocyte expansion (Delta = 40.0%) coupled with CD8⁺ Teff collapse (Delta = −9.2%). Preliminary data also suggest BO may be marked by CD56⁻CD11C⁺ monocyte depletion (Delta = −23.1%, n = 3), warranting further validation. Imaging subgroup profiling revealed that SMPP-GC featured major B-cell maturation alterations (HLADR⁺CD45RA^high expansion and CD38⁺CD25^low depletion). Composite indices including the Adaptive Exhaustion Score suggested discriminatory power (AUC = 0.90 for PE vs. uncomplicated SMPP). The Innate-Adaptive Ratio increased progressively with severity (Spearman rho = +0.40, p = 0.0002). Conclusions: SMPP is not immunologically uniform. Innate-adaptive rebalancing underlies complication-specific phenotypes: inflammatory monocyte mobilization with adaptive exhaustion is strongly associated with PE, whereas B-cell maturation alterations characterize diffuse bronchiolitis. These findings identify potential biomarker candidates for patient stratification, though small-cohort observations require extensive validation in larger prospective studies. Full article

Efficient water vapor removal is important for both building humidity control and industrial gas dehydration, where operating conditions may span broader temperature and pressure ranges. Driven by a pressure gradient, membrane-based dehumidification has emerged as an energy-efficient alternative, employing polymeric composite membrane materials to achieve effective moisture separation. However, traditional development of such membranes remains heavily reliant on inefficient trial-and-error approaches. To overcome this limitation, this study employs machine learning to directly predict the relationships between physicochemical structure, operational conditions, and water vapor permeation performance of composite membrane materials. A dataset comprising 138 experimental samples from 26 published studies was compiled, featuring five input features: selective layer thickness, operating temperature, feed pressure, relative humidity, and a newly proposed hydrophilicity score based on functional group composition. Among six machine learning models evaluated, the Gradient Boosting Decision Tree (GBDT) achieved superior predictive performance, yielding a test R² of 0.912. SHAP analysis identified selective layer thickness as the dominant descriptor, followed by feed pressure, hydrophilicity score, operating temperature, and relative humidity, contributing 34.4%, 26.5%, 15.8%, 11.9%, and 11.5% to the model predictions, respectively. Within the investigated parameter space, a genetic algorithm integrated with the GBDT model identified a permeability-oriented parameter combination (18.25 μm thickness, 111.43 °C, 0.94 bar, 52.02%RH, and a hydrophilicity score of 5), achieving a predicted permeability of 136,418 Barrer. The framework offers a transferable strategy for accelerating the rational design of advanced membrane materials, significantly reducing the need for exhaustive experimental screening. Full article

Classification of vehicle engines using the chemical composition of the exhaust from these engines can be used to identify the engine’s design and verify compliance with environmental regulations through the vehicle’s emissions. This paper describes a method to identify the type of vehicles using machine learning (ML), where low-cost MQ series sensors measure the gases and particle emissions from a vehicle exhaust system, while simultaneously collecting and measuring the vehicle’s temperature and humidity levels. A custom-designed multi-sensor exhaust sensing module is employed to capture real-time exhaust emissions prior to entering the atmosphere. Exhaust samples are collected from vehicles representing three major engine categories: petrol, diesel, and compressed natural gas (CNG). In addition, fresh air samples are collected as a baseline environmental reference for comparison. All exhaust measurements are collected under controlled and consistent engine operating conditions to ensure comparable emission profiling across vehicle classes. To ensure consistent combustion-based emission profiling, this study focuses on conventional fuel-powered vehicles. MQ-series gas sensors are sensitive to combustion by-products emitted during engine operation, such as carbon monoxide (CO), hydrocarbons (HC), while also exhibiting cross-sensitivity to other gaseous components present in exhaust mixtures. Nevertheless, the proposed system performs pattern-based classification using relative sensor response signatures. Standardization of data is achieved through z-score normalization. The best models developed (based on three separate experimental designs) are trained and validated using six supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (RBF), k-Nearest Neighbors, Random Forest, Gradient Boosting Decision Tree, and XGBoost and are compared against one another. Evaluation of the tested algorithms using various evaluation metrics demonstrated that ensemble models outperformed all other algorithms, achieving the highest accuracy of 99.5%. Furthermore, noise analysis confirms that the proposed solution maintains high classification accuracy (more than 89%) even under substantial sensor perturbations mimicking the real-world deployment. The solution proposed below illustrates how using gas sensors and advanced algorithms can provide accurate exhaust identification and identify engines in real-time. Full article

