Advancing renewable fuel integration: A comprehensive response surface methodology approach for internal combustion...

Area of Science:

• * Internal Combustion Engine Technology
• * Renewable Energy Sources
• * Statistical Modelling

Background:

• * Growing demand for renewable alternative fuels in internal combustion engines (ICEs) to reduce consumption and emissions.
• * Limitations of experimental analysis alone for addressing complex combustion, performance, and emission challenges.
• * Increasing significance of engine modelling using machine learning and statistical methods like RSM.

Purpose of the Study:

• * To explore contemporary applications of the Response Surface Method (RSM) for assessing renewable alternative fuels in ICEs.
• * To provide an introduction to RSM principles and procedures.
• * To comprehensively examine recent applications of alternative fuels and the role of RSM in evaluating their impact on combustion, performance, and emissions.

Main Methods:

• * Application of the Response Surface Method (RSM), a statistical technique.
• * Integration of machine learning methodologies with statistical data.
• * Analysis of combustion, performance, and emission data from engines utilizing renewable fuels.

Main Results:

• * RSM facilitates enhanced analytical outcomes in engine modelling.
• * The study details the impact of various renewable alternative fuels on engine performance and emissions.
• * Advantages and limitations of using RSM are identified, alongside potential for integration with other modelling techniques.

Conclusions:

• * RSM is a valuable tool for evaluating the feasibility of renewable alternative fuels in internal combustion engines.
• * The study contributes to understanding the efficacy of RSM in optimizing engine performance and reducing emissions.
• * Combining RSM with other modelling approaches offers potential for further optimization.