Modeling the emission characteristics of the hydrogen-enriched natural gas engines by multi-output least-squares support vector regression: Comprehensive statistical and operating analyses

[1]  Ben Niu,et al.  Adaptive neural self-triggered bipartite secure control for nonlinear MASs subject to DoS attacks , 2023, Inf. Sci..

[2]  Jincheng Wang,et al.  Development and research of triangle-filter convolution neural network for fuel reloading optimization of block-type HTGRs , 2023, Appl. Soft Comput..

[3]  Yuehua Wu,et al.  Numerical Investigation on the Combustion and Emission Characteristics of Diesel Engine with Flexible Fuel Injection , 2023, Machines.

[4]  E. Varuvel,et al.  Application of machine learning algorithms for predicting the engine characteristics of a wheat germ oil–Hydrogen fuelled dual fuel engine , 2022, International Journal of Hydrogen Energy.

[5]  L. Zhang,et al.  Adaptive neural prescribed performance control for switched pure-feedback non-linear systems with input quantization , 2022, Assembly Automation.

[6]  Shuofeng Wang,et al.  Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm , 2022, Energy.

[7]  Yang Wang,et al.  Feasibility study of hydrogen jet flame ignition of ammonia fuel in marine low speed engine , 2022, International Journal of Hydrogen Energy.

[8]  P. Shukla,et al.  Alcohols as alternative fuels in compression ignition engines for sustainable transportation: a review , 2022, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

[9]  Lirong Yin,et al.  A Semi-Supervised Extreme Learning Machine Algorithm Based on the New Weighted Kernel for Machine Smell , 2022, Applied Sciences.

[10]  S. Hosseini Development of a reliable empirical correlation to calculate hydrogen solubility in seventeen alcoholic media , 2022, Scientific Reports.

[11]  B. Vaferi,et al.  Application of machine learning methods for estimating and comparing the sulfur dioxide absorption capacity of a variety of deep eutectic solvents , 2022, Journal of Cleaner Production.

[12]  S. Wongwises,et al.  A review of recent progress in biogas upgrading: With emphasis on carbon capture , 2022, Biomass and Bioenergy.

[13]  S. K. Mahla,et al.  Effect of utilization of hydrogen-rich reformed biogas on the performance and emission characteristics of common rail diesel engine , 2022, International Journal of Hydrogen Energy.

[14]  Long Liu,et al.  Investigation of future low-carbon and zero-carbon fuels for marine engines from the view of thermal efficiency , 2022, Energy Reports.

[15]  A. Bagherzadeh,et al.  Developing a global approach for determining the molar heat capacity of deep eutectic solvents , 2021, Measurement.

[16]  Yufeng Wei,et al.  Prediction of the bond strength of FRP-to-concrete under direct tension by ACO-based ANFIS approach , 2021, Composite Structures.

[17]  Hosein Naderpour,et al.  Failure mode prediction of reinforced concrete columns using machine learning methods , 2021, Engineering Structures.

[18]  A. K. Tripathi,et al.  Alternative fuels for decarbonisation of road transport sector in India: Options, present status, opportunities, and challenges , 2021 .

[19]  Tingting Ding,et al.  Photovoltaic power forecast based on satellite images considering effects of solar position , 2021 .

[20]  D. Srivastava,et al.  Quantitative analysis of engine parameters of a variable compression ratio CNG engine using machine learning , 2021, Fuel.

[21]  Behzad Vaferi,et al.  Robust intelligent topology for estimation of heat capacity of biochar pyrolysis residues , 2021 .

[22]  K. Nishida,et al.  Experimental investigation on performance of hydrogen additions in natural gas combustion combined with CO2 , 2021, International Journal of Hydrogen Energy.

[23]  Ozgur Kisi,et al.  Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm , 2021, Sustainability.

[24]  Peng Zhou,et al.  GVRP considered oil-gas recovery in refined oil distribution: From an environmental perspective , 2021, International Journal of Production Economics.

[25]  B. Vaferi,et al.  Determination of Methanol Loss Due to Vaporization in Gas Hydrate Inhibition Process Using Intelligent Connectionist Paradigms , 2021, Arabian Journal for Science and Engineering.

[26]  A. Agarwal,et al.  Experimental evaluation of laser ignited hydrogen enriched compressed natural gas fueled supercharged engine , 2021 .

[27]  D. Wood,et al.  Auto-characterization of naturally fractured reservoirs drilled by horizontal well using multi-output least squares support vector regression , 2021, Arabian Journal of Geosciences.

[28]  Ming Yang,et al.  Confidence Interval Based Distributionally Robust Real-Time Economic Dispatch Approach Considering Wind Power Accommodation Risk , 2021, IEEE Transactions on Sustainable Energy.

[29]  Fanhua Ma,et al.  Study of laminar burning speed and calibration coefficients of quasi-dimensional combustion model for hydrogen enriched compressed natural gas fueled internal combustion engine along with exhaust gas recirculation , 2021 .

[30]  Xiaochen Wang,et al.  A review of NOx and SOx emission reduction technologies for marine diesel engines and the potential evaluation of liquefied natural gas fuelled vessels. , 2020, The Science of the total environment.

[31]  M. Maslehuddin,et al.  Ensemble machine learning model for corrosion initiation time estimation of embedded steel reinforced self-compacting concrete , 2020 .

[32]  David A. Wood,et al.  Auto-detection interpretation model for horizontal oil wells using pressure transient responses , 2020, Advances in Geo-Energy Research.

