Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes
Researchers have developed a new reinforcement learning (RL) approach called Y-wise Affine Neural Network (YANN-RL) designed for control in chemical process systems. This method aims to overcome the typical challenges of trust and lengthy training times associated with RL in this domain. By providing confident and interpretable starting points for control schemes, YANN-RL demonstrated reduced training time and data requirements in case studies involving a CSTR, a four-tank system, and an extraction column. AI
IMPACT This new RL approach could accelerate AI adoption in chemical engineering by reducing training time and increasing trust in AI control systems.