A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel

Yusri, I.M.; Majeed, Anwar P. P. Abdul; Mamat, Rizalman; Ghazali, Mohd Imran; Awad, Omar I.; Azmi, W.H.

doi:10.1016/j.rser.2018.03.095

Cited by 150 publications

(43 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The final goal of the application of RSM is to describe in a mathematically simple and intuitive way (with multilinear models only) the behaviour of a dataset in order to carry out statistical previsions. A response of interest relies on several significant variables, and the aim of the method is to model and optimise such response [36]. In the last decade, several researchers in the field of alternative fuels began applying RSM to the modelling and optimization issues.…”

Section: Emissions and Performances Prediction: Application Of The Rementioning

confidence: 99%

“…A typical engine-related RSM analysis involves engine parameters such as load, speed, fuel injection timing to reduce fuel consumption, exhaust emissions and noise, and aims to improve engine performance [37][38][39] or to study the behaviour of auxiliary organs [19]. A selection of works from the extensive review paper by Yusri et al [36], and how to proceed with RSM application in the field of biodiesel and bioethanol, is presented hereinafter.…”

Section: Emissions and Performances Prediction: Application Of The Rementioning

confidence: 99%

See 1 more Smart Citation

Proposal of a Predictive Mixed Experimental- Numerical Approach for Assessing the Performance of Farm Tractor Engines Fuelled with Diesel- Biodiesel-Bioethanol Blends

et al. 2019

View full text Add to dashboard Cite

The effect of biofuel blends on the engine performance and emissions of agricultural machines can be extremely complex to predict even if the properties and the effects of the pure substances in the blends can be sourced from the literature. Indeed, on the one hand, internal combustion engines (ICEs) have a high intrinsic operational complexity; on the other hand, biofuels show antithetic effects on engine performance and present positive or negative interactions that are difficult to determine a priori. This study applies the Response Surface Methodology (RSM), a numerical method typically applied in other disciplines (e.g., industrial engineering) and for other purposes (e.g., set-up of production machines), to analyse a large set of experimental data regarding the mechanical and environmental performances of an ICE used to power a farm tractor. The aim is twofold: i) to demonstrate the effectiveness of RSM in quantitatively assessing the effects of biofuels on a complex system like an ICE; ii) to supply easy-to-use correlations for the users to predict the effect of biofuel blends on performance and emissions of tractor engines. The methodology showed good prediction capabilities and yielded interesting outcomes. The effects of biofuel blends and physical fuel parameters were adopted to study the engine performance. Among all possible parameters depending on the fuel mixture, the viscosity of a fuel blend demonstrated a high statistical significance on some system responses directly related to the engine mechanical performances. This parameter can constitute an interesting indirect estimator of the mechanical performances of an engine fuelled with such blend, while it showed poor accuracy in predicting the emissions of the ICE (NOx, CO concentration and opacity of the exhaust gases) due to a higher influence of the chemical composition of the fuel blend on these parameters; rather, the blend composition showed a much higher accuracy in the assessment of the mechanical performance of the ICE.

show abstract

Section: Emissions and Performances Prediction: Application Of The Rementioning

confidence: 99%

Section: Emissions and Performances Prediction: Application Of The Rementioning

confidence: 99%

Proposal of a Predictive Mixed Experimental- Numerical Approach for Assessing the Performance of Farm Tractor Engines Fuelled with Diesel- Biodiesel-Bioethanol Blends

et al. 2019

View full text Add to dashboard Cite

show abstract

“…According to Equation (26), N c increases to a great extent as j increases. Therefore, the combined use of the Pearson correlation and partial correlation analysis is a very useful and efficient way of decreasing the computational effort of the sensitivity analysis, since it allows the poorly correlated variables to be excluded a-priori, or the highly correlated variables to be selected, so that the sensitivity analysis (which is highly time consuming, since it requires model fitting of each possible combination), is only carried out for a reduced set of variables.…”

