Integration of Artificial Intelligence into Industrial Process Automation Systems

Abstract

The article investigates the introduction of artificial intelligence into the automation of industrial processes, which is a key area of digital transformation of production. It has been established that the use of machine learning algorithms, neural networks, computer vision, and cognitive computing helps increase productivity, optimise resource management, and minimise human intervention in technological operations. The article aims to analyse the role of artificial intelligence in the automation of industrial processes and develop approaches to its effective integration to increase the productivity and adaptability of production systems. To achieve this goal, the article uses system analysis to determine the impact of artificial intelligence (AI) on industrial automation, comparative analysis methods to assess existing approaches to the implementation of intelligent technologies, predictive modelling to assess the efficiency of autonomous systems and a structural-functional approach to developing an integration model. The main problems of introducing artificial intelligence into industrial automation include the high integration cost, the complexity of adapting AI algorithms to changing production conditions, the need to modernise technological infrastructure, and cybersecurity risks. It is proved that the effectiveness of integrating artificial intelligence into production processes largely depends on the choice of an appropriate approach: centralised, decentralised or hybrid. The article proposes a conceptual model of such integration, which includes a sensor level of data collection, an analytical level of processing, and a managerial level of decision-making, which allows for the management of production systems to be optimised in real time. The article formulates recommendations for the effective implementation of artificial intelligence in industrial processes, including analyzing an enterprise's technological capabilities, adapting algorithms to the specifics of production, ensuring cybersecurity, and training personnel to work with intelligent technologies. Prospects for further research include improving adaptive machine-learning methods, creating self-learning AI systems, optimising the interaction of autonomous AI modules with centralised management platforms, and developing methods for assessing the effectiveness of artificial intelligence in automated production environments.