Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective
نویسندگان
چکیده
Owing to the ever-growing impetus towards development of eco-friendly and low carbon footprint energy solutions, biodiesel production usage have been subject tremendous research efforts. The process is driven by several parameters, which must be maintained at optimum levels ensure high productivity. Since productivity quality are also dependent on various raw materials involved in transesterification, physical experiments necessary make any estimation regarding them. However, a brute force approach carrying out until optimal parameters achieved will not succeed, due large number underlying non-linear relation between responses. In this regard, machine learning-based prediction used paper quantify response features as function parameters. Three powerful learning algorithms—linear regression, random forest regression AdaBoost comprehensively studied work. Furthermore, two separate examples—one involving yield, other free fatty acid conversion percentage—are illustrated. It seen that both can achieve accuracy predictive modelling yield percentage. may more suitable for modelling, it achieves best amongst tested algorithms. Moreover, quickly deployed, was insensitive regressors used.
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ژورنال
عنوان ژورنال: Energies
سال: 2021
ISSN: ['1996-1073']
DOI: https://doi.org/10.3390/en14041122