The paper presents the implementation of a model based predictive control algorithm of an industrial drying process for Viscose staple fibres. A short review of the earlier published modelling procedure presents the linearised multi-input multi-output (MIMO) state space formulation of the design model. The design of the predictive control scheme incorporates constraints on the manipulated variables, set-points for the manipulated variables, and active disturbance rejection for the main disturbance variables of the drying process. Some simulation results demonstrate the beneficial effects of this structure, and the choice of proper weightings is also briefly discussed. Implementation issues and various measured results are presented. The problem of redundant but biased measurements of fibre humidity is addressed and the superior performance of the new concept compared to the existing PID-control is demonstrated. Both energy consumption and variation of humidity in the final product are significantly reduced by the successful implementation.

A dynamic model of a through-air-drying process for viscose staple fibres is presented in this article. In this process fibres formed to a porous web are transported through a convective dryer that consists of numerous rotating drum sieves. Finally, the fibres pass through two remoistening drums. The structure of the model is modular and scalable. On applying spatial discretization the originally partial differential system equations (conservation of mass and energy) turn into a system of ordinary differential equations. Drying rates and heat transfer rates are calculated using phenomenological equations for heat and mass transfer. Kinetics of drying is separated into three phases, where viscose fibres are hygroscopic. The process model is able to simulate transient behaviour of the dryer like changes of the incoming fibre moisture, changes of the drying air temperature and humidity and changes of the thickness of fibre layer on the drums. Stationary validation of the longitudinal fibre moisture distribution along the dryer shows good accordance with measurement data at different operating points, for example, different temperature profiles. Dynamic data like temperature transients are utilized for both model fitting and validation of the dynamic model. For the remoistening process and disturbance behaviour concerning the thickness of the fibre web, black box models have been identified. Results of a successful application of the model in a predictive control algorithm are shown.

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