Process Optimization

Process optimization aims to enhance efficiency and resource utilization across diverse industrial settings, from semiconductor manufacturing to chemical production and additive manufacturing. Current research heavily emphasizes data-driven approaches, employing machine learning models like neural networks (including deep learning architectures such as LSTMs, CNNs, and Transformers), reinforcement learning, and Bayesian optimization to analyze process data, predict outcomes, and optimize control parameters. These advancements are significantly impacting various sectors by improving production efficiency, reducing waste, and enabling more sustainable operations, particularly in carbon-neutral initiatives and resource-intensive industries.