AI Technology Autonomously Optimizes Complex Chemical Processes

Yokogawa's Vaaler Award-winning reinforcement learning algorithm reduces implementation time, balances plant objectives and achieves rapid learning in trials.

Factorial Kernel Dynamic Policy Programming, or FKDPP, a reinforcement learning AI developed by Yokogawa and the NARA Institute of Science and Technology and applied by Yokogawa to process industries is the first reinforcement learning AI to autonomously control complex chemical processes, FKDPP complements manual and conventional control methods like PID and advanced process control.

Karthik Gopalakrishnan, part of the digital transformation, smart manufacturing, artificial intelligence, cybersecurity and industrial automation team at Yokogawa, discusses the award-winning tech with EIC Traci Purdum