Plant-Wide Neurocontrol of the Tennessee Eastman Challenge Process Using Evolutionary Reinforcement Learning A.v.E. Conradie and C. Aldrich * Department of Chemical Engineering, University of Stellenbosch, Private Bag X1, Matieland, 7602, South Africa. Email: ca1@ing.sun.ac.za Abstract The Tennessee Eastman (TE) Control Challenge proposed by Downs and Vogel [1] is a test bed problem for use in evaluating advanced process control methodologies from a plant-wide perspective. The dynamic model for the process (based on an actual industrial process) integrates the operation of five unit operations; viz. an exothermic, two-phase reactor, a partial condenser, a centrifugal compressor, a flash drum and a reboiled stripper. The process incorporates 41 process variables and 12 manipulated variables. Several researchers have considered various controller designs for the process, which include multi-loop Single- Input-Single-Output (SISO) control strategies, Dynamic Matrix Control (DMC) and linear/nonlinear Model Predictive Control (MPC). These approaches entail a significant degree of design effort in that the selection of the optimal (economic) set points and the selection of the optimal pairing of process and manipulated variables, are non-trivial tasks. Furthermore, linear controller designs may not be optimal in dealing with the non-linear behaviour inherent in the process dynamics. The success of biological organisms in controlling complex and uncertain environments may serve as significant motivation for a more biological (as opposed to algorithmic) approach to developing control strategies. Evolutionary reinforcement learning provides a framework for the development of control policies from direct cause-effect interactions with a simulated dynamic environment. In this paper the use of a novel evolutionary reinforcement learning algorithm, SANE (Symbiotic, Adaptive Neuro-Evolution), is demonstrated for the development of neural network controllers. The SANE algorithm performs the global search for an optimal plant-wide control strategy. The SANE algorithm is a genetic algorithm based on implicit fitness sharing, which requires neurons in the genetic population to cooperate (to form a neural network) in performing the required control task. This cooperative approach ensures that genetic diversity is maintained in the genetic population, which favours a continued global search to the problem at hand. Also, several parallel searches on the neuron level for different aspects of the control solution should be more effective than a single search for the entire solution. The SANE algorithm offers several key advantages over conventional controller design approaches. Near optimal neurocontrollers were developed without prior knowledge of the optimal (economic) set points. Also, the pairing of process and manipulated variables and the elimination of control interactions were implicit in the SANE search process. All process variables and manipulated variables were available to SANE for neurocontroller development, whereas other approaches constrain the number of manipulated variables to be used for controller development. The neurocontroller was also developed utilising the open loop unstable process, whereas a nonlinear MPC design required prestabilisation of the plant with PI controllers. The robust and high performance of the neurocontrollers during set point changes and in the presence of disturbances, is verified. As shown for the Tennessee Eastman Control Challenge; evolutionary reinforcement learning may be used in process control to established a plant-wide control strategy by developing neural network controllers with minimal prior process analysis. Introduction Downs and Vogel [1] developed the Tennessee Eastman (TE) Control Challenge as a realistic process model for testing control methodologies. The Tennessee Eastman Control Challenge offers numerous