[33]  R. Mehra,et al.  Experimental study of hydrogen enriched compressed natural gas (HCNG) engine and application of support vector machine (SVM) on prediction of engine performance at specific condition , 2020 .

[34]  Xiaowu Zhang,et al.  Performance and emissions characteristics of a lean-burn marine natural gas engine with the addition of hydrogen-rich reformate , 2019, International Journal of Hydrogen Energy.

[35]  Yi Sun,et al.  Stock intelligent investment strategy based on support vector machine parameter optimization algorithm , 2019, Neural Computing and Applications.

[36]  Francisco C. Pereira,et al.  Multi-output bus travel time prediction with convolutional LSTM neural network , 2019, Expert Syst. Appl..

[37]  Li-Chiu Chang,et al.  Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts , 2019, Journal of Cleaner Production.

[38]  S. Verhelst,et al.  Methanol as a fuel for internal combustion engines , 2019, Progress in Energy and Combustion Science.

[39]  Sung-Bae Cho,et al.  Hierarchical modular Bayesian networks for low-power context-aware smartphone , 2017, Neurocomputing.

[40]  Roopesh Kumar Mehra,et al.  Study on influencing factors of prediction accuracy of support vector machine (SVM) model for NOx emission of a hydrogen enriched compressed natural gas engine , 2018, Fuel.

[41]  G. Blasio,et al.  Advances of the Natural Gas/Diesel RCCI Concept Application for Light-Duty Engines: Comprehensive Analysis of the Influence of the Design and Calibration Parameters on Performance and Emissions , 2018, Energy, Environment, and Sustainability.

[42]  V. Padmanaban,et al.  Natural gas vehicles in heavy-duty transportation-A review , 2018, Energy Policy.

[43]  Roopesh Kumar Mehra,et al.  Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios , 2018, Applied Energy.

[44]  Shahaboddin Shamshirband,et al.  Modeling interfacial tension in N2/n-alkane systems using corresponding state theory: Application to gas injection processes , 2018, Fuel.

[45]  Roopesh Kumar Mehra,et al.  Progress in hydrogen enriched compressed natural gas (HCNG) internal combustion engines - A comprehensive review , 2017 .

[46]  Fanhua Ma,et al.  Intelligent regression algorithm study based on performance and NOx emission experimental data of a hydrogen enriched natural gas engine , 2016 .

[47]  Guo Dongwei,et al.  Soft-sensor modeling of silicon content in hot metal based on sparse robust LS-SVR and multi-objective optimization , 2016 .

[48]  Ashkan Moosavian,et al.  Support vector machine to predict diesel engine performance and emission parameters fueled with nano-particles additive to diesel fuel , 2015 .

[49]  Mohd Azli Salim,et al.  Performance Analysis of A Spark Ignition Engine Using Compressed Natural Gas (CNG) as Fuel , 2015 .

[50]  Pravin M. Singru,et al.  Fouling analysis of a shell and tube heat exchanger using local linear wavelet neural network , 2014 .

[51]  Jan Adamowski,et al.  Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS , 2014, Expert Syst. Appl..

[52]  Wang Shi-tong,et al.  A Shared Latent Subspace Transfer Learning Algorithm Using SVM , 2014 .

[53]  Linjing Zhao,et al.  An intelligent approach for engine fault diagnosis based on Hilbert–Huang transform and support vector machine , 2014 .

[54]  Lin Li,et al.  Multi-output least-squares support vector regression machines , 2013, Pattern Recognit. Lett..

[55]  Andrea Unich,et al.  Numerical evaluation of internal combustion spark ignition engines performance fuelled with hydrogen – Natural gas blends , 2012 .

[56]  Yituan He,et al.  Experimental study on combustion and emission characteristics of a hydrogen-enriched compressed natural gas engine under idling condition , 2011 .

[57]  Chi-Man Vong,et al.  Engine ignition signal diagnosis with Wavelet Packet Transform and Multi-class Least Squares Support Vector Machines , 2011, Expert Syst. Appl..

[58]  Fanhua Ma,et al.  Performance and emission characteristics of a turbocharged spark-ignition hydrogen-enriched compressed natural gas engine under wide open throttle operating conditions , 2010 .

[59]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[60]  Fanhua Ma,et al.  Performance and emission characteristics of a turbocharged CNG engine fueled by hydrogen-enriched compressed natural gas with high hydrogen ratio , 2010 .

[61]  Pak Kin Wong,et al.  Engine idle-speed system modelling and control optimization using artificial intelligence , 2010 .

[62]  Mike Preuss,et al.  Support vector machine learning with an evolutionary engine , 2009, J. Oper. Res. Soc..

[63]  K. Boulouchos,et al.  Hydrogen–natural gas blends fuelling passenger car engines: Combustion, emissions and well-to-wheels assessment , 2008 .

[64]  Yong Li,et al.  Experimental study on thermal efficiency and emission characteristics of a lean burn hydrogen enriched natural gas engine , 2007 .

[65]  Peter E. Rossi,et al.  Hierarchical Bayes Models , 2006 .

[66]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[67]  Johan A. K. Suykens,et al.  Optimal control by least squares support vector machines , 2001, Neural Networks.

[68]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[69]  K. Zeng,et al.  Regression prediction of hydrogen enriched compressed natural gas (HCNG) engine performance based on improved particle swarm optimization back propagation neural network method (IMPSO-BPNN) , 2022, Fuel.