APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN CHEMISTRY AND CHEMICAL ENGINEERING

Abstract

Problems in chemistry and chemical engineering are composed of complex systems. Various chemical processes in chemistry and chemical engineering can be described by different mathematical functions as, for example, linear, quadratic, exponential, hyperbolic et al. There are many of calculated and experimental descriptors/molecular properties to describe the chemical behavior of the substances. It is also possible that many variables can influence the desired response. Usually, chemometrics is widely used as a valuable tool to deal chemical data, and to solve complex problems. In this context, Artificial Neural Networks (ANN) is a chemometric tool that may provide accurate results for complex and non-linear problems that demand high computational costs. The main advantages of ANN techniques include learning and generalization ability of data, fault tolerance and inherent contextual information processing in addition to fast computation capacity. Due to the popularization, there is a substantial interest in ANN techniques, in special in their applications in various fields. The following types of applications are considered: data reduction using neural networks, overlapped signal resolution, experimental design and surface response, modeling, pattern recognition, and multivariate regression.