Review on Artificial Neural Network and its Application in the Field of Engineering

Abstract

The engineers have been utilising artificial neural networks (ANNs), one of the most effective and adaptable tools offered by artificial intelligence, for many years in a variety of applications. A simple mathematical model of brain functions is provided by ANNs, which are computational tools. They can be used for tasks like modelling, categorisation, and prediction when combined with raw data and a learning system. They have recently experienced a sharp increase in popularity and are currently among the most important study topics in the disciplines of artificial intelligence and machine learning. Large groups of basic classifiers known as neurons make up an ANN. Chemical engineers use them to automate process controls, model complex linkages, and forecast reactor performance. Large data sets can benefit from ANNs' capacity for learning, but these systems can also overfit or become stuck in local minima and are challenging to reverse engineer. The function of artificial neural networks (ANNs) in chemical engineering is explored in this article. For creating chemical engineering processes, the ANN is quite helpful. The process is very quick and trustworthy. We also gathered several journal publications and current research articles, summarising the use of ANN in various chemical engineering fields and its processes.