Abstract
AbstractA new algorithm for the continuous tracking of the optimal economic operating conditions of an integrated chemical plant has been developed. The method does not make use of fundamental models of the plant but is based on an on‐line search using experimental moves of the independent variables. The results of these moves provide the data for the identification of a dynamic process model. This model allows one to determine the gradient of the objective function and thus guides the next move of the independent variable eventually leading to the optimum. The advantages of the new technique over other methods are demonstrated in simulation examples: The speed of tracking is equally fast or faster than the relaxation speed of the system after a step change. The noise insensitivity allows safe tracking of the optimum despite significant measurement errors. As a key feature a decentralized form of the algorithm is developed making it suitable for distributed microprocessor implementation.
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