A Data Driven Approach to Process Control Modelling Using Machine Learning

The optimization of process control loops has long been a critical aspect of industrial operations. In recent years, the landscape of process optimization has witnessed significant advancements driven by the integration of machine learning and artificial intelligence technologies. These innovations have opened new avenues to enhance model performance and accuracy, offering opportunities to revolutionize traditional optimization approaches. Traditionally, process optimization has adhered to well-established frameworks and methodologies. However, the rapid evolution and diversification of available tools and techniques have ushered in a transformative era. Despite the proven effectiveness of conventional methods, they often fall short in harnessing critical benefits, such as reduced processing time and enhanced efficiency. This paper advocates for exploring alternative approaches to process optimization by harnessing readily available machine learning and artificial intelligence techniques, which have the potential to deliver expedited and precise results using operational data. These advanced models meticulously analyze process data and are rigorously evaluated, allowing for a direct comparison with existing optimization results. Consequently, this comparison aids in the identification of the most suitable model for the given process. As a result, the proposed model not only achieves heightened accuracy but also facilitates improved feature engineering. This leads to better control and enhanced product quality, ultimately driving increased production efficiency and profitability. By embracing the evolving landscape of process optimization, industries can remain competitive and adaptive in today's dynamic manufacturing environments.