LPG is gaining relevance as an alternative fuel for medium- and heavy-duty vehicles due to its lower carbon content compared to gasoline and diesel. However, its successful application depends on the availability of efficient three-way catalysts capable of ensuring emission control under transient operating conditions. This study examines the influence of catalyst design parameters, particularly volume and precious metal loading, on the reactive behavior of a TWC under transient conditions representative of a medium-duty spark-ignition (MD-SI) engine fueled with LPG. Particular emphasis is placed on the oxidation and reduction characteristics of propane (C3H8) and butane (C4H10), the main components of LPG, which remain scarcely investigated in the context of TWC performance. A reference catalyst was characterized through light-off and oxygen storage capacity tests at various space velocities and air-fuel ratios. These experiments were used to calibrate and validate a computational model of the catalyst developed in Exothermia Suite®. The combined experimental and modeling approach enabled the analysis of CO, UHC, and NO conversion, as well as the formation of undesired byproducts such as NH3 and N2O, under both cold-start and hot-start conditions. Results demonstrate that increasing or decreasing catalyst volume or PGM loading by 25 to 50
Wall-flow Particulate Filters (PFs) remove harmful particulate matter from the exhaust of internal combustion engines. They also create a backpressure which is detrimental to engine performance. There are many contributions to PF backpressure: entrance and exit effect pressure changes, along-channel inertial losses, along-channel viscous losses and across-wall and -soot-cake viscous losses. This work uses a recently-developed analytic model to quantify how these contributions to backpressure vary with process conditions and PF properties. The study covers symmetric, square-channelled asymmetric and octo-square asymmetric PFs. The model includes compressible flow. Terms relating to different backpressure contributions are readily identifiable in the analytic expression for backpressure and therefore separable; this cannot be done with a numeric model. Increasing mass flow, temperature, soot-loading and PF asymmetry and decreasing PF wall permeability are all predicted to increase backpressure but have different effects on individual contributions to backpressure. Inertial contributions to backpressure increase more rapidly with mass flow than viscous contributions. Viscous contributions to backpressure increase more rapidly with temperature than inertial contributions. Increasing PF length decreases across-wall viscous losses but increases along-channel viscous losses, which results in backpressure initially decreasing dramatically as PF length is increased from a very short length, passing through a shallow minimum and then increasing gradually as the length is further increased. Along-channel inertial losses show little dependency on length but depend predominantly on the difference in flow of momentum into and out of the PF. The exit effect pressure change is a pressure recovery (pressure rise), offsetting the other backpressure contributions.
Reliable multi-pollutant emission forecasts are needed to coordinate climate mitigation and air quality management. This study developed a Pollutant Adaptive Bayesian Dynamic Regression and Conformal Prediction framework (PA-BDR-CP-X) for India’s methane (CH4), carbon dioxide (CO2), nitrous oxide (N2O), oxides of nitrogen (NOx) and particulate matter 2.5 micrometers (PM2.5) emissions using annual EDGAR emissions, version EDGAR_2025_GHG, and socioeconomic, energy and agricultural covariates for 1970–2024. Its contribution is a workflow that selects a forecasting structure separately for each pollutant from trend-only or covariate augmented Bayesian dynamic regression, autoregressive integrated moving average (ARIMA) and exponential smoothing (ETS), and then applies conformal uncertainty calibration. Models were assessed using a fixed 2020–2024 test and 90 expanding window rolling origin forecasts per pollutant. In the fixed test, covariate augmented PA-BDR-CP-X reduced root mean square error (RMSE) relative to the best ARIMA/ETS benchmark by 35.07
Performance and emission characteristics are important considerations when using high-viscosity biofuels in oil burners. Burner performance is mainly influenced by flame temperature, while emission parameters indicate the level of pollutants in the exhaust. This study examines the feasibility of sludge palm oil (SPO) blends in a waste oil burner, focusing on flame temperature and emissions of carbon monoxide (CO), carbon dioxide (CO₂), oxides of nitrogen (NOx), and flue gas temperature. The objective is to identify a suitable equivalence ratio and the optimum SPO blend. Experimental investigations showed that the SPO80 blend at an equivalence ratio of 1.0 provided favorable results. At 300 mm from the flame front, the flame temperature reached 1259.5 °C, and at 900 mm it was 641.5 °C. These values are increment of 9.5
