Fuel最新文献

{"title":"Reduction characteristics and mechanism of mechanically activated iron ore powder by lignin","authors":"","doi":"10.1016/j.fuel.2024.133208","DOIUrl":"10.1016/j.fuel.2024.133208","url":null,"abstract":"<div><div>As a primary constituent of renewable biomass fuel, lignin can be effectively utilized as a reducing agent in the ironmaking process, thereby significantly mitigating CO₂ emissions throughout the procedure. This study meticulously evaluates the impact of lignin on the reduction of iron ore powder across diverse levels of mechanical activation through thermogravimetric analysis. It elucidates the intricate reduction mechanism at play. The reduction process is characterized by two pivotal stages: the initial reduction of pyrolysis gases followed by the reduction of fixed carbon, which predominantly drives the transformation of iron ore powder. While mechanical activation exhibits a negligible influence on the first stage, it exerts a pronounced effect on the second. An extended activation time elevates the depth of reduction in the latter stage. Furthermore, the heating rate plays a crucial role in the reduction process, with slower rates favoring the reduction of pyrolysis gases and faster rates boosting carbon fixation reduction. Enhanced activation durations lead to improve linear regression fits for the reduction reaction data. Utilizing the carbon gasification model for iron ore powder treated with 480 min of activation yields a calibration variance of 0.99, with the derived activation energy of 48.75 kJ/mol aligning closely with empirical values. This research provides a thorough analysis of the reduction kinetics and mechanism, underscoring the transformative potential of lignin as a reducer in ironmaking processes, thus contributing to the development of sustainable, low-carbon technologies.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing the energy values of solid biofuel through acidic pre-treatment: An evolutionary-based neuro-fuzzy modelling and feature importance analysis","authors":"","doi":"10.1016/j.fuel.2024.133182","DOIUrl":"10.1016/j.fuel.2024.133182","url":null,"abstract":"<div><div>The increasing focus on sustainable energy solutions and eco-friendly fuel alternatives has sparked interest in exploring methods to enhance the energy content of solid biofuels through pre-treatment processes. This study investigates the impact of acidic (H₂SO₄) pretreatment on the energy content of solid biofuel derived from watermelon peel waste using machine learning methods. The biofuel properties, and calorific value (CV) were determined experimentally for both H₂SO₄-treated and untreated biofuel samples. Subsequently, an Adaptive neuro-fuzzy inference system (ANFIS) model with Genetic Algorithm (GA) and Grid Partitioning (GP) clustering techniques was developed to predict heating value of the biofuel, with performance evaluation based on root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), Mean Absolute Error (MAE) and R²-values. The decision tree regressor was employed to evaluate the feature importance, predictive power and impact of each input variable on CV prediction based on Gini importance metrics. To establish its superior performance, the hybrid ANFIS-GA-GP model was compared with ordinary-ANFIS and ANN model using same metrics. Results show that acid-pretreatment increased the CV of the solid biofuel by 3.53 MJ/kg (31.83 % improvement) and reduced ash content by 1.77 %. FTIR analysis revealed surface modifications, and a shift in the C-O vibrational stretch, while SEM micrographs displayed denser surfaces and reduced porosity as indicated by a lesser fiber diameter in treated samples. The GP-clustered ANFIS-GA model with a triangular membership function (tri-MF) exhibited superior performance and higher accuracy (RMSE of 0.1309, MAPE of 9.343, MAD of 0.1036, MAE of 0.1110, and R²-value of 0.9773 at model training). Furthermore, a tree decision regressor identified Fixed carbon as the most significant predictor for CV with a Gini importance of 0.988375. This study demonstrates the substantial enhancement in the energy content of biofuel using acidic pre-treatment and insights into a cutting-edge approach of improving the combustion properties of solid biofuel with machine learning models. This significantly contributes to the field of sustainable bioenergy research.