Abstract
Industrial processes are measured and controlled using high-dimensional process variables, but its overall operation is usually characterised by low-dimensional patterns. The changes in the pattern are dominated by three features: free motion, controlled motion, and uncertainty. In this paper, all three features are taken into consideration to propose a new probabilistic dynamic-controlled latent variable (PDCLV) model structure using a dynamic Bayesian network for process modelling in the pattern space. To this end, the linear dynamic system characterised by control inputs is introduced, and the expectation maximisation algorithm is specially designed for learning the PDCLV model. Benefitting from the dynamic causality between control inputs and the explicit modelling of the pattern, a method for pattern-based stochastic model predictive control (SMPC) is implemented successfully to realise process optimisation. A case study on an industrial boiler combustion process demonstrates the benefits of the proposed PDCLV structure for pattern-space modelling and pattern-based SMPC.
Published Version
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