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The choice of a pump schedule has a great influence on the cost efficiency of a system, when the energy consumption charge changes during the day. The pump scheduling problem for a simplified regional water network is considered, and the formulation of the problem as a Markov decision process is given. The experimental section shows computer simulations and compares the results with a novel evolutionary approach based on neutral genetic algorithm techniques.

In this study, the fault tolerant Kappa number control in continuous cooking application is investigated. The Kappa number is one of the quality measures in the pulp cooking process and usually the only on-line measurement. It is a measure of the residual lignin content in the pulp. The Kappa number is mainly controlled by the cooking temperature. In this study, Kappa number control (temperature control) is carried out using Gustafson's Kappa number model for the prediction of the blowline Kappa number. New temperature set point is solved iteratively based on the difference between the predicted and target blow-line kappa numbers. The input variables are monitored using self-organizing map (SOM). The data is collected from industrial conventional continuous cooking digester. Good results were achieved using the proposed approach.

Typically in pulp and paper processes raw material quality variation due to seasonal deviations as well as measurement drifts cause difficulties in setting the tight alarm thresholds for quality and control measurements. By using adaptive thresholds, more sensitive measurement range and thus reduced quality variation can be achieved. In this paper, new adaptive classification algorithm is proposed and validated using simulated and real mill data.

Two industrial continuous cooking processes producing both softwood and hardwood pulp were studied. Real time kappa number profiles of conventional and Downflow Lo-Solids cooking were modelled. Gustafson's kappa number model with optimised parameters was used. Blow-line kappa numbers were successfully predicted before cooking zone. New kappa number control strategy is presented. In control strategy temperature set point is determined iteratively using prediction model of kappa number.

Industrial systems generate a lot of information for operators. Increased measurement information flow might cause difficulties for the process operators to observe the process states and faulty process operation. In this study, measurements and statistical variables are combined by fuzzy logic to generate key factors for several points in the continuous cooking digester. The overall diagnosis system combines the key factors into one system which is used for the operational purposes and as a helping tool for process condition monitoring.

In industrial digester control systems Kappa number models are often used. There are publications using both physical and experimental methods. In this study Kappa number is predicted in the top of the digester using both modelling techniques. Physical Kappa number model is taking into account the reaction kinetics and the experimental models are used in the prediction of the temperature and alkali profiles, which are used as inputs to the physical model. The comparison of the prediction is done with neural network model.

The aim of the research is to control the quality of the pulp and the functional condition of the refiners using statistical process control (SPC) and fuzzy modelling techniques. The on-line measurements and the SPC parameters are combined with the Mamdani type fuzzy models. The models and the “indexes” are used for the optimisation and the fault diagnosis purposes in the industrial scale pulp mill. The methodology is applied to the reject refining line in the thermo mechanical pulp (TMP). The preliminary results are promising and the methodology can be applied to all type of TMP mills.