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0016236124023317/pdfft?md5=b498a4cce54fa088864f720b3901e222&pid=1-s2.0-S0016236124023317-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An auto encoder as a feature-extraction tool for Raman spectroscopic compositional analysis of hydrocarbon mixtures and investigation of correlations of auto encoder–extracted variables with component concentrations","authors":"","doi":"10.1016/j.fuel.2024.133167","DOIUrl":"10.1016/j.fuel.2024.133167","url":null,"abstract":"<div><div>This paper examines the ability of an auto encoder (AE) to serve as a feature-extraction tool in Raman spectroscopic compositional analysis of mixtures of 11 hydrocarbons (<em>n</em>-hexane, <em>n</em>-heptane, <em>n</em>-octane, <em>n</em>-nonane, isooctane, cyclohexane, methyl cyclohexane, benzene, toluene, xylene, and indan) and explores its potential application to vibrational spectroscopic analysis of petroleum and petrochemicals. We found the use of AE-extracted variables enhanced analytical accuracy when determining the concentrations of the four linear hydrocarbons and xylene compared with those using the original spectra. Although the peaks of the linear hydrocarbons were relatively indistinct and similar to each other, the AE effectively extracted the relevant features from the highly overlapped spectra, improving the accuracy. To investigate how the AE variables describe variation in the concentrations of the components, correlations between the sets of AE variables and component concentrations were examined using canonical-correlation analysis. The resulting absolute R values were high, ranging from 0.936 to 0.979. This suggests that the AE variables are mutually complementary and can track spectral variation induced by changes in the concentration of the components.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the effect of composition on dielectric constant of sustainable aviation fuel","authors":"","doi":"10.1016/j.fuel.2024.133230","DOIUrl":"10.1016/j.fuel.2024.133230","url":null,"abstract":"<div><div>One of the challenges in developing 100% sustainable aviation fuels is the effect of synthetic blend components on the dielectric constant. Modern aircraft often employ capacitance-based gauging systems that rely on the dielectric constant of the fuel onboard to determine fuel quantity. Aircraft manufacturers have expressed concern over inaccuracies in fuel gauging attributable to variances in the dielectric constant between conventional jet fuels and 100% paraffinic sustainable aviation fuel. In our study, dielectric constant and density data were gathered from 172 conventional jet fuel samples to establish a baseline “experience range.” Subsequently, thirty-five individual hydrocarbon molecules from the jet fuel range and nine fuels were acquired, characterized, and reported herein according to the Clausius-Mossotti relationship. Our findings indicate that different hydrocarbon group types exert varying effects on the Clausius-Mossotti relationship. To align with the established experience range for both the dielectric constant and the Clausius-Mossotti relationship, it appears that 100% drop-in SAF will need to incorporate some aromatic compounds. Finally, we explored two blending rules for the dielectric constant of jet fuel range hydrocarbons and achieved excellent coefficients of determination (R² values of 0.9942 and 0.9983, respectively).</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0016236124023792/pdfft?md5=b27ef0b6b0d19a6502502a2792406472&pid=1-s2.0-S0016236124023792-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of hydrate formation boundaries in pure water and salt/alcohol containing systems based on prior knowledge and artificial intelligence","authors":"","doi":"10.1016/j.fuel.2024.133193","DOIUrl":"10.1016/j.fuel.2024.133193","url":null,"abstract":"<div><div>As an efficient and clean energy source, natural gas plays a crucial role in optimizing the global energy consumption structure. However, the formation and accumulation of hydrates in pipelines during the exploitation and transportation of natural gas greatly jeopardize the production safety. Accurately predicting hydrate formation boundaries remains challenging due to the complex interation among various influencing factors. Although mechanistic models provide insights, they often fall short in accuracy. Conversely, machine learning models, while promising, may face limitations related to small sample sizes and a lack of physical interpretability. To overcome these limitations, this study proposed a physically guided neural network (PGNN) based on the Chen-Guo model, which integrated thermodynamic mechanisms with a neural network framework. This proposed model utilized pressure calculated from the Chen-Guo model as an input variable and incorporates physical inconsistencies into its loss function. A comparative analysis using literature data that PGNN achieved superior prediction accuracy, with an R² value of 0.9768 for gas hydrate formation pressure prediction. The integration of thermodynamic mechanisms enhanced the prediction accuracy, as evidenced by an increase in the R² value by 0.036, and a reduction in the MSE value by 5.56. Furthermore, the PGNN maintained a high level of prediction accuracy, ranging from 0.96 to 0.98, even with limited sample sizes, thus confirming its applicability and stability. This study validated the feasibility of PGNN for predicting gas hydrate formation conditions and offered insights into hydrate-based applications and hydrate management strategies.