Yokogawa’s white paper highlights how reinforcement learning AI (FKDPP) enables autonomous chemical process control. Successfully tested at ENEOS Materials, the AI stabilized distillation operations with only 30 learning trials, cutting steam use and CO2 emissions by 40% while reducing costs for feedstock, fuel, and labor. Unlike traditional PID controls, it shortened settling time by 65% and extended equipment life by reducing overshoot. Integrated across edge, cloud, and 5G, the technology enhances safety, productivity, and sustainability. Its scalability positions it to transform industries such as energy and pharmaceuticals with strong ROI impact.

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