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    Continuous Control of Complex Chemical Reaction Network with Reinforcement Learning

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    Type
    Conference Paper
    Authors
    Alhazmi, Khalid
    Sarathy, S. Mani
    KAUST Department
    Chemical Engineering
    Chemical Engineering Program
    Clean Combustion Research Center
    Combustion and Pyrolysis Chemistry (CPC) Group
    Physical Science and Engineering (PSE) Division
    Date
    2020-05
    Permanent link to this record
    http://hdl.handle.net/10754/664464
    
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    Abstract
    The goal of process control is to maintain a process at the desired operating conditions. Disturbances, measurement uncertainties, and high-order dynamics in complex and highly integrated chemical processes pose a challenging control problem. Even though advanced process controllers, such as Model Predictive Control (MPC), have been successfully implemented to solve hard control problems, they are difficult to develop, rely on a process model, and require high performance computers and continuous maintenance. Reinforcement learning presents an appealing option for such complex systems, but little work has been done to apply reinforcement learning in chemical reactions with practical significance, to discuss the structure of the RL agent, and to evaluate the performance against benchmark measures. This work (1) applies a state-of-the-art reinforcement learning algorithm (DDPG) to a network of reactions with challenging dynamics and practical significance. (2) Disturbances and measurement uncertainties have been simulated. In addition, (3) we defined an observation space that is based on the working concept of a PID controller, optimized the reward function to achieve the desired controller performance, and evaluated the performance of the RL controller in terms of setpoint tracking, disturbance rejection, and robustness to parameter uncertainties.
    Citation
    Alhazmi, K., & Sarathy, S. M. (2020). Continuous Control of Complex Chemical Reaction Network with Reinforcement Learning. 2020 European Control Conference (ECC). doi:10.23919/ecc51009.2020.9143688
    Publisher
    IEEE
    Conference/Event name
    2020 European Control Conference (ECC)
    ISBN
    9783907144022
    DOI
    10.23919/ecc51009.2020.9143688
    Additional Links
    https://ieeexplore.ieee.org/document/9143688/
    ae974a485f413a2113503eed53cd6c53
    10.23919/ecc51009.2020.9143688
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