Exploring the Exhaust Emission and Efficiency of Algal Biodiesel Powered Compression Ignition Engine: Application of Box–Behnken and Desirability Based Multi‐Objective Response Surface Methodology

Prabhakar Sharma, Ajay Chhillar, Zafar Said, Saim Memon

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Sustainable Development Goals were established by the United Nations General Assem-bly to ensure that everyone has access to clean, affordable, and sustainable energy. Third‐generation biodiesel derived from algae sources can be a feasible option in tackling climate change caused by fossil fuels as it has no impact on the human food supply chain. In this paper, the combustion and emission characteristics of Azolla Pinnata oil biodiesel‐diesel blends are investigated. The multi‐ob-jective response surface methodology (MORSM) with Box–Behnken design is employed to decrease the number of trials to conserve finite resources in terms of human labor, time, and cost. MORSM was used in this study to investigate the interaction, model prediction, and optimization of the operating parameters of algae biodiesel‐powered diesel engines to obtain the best performance with the least emission. For engine output prediction, a prognostic model is developed. Engine operating parameters are optimized using the desirability technique, with the best efficiency and lowest emission as the criteria. The results show Theil’s uncertainty for the model’s predictive capability (Theil’s U2) to be between 0.0449 and 0.1804. The Nash–Sutcliffe efficiency is validated to be excellent between 0.965 and 0.9988, whilst the mean absolute percentage deviation is less than 4.4%. The optimized engine operating conditions achieved are 81.2% of engine load, 17.5 of compression ratio, and 10% of biodiesel blending ratio. The proposed MORSM‐based technique’s dependability and robustness validate the experimental methods.

Original languageEnglish
Article number5968
Number of pages22
JournalEnergies
Volume14
Issue number18
DOIs
Publication statusPublished - 20 Sep 2021
Externally publishedYes

Fingerprint

Dive into the research topics of 'Exploring the Exhaust Emission and Efficiency of Algal Biodiesel Powered Compression Ignition Engine: Application of Box–Behnken and Desirability Based Multi‐Objective Response Surface Methodology'. Together they form a unique fingerprint.

Cite this