Deep Learning for Process Optimization

Data-driven, intelligent, and adaptable approaches to complicated problem-solving have been made possible by deep learning (DL), which has completely changed the field of process optimization. This chapter examines how DL approaches can be used with conventional optimization techniques to improve productivity, quality, and decision-making in various fields, including supply chain management, manufacturing, energy systems, and industrial operations. Important DL models are examined for their uses in process control, anomaly detection, and predictive modeling. These models include transformers, autoencoders, recurrent neural networks, and convolutional neural networks. The potential of sophisticated methods such as surrogate modeling and reinforcement learning in achieving dynamic optimization and reducing computing costs is emphasized. The chapter also discusses issues such as interpretability, computational complexity, and data needs while outlining new approaches using edge computing, explainable artificial intelligence, and hybrid models. This research integrates theory and experience to present a comprehensive application of DL for transformational process improvement.