Preprint Review Version 1 This version is not peer-reviewed

Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research

Version 1 : Received: 15 August 2019 / Approved: 17 August 2019 / Online: 17 August 2019 (03:48:28 CEST)

How to cite: Faizollahzadeh Ardabili, S.; Mosavi, A.; R. Várkonyi-Kóczy, A. Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research . Preprints 2019, 2019080179 (doi: 10.20944/preprints201908.0179.v1). Faizollahzadeh Ardabili, S.; Mosavi, A.; R. Várkonyi-Kóczy, A. Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research . Preprints 2019, 2019080179 (doi: 10.20944/preprints201908.0179.v1).

Abstract

Biofuels construct an essential pillar of energy systems. Biofuels are considered as a popular resource for electricity production, heating, household, and industrial usage, liquid fuels, and mobility around the world. Thus, the need for handling, modeling, decision-making, demand, and forecasting for biofuels are of utmost importance. Recently, machine learning (ML) and deep learning (DL) techniques have been accessible in modeling, optimizing, and handling biofuels production, consumption, and environmental impacts. The main aim of this study is to review and evaluate ML and DL techniques and their applications in handling biofuels production, consumption, and environmental impacts, both for modeling and optimization purposes. Hybrid and ensemble ML methods, as well as DL methods, have found to provide higher performance and accuracy in modeling the biofuels.

Subject Areas

biofuels; deep learning; big data; machine learning models; biodiesel

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