Over the past few years, with the advent of Industry 4.0, it is noticeable how the Digital Twin concept fits well to fulfill the required needs of increasing efficiency, productivity, and automation. Optimization in Industry 4.0 is important to reduce costs, energy waste and increases productivity, thereby enabling more sustainable processes and environmental benefits. The introduction of Artificial Intelligence in Digital Twins helps create data-driven modeling approaches to monitor, simulate, predict, and optimize visualized entities and contribute to their continuous improvement. This paper reports an approach for the design of a Digital Twin and Artificial Intelligence used to optimize a decanter centrifuge. The case study uses centrifugal machinery as a reference and outlines a data-driven approach to model the system and determine the optimal working parameters.