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Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends

https://doi.org/10.1016/j.apenergy.2011.08.027
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s10098-009-0235-2openalex-snapshot
Recent trends in policies, socioeconomy and future directions of the biodiesel industry
inferredcites →https://doi.org/10.1016/j.apenergy.2004.07.008openalex-snapshot
Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine
inferredcites →https://doi.org/10.1016/j.apenergy.2008.09.017openalex-snapshot
Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network
inferredcites →https://doi.org/10.1016/j.apenergy.2009.03.001openalex-snapshot
Optimization of operating conditions for compressor performance by means of neural network inverse
inferredcites →https://doi.org/10.1016/j.apenergy.2009.08.016openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.apenergy.2009.10.009openalex-snapshot
CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network
inferredcites →https://doi.org/10.1016/j.apenergy.2010.12.030openalex-snapshot
Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings
inferredcites →https://doi.org/10.1016/j.apenergy.2010.12.075openalex-snapshot
Evaluation of non-premixed combustion and fuel spray models for in-cylinder diesel engine simulation
inferredcites →https://doi.org/10.1016/j.apenergy.2011.01.028openalex-snapshot
Characterisation of engine-out responses from a light-duty diesel engine fuelled with palm methyl ester (PME)
inferredcites →https://doi.org/10.1016/j.apenergy.2013.03.086openalex-snapshot
Green energy: Water-containing acetone–butanol–ethanol diesel blends fueled in diesel engines
inferredcites →https://doi.org/10.1016/j.apenergy.2013.05.010openalex-snapshot
The influence of biodiesel fuel on injection characteristics, diesel engine performance, and emission formation
inferredcites →https://doi.org/10.1016/j.apenergy.2013.08.005openalex-snapshot
Effects of waste cooking oil-based biodiesel on the toxic organic pollutant emissions from a diesel engine
inferredcites →https://doi.org/10.1016/j.energy.2009.08.034openalex-snapshot
Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends
inferredcites →https://doi.org/10.1016/j.energy.2013.03.057openalex-snapshot
Modeling and optimization of biodiesel engine performance using advanced machine learning methods
inferredcites →https://doi.org/10.1016/j.fuel.2011.03.034openalex-snapshot
Engine-out characterisation using speed–load mapping and reduced test cycle for a light-duty diesel engine fuelled with biodiesel blends
inferredcites →https://doi.org/10.1016/j.renene.2008.08.008openalex-snapshot
Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network
inferredcites →https://doi.org/10.1016/s0016-2361(00)00111-3openalex-snapshot
Modeling diesel particulate emissions with neural networks
inferredcites →https://doi.org/10.1016/s0167-7012(00)00201-3openalex-snapshot
Artificial neural networks: fundamentals, computing, design, and application
inferredcites →https://doi.org/10.1016/s0893-6080(99)00030-1openalex-snapshot
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Diesel Engine Exhaust Emissions Modelling Using Artificial Neural Networks
inferredcites →https://doi.org/10.4271/2002-01-2772openalex-snapshot
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inferredcites →https://doi.org/10.4271/2003-01-3227openalex-snapshot
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inferredcites →https://doi.org/10.4271/2003-01-3232openalex-snapshot
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Neural Network Application to Evaluate Thermodynamic Properties of ICE's Combustion Gases
inferredcites →https://doi.org/10.4271/2006-01-3298openalex-snapshot
Fast Prediction of HCCI Combustion with an Artificial Neural Network Linked to a Fluid Mechanics Code
inferredcites →https://doi.org/10.5860/choice.26-0943openalex-snapshot
Internal combustion engine fundamentals

