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Adaptive model predictive inventory controller for multiproduct batch plant
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DOI：10.1002/aic.14783.png)
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Abstract
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An inventory control system was developed for multiproduct batch plants with an arbitrary number of batch processes and storage units. Customer orders are received by the plant at order intervals and in order quantities that are subject to random fluctuations. The objective of the plant operation is to minimize the total cost while maintaining inventory levels within the storage or warehouse capacity by adjusting the startup times, the quantities of raw material orders, and production batch sizes. An adaptive model predictive control algorithm was developed that uses a periodic square wave model to represent the flows of the material between the processes and the storage units. The boundedness of the control output and the convergence of the estimated parameters in implementations of the proposed algorithm were mathematically proven under the assumption that disturbances in the orders are bounded. The effectiveness of this approach was demonstrated by performing simulations. (c) 2015 American Institute of Chemical Engineers AIChE J, 61: 1867-1880, 2015
Keywords:
adaptive model predictive control
multiproduct batch plant
periodic square wave model
batch-storage network
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