Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Version 1 : Received: 8 August 2020 / Approved: 2 September 2020 / Online: 2 September 2020 (07:56:48 CEST)

How to cite: Ahmad, I.; Kano, M.; C. Menezes, B.; Iqbal Cheema, I.; Sana, A.; Shahzad, J.; Ullah, Z.; Khan, M.; Habib, A. Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions. Preprints 2020, 2020090033 (doi: 10.20944/preprints202009.0033.v1). Ahmad, I.; Kano, M.; C. Menezes, B.; Iqbal Cheema, I.; Sana, A.; Shahzad, J.; Ullah, Z.; Khan, M.; Habib, A. Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions. Preprints 2020, 2020090033 (doi: 10.20944/preprints202009.0033.v1).

Abstract

Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. The purpose of the present study is to review the state-of-the-art ML applications in the biofuels' life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.

Subject Areas

Bio-energy; Artificial intelligence; Industry 4.0; Biodiesel; Biogas; Renewable energy; Supply Chain

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.