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Declaration of Competing Interest the Authors Mr.Mahesh Chand Saini and Dr. O. P Jakhar Declare that They Have No Known Competing Financial Interests or Personal Relationships that Could Have Appeared to Influence the Research Work Reported in this Paper
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Development of artificial neural network to predict the performance of spark ignition engine fuelled with waste pomegranate ethanol blends
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Appraisal of artificial neural networks to the emission analysis and prediction of CO2, soot, and NOx of n-heptane fueled engine
inferred← citeshttps://doi.org/10.1016/j.jece.2022.108025openalex-snapshot
Machine learning approach for the prediction of biomass pyrolysis kinetics from preliminary analysis
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2014.04.013openalex-snapshot
Emission reduction of NOx, PM, PM-carbon, and PAHs from a generator fuelled by biodieselhols
inferred← citeshttps://doi.org/10.1016/j.jngse.2014.08.002openalex-snapshot
Development of an ANN based system identification tool to estimate the performance-emission characteristics of a CRDI assisted CNG dual fuel diesel engine
inferred← citeshttps://doi.org/10.1016/j.jngse.2014.10.024openalex-snapshot
A comparative study of GEP and an ANN strategy to model engine performance and emission characteristics of a CRDI assisted single cylinder diesel engine under CNG dual-fuel operation
inferred← citeshttps://doi.org/10.1016/j.jngse.2015.06.041openalex-snapshot
Development of ANN model for prediction of performance and emission characteristics of hydrogen dual fueled diesel engine with Jatropha Methyl Ester biodiesel blends
inferred← citeshttps://doi.org/10.1016/j.joei.2015.10.007openalex-snapshot
Application of artificial neural network for kinetic parameters prediction of biomass oxidation from biomass properties
inferred← citeshttps://doi.org/10.1016/j.measurement.2021.110566openalex-snapshot
Multi-objective optimization to minimize pumping power and flow non-uniformity at the outlets of a distributor manifold using CFD simulations and ANN rapid predictions
inferred← citeshttps://doi.org/10.1016/j.pecs.2021.100904openalex-snapshot
Machine learning technology in biodiesel research: A review
inferred← citeshttps://doi.org/10.1016/j.pecs.2021.100967openalex-snapshot
Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: A review and future directions
inferred← citeshttps://doi.org/10.1016/j.pecs.2022.101010openalex-snapshot
Combustion machine learning: Principles, progress and prospects
inferred← citeshttps://doi.org/10.1016/j.proeng.2014.09.061openalex-snapshot
Surrogate-based Sensitivity Analysis for Safety Assessment of General Aviation Heavy-fueled Engines
inferred← citeshttps://doi.org/10.1016/j.protcy.2013.04.041openalex-snapshot
Wavelet Neural Networks for Predicting Engine Emissions
inferred← citeshttps://doi.org/10.1016/j.renene.2014.08.075openalex-snapshot
Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search
inferred← citeshttps://doi.org/10.1016/j.renene.2017.10.101openalex-snapshot
Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural network
inferred← citeshttps://doi.org/10.1016/j.renene.2019.10.080openalex-snapshot
Artificial neural network modeling of performance, emission, and vibration of a CI engine using alumina nano-catalyst added to diesel-biodiesel blends
inferred← citeshttps://doi.org/10.1016/j.renene.2023.119031openalex-snapshot
Model construction and optimization for raising the concentration of industrial bioethanol production by using a data-driven ANN model
inferred← citeshttps://doi.org/10.1016/j.rser.2018.03.095openalex-snapshot
A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel
inferred← citeshttps://doi.org/10.1016/j.scs.2019.102010openalex-snapshot
A review on machine learning forecasting growth trends and their real-time applications in different energy systems
inferred← citeshttps://doi.org/10.1016/j.seta.2021.101416openalex-snapshot
A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels
inferred← citeshttps://doi.org/10.1021/acs.iecr.3c01662openalex-snapshot
