Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective

Krishna Kumar Gupta, Kanak Kalita, Ranjan Kumar Ghadai, Manickam Ramachandran and Xiao-Zhi Gao
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Krishna Kumar Gupta: Department of Mechanical Engineering, MPSTME, SVKM’s Narsee Monjee Institute of Management Studies (NMIMS), Shirpur Campus, Dhule 425 405, India
Kanak Kalita: Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi 600 062, India
Ranjan Kumar Ghadai: Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar 737 136, India
Manickam Ramachandran: Data Analytics Lab, REST Labs, Kaveripattinam, Krishnagiri 635 112, India
Xiao-Zhi Gao: School of Computing, University of Eastern Finland, FI-70211 Kuopio, Finland

Energies, 2021, vol. 14, issue 4, 1-16

Abstract: Owing to the ever-growing impetus towards the development of eco-friendly and low carbon footprint energy solutions, biodiesel production and usage have been the subject of tremendous research efforts. The biodiesel production process is driven by several process parameters, which must be maintained at optimum levels to ensure high productivity. Since biodiesel productivity and quality are also dependent on the various raw materials involved in transesterification, physical experiments are necessary to make any estimation regarding them. However, a brute force approach of carrying out physical experiments until the optimal process parameters have been achieved will not succeed, due to a large number of process parameters and the underlying non-linear relation between the process parameters and responses. In this regard, a machine learning-based prediction approach is used in this paper to quantify the response features of the biodiesel production process as a function of the process parameters. Three powerful machine learning algorithms—linear regression, random forest regression and AdaBoost regression are comprehensively studied in this work. Furthermore, two separate examples—one involving biodiesel yield, the other regarding biodiesel free fatty acid conversion percentage—are illustrated. It is seen that both random forest regression and AdaBoost regression can achieve high accuracy in predictive modelling of biodiesel yield and free fatty acid conversion percentage. However, AdaBoost may be a more suitable approach for biodiesel production modelling, as it achieves the best accuracy amongst the tested algorithms. Moreover, AdaBoost can be more quickly deployed, as it was seen to be insensitive to number of regressors used.

Keywords: biodiesel; machine learning; linear regression; random forest regression; AdaBoost regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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