Optimization of diesel engine performance and emission parameters employing cassia tora methyl esters-response surface methodology approach

Singh, Yashvir; Sharma, Abhishek; Tiwari, Sumit; Singla, Amneesh

doi:10.1016/j.energy.2018.12.013

Cited by 85 publications

(22 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The areas where it has been applied are diverse, in engineering fields such as the synthesis of oxygen in nanocomponents, study of the effects of low-frequency oscillations in the generation of energy by wind generators, or even the elimination of iron components in binary mixtures of dyes [24][25][26], structural fields, where Response Surface Models have been used in the study of the quality of welds in metal structures, predicting parameters such as the tensile strength, impact toughness and hardness of friction [27,28]; in construction, this methodology has been used in the study of mixtures' optimization for mortar and concrete, analysing properties required by EN regulations for these materials [29][30][31], among others. Within the field of energy, application studies have been found in the optimization of energy processes and fuel consumption or BTE and NOx Optimization using ANOVA analysis [32,33].…”

Section: Response Surface Methodology As An Alternative Methodsmentioning

confidence: 99%

Response Surface Method to Calculate Energy Savings Associated with Thermal Comfort Improvement in Buildings

et al. 2022

View full text Add to dashboard Cite

In developed countries, a large part of the building stock in 2050 will consist of currently existing buildings. Consequently, in order to achieve the objectives in terms of energy efficiency in the building sector we must consider not only new infrastructures but also the old ones. A reduction in energy consumption for climate control of between 50 and 90% can be achieved by rehabilitation and the implementation of different energy efficiency measures. Currently, these measures to reduce energy consumption and associated CO2 emissions can be modelled using computer tools. However, high precision and detail of thermal behaviour models through simulations can mean a great computational cost for companies, which results in a blockage of servers and workers. In this paper, the Response Surface Methodology (RSM) is presented as an innovative methodology for the simplification of models for calculation of the energy savings associated with thermal comfort improvement in buildings. A single-family house model, located in three different climates, is presented as a case study in order to validate the proposed methodology. Different scenarios were simulated, addressing heating and cooling temperature set points and external wall insulation represented by the transmittance (U-value). Results obtained from energy simulation using Design Builder were contrasted against those estimated from the simplified model extracted from the RSM analysis. The results revealed a deviation lower than 3% when comparing both methods. Therefore, the simplified mathematical prediction models are demonstrated to be suitable for the study of the energy performance of buildings, saving computational time, costs and associated human resources.

show abstract

Section: Response Surface Methodology As An Alternative Methodsmentioning

confidence: 99%

Response Surface Method to Calculate Energy Savings Associated with Thermal Comfort Improvement in Buildings

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In the last decade, several researchers in the field of alternative fuels began applying RSM to the modelling and optimization issues. 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%

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

“…Determination of optimum concentrations in a three-component mixture is a performance-requiring process both in terms of time and economy [24][25][26][27][28]. At this point, the response surface methodology, which is one of the statistical methods applied based on the experimental design, stands out as an effective method in determining the optimum concentration of the three components [28][29][30][31][32]. In addition, RSM is advantageous compared to other methods in terms of gaining maximum information from a small number of experiments, changing the effective parameters simultaneously, and optimizing [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Determination of the Optimum Blend Ratio of Diesel, Waste Oil Derived Biodiesel and 1-Pentanol Using the Response Surface Method

et al. 2022

View full text Add to dashboard Cite

Higher alcohols can be included as a third component in biodiesel-diesel mixtures to improve fuel properties and reduce emissions. Determining the optimum concentrations of these fuels according to the purpose of engine use is important both environmentally and economically. In this study, eight different concentrations of diesel (D), waste oil derived biodiesel (WOB), and 1-pentanol (P) ternary mixtures were determined by the design of experimental method (DOE). In order to determine the engine performance and exhaust emission parameters of these fuels, they were tested on a diesel engine with a constant load of 6 kW and a constant engine speed of 1800 rpm. Using the test results obtained, a full quadratic mathematical model with a 95% confidence level was created using the Response Surface Method (RSM) to predict five different output parameters (BSFC, BTE, CO, HC, and NOx) according to the fuel mixture ratios. The R2 accuracy values of the outputs were found at the reliability level. According to the criteria that BTE will be maximum and BSFC, CO, HC, and NOx emissions will be minimum, the optimization determined that the fuel mixture 79.09% D-8.33% WOB-12.58% P concentration (DWOBPopt) will produce the desired result. A low prediction error was obtained with the confirmation test. As a result, it is concluded that the optimized fuel can be an alternative to the commonly accepted B7 blend and can be used safely in diesel engines.

show abstract

Optimization of diesel engine performance and emission parameters employing cassia tora methyl esters-response surface methodology approach

Cited by 85 publications

References 32 publications

Response Surface Method to Calculate Energy Savings Associated with Thermal Comfort Improvement in Buildings

Response Surface Method to Calculate Energy Savings Associated with Thermal Comfort Improvement in Buildings

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

Determination of the Optimum Blend Ratio of Diesel, Waste Oil Derived Biodiesel and 1-Pentanol Using the Response Surface Method

Contact Info

Product

Resources

About