Artificial Neural Network-Based Models for the Prediction of Biomass Pyrolysis Products from Preliminary Analysis
inferred← citeshttps://doi.org/10.1021/acs.iecr.3c03105openalex-snapshot
Hydrodynamics Behavior of Ternary Biomass Mixtures with Silica Sand in Fluidized Beds: Experiment and Artificial Neural Network Model Development
inferred← citeshttps://doi.org/10.1021/ef300862uopenalex-snapshot
Development of Thermophysical and Transport Properties for the CFD Simulations of In-Cylinder Biodiesel Spray Combustion
inferred← citeshttps://doi.org/10.1021/ef301162dopenalex-snapshot
Investigation of Biodiesel–Diesel Fuel Blends on Combustion Characteristics in a Light-Duty Diesel Engine Using OpenFOAM
inferred← citeshttps://doi.org/10.1038/s41598-026-45011-4openalex-snapshot
A sustainable hybrid FEA–AI optimization framework for multistage deep drawing of unidirectionally rolled copper micro-cups
inferred← citeshttps://doi.org/10.1061/(asce)ey.1943-7897.0000363openalex-snapshot
Prediction of NOx Emissions and Fuel Consumption of a City Bus under Real Operating Conditions by Means of Biharmonic Maps
inferred← citeshttps://doi.org/10.1063/1.5095360openalex-snapshot
Optimization of gasoline engine to maximize brake power and minimize brake specific fuel consumption with artificial neural network and Killer Whale algorithm
inferred← citeshttps://doi.org/10.1080/01430750.2015.1023466openalex-snapshot
Artificial neural network-based prediction of performance and emission characteristics of CI engine using polanga as a biodiesel
inferred← citeshttps://doi.org/10.1080/10407782.2019.1665451openalex-snapshot
Investigation on the effect of boost pressure on performance characteristics of a diesel engine
inferred← citeshttps://doi.org/10.1080/10916466.2022.2025832openalex-snapshot
Prediction of spark ignition engine performance with bioethanol-gasoline mixes using a multilayer perception model
inferred← citeshttps://doi.org/10.1080/15435075.2014.891513openalex-snapshot
Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine
inferred← citeshttps://doi.org/10.1080/15435075.2021.1891908openalex-snapshot
Application of artificial neural network to map the performance characteristics of boiler using different algorithms
inferred← citeshttps://doi.org/10.1080/15567036.2018.1550540openalex-snapshot
Artificial intelligence techniques for the vibration, noise, and emission characteristics of a hydrogen-enriched diesel engine
inferred← citeshttps://doi.org/10.1080/15567036.2019.1651791openalex-snapshot
Determining the effects of 2-ethylhexyl nitrate blend on isolated diesel engine attributes using the experimental and ANN approaches
inferred← citeshttps://doi.org/10.1080/16258312.2024.2303307openalex-snapshot
Artificial intelligence based supply chain management strategy during COVID-19 situation
inferred← citeshttps://doi.org/10.1080/17597269.2018.1489675openalex-snapshot
Combustion analysis of a compression-ignition engine fuelled with an algae biofuel blend and diethyl ether as an additive by using an artificial neural network
inferred← citeshttps://doi.org/10.1088/1757-899x/376/1/012038openalex-snapshot
Prediction of performace parameters using artificial neural network for 4-stroke C I engine fueled with esterified neem oil and biogas
inferred← citeshttps://doi.org/10.1108/wje-07-2019-0210openalex-snapshot
Optimization of VCR engine parameters using particle swarm optimization-based desirability approach
inferred← citeshttps://doi.org/10.1108/wje-12-2020-0626openalex-snapshot
Optimization of injection parameters for ethanol blends of plastic oil in VCR engine using RSM and ANN
inferred← citeshttps://doi.org/10.1109/access.2021.3098657openalex-snapshot
Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks
inferred← citeshttps://doi.org/10.1109/cogmob55547.2022.10118200openalex-snapshot
Artificial Neural Network Based Prediction of Engine Combustion and Emissions from a High Resolution Dataset
inferred← citeshttps://doi.org/10.1111/gcbb.12816openalex-snapshot
Applications of artificial intelligence‐based modeling for bioenergy systems: A review
