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

Abstract

Sustainable Development Goals were established by the United Nations General Assembly 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-objective 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.

Experimental Assessment of the Impact of Replacing Diesel Fuel with CNG on the Concentration of Harmful Substances in Exhaust Gases in a Dual Fuel Diesel Engine

“…As a part of our research, the engine operation points were determined on the basis of good engineering judgment and as equivalents of the operating points in which ISO 8178 [34] tests are carried out. In many cases found in the literature, the research program is prepared with the use of certain algorithms [28,31,35] however, the authors' experience shows that this type of methodology is not fully effective in the areas in which the authors of this work operate. The research presented in the paper is an investigation of an innovative, high-pressure dual fFuel installation.…”

“…Many fuels can be used as high-cetane fuel, the properties of which do not differ significantly from those of diesel oil. Very promising are the possibilities of using, together with ecological gaseous fuels, also ecological, alternative fuels replacing diesel oil in this case, such as oil made of algae [28], rice [29], pongamia, palm, peanuts, mushrooms, jatrophy, coffee grounds, soybean, coconut, linseed, olives or even animal fat, which the authors described in detail in [30]. Also, a gas used as a fuel corresponding to the properties of natural gas can be produced in many ways and has great potential as a clean alternative fuel for additional reduction of GHG emissions in the process of its combustion or production [29].…”

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

“…Statistical assessment is one of the most common approaches for the evaluation of the prognostic efficacy of the models. The general indicators such as coefficient of determination, coefficient of regression, mean squared error, mean absolute percentage error, and Theil’s statistics have been utilized by various published investigations . The appeal of statistical approaches for regression issues arises from their simplicity of implementation and flexibility irrespective of the size of the data set.…”

“…Despite having more data than anybody else, the data are scattered, unorganized, distributed across several formats, and kept locally. While the business makes a lot of money, it is also vulnerable to antiquated systems. , …”

“…The general indicators such as coefficient of determination, coefficient of regression, mean squared error, mean absolute percentage error, and Theil's statistics have been utilized by various published investigations. 114 The appeal of statistical approaches for regression issues arises from their simplicity of implementation and flexibility irrespective of the size of the data set. Finally, when comparing machine learning algorithms with limited data sets, additional algorithm selection procedures such as the combined F-test, 5 × 2 cross-validation, and nested cross-validation are also advocated.…”