This research study focuses on proposing a novel multivariate experimental data algorithm for optimization of water-air ratio of indirect water-injected automobile diesel engine. The proposed novel algorithm identifies the point of diminishing returns between two selected diesel engine performance parameters by plotting them simultaneously against varying water-air ratios. The point of diminishing returns pin-points the optimal water-air ratio for a given test configuration. In this way, a range of optimal water-air ratios can be determined by this proposed algorithm for any indirect water injected automobile diesel engine against its entire operational range. Fuel consumption, torque, NOx emissions, and CO2 emissions are set as performance parameters. For this study, the working of proposed algorithm was demonstrated through experimentation using four different engine operating conditions, where multiple water-air ratios were maintained in the water injection system. Measurements of torque, fuel consumption, NOx and CO2 emissions were taken for each test configuration. The proposed algorithm then conducted the graphical interpretation of the plotted data and identified the optimal water-air ratio which in this case was from 0.05 to 0.07 by balancing torque, NOx and CO2 emissions with fuel consumption demonstrating its practicality for determination of optimized water-air ratio.
Diesel Particulate Filter (DPF) is a key technology for reducing particulate matter emissions from diesel engines. The plateau environment imposes higher demands on DPF performance. This study focuses on a square asymmetric channel DPF structure and investigates the influence of structural and exhaust parameters on DPF filtration performance under plateau conditions by establishing simulation model. The results show that the lowest pressure drop of 9218.1 Pa occurs at RCD = 1.3. A CPSI of 250–300 yields the minimum pressure drop, approximately 9210–9218 Pa. Reducing wall thickness, exhaust flow rate, and exhaust temperature, while increasing substrate length and diameter, significantly reduces the pressure drop. The initial filtration efficiency increases nonlinearly with wall thickness, CPSI, substrate diameter, substrate length, and exhaust temperature. In contrast, it decreases significantly with increasing RCD and exhaust flow rate. Furthermore, sensitivity analysis of the DPF filtration process based on orthogonal experiments indicates that substrate diameter and exhaust flow rate are the most influential factors controlling pressure drop and initial efficiency, respectively. This study provides important theoretical support for the structural optimization of DPFs in plateau environments.
The biggest obstacle is the need to comply with rigorous pollution regulations while achieving the needed performance level in diesel engines. Integrated usage of biofuels with oxygenated additives approach can reduce climate change and boost energy self-sufficiency by reducing fossil fuel imports. This work aims to optimize the output parameters in variable compression ratio (VCR) diesel engine by employing response surface methodology (RSM) software to the input factors containing n-heptane (0–20 RSM is employed to analyse the performance, combustion and emission parameters. Optimization using RSM identified optimal conditions at CR ≈ 18.3, TiO2 ≈ 50 ppm, APME ≈ 11
This study presents a numerical assessment of the thermodynamic and environmental performance of a hydrogen-fueled internal combustion engine using a validated zero-dimensional, single-zone model developed in OpenModelica. The engine analyzed is a single-cylinder, four-stroke, spark-ignition unit with direct hydrogen injection, based on a research platform developed at Sandia National Laboratories. The model incorporates essential submodels for engine kinematics, mass and energy balances, gas exchange, combustion heat release, heat transfer, and simplified chemical kinetics. Combustion is represented by the Wiebe function, with its parameters calibrated through comparison with experimentally measured in-cylinder pressure profiles. Initial simulations predict NOₓ emissions of 1.33 g/kWh—exceeding EURO VI regulatory limits. To address this, the study investigates direct water injection (DWI) as an emissions reduction strategy. Results demonstrate that a 10
The decarbonization of maritime transport necessitates the adoption of carbon-free fuels, with ammonia (NH₃) emerging as a promising candidate. However, its incomplete combustion, resulting in unburnt NH3, and the formation of nitrogen oxides (NOₓ) and nitrous oxide (N₂O), a potent greenhouse gas (GHG), pose significant challenges for emission control. To address these challenges, experimental kinetic data of Fe-BEA, Co-based, and Pt-based catalysts are incorporated into physico-chemical models. Model validation against the experimental data confirms the accurate prediction of NH₃, NOₓ, and N₂O conversion trends. The model is subsequently employed to design and optimize the Exhaust Aftertreatment System (EATS) layouts, including Selective Catalytic Reduction (SCR), SCR with Ammonia Slip Catalyst (ASC), and hybrid Co–Pt ASC configurations, across a range of engine-out NH₃/NOₓ ratios and combustion efficiencies. The results demonstrate that while NOₓ and NH₃ emissions can be effectively mitigated, N₂O formation in ASC-equipped systems constrains total GHG reduction to approximately 76