Section: Dependent Output Variables (Combustion Metrics For Use For Mmentioning

confidence: 99%

“…Moreover, they require a short computational time, which makes them suitable for control applications. An example of a semi-empirical model for the estimation of combustion metrics is reported in [23].Artificial intelligence systems, which include methods such as support vector machine (SVM) and artificial neural networks (ANNs) [13,[24][25][26][27][28], can also be adopted for control-oriented applications. The use of ANNs has increased more and more in the last few years, due to their capacity to accurately predict the behavior of complex systems with short computational times.…”

mentioning

confidence: 99%

“…The use of ANNs has increased more and more in the last few years, due to their capacity to accurately predict the behavior of complex systems with short computational times. Many applications based on ANNs can be found in the literature on engine control and diagnostics [13,[26][27][28][29][30][31][32][33][34]. From an analysis of the literature, it may be observed that the predictive performance of ANNs is influenced to a great extent by the selection of their main parameters, such as the number of layers, the training algorithm and the number of neurons [31][32][33][34][35][36][37].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Physics-Based, Semi-empirical and Neural Network-based Models for Model-based Combustion Control in a 3.0 L Diesel Engine

D’Ambrosio

Finesso

et al. 2019

Energies

View full text Add to dashboard Cite

A comparison of four different control-oriented models has been carried out in this paper for the simulation of the main combustion metrics in diesel engines, i.e., combustion phasing, peak firing pressure, and brake mean effective pressure. The aim of the investigation has been to understand the potential of each approach in view of their implementation in the engine control unit (ECU) for onboard combustion control applications. The four developed control-oriented models, namely the baseline physics-based model, the artificial neural network (ANN) physics-based model, the semi-empirical model, and direct ANN model, have been assessed and compared under steady-state conditions and over the Worldwide Harmonized Heavy-duty Transient Cycle (WHTC) for a Euro VI FPT F1C 3.0 L diesel engine. Moreover, a new procedure has been introduced for the selection of the input parameters. The direct ANN model has shown the best accuracy in the estimation of the combustion metrics under both steady-state/transient operating conditions, since the root mean square errors are of the order of 0.25/1.1 deg, 0.85/9.6 bar, and 0.071/0.7 bar for combustion phasing, peak firing pressure, and brake mean effective pressure, respectively. Moreover, it requires the least computational time, that is, less than 50 s when the model is run on a rapid prototyping device. Therefore, it can be considered the best candidate for model-based combustion control applications.

show abstract

Investigation of RBFNN Based on Improved PSO Optimization Algorithm for Performance and Emissions Prediction of a High‐Pressure Common‐Rail Diesel Engine

Wang

Yao

et al. 2023

Advcd Theory and Sims

View full text Add to dashboard Cite

The purpose of this study is improve calibration efficiency and obtain accurate diesel engine operating parameters, achieving improved diesel engine emissions and fuel efficiency. A PSO‐RBF (particle swarm optimization‐radial basis function) diesel engine performance prediction model combining an improved PSO (particle swarm optimization algorithm and an RBF neural network is proposed. A space‐filling experimental design method for diesel engine performance prediction is proposed based on the actual operating conditions of diesel engines. Training data are collected at the bench to build the RBF prediction model. An optimization PSO search method is proposed to improve the PSO optimization capability. An improved PSO algorithm is used to optimize the model and improve prediction accuracy. Then the BSFC (diesel brake‐specific fuel consumption), NOx ((Nitrogen Oxid), CO (Carbon Monoxide), and HC (Hydrocarbon) prediction models are constructed. Results show that the PSO‐RBF can find the global solution with good prediction accuracy and generalization ability during small amounts of data. The PSO‐RBF model fitting degrees of BSFC, NOx, CO, and HC are 0.9952, 0.9910, 0.9820, and 0.9870 respectively. Mean relative errors are 3.02%, 2.78%, 1.39%, and 2.01% respectively. Mean absolute percentage errors are 1.58%, 3.26%, 3.69%, and 2.96% respectively. The optimized model R2 (Model determination coefficient) is improved by 0.065, 0.102, 0.10, and 0.085, respectively.

show abstract

A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel

Cited by 150 publications

References 99 publications

Proposal of a Predictive Mixed Experimental- Numerical Approach for Assessing the Performance of Farm Tractor Engines Fuelled with Diesel- Biodiesel-Bioethanol Blends

Proposal of a Predictive Mixed Experimental- Numerical Approach for Assessing the Performance of Farm Tractor Engines Fuelled with Diesel- Biodiesel-Bioethanol Blends

Comparison of Physics-Based, Semi-empirical and Neural Network-based Models for Model-based Combustion Control in a 3.0 L Diesel Engine

Investigation of RBFNN Based on Improved PSO Optimization Algorithm for Performance and Emissions Prediction of a High‐Pressure Common‐Rail Diesel Engine

Contact Info

Product

Resources

About