Conclusion

The integration of AI and big data processing methods into the field of process and product engineering represents a significant advancement. These technologies enable the rapid modeling of complex phenomena and the automatic resolution of problems that are difficult to formulate. By leveraging techniques such as supervised, unsupervised, and deep learning, it is possible to implement effective modeling and optimization approaches across a very wide range of applications, from energy cycles to pharmaceutical production. However, the application of these methods is not limited to process modeling and optimization. They are also applied to the design of new materials and products with targeted properties, as well as to process monitoring, control, and maintenance. Clustering and dimensionality reduction methods enable the detection of hidden trends or anomalies; regression models accelerate...