While the catalyst development for emission control mostly starts at the powder level, structured coated catalysts are crucial for application. Hence, the transfer of manual catalysts preparation methods to robot-controlled processes is a key step toward reproducibility, scalability, and optimization, all needed in academia and industry. In this study, a laboratory-scale robot-controlled dip-coating device was developed and systematically evaluated for washcoating of monolithic honeycomb substrates. Parameters such as dwell time and dipping speed were independently varied. The washcoats were quantified using a photo-analysis method, providing descriptors such as open channel area and channel clogging. Using γ-Al2O3 as a model material, optimized robot-controlled parameters were identified. Next, 2 wt
High-fidelity models combined with machine-learning (ML) surrogates offer a powerful framework for accelerating the design of chemical process systems. In this study, independent ML models for two different unit operations (a chemical reactor and a recuperative heat exchanger) were coupled to optimize a flowsheet. Large datasets were generated using DETCHEM CHANNEL for catalytic methane combustion and a finite-difference model for a microchannel recuperative heat exchanger. High-fidelity and fast reactor design tools facilitate the generation of datasets of suitable size for ML, with 60,000 model-driven reactor data points established in 18 h of wall-clock time. Separate ML regression models were trained for each unit operation and then coupled through a thermodynamic consistency optimization loop that enforces energy-balance closure between combustor exhaust temperature and heat-exchanger performance. Because the catalytic light-off transition produces a sharp response surface, the combustor surrogate was used to identify ignition, while post-ignition exhaust temperature and composition were obtained from a thermodynamic equilibrium calculation to ensure energy and species balance closure. This coupled surrogate enabled rapid system-level evaluation and was embedded within a genetic algorithm to minimize device volume while maintaining thermal effectiveness. The optimized configuration reduced the volume-to-throughput ratio by approximately 60
In the control of exhaust gas pollution by selective catalytic reduction (SCR), computational fluid dynamics (CFD) gained a major role for design purposes. A crucial primary step is the characterization of the spray behavior of the reductant. Secondary, and not less important, is to capture the complete evaporation process of the urea-water solution (UWS). This work presents a new approach that utilizes the apparent properties of pure substances within the process-oriented framework of a two-liquid mixture. All properties are directly compared to accessible experimental and correlation-based data. Within the framework, a thermodynamically consistent vapor pressure for urea is derived that agrees with observations in the literature. Furthermore, an application-specific approach for urea decomposition kinetics is presented. Evaporation rates and spray applications are simulated and compared to own as well as experimental and simulation data from different authors. The limitations of the model are identified, presented, and discussed.
Since PM10 air pollution from braking systems was recognized, the number of studies carried out to measure brake wear particle emissions has steadily increased, especially in the last ten years. To understand the origin of these brake particle emissions, researchers developed their own measuring benches equipped with measuring devices to characterize brake wear particles in number, mass, size, shape and chemically. Test benches adapted for the measurement of brake particles are very different from one study to another, in particular before the publication of the test procedure (UN GTR No.24) that will be used to homologate light-duty vehicles when the future environmental emission standard (Euro 7) is implemented in November 2026. This literary review has two objectives. The first is to describe the systems of measurement (test benches and measuring devices) used by the different authors to characterize brake wear particles and to compare them with the layout described by UN GTR No.24. The second objective is to give the characteristics of the brake wear particle emissions measured by the researchers (PM10 emission factors, size, shape and chemical composition of the particles).
