Angeles Blanco, Erik Dahlquist, Johannes Kappen, Jussi Manninen, Carlos Negro, and Risto Ritala
CONTENTS
12.1 Introduction…………………………………………………………………………………………….. 311
12.2 Modelling and Simulation of Pulp and Paper Production……………………….. 312
12.2.1 Availability of Data as an Important Success Factor……………… 313
12.2.2 Methods for the Analysis of Data……………………………………………. 313
12.2.3 Modelling and Simulation Software………………………………………… 314
12.2.4 Off-Line Use of Simulation and Simulation-Based Optimisation..315
12.2.5 Online Use of Simulation and Simulation-Based Optimisation.. 317
12.3 Human Software and Machine Interaction Needs…………………………………. 321
12.4 Research Needs……………………………………………………………………………………….. 322
12.5 Conclusions…………………………………………………………………………………………….. 323
References and Further Reading……………………………………………………………………….. 323
12.1 INTRODUCTION
Although the pulp and paper industries have used balancing calculations and process control for a very long time, the scope of modelling and simulation applications is not yet as complete as it could be. One reason is that we seldom have steady-state conditions and we lack adequate measurements for on-line control for many quality variables. Also the final quality of paper depends on many process elements in a complicated and nonlinear way. Control actions for these processes are usually based on skill and experience of the operators. There is a need to improve paper machine performance to increase the competitiveness of the mills. In turn, this makes necessary the application of operator decision-support tools based on dynamic optimisation in order to allow process operators and engineers to manage complex dynamic problems in an efficient and ecologically sustainable manner.
New techniques, such as multivariate statistics, soft-sensors, that is, regression algorithms made from process signals and lab measurements of quality variables, and general stochastic distribution modelling and control in combination with physical models will be used to enhance the controllability of the process. By using and combining these techniques, it will be possible to make further improvements in the papermaking processes. Some of these techniques have already been explored in the pulp and paper industry for data analysis tools, improved process efficiency, development of soft sensors, and stochastic distribution-control algorithms. However, the complexity of the papermaking process makes it difficult to achieve robust models that can be used without frequent work with updating. Producing robust models is therefore a key issue for researchers and suppliers of control systems.
As this is a key issue for the industry, the COST Action E36 ‘Modelling and Simulation in the Pulp and Paper Industry’ (COST, 2005) was established in order to facilitate the coordination of activities and the exchange of knowledge at the European level. The main objective of the COST Action is to advance the development and application of simulation techniques in the pulp and paper manufacturing processes in order to minimise the environmental impact and to increase mill productivity and cost-competitiveness. Among the benefits will be a better understanding of the operation of the processes and their involved control loops. At the current time, the paper industry has a number of pending issues such as increasing stability, improving product performance, achieving higher paper quality, optimising wet end chemistry, better runnability, and lowering environmental impact.
At this moment, 50 researchers from more than 20 organisations from 12 countries participate in the Action. There is also close cooperation with industrial and academic research partners. The following aspects have been considered: available modelling and simulations tools, current use of these tools off-line and online, recommendations on the exchange of know-how contained in models, research needs, and main requirements for further software development. There is also a book entitled The Use of Modelling and Simulation in the Pulp and Paper Industry, (Dahlquist, 2008).