The transition toward sustainable aviation fuels requires dedicated experimental platforms capable of evaluating alternative fuels under realistic propulsion conditions. This study presents the development and laboratory experimental validation of a modular testing installation designed for sustainable fuels derived from plastic waste pyrolysis, intended for aerospace engine applications. The proposed system is conceived as an integrated small-scale gas turbine assembly that reproduces the functional characteristics of a jet engine and enables controlled laboratory investigations of dynamic behavior, combustion stability, and performance. The installation comprises a compressor, annular combustion chamber, and turbine mounted on a common shaft, along with a fully autonomous fuel supply system equipped with electronically controlled pumping, safety devices, and thermal conditioning of the fuel mixture via an attached Stirling engine. Combustion processes are continuously evaluated using an exhaust gas analysis system to assess fuel composition and combustion quality, while a high-speed camera operating at 50,000 fps enables detailed visualization of flame stability. Operating parameters, including temperatures, pressures, rotational speed, mass flow rates, and thrust, are monitored and recorded through an integrated control and data acquisition system with real-time analysis capabilities. Experimental results demonstrate stable operation and reliable ignition using alternative fuel mixtures, confirming the suitability of the modular installation as a versatile research platform for the assessment and comparative analysis of sustainable aerospace fuels. Full article

CO₂-enhanced gas recovery (CO₂-EGR) is a crucial technology for achieving both natural gas production increase and CO₂ geological storage. While pure CO₂ flooding demonstrates favorable recovery performance, the technical challenges and high costs associated with purifying CO₂ remain significant. CO₂ purification from exhaust gas incurs prohibitive costs, while direct injection of an unpurified CO₂–N₂ mixture can greatly cut engineering expenditure. Nitrogen also provides synergistic pressure support, working with CO₂ to drive natural gas displacement. Therefore, from an economic and practical standpoint, employing impure CO₂ mixtures (e.g., CO₂–N₂) for flooding presents a more advantageous approach. To clarify the factors influencing the recovery enhancement in tight sandstone gas reservoirs using CO₂–N₂ mixtures, long-core flooding experiments were conducted at 100 °C. This study systematically investigates the impact patterns of three key factors—injection timing, injection rate, and injection gas composition—on the enhanced recovery of tight sandstone gas reservoirs. The experimental results indicate that: (1) Advancing the injection timing significantly improves the recovery performance for both CO₂ and N₂ flooding. However, the cumulative recovery factor (sum of the depletion recovery and the incremental recovery from gas injection) shows a declining trend. (2) The enhanced recovery effect exhibits a trend of first increasing and then decreasing with the increase in injection rate. When the injection rate exceeds 0.05 mL/min, it tends to cause premature breakthrough of the injected gas, thereby reducing the displacement efficiency. (3) As the proportion of CO₂ in the injected gas increases, the enhanced recovery effect shows a nonlinear rise. The highest incremental recovery (17.02%) was achieved with pure CO₂ flooding, while pure N₂ flooding yielded the lowest result (14.64%). The research findings, from a macroscopic perspective, elucidate the influence patterns of three distinct factors on enhancing gas recovery in tight sandstone reservoirs, thereby providing theoretical foundation and scientific guidance for the development of such reservoirs. In summary, the injection timing, injection rate and CO₂ proportion in injected gas are the key controlling factors for gas flooding enhanced recovery in tight sandstone reservoirs. This study clarifies the macroscopic influence law of each factor, and the optimized development parameters proposed can provide direct theoretical support and technical guidance for the on-site application of gas flooding in tight sandstone reservoirs. Full article

The non-repeatability of the internal combustion engine’s cycle-by-cycle (CCN-R) operation directly affects pollutant emissions, fuel consumption, and energy efficiency. Reducing this non-repeatability is an important part of efforts to improve the environmental performance of power units. Cycle variability analysis allows the identification of engine operating areas that promote unstable combustion and increased emissions of harmful exhaust components. The aim of the study was to quantitatively assess the cycle-to-cycle non-repeatability COV of selected operating parameters of the Perkins 1104D-E44TA diesel engine. The analyses covered the maximum cylinder pressure (p_max), the mean indicated pressure (IMEP), and the crankshaft rotation angle corresponding to the occurrence of maximum pressure (α). The measurements were carried out on an engine dynamometer at 25 operating points, covering speeds 1000–2200 r./min and load torques 200–400 N × m, recording 500 consecutive operating cycles at each point. The results showed that the most stable engine operation occurred at medium rotational speeds and moderate loads, where COV_pmax values did not exceed 0.5% and COV_IMEP values were lower than 1.0%. Increased pmax non-repeatability (up to 2.10%) and very high α angle variability (up to 100–140%) were observed at high rotational speeds and high loads. Only in the case of COV_IMEP was a significant reduction in repeatability observed compared to idling. The results obtained from cycle-by-cycle non-repeatability analyses can ultimately, after being supplemented with exhaust gas composition testing, be used as tools to support engine control optimization in order to reduce pollutant emissions and improve combustion efficiency. Full article

