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Ahmad, Junaid, Awais, Muhammad 
ORCID: https://orcid.org/0000-0001-6421-9245, Rashid, Umer, Ngamcharussrivichai, Chawalit, Naqvi, Salman Raza and Ali, Imtiaz
(2023)
A systematic and critical review on effective utilization of artificial intelligence for bio-diesel production techniques.
Fuel, 338.
ISSN 0016-2361
Abstract
Since industrial development and globalization of the world, fossil fuels remain a major source of energy for almost all sectors of life. Fossil fuels, without doubt, play a vital role in the development of today’s world. However, there are increasing reservations about the consumption of fossil fuels due to their detrimental impact on our ecosystem. In recent decades, biodiesel has attracted much attention as a promising replacement for fossil fuel-based diesel, especially in the transportation sector. Biodiesel is a non-toxic, environmentally friendly, and carbon-free fuel that can be made from any kind of vegetable oil or fat. In recent developments, numerous techniques have been developed to efficiently prepare biodiesel from oil/fats. Therefore, in this paper, we cover a new advancement in techniques used for the conversion of oil/fat to biodiesel and the role of artificial intelligence (AI). AI approaches help predict the effectiveness of biodiesel production techniques and optimize the process, in addition to minimizing the cost of the process. The AI-enabled biodiesel prediction methods consist of several stages, i.e., biodiesel data collection, biodiesel data preprocessing, developing, and tuning machine learning (ML) algorithm on biodiesel data, and predicting unknown biodiesel properties. Therefore, the main purpose of applying AI to the biodiesel production process is to improve the process optimization and to develop AI models for fuel properties measurements and ultimately reduce the cost of the product. Because these problems have not been addressed previously, we hope that our analysis will help future researchers identify the appropriate technique and feedstock to produce high-quality biodiesel.
| Item Type: | Article |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Depositing User: | LivePure Connector |
| Date Deposited: | 25 Nov 2023 03:18 |
| Last Modified: | 10 Dec 2024 01:42 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/93752 |
| DOI: | 10.1016/j.fuel.2022.127379 |
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