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Chizoo E, Chimamkpam AS, Gabriel CE, Gerald U. Adaptive neuro-fuzzy inference system-genetic algorithm versus response surface methodology-desirability function algorithm modelling and optimization of biodiesel synthesis from waste chicken fat. J Taiwan Inst Chem Eng 2022. [DOI: 10.1016/j.jtice.2022.104389] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Biodiesel production from mixed oils: A sustainable approach towards industrial biofuel production. CHEMICAL ENGINEERING JOURNAL ADVANCES 2022. [DOI: 10.1016/j.ceja.2022.100284] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
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Facile and low-cost synthesis route for graphene deposition over cobalt dendrites for direct methanol fuel cell applications. J Taiwan Inst Chem Eng 2020. [DOI: 10.1016/j.jtice.2020.10.019] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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State of the Art of Machine Learning Models in Energy Systems, a Systematic Review. ENERGIES 2019. [DOI: 10.3390/en12071301] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.
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Choo MY, Oi LE, Show PL, Chang JS, Ling TC, Ng EP, Phang SM, Juan JC. Recent progress in catalytic conversion of microalgae oil to green hydrocarbon: A review. J Taiwan Inst Chem Eng 2017. [DOI: 10.1016/j.jtice.2017.06.028] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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