Data-Driven Control Algorithms for Process Optimization

Abstract

In the ever-evolving landscape of industrial process control, the reliance on data-driven control algorithms has emerged as a powerful solution for optimizing complex processes. Traditional model-based control approaches often struggle to keep pace with the complexity and variability inherent in modern industrial systems. Data-driven control algorithms, however, leverage vast amounts of real-time data to develop control strategies without relying on predefined mathematical models. This paper explores the role of data-driven control algorithms in process optimization, discussing their applications, advantages, and limitations in various industries such as manufacturing, energy, and chemical processing. The paper also highlights future research directions and emerging trends in the field, focusing on machine learning, adaptive systems, and real-time decision-making.