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Laminar flame speed modeling of pre-vaporized jet fuel/hydrogen mixtures under engine conditions","authors":"","doi":"10.1016/j.fuel.2024.133149","DOIUrl":"10.1016/j.fuel.2024.133149","url":null,"abstract":"<div><div>Hydrogen combustion stands out as a key transition strategy to a carbon–neutral society. As hydrogen features unique properties, it poses great challenges for flame stabilization in hydrogen-fueled combustors. This study firstly uses the laminar flame speed (LFS) data of jet fuel/H₂ mixture measured by experiment to validate the one-dimensional laminar flame simulations with a 56-species mechanism for n-dodecane/H₂ mixtures. Then the coupled thermal-chemical effects on n-dodecane/H₂ flames are analyzed. It is found that hydrogen may strongly affect NTC-related flame structure under engine-relevant pressure and temperature. In addition, the unburnt temperature and pressure are found to exhibit strong coupling effects on LFS for n-dodecane/H₂ flames which further complicates the modelling of LFS for n-dodecane/H₂ mixtures. Both analytical and ANN models have been constructed for pre-vaporized n-dodecane/H₂ flames under a wide range of thermodynamic conditions including pressure, equivalence ratio, and hydrogen content. The former one utilizes a double-Gaussian formulation, in which the hydrogen content is directly incorporated to account for the effect of hydrogen doping on LFS. Isentropic compression with variable compression efficiency is applied to correlate unburnt temperature and pressure. The latter one applies a three-layer ANN with the pressure, equivalence ratio and hydrogen content as the input and the LFS as the output. Both models demonstrate reasonable predictive capabilities for LFS compared to one-dimensional simulations of freely propagating flames. The analytical and ANN models provide complementary modeling strategies to capture the impacts of hydrogen doping on LFS under engine conditions. The analytic model is more efficient than the ANN model, while the ANN model provides higher accuracy.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic effect for enhancing reactivity of hydrogen evolution and ethylene glycol oxidation reactions by anchoring isolated-Pt nanoparticles on high-indexed Au concave nanocrystals","authors":"","doi":"10.1016/j.fuel.2024.133227","DOIUrl":"10.1016/j.fuel.2024.133227","url":null,"abstract":"<div><div>Constructing heterointerface by incorporating two/multi-functional components has been considered an effective strategy for optimizing chemisorption behaviors between the active sites and reactants or intermediates, thus achieving the enhanced catalytic performance of electrocatalysts for hydrogen evolution reaction (HER) and ethylene glycol electrooxidation reaction (EGOR). Inspired by this, a series of heterointerface structural Pt-around-Au (Pt_m^Au_TOH) nanomaterials with synergistic effect are constructed for HER and EGOR by loading the small Pt active entities on the high-indexed faceted Au trisoctahedron (TOH). Among them, the optimal Pt_0.11^Au_TOH catalyst with more isolated-Pt entities exhibits outstanding HER performance with an overpotential of only 39 mV to achieve 10 mA/cm², and displays higher activity for EGOR with 4.15 mA/cm² specific activity as well as excellent anti-CO poisoning ability. Compared with other-prepared catalysts and the state-of-the-art Pt/C catalyst, the enhanced catalytic activity of Pt_0.11^Au_TOH catalyst are attributed to the unique structural properties including ultrafine nanoparticles, higher density of available exposed active sites, as well as optimal heterointerface effect, synergistic effect between the Pt entities and high-index facets Au supports. This work provides a novel<!--> <!-->perspective for the design promising multi-functional catalysts with enhanced performance for practical applications in energy<!--> <!-->conversions.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of CO2 uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence","authors":"","doi":"10.1016/j.fuel.2024.133183","DOIUrl":"10.1016/j.fuel.2024.133183","url":null,"abstract":"<div><div>This study introduces comprehensive research on the prediction of the carbon dioxide (CO₂) uptake from the biomass-waste derived-porous carbons (BWDPCs), by using scientometrics and model agnostic multi-layered explainable artificial intelligence (XAI) techniques. It aims to identify the main characteristics, and trends that are specific to this domain, and to establish, compare and analyse the four different black box machine learning (ML) models for CO₂ uptake prediction. For this study, through model evaluation parameters, and scatter plots, statistical analysis supports the fact that the Extreme Gradient Boosting (XGBoost) model is found to be the best performing model for CO₂ uptake