The widespread use of gasoline blended with 10% ethanol (E10) has raised questions regarding engine performance and emissions under conditions where ethanol supply may be disrupted, and pure gasoline (E0) is temporarily used instead. This study experimentally investigates the effects of E0 and E10 fuels on fuel consumption and exhaust emissions in spark-ignition engines equipped with two different fuel supply systems: multi-point fuel injection (MPI) and carburetion (CARB). Chassis dynamometer tests were performed on two passenger vehicles under steady-state part-load conditions at vehicle speeds of 60, 90, and 120 km/h, as well as during full-throttle operation. E0 and E10 were tested separately under identical operating points. Fuel consumption, brake-specific fuel consumption, air–fuel ratio, and exhaust gas components (CO, CO₂, HC, O₂) were measured and analysed. The results show that the MPI-equipped vehicle exhibited consistently lower fuel consumption when operating on E0 compared to E10, primarily due to the lower volumetric heating value of ethanol. In contrast, the carbureted engine demonstrated a stronger sensitivity to fuel composition, with E10 leading to leaner mixture formation and pronounced changes in fuel consumption and emissions. CO and HC emissions were significantly lower in the MPI engine, mainly due to closed-loop stoichiometric control combined with the presence of a three-way catalytic converter, while E10 substantially reduced these emissions in the carbureted engine. CO and HC emissions were significantly lower in the MPI configuration, mainly due to closed-loop stoichiometric control combined with the presence of a three-way catalytic converter. In the carbureted configuration, E10 substantially reduced CO and HC emissions compared to E0, primarily as a result of leaner mixture formation. Overall, the findings indicate that modern MPI engines are less sensitive to whether the supplied fuel is E10 and E0, whereas carbureted engines may show notable changes in performance and emissions under the same operating conditions. Full article

To address the issues of parameter setting, reliance on human experience, and the limitations of traditional model-driven control methods in handling complex nonlinear dynamics in the tin smelting industrial process, this paper proposes a data-driven control approach based on improved deep reinforcement learning (RL). Aiming to reduce the tin entrainment rate in smelting slag and CO emissions in exhaust gas, we construct a data-driven environment model with an 8-dimensional state space (including furnace temperature, pressure, gas composition, etc.) and an 8-dimensional action space (including lance parameters such as material flow, oxygen content, backpressure, etc.). We innovatively design a Dual-Action Discriminative Deep Deterministic Policy Gradient (DADDPG) algorithm. This method employs an online Actor network to simultaneously generate deterministic and exploratory random actions, with the Critic network selecting high-value actions for execution, consistently enhancing policy exploration efficiency. Combined with a composite reward function (integrating real-time Sn/CO content, their variations, and continuous penalty mechanisms for safety constraints), the approach achieves multi-objective dynamic optimization. Experiments based on real tin smelting production line data validate the environment model, with results demonstrating that the tin content in slag is reduced to between 3.5% and 4%, and CO content in exhaust gas is decreased to between 2000 and 2700 ppm. Full article

