Optimization the Process of Catalytic Cracking Using Artificial Neural Networks

Abstract

Industrial production is one of the promising areas of application of artificial neural networks (ANN). There is a tangible trend towards manufacturing modules with a high level of automation in this area, which requires an increase in the number of intelligent self-regulating and self-adjusting objects. However, industrial processes are characterized by a large variety of dynamically interacting parameters, which complicate the creation of adequate analytical models. Modern industrial production is constantly becoming more complicated. This slows down the introduction of new technological solutions. In this regard, there is an increasing interest in alternative approaches to modeling industrial processes using ANN, which provide the possibility to create models that operate in real time with small errors that can be trained in the process of use. The advantages of neural networks make their use attractive for solving problems such as: forecasting, planning, designing of automated control systems, quality management, manipulator and robotics management, process safety management: fault detection and emergency situations prevention, process management: optimization of industrial process regimes, monitoring and visualization of supervisory reports. Neural networks can be useful in industrial production, for example, when creating an enterprise risk management model, planning a production cycle. Modeling and optimization of production is characterized by high complexity, a large number of variables and constants, defined not for all possible systems. Traditional analytical models can often be built only with considerable simplification, and they mostly have evaluative nature. While the ANN is trained on the basis of data from a real or numerical experiment.