prediction with low errors and high coefficient of correlation for both training (<em>MSE</em>: 0.157, <em>RMSE</em>: 0.397, <em>MAE</em>: 0.294, <em>MAPE</em>: 0.112, <em>R²</em>: 0.931) and testing phases (<em>MSE</em>: 0.345, <em>RMSE</em>: 0.588, <em>MAE</em>: 0.461, <em>MAPE</em>: 0.121, <em>R²</em>: 0.860). Now, with the best performing black box ML model as XGBoost model, it serves as the basis for the multi-layered XAI analysis. Using multi-layered XAI techniques to interpret the black box ML model and covert it to a white box model, it makes clearer insights into the significant key features that affect the CO₂ uptake both at the global and local level. The study demonstrates that using multi-layered XAI analysis helps in improving the trust of the predictive model and provides a way forward for the application of white box models in CO₂ uptake.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0016236124023329/pdfft?md5=dfea9b303b861e1686005e6334004a07&pid=1-s2.0-S0016236124023329-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of combustion pressure with deep learning using flame images","authors":"","doi":"10.1016/j.fuel.2024.133203","DOIUrl":"10.1016/j.fuel.2024.133203","url":null,"abstract":"<div><div>Deep learning methods provide data-driven techniques for handling large amounts of combustion data, thus finding the hidden patterns underlying these data. This study aims to predict combustion pressure from flame images, which provide more comprehensive information about the combustion process than traditional pressure sensors. The flame images were captured from a single-cylinder 4-stroke optical gasoline direct injection (GDI) engine at 1000 rpm, 5.7 bar IMEP, and stoichiometric combustion conditions using a high-speed camera. To achieve this prediction, we employed five different models: EfficientNetB4, ResNet50, Ensemble Adversarial Inception ResNet, convolutional neural network (CNN), and CNN-XGBoost. The training dataset comprised 1350 flame images captured from a single-cylinder optical GDI engine across different combustion stages. To ensure robustness, 150 images were used for validation. The models were subjected to a testing set of 4500 flame images obtained from different cycles, to evaluate how well they could perform on new, unseen data. The results showed that EfficientNetB4 achieved an impressive R² of 0.94 and a low RMSE of 0.70 compared to other tested models. Saliency analysis revealed that the model focuses on subtle flame characteristics and areas without intense flames, which suggests that it detects features invisible to the human eye. Additionally, the proposed deep learning approach is applied for the sake of monitoring cycle-to-cycle variations based on in-cylinder flame propagation where it is found that it produces high accuracy compared to those obtained through pressure sensors. Our findings are intended to advance the adoption of machine learning approaches for assisting in engine design and optimization.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of active site size in dispersed MoS2 nanocatalysts on slurry-phase hydrocracking of residue","authors":"","doi":"10.1016/j.fuel.2024.133207","DOIUrl":"10.1016/j.fuel.2024.133207","url":null,"abstract":"<div><div>This work investigates the influence of active site size in dispersed MoS₂ on the slurry-phase hydrocracking of Merey residue (MRR). MoS₂ nanocatalysts with varying morphologies were synthesized using the ligand sulfurization method, employing molybdenum oleate as the Mo precursor. The results demonstrate that mono-layered or double-layered MoS₂ plates with slab sizes of 3 ∼ 10 nm can be synthesized at a sulfurization temperature of 400 °C and a sulfurization time of 0.5 h. Prolonged sulfurization time results in the growth and agglomeration of MoS₂ plates, leading to multi-layered slabs with increased length and larger particle size, as well as a wider distribution range. The MoS₂-0.5 (prepared with a sulfurization time of 0.5 h) exhibits superior hydrocracking activity, effectively converting resin and asphaltene into light fractions and significantly suppressing coke formation. Specifically, the conversion rates of resin and asphaltene are 51.3 wt% and 92.1 wt%, respectively, with the minimal coke yield of 0.6 wt% under conditions of 430 °C, 1 h, 7 MPa initial H₂, and a catalyst dosage of 500 μg/g. The catalytic activity of the MoS₂ nanocatalysts exhibits a strong correlation with their size and morphology obtained at different sulfurization times. Density functional theory (DFT) calculations reveal that MoS₂ with smaller slab sizes are more beneficial for the adsorption and dissociation of H₂, due to higher adsorption energy (<em>E_ads</em>) and lower activation barrier (<em>E_a</em>). This facilitates the generation of sufficient active hydrogen to encourage the hydrocracking of residue and suppress coke formation.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}