Solving process engineering problems using artificial neural networks

This chapter reveals the advantages of artificial neural networks (ANNs) by means of prediction success and effects on solutions for various problems. With this aim, initially, multilayer ANNs and their structural properties are explained. Then, feed-forward ANNs and a type of training algorithm called back-propagation, which was benefited for these type networks, are presented. Different structural design problems from civil engineering are optimized, and handled intended for obtaining prediction results thanks to usage of ANNs.

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Applications of Artificial Neural Networks in Bridge Engineering

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.243-249.1984 ◽

2011 ◽

Vol 243-249 ◽

pp. 1984-1987

Author(s):

Xing Wei ◽

Jun Li

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Life Prediction ◽

Bridge Engineering ◽

General View ◽

Bridge Health Monitoring ◽

Engineering Problems ◽

Artificial Neural ◽

Prediction Reliability ◽

Model Slope

Artificial neural networks (ANNs) have been widely applied to many bridge engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in member capacity prediction, reliability analysis, optimal design of structural systems, fatigue life prediction, construction control, material constitutive model , slope stability, bridge health monitoring. The objective of this paper is to provide a general view of some ANNs applications for solving some types of bridge engineering problems. A brief introduction to ANNs is given. Problems such as what is a neural network, how it works and what kind of advantages it has are discussed. After this, several applications in bridge engineering are presented.

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The Use of Artificial Neural Networks (ANN) in Food Process Engineering

ETP International Journal of Food Engineering ◽

10.18178/ijfe.5.1.15-21 ◽

2019 ◽

pp. 15-21 ◽

Cited By ~ 2

Author(s):

Raquel P. F. Guiné ◽

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Process Engineering ◽

Artificial Neural

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Using Artificial Neural Networks for Solving Engineering Problems

Proceedings of the Thirty-First International Matador Conference ◽

10.1007/978-1-349-13796-1_95 ◽

1995 ◽

pp. 635-640 ◽

Cited By ~ 2

Author(s):

Y. F. Lou ◽

P. Brunn

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Engineering Problems ◽

Artificial Neural

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Integrating knowledge-based systems and artificial neural networks for engineering

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060400002055 ◽

1995 ◽

Vol 9 (1) ◽

pp. 13-22 ◽

Cited By ~ 7

Author(s):

Nabil Kartam ◽

Ian Flood ◽

Tanit Tongthong

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Generalized Solution ◽

Knowledge Based Systems ◽

Knowledge Based ◽

Engineering Problems ◽

Scope Of Application ◽

Artificial Neural ◽

The Individual

AbstractThe feasibility and relative merits of integrating knowledge-based systems (KBSs) and artificial neural networks (ANNs) for application to engineering problems are presented and evaluated. The strength of KBSs lies in their ability to represent human judgment and solve problems by providing explanations from and reasoning with heuristic knowledge. ANNs demonstrate problem solving characteristics not inherent in KBSs, including an ability to learn from example, develop a generalized solution applicable to a range of examples of the problem, and process information extremely rapidly. In this respect, KBSs and ANNs are complementary, rather than alternatives, and may be integrated into a system that exploits the advantages of both technologies. The scope of application and quality of solutions produced by such a hybrid extend beyond the boundaries of the individual technologies. This paper identifies and describes how KBSs and ANNs can be integrated, and provides an evaluation of the advantages that will accrue in engineering applications.

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Application of Artificial Neural Networks to Chemical and Process Engineering

10.5772/intechopen.96641 ◽

2021 ◽

Author(s):

Fabio Machado Cavalcanti ◽

Camila Emilia Kozonoe ◽

Kelvin André Pacheco ◽

Rita Maria de Brito Alves

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Process Analysis ◽

Large Data ◽

Empirical Method ◽

Computational Effort ◽

Process Engineering ◽

Future Research ◽

Artificial Neural ◽

And Control

The accelerated use of Artificial Neural Networks (ANNs) in Chemical and Process Engineering has drawn the attention of scientific and industrial communities, mainly due to the Big Data boom related to the analysis and interpretation of large data volumes required by Industry 4.0. ANNs are well-known nonlinear regression algorithms in the Machine Learning field for classification and prediction and are based on the human brain behavior, which learns tasks from experience through interconnected neurons. This empirical method can widely replace traditional complex phenomenological models based on nonlinear conservation equations, leading to a smaller computational effort – a very peculiar feature for its use in process optimization and control. Thereby, this chapter aims to exhibit several ANN modeling applications to different Chemical and Process Engineering areas, such as thermodynamics, kinetics and catalysis, process analysis and optimization, process safety and control, among others. This review study shows the increasing use of ANNs in the area, helping to understand and to explore process data aspects for future research.

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