Educational applications of neural networks using Excel® for Chemical Engineering and Biochemistry
Keywords:
Applied Artificial Intelligence, Neural Network Models, Optimization in Engineering, Algebraic Models.Abstract
Currently, applications of artificial neural networks can be found in various fields due to the rapid development of information technology, ushering in a new era of System Identification (SI) that encompasses every aspect of our society. This paper aims to give students an overview of general methodologies to help them understand System Identification and its potential applications in chemical and biochemical engineering. So, it explains the basics of Artificial Neural Network algorithms using spreadsheet software (Microsoft Excel®). The handling of experimental results, the construction models with algebraic equations, use of mathematical minimization functions and models, and the use of the optimization tool (SOLVER™) were employed to convey the essential concepts. This procedure was applied in a didactic manner to various case studies using static-feedforward neural networks, dynamic-feedback neural networks, continuous differential dynamic neural networks (black box model), and complementary hybrid neural networks (grey box models). These models require information through input data and generate an output that describes complex and nonlinear behaviors. This work introduces students to the use of basic artificial intelligence models applied to chemical and biochemical engineering.
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