inferred← citeshttps://doi.org/10.1115/1.4035886openalex-snapshot
Artificial Neural Network Prediction of Diesel Engine Performance and Emission Fueled With Diesel–Kerosene–Ethanol Blends: A Fuzzy-Based Optimization
inferred← citeshttps://doi.org/10.1115/1.4038709openalex-snapshot
A Comparative Study of Artificial Intelligence Based Models to Predict Performance and Emission Characteristics of a Single Cylinder Diesel Engine Fueled With Diesosenol
inferred← citeshttps://doi.org/10.1115/1.4040380openalex-snapshot
Artificial Neural Network-Based Prediction of Performances-Exhaust Emissions of Diesohol Piloted Dual Fuel Diesel Engine Under Varying Compressed Natural Gas Flowrates
inferred← citeshttps://doi.org/10.1115/1.4047761openalex-snapshot
Random Forest Machine Learning Model for Predicting Combustion Feedback Information of a Natural Gas Spark Ignition Engine
inferred← citeshttps://doi.org/10.1115/1.4053301openalex-snapshot
Comparison of Random Forest and Neural Network in Modeling the Performance and Emissions of a Natural Gas Spark Ignition Engine
inferred← citeshttps://doi.org/10.1115/1.4053920openalex-snapshot
Implementation of Soft Computing Techniques in Predicting and Optimizing the Operating Parameters of Compression Ignition Diesel Engines: State-of-the-Art Review, Challenges, and Future Outlook
inferred← citeshttps://doi.org/10.1115/1.4062411openalex-snapshot
Evaluating the Operating Parameters of a Compression Ignition Engine Fueled With Waste Cooking Oil—Diesel Blends Using Artificial Neural Network and Ensemble Methods
inferred← citeshttps://doi.org/10.1134/s1061830922601131openalex-snapshot
Multiple Damage Prediction in Tubular Rectangular Beam Model Using Frequency Response-Based Mode Shape Curvature with Back-Propagation Neural Network
inferred← citeshttps://doi.org/10.1139/cjce-2019-0008openalex-snapshot
Influence of moisture infiltration on flexible pavement cracking and optimum timing for surface seals
inferred← citeshttps://doi.org/10.1142/s0218488513400187openalex-snapshot
DIESEL ENGINE MODELLING USING EXTREME LEARNING MACHINE UNDER SCARCE AND EXPONENTIAL DATA SETS
inferred← citeshttps://doi.org/10.1155/2014/240942openalex-snapshot
Prediction of Waste Heat Energy Recovery Performance in a Naturally Aspirated Engine Using Artificial Neural Network
inferred← citeshttps://doi.org/10.1155/2020/7179801openalex-snapshot
A Case Study on Computer-Based Analysis of the Stochastic Stability of Mechanical Structures Driven by White and Colored Noise: Utilizing Artificial Intelligence Techniques to Design an Effective Active Suspension System
inferred← citeshttps://doi.org/10.1177/09544062221137198openalex-snapshot
Hybrid optimisation of input process parameters of deep-drawn cylindrical cups from directional rolled copper strips
inferred← citeshttps://doi.org/10.1177/0954407016650545openalex-snapshot
A novel volumetric efficiency model for spark ignition engines equipped with variable valve timing and variable valve lift Part 1: model development
inferred← citeshttps://doi.org/10.1177/0954407020931686openalex-snapshot
Semi-physical NOx and soot model for CI engines: Study of its calibration procedure and portability
inferred← citeshttps://doi.org/10.1177/09544070221104893openalex-snapshot
Data-driven control of automotive diesel engines and after-treatment systems: State of the art and future challenges
inferred← citeshttps://doi.org/10.1177/09544089241311377openalex-snapshot
Lube oil life prediction for heavy earth moving machinery (HEMM): A machine learning approach
inferred← citeshttps://doi.org/10.1177/0958305x18779576openalex-snapshot
Prediction of performance and exhaust emissions of diesel engine fuelled with adulterated diesel: An artificial neural network assisted fuzzy-based topology optimization
inferred← citeshttps://doi.org/10.1177/09596518241302483openalex-snapshot
Convex modeling and control of multi-fuel compression ignition engines
inferred← citeshttps://doi.org/10.1177/14680874211034409openalex-snapshot
On the use of artificial neural networks to model the performance and emissions of a heavy-duty natural gas spark ignition engine
