Environmental Impact Assessment by Green Processes

Giuliano, Aristide; Errico, Massimiliano; Salehi, Hamid; Avino, Pasquale

doi:10.3390/ijerph192315575

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…The primary energy consumption on earth has been increasing since the industrial revolution, which has created serious social problems such as environmental pollution and global climate change [1]. Developing clean and renewable energy sources and conversion techniques is becoming a necessity for the survival of human beings.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Fuel Cell Research

Zheng

et al. 2023

Energies

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A fuel cell is an energy conversion device that utilizes hydrogen energy through an electrochemical reaction. Despite their many advantages, such as high efficiency, zero emissions, and fast startup, fuel cells have not yet been fully commercialized due to deficiencies in service life, cost, and performance. Efficient evaluation methods for performance and service life are critical for the design and optimization of fuel cells. The purpose of this paper was to review the application of common machine learning algorithms in fuel cells. The significance and status of machine learning applications in fuel cells are briefly described. Common machine learning algorithms, such as artificial neural networks, support vector machines, and random forests are introduced, and their applications in fuel cell performance prediction and optimization are comprehensively elaborated. The review revealed that machine learning algorithms can be successfully used for performance prediction, service life prediction, and fault diagnosis in fuel cells, with good accuracy in solving nonlinear problems. Combined with optimization algorithms, machine learning models can further carry out the optimization of design and operating parameters to achieve multiple optimization goals with good accuracy and efficiency. It is expected that this review paper could help the reader comprehend the state of the art of machine learning applications in fuel fuels and shed light on further development directions in fuel cell research.

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Section: Introductionmentioning

confidence: 99%