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Experimental and artificial intelligence for determination of stable criteria in cyclic voltammetric process of medicinal herbs for biofuel cells
Author(s) -
Shaosen Su,
Chen Dezhi,
Srinivasan Kathiravan,
Chen BorYann,
Meijuan Xu,
Garg Akhil,
Gao Liang,
Sandoval Jayne
Publication year - 2019
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.4634
Subject(s) - reticulate , biological system , syzygium , artificial neural network , process (computing) , artificial intelligence , voltage , computer science , mathematics , engineering , biology , botany , electrical engineering , operating system
Summary This study proposed an expert system approach on the basis of artificial intelligence (AI) in the modeling of cyclic voltammogram (CV) profiles of green tea extracts. AI approach of artificial neural networks is applied to generate the model phase‐plane portraits of current output versus applied voltage through CV scan cycles. The predicted current values were validated using experiments, and generic ability of approach was examined by testing on the CV scan cycles generated from Syzygium aromaticum and Citrus reticulate . It was concluded that AI approach can be employed to reveal stable point (cycle and voltage) in CV profiles for bioenergy applications.