Reducing greenhouse gas (GHG) emissions, particularly CO2, is a significant issue in addressing global warming and climate change. Marine CO2 emissions are also a major concern. The International Maritime Organization (IMO) has implemented numerous regulations to reduce GHG emissions from international shipping. Due to certain limitations and challenges for shipboard operation, post-combustion carbon capture technology is considered the most promising strategy in the near term for maritime applications. In this work, two different absorbents, an amine solution and a sodium hydroxide solution, are considered for the absorption of CO2 from marine exhaust. For each absorbent, two processes are considered: a single-tower process and two-tower process. In a two-tower process the absorbent is regenerated on the ship, while in a single-tower process fresh and spent solution are stored onboard before spent solution is offloaded at port. Although single-tower processes reduce onboard equipment complexity, they require substantial volumes for lean and rich solutions, which impose practical limitations on the achievable CO₂ removal rate under shipboard space constraints. Aspen Plus and sea-trial data are used to model the processes for application on a bulk goods transport ship. The study aims to compare the operational feasibility of both systems under the constraints of a moving ship.
Accurate carbon emission forecasting is vital for energy system optimization and carbon market decision-making. However, carbon emission data typically exhibit nonlinear and multi-scale characteristics, making them difficult to model using traditional forecasting methods. Moreover, conventional models may suffer from data leakage when future information is inadvertently used during training. To address these challenges, this study proposes an innovative forecasting framework that integrates wavelet transform (WT), rolling variational mode decomposition (RVMD), the tornado optimizer with coriolis (TOC), and the TimeXer model. In this framework, WT is first applied to filter out high-frequency noise. RVMD, combined with a sliding window mechanism, is then used to decompose the series while preventing future information leakage. The TOC algorithm adaptively optimizes RVMD parameters to enhance decomposition fidelity. Finally, the TimeXer model is employed to achieve achieves superior predictive accuracy for each mode. An empirical analysis using daily carbon emission data from China and the United States demonstrates that the proposed WT-RVMD-TOC-TimeXer framework significantly outperforms existing methods in both point and interval forecasting. The model exhibits superior accuracy, stability, and cross-regional generalization capability, achieving a favorable balance between interval coverage and compactness. Statistical tests further confirm its advantages. This study provides a systematic and practical solution for modeling complex carbon emission time series, offering both theoretical innovation and engineering applicability.
Motorcycles are a common mode of transportation and a key source of air pollution in many Asian cities. To mitigate idling emissions and fuel loss from motorcycles, idle-stop (IS) regulations have been implemented in Taiwan. However, the effectiveness of motorcycle IS systems under real-driving conditions remains controversial due to spikes in emissions during the engine restart phase. This study evaluated the effectiveness of IS systems in recent-model, in-use motorcycles in Taiwan using a miniature onboard emissions measurement system. Real-world emission factors with and without IS system activation were compared to determine breakeven idling times beyond which IS systems reduce emissions. Additionally, a city-wide CCTV survey of traffic intersections was conducted in Taichung City, Taiwan, to analyze real-world motorcycle idling times under different traffic conditions. Results showed that IS systems effectively reduced carbon dioxide (CO2) emissions and fuel consumption (FC). However, their effectiveness in reducing carbon monoxide (CO) and hydrocarbon (HC) emissions was uncertain due to high variability across motorcycle models. For CO, breakeven times ranged from 106 to 233 s, whereas the mean real-world waiting time was 50 s, suggesting that IS systems may not significantly reduce CO emissions. For HC, breakeven times ranged from 30 to 98 s, suggesting potential reductions under real-world conditions, but with considerable variability. In contrast, IS breakeven times for CO2 and FC ranged from 0 to 4 s, suggesting a strong potential to reduce CO2 emissions and FC. Our findings provide valuable evidence for policy development and technological improvements for motorcycle IS systems.
