Applicability and Trend of the Artificial Intelligence (AI) on Bioenergy Research between 1991–2021: A Bibliometric Analysis

Abstract

The bibliometric analysis investigated the impact of publications on trends in the literature and bioenergy research using artificial intelligence (AI) from 1991 to 2021. In this study, 1721 publications were extracted from the Web of Science, and an analysis of the countries, authorship, institutions, journals, and keywords was visualised. In the recent decades, this field has entered an outbreak phase. India was the most productive country in this area, followed by China, Iran, and the US. It also noted several notable differences between trends and subjects in developed and developing countries. The former led this field at the initial stage and later attached importance to using AI for research feedstock and impact assessment. Developing countries encouraged the advancement of this area and emphasised the feedstock usage of phase treatment and process optimisation. In addition, a co-authorship and institutes study revealed that authors and institutes in distant regions rarely collaborated. The journal analysis shows strong links between Energy, Fuel, and Energy Conversion and Management. Machine learning is by far the most common application of artificial intelligence (AI) technology in bioenergy research, with 53% of the articles using it. In these AI-related publications, the keyword artificial neural network (ANN) appeared most frequently in the articles.

Publication DOI: https://doi.org/10.3390/en16031235
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Chemical Engineering & Applied Chemistry
College of Engineering & Physical Sciences > Energy and Bioproducts Research Institute (EBRI)
College of Engineering & Physical Sciences > Aston Institute of Materials Research (AIMR)
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Advanced Materials
Additional Information: Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Funding Information: The research was funded by The European Research Executive Agency (H2020-MSCA-IF-2020, No. 101025906) and Guangdong Science and Technology Program (No. 2021A0505030008).
Uncontrolled Keywords: ANN,artificial intelligence,bibliometric analysis,bioenergy,web of science,Renewable Energy, Sustainability and the Environment,Building and Construction,Fuel Technology,Engineering (miscellaneous),Energy Engineering and Power Technology,Energy (miscellaneous),Control and Optimization,Electrical and Electronic Engineering
Publication ISSN: 1996-1073
Last Modified: 25 Apr 2024 07:20
Date Deposited: 03 Feb 2023 10:01
Full Text Link:
Related URLs: https://www.mdp ... -1073/16/3/1235 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Review article
Published Date: 2023-02
Published Online Date: 2023-01-23
Accepted Date: 2023-01-19
Authors: Cheng, Yi
Zhao, Chuzhi
Neupane, Pradeep
Benjamin, Bradley
Wang, Jiawei (ORCID Profile 0000-0001-5690-9107)
Zhang, Tongsheng

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