For mine ventilation-on-demand (VOD) scenarios, conventional joint optimisation of airflow augmentation and energy saving in mine ventilation systems is often constrained in practical engineering applications by shrinkage of the feasible region, limited adjustable resistance margins, and strongly multi-modal objective functions. These factors tend to result in low solution efficiency, pronounced sensitivity to initial values and insufficient solution robustness. In response to these challenges, a two-layer intelligent evolutionary optimisation framework, termed ES–Hybrid JADE with Competitive Niching, is developed in this study. In the outer layer, four classes of evolutionary algorithms—CMAES, DE, ES, and GA—are comparatively assessed over 50 repeated test runs, with a combined ranking based on convergence speed and solution quality adopted as the evaluation metric. ES, with a rank_mean of 2.0, is ultimately selected as the global hyper-parameter self-adaptive regulator. In the inner layer, four algorithms—COBYLA, JADE, PSO and TPE—are compared. The results indicate that JADE achieves the best overall performance in terms of terminal objective value, multi-dimensional performance trade-offs and robustness across random seeds. Furthermore, all four inner-layer algorithms attain feasible solutions with a success rate of 1.0 under the prescribed constraints, thereby ensuring that the entire optimisation process remains within the feasible domain. The proposed framework is applied to an exhaust-type dual-fan ventilation system in a coal mine in Shaanxi Province as an engineering case study. By integrating GA-based automatic ventilation network drawing (longest-path/connected-path) with roadway sensitivity analysis and maximum resistance increment assessment, two solution schemes—direct optimisation and composite optimisation—are constructed and compared. The results show that, within the airflow augmentation interval [0.40, 0.55], the two schemes are essentially equivalent in terms of the optimal augmentation effect, whereas the computation time of the composite optimisation scheme is reduced significantly from approximately 29 min to about 13 s, and a set of multi-modal elite solutions can be provided to support dispatch and decision-making. Under global constraints, a maximum achievable airflow increment of approximately 0.66 m³·s⁻¹ is obtained for branch 10, and optimal dual-branch and triple-branch cooperative augmentation combinations, together with the corresponding power projections, are further derived. To the best of our knowledge, prior VOD airflow-augmentation studies have not combined feasibility-region contraction (via sensitivity- and resistance-margin gating) with a two-layer ES-tuned JADE optimiser equipped with Competitive Niching to output multiple feasible optima. This work provides new insight that the constrained airflow-augmentation problem is intrinsically multimodal, and that retaining multiple basins of attraction yields dispatch-ready elite solutions while achieving orders-of-magnitude runtime reduction through prediction-based constraints. The study demonstrates that the proposed two-layer intelligent evolutionary framework combines fast convergence with high solution stability under strict feasibility constraints, and can be employed as an engineering algorithmic core for energy-efficiency co-ordination in mine VOD control. Full article

Reducing fuel costs, maximizing waste utilization, and improving energy efficiency are critical challenges in agricultural thermal processes. This study addresses these issues by developing and evaluating a mixed-fuel burner and furnace system for steaming mushroom substrate cubes using crude glycerol and recycled vegetable oil as low-cost alternative energy sources. The experimental investigation assessed boiler thermal efficiency, combustion efficiency, exhaust-gas composition, temperature distribution, steam generation, and combustion-gas dispersion within the furnace. In parallel, analytical modeling of pressure, temperature, and gas-flow behavior was performed to validate the experimental observations. Five fuel compositions were examined, including 100% used vegetable oil, 100% crude glycerol, and blended ratios of 50/50, 25/75, and 10/90 (glycerol/vegetable oil), with all tests conducted in accordance with DIN EN 203-1 standards. The results demonstrate that blending used vegetable oil with glycerol significantly improves flame stability, increases peak combustion temperatures, and suppresses incomplete-combustion byproducts compared with pure glycerol operation. Combustion efficiencies of 90–99% and boiler thermal efficiencies of 72–73% were achieved. Among the tested fuels, the optimal balance between combustion stability, efficiency, and cost was achieved with a 25% glycerol and 75% used vegetable oil mixture. Economic analysis revealed that the proposed mixed-fuel system offers superior viability compared with LPG, reducing annual fuel costs by approximately 50%, shortening steaming time by 2 h per batch, and achieving a payback period of only 3.26 months. These findings confirm the feasibility of the proposed waste-to-energy system for small- and medium-scale agricultural applications. To further enhance sustainability and renewable fuel utilization, future work should focus on improving air–fuel mixing for higher glycerol fractions, scaling the system for larger farms, and extending its application to other agricultural thermal processes. Full article

A plasma-assisted ammonia jet flame igniter was developed in this study to address the limitations of conventional spark ignition at high pressures. The effect of pressure on plasma discharge characteristics, optical emission spectra, and exhaust gas emission was systematically investigated, providing new insights into the mechanisms of plasma-assisted ammonia ignition under high-pressure conditions. The results indicate that increased chamber pressure elevates gas density, which in turn raises the voltage required to sustain an arc discharge at 0.4 MPa and markedly reduces the frequency of arc drift. Spectral analysis shows that higher pressure inhibits atomic oxygen lines (777.2 nm and 844.6 nm) while intensifying the molecular nitrogen bands between 350–450 nm. A corresponding decrease in electron excitation temperature is also observed. In terms of exhaust composition, hydrogen concentration demonstrates a bifurcated behavior, rising with pressure under fuel-rich conditions (the equivalence ratio φ > 1.2) and falling under fuel-lean conditions (φ ≤ 1). Conversely, NO concentration consistently decreases with increasing pressure across all test conditions. The ammonia concentration in the exhaust gas shows opposite pressure dependencies at different equivalence ratios. It increases with rising pressure for φ ≥ 1, while it decreases with increasing pressure for φ < 1. Full article