This work provides a longitudinal analysis of carbon emissions and energy-efficiency behavior within the European maritime sector from 2018 to 2023, drawing on the official records of the EU Monitoring, Reporting and Verification (MRV) system. Regulation (EU) 2015/757 obliges vessels above 5000 GT calling at ports of the European Economic Area (EEA) to annually report CO₂ emissions, fuel use, operational activity, and monitoring-method details. By examining more than 78,000 vessel-year reports, this study characterizes sector-wide changes in emissions intensity, fuel-use patterns, and methodological practices across the six-year observation window. The analysis reveals a moderate downward trend in average CO₂ emissions per vessel, accompanied by changes in emissions intensity measured per unit distance, and a marked shift away from heavy fuel oil toward low-sulfur alternatives and liquefied natural gas, primarily reflecting compliance with the IMO 2020 sulfur cap rather than intrinsic reductions in CO₂ intensity. Adoption of higher-accuracy monitoring methods also increased steadily. Pandemic-related disruptions appear as short-lived emission reductions associated with temporary declines in shipping activity. The observed trends provide a descriptive pre-ETS and early-CII/EEXI baseline for assessing the impacts of recent and forthcoming policy measures, including the Carbon Intensity Indicator (CII), the Energy Efficiency Existing Ship Index (EEXI), and the incorporation of maritime transport into the EU Emissions Trading System (EU ETS). Together, these developments reflect an accelerating regulatory focus on maritime decarbonization. By offering a comprehensive, data-driven assessment of six years of MRV implementation, this study addresses an important gap in maritime-climate research and provides evidence-based guidance for the design of future EU and global emissions-reduction policies.
In this study, performance and emission characteristics of fuel blends of Gasoline (G), Ethanol (E) and di-tert-butyl peroxide (DTBP) are measured for a four-stroke, single- cylinder, multi-fuel, spark-ignited 661cc engine test rig. Previous studies have identified several limitations of ethanol blending in gasoline, such as higher fuel consumption, lean combustion and relatively lower reduction of unburnt hydrocarbons and nitrogen oxides. In this work, we investigate the possibilities to overcome these drawbacks by adding octane improver DTBP in gasoline-ethanol binary blends. The content of DTBP was varied between 1
Controlling exhaust pollution by selective catalytic reduction (SCR) requires the precise and efficient injection of reductant, which highlights the crucial role played by nozzles. Their characteristics, such as droplet size distribution, penetration depths, and spray angle, significantly impact the overall functionality of this application. This work provides insights into an external atomizing air-assist nozzle, which is commonly utilized in SCR applications. A set of 10 nozzles of the same type were analyzed via shadowgraphy, structural image Velocimetry (SIV), and Phase-Doppler-Anemometry (PDA) methods to ascertain their spray characteristics and behavior. Based on the averaged experimental data, inlet boundary conditions for computational fluid dynamics (CFD) simulations are derived. The spray properties simulated following two different workflows, such as droplet size and droplet velocity distribution, are evaluated on two planes in the spray direction using experimental data. It is demonstrated that common spray models can achieve an acceptable degree of accuracy across a broad range of operation points. The limitations are identified and a sensitivity analysis on the workflow demonstrates the significant dependency on physical models as well as numerical settings.
The present work studies the effect of injecting hydrogen (H2) gas at different flow rates in combination with algae biodiesel on the performance, combustion and emission of a stationary engine. To improve thermal efficiency and combustion behaviour while lowering emissions Hydrogen (H₂) was supplied through the intake manifold at flow rates of 20 L min⁻¹, 22 L min⁻¹ and 25 L min⁻¹ with algae biodiesel (B25) given via the injector as the primary fuel. All the test results of the modified fuel combinations are compared with the results of diesel fuel. At full load B25 (20 L min⁻¹) showed 2