inferred← citeshttps://doi.org/10.1177/1468087421990476openalex-snapshot
Prediction and optimization of dual-fuel marine engine emissions and performance using combined ANN with PSO algorithms
inferred← citeshttps://doi.org/10.1177/14680874221088537openalex-snapshot
Investigation of an efficient model-based fuel economy optimization methodology for diesel engines focusing on real world driving emission cycles
inferred← citeshttps://doi.org/10.1177/14680874221138126openalex-snapshot
Optimization of homogeneous charge compression ignition combustion in a light-duty diesel engine operated using ethyl acetate-gasoline blends
inferred← citeshttps://doi.org/10.1177/14680874251382294openalex-snapshot
State estimation model for NOx storage-reduction catalyst using modeling method based on machine learning
inferred← citeshttps://doi.org/10.1177/14680874261487974openalex-snapshot
A physics–machine learning hybrid model for ignition-delay estimation considering negative temperature coefficient behavior
inferred← citeshttps://doi.org/10.1252/jcej.15we309openalex-snapshot
An Insight into Physical and Chemical Impacts of Cavitation under Different Operational Conditions in Biodiesel Synthesis under Ultrasound Irradiation
inferred← citeshttps://doi.org/10.1371/journal.pone.0184561openalex-snapshot
A neural network based computational model to predict the output power of different types of photovoltaic cells
inferred← citeshttps://doi.org/10.1515/ijcre-2023-0183openalex-snapshot
Emission and performance investigation of mango seed oil biodiesel supplied with n -pentanol and n -hexanol additives and optimization of fuel blends using modified deep neural network
inferred← citeshttps://doi.org/10.1631/jzus.a1300010openalex-snapshot
Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms
inferred← citeshttps://doi.org/10.17159/2413-3051/2015/v26i2a2198openalex-snapshot
Prediction of diesel engine performance, emissions and cylinder pressure obtained using bioethanol-biodiesel-diesel fuel blends through an artificial neural network
inferred← citeshttps://doi.org/10.19026/rjaset.8.1068openalex-snapshot
Simulating the Diesel Engine Vibration with Fuzzy Neural Network
inferred← citeshttps://doi.org/10.2298/tsci160302151topenalex-snapshot
Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network
inferred← citeshttps://doi.org/10.2339/politeknik.567865openalex-snapshot
Estimation of the COVIMEP Variation in a HCCI Engine
inferred← citeshttps://doi.org/10.26701/ems.320490openalex-snapshot
Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives
inferred← citeshttps://doi.org/10.31217/p.32.2.1openalex-snapshot
Mathematical Modelling of Diesel Engine Operational Performance Parameters in Transient Modes
inferred← citeshttps://doi.org/10.31857/s0130308223040024openalex-snapshot
Multiple damage prediction in tubular rectangular beam model using frequency response-based mode shape curvature with back-propagation neural network
inferred← citeshttps://doi.org/10.3390/app12042246openalex-snapshot
Comparing Polynomials and Neural Network to Modelling Injection Dosages in Modern CI Engines
inferred← citeshttps://doi.org/10.3390/app15189868openalex-snapshot
Prediction of Combustion Parameters and Pollutant Emissions of a Dual-Fuel Engine Based on Recurrent Neural Networks
inferred← citeshttps://doi.org/10.3390/en12183423openalex-snapshot
Comparison of Physics-Based, Semi-Empirical and Neural Network-Based Models for Model-Based Combustion Control in a 3.0 L Diesel Engine
inferred← citeshttps://doi.org/10.3390/en14165072openalex-snapshot
Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions
inferred← citeshttps://doi.org/10.3390/math10030420openalex-snapshot
Modeling and Optimization of a Compression Ignition Engine Fueled with Biodiesel Blends for Performance Improvement
inferred← citeshttps://doi.org/10.3390/polym11111853openalex-snapshot
The Yield Prediction of Synthetic Fuel Production from Pyrolysis of Plastic Waste by Levenberg–Marquardt Approach in Feedforward Neural Networks Model
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