Response surface methodology (RSM) for optimizing engine performance and emissions fueled with biofuel: Review of RSM for sustainability energy transition
Response Surface Methodology (RSM) is a statistical method to design experiments and optimize the effect of process variables. RSM is based on the principles of design of experiments or DOE. Design of experiments or DOE is a field of applied statistics that plans, conducts, analyses, and interprets...
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| Published in | Results in engineering Vol. 18; p. 101213 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.06.2023
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Response Surface Methodology (RSM) is a statistical method to design experiments and optimize the effect of process variables. RSM is based on the principles of design of experiments or DOE. Design of experiments or DOE is a field of applied statistics that plans, conducts, analyses, and interprets controlled tests to assess factors that affect parameter values. Response surface methodology or RSM uses a statistical method for designing experiments and optimization. Despite the potential of response surface methodology to predict and optimize engine performance and emissions characteristics, a comprehensive review on RSM for biofuels, particularly for internal combustion engines (ICEs), is difficult to find. The review of response surface methodology is sometimes included together with other machine learning approaches such as ANN. Therefore, a review article that is exclusively written to address the specific of RSM for biofuel and ICE is required. This review article offers a fresh perspective on the application of RSM for biofuel in ICE. This article aims to critically review the RSM to optimize engine performance and emissions using biofuel. The study concludes with several possible research gaps for future works of RSM biofuel application. Although response surface methodology or RSM has drawbacks such as extrapolation inaccuracy outside the investigational ranges and discrete variables error, RSM has numerous advantages to design, model, estimate, and optimize biofuel for ICE with satisfactory accuracy. With its prediction and optimization capability, response surface methodology has the potential to assist the development of ICE optimization powered by biofuel for sustainability energy transition.
•RSM has been systematically employed for ICE fueled with biofuels.•RSM is sometimes applied together with other soft computing approaches such as ANN.•This review article offers a fresh perspective on the application of RSM for biofuel in ICE.•It concludes with several possible research gaps for future works of RSM biofuel application.•RSM has the potential to assist the development of ICE optimization powered by biofuel. |
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| AbstractList | Response Surface Methodology (RSM) is a statistical method to design experiments and optimize the effect of process variables. RSM is based on the principles of design of experiments or DOE. Design of experiments or DOE is a field of applied statistics that plans, conducts, analyses, and interprets controlled tests to assess factors that affect parameter values. Response surface methodology or RSM uses a statistical method for designing experiments and optimization. Despite the potential of response surface methodology to predict and optimize engine performance and emissions characteristics, a comprehensive review on RSM for biofuels, particularly for internal combustion engines (ICEs), is difficult to find. The review of response surface methodology is sometimes included together with other machine learning approaches such as ANN. Therefore, a review article that is exclusively written to address the specific of RSM for biofuel and ICE is required. This review article offers a fresh perspective on the application of RSM for biofuel in ICE. This article aims to critically review the RSM to optimize engine performance and emissions using biofuel. The study concludes with several possible research gaps for future works of RSM biofuel application. Although response surface methodology or RSM has drawbacks such as extrapolation inaccuracy outside the investigational ranges and discrete variables error, RSM has numerous advantages to design, model, estimate, and optimize biofuel for ICE with satisfactory accuracy. With its prediction and optimization capability, response surface methodology has the potential to assist the development of ICE optimization powered by biofuel for sustainability energy transition. Response Surface Methodology (RSM) is a statistical method to design experiments and optimize the effect of process variables. RSM is based on the principles of design of experiments or DOE. Design of experiments or DOE is a field of applied statistics that plans, conducts, analyses, and interprets controlled tests to assess factors that affect parameter values. Response surface methodology or RSM uses a statistical method for designing experiments and optimization. Despite the potential of response surface methodology to predict and optimize engine performance and emissions characteristics, a comprehensive review on RSM for biofuels, particularly for internal combustion engines (ICEs), is difficult to find. The review of response surface methodology is sometimes included together with other machine learning approaches such as ANN. Therefore, a review article that is exclusively written to address the specific of RSM for biofuel and ICE is required. This review article offers a fresh perspective on the application of RSM for biofuel in ICE. This article aims to critically review the RSM to optimize engine performance and emissions using biofuel. The study concludes with several possible research gaps for future works of RSM biofuel application. Although response surface methodology or RSM has drawbacks such as extrapolation inaccuracy outside the investigational ranges and discrete variables error, RSM has numerous advantages to design, model, estimate, and optimize biofuel for ICE with satisfactory accuracy. With its prediction and optimization capability, response surface methodology has the potential to assist the development of ICE optimization powered by biofuel for sustainability energy transition. •RSM has been systematically employed for ICE fueled with biofuels.•RSM is sometimes applied together with other soft computing approaches such as ANN.•This review article offers a fresh perspective on the application of RSM for biofuel in ICE.•It concludes with several possible research gaps for future works of RSM biofuel application.•RSM has the potential to assist the development of ICE optimization powered by biofuel. |
| ArticleNumber | 101213 |
| Author | Idris, Muhammad Fattah, I.M. Rizwanul Veza, Ibham Spraggon, Martin |
| Author_xml | – sequence: 1 givenname: Ibham orcidid: 0000-0002-1674-4798 surname: Veza fullname: Veza, Ibham email: ibham.veza@utp.edu.my organization: Mechanical Engineering Department, Universiti Teknologi PETRONAS (UTP), 32610, Seri Iskandar, Perak, Malaysia – sequence: 2 givenname: Martin surname: Spraggon fullname: Spraggon, Martin organization: Research & Innovation Center Division, Rabdan Academy, Abu Dhabi, United Arab Emirates – sequence: 3 givenname: I.M. Rizwanul surname: Fattah fullname: Fattah, I.M. Rizwanul email: IslamMdRizwanul.Fattah@uts.edu.au organization: Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, 2007, NSW, Australia – sequence: 4 givenname: Muhammad surname: Idris fullname: Idris, Muhammad organization: School of Environmental Science, University of Indonesia, Jakarta, 10430, Indonesia |
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| Keywords | RSM DOE optimization RSM full factorial design RSM Box-Behnken design RSM biofuel Response surface methodology (RSM) |
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