Off-LiNE Use of Simulation and Simulation-Based Optimisation

Perhaps the most typical applications of off-line simulation in pulp and paper pro­duction processes are various design problems starting from defining elementary mass and energy balances for a process concept with steady-state simulation to using dynamic simulation to integrated design of process and control (Kokko, 2002). As the design task progresses, the model fidelity increases but the scope of simulation often decreases from plant-wide models to subprocesses or individual unit opera­tions. Unit operation development is often supplemented by model development. In this application, models are not necessarily used for getting quantitative results but rather for increasing knowledge on the underlying phenomena behind the unit opera­tion. Examples include use of computational fluid dynamics (CFD) for head-box design (Hamalainen and Tarvainen, 2002) and wet-press models (Gustafsson and Kaul, 2001; Honkalampi and Kataja, 2002) for press section.

A second important application is operator training using simulators with all the process functions alone or normally also including the complete distributed control system (DCS). In this way the operators can train both to understand the processes as well as the interaction between the processes and the control system (see Chapter 4). The third application is to use modelling and simulation for production support and optimisation. Techniques and challenges for these applications are covered in detail in the next section on on-line simulation. Typical off-line applications include periodic tracking of material balances and process operation improvement, like opti­misation of grade changes (Lappalainen et al., 2003). Steady-state tools can be used to keep track of material balances, but for most applications dynamic simulation is the tool of choice. One recent application is maximising total profitability with dynamic production planning (Vanni and Launonen, 2005).

As design and operational problems are often multiobjective by nature, multiob­jective optimisation techniques have been lately applied to pulp and paper problems (Hakanen et al., 2004). These techniques allow optimising simultaneously against several criteria and finding balanced solutions without having to constrain some of the criteria a priori. One of the most important design and operation criteria is prod­uct quality, and models linking the process conditions to the end-product property are still very much under development. Statistical models have been built to estimate the effect of process conditions on the product properties, but due to their nature they are suited for optimisation of an existing pulp or paper production line where enough data can be collected for the models (Blanco et al., 2005; Scott and Shirt, 2001).

One of the complicating facts in defining mechanistic quality models is the com­plex chemistry of pulp and papermaking. Cooking and bleaching chemistry has been traditionally modelled with kinetic expressions (Andersson, Wilson, and Germgard, 2003), whereas multiphase equilibrium chemistry has been applied successfully lately for scale formation and metal management in pulp manufacturing (Bryant, Samuelsson, and Basta, 2003; Rasanen, 2003; Sundquist et al., 2004) as well as for pH control in neutral paper production (Ylen, 2001).

One of the recent joint efforts in the process modelling and simulation com­munity is to define standard interfaces for the various simulation software compo­nents, like solvers, unit operation models, and thermal packages (Computer-Aided Process Engineering [CAPE-OPEN] Laboratories Network, CO-LaN, 2005)). This development makes it, from the user’s point of view, possible to mix and match software components from various sources without having to purchase a complete set of software packages. From the vendor’s point of view, it obviously gives the pos­sibility to specialise in, for example, model development for the different platforms. Some major players in process simulation, like Aspen Plus, are already partially CAPE-OPEN compliant but to our knowledge no compliance yet exists in the pulp and paper software.

In addition to already referenced papers, some additional off-line applications of simulation models are: maximising total profitability with dynamic production planning by combining production planning and scheduling tools with cost manage­ment (Vanni and Launonen, 2005); optimal reutilisation of waste heat in paper mills (Kappen, Mueller, and Kamml, 2004); identification and removal of stickies (Kap – pen, Hamann, and Cordier, 2004), and so on.

It has to be noted that the above-mentioned examples have been solved using different types of models and tools, and so far no single general tool or set of models exists that can be used for every type of problem. Instead of attempting to build a general tool that would be nonoptimal for most applications, information and model transfer between the tools should be improved to avoid tedious redefining of data. Often process models are built for a specific purpose only—for example, process retrofit—and not utilised later. The biggest advantage of models would be obtained if they were utilised through the whole life-cycle of the plant. The plant model would be then built in the design phase of the project and subsequently used to:

• Check the dimensioning of equipment and the feasibility of control loops.

• Train operators on a system that is based on the simulation model.

• Verify DCS logic; perform DCS checkout based on the model connected to the DCS instead of the real process.

The benefit of this approach is a rather steep start-up curve providing the payback of an investment of US$500,000 to $3 million (Bogo, 2004). Further use could be made in using the model to check on-line sensors and identify the deviation from the set point of individual equipment or the process as a whole. This would call for the model run in real time in parallel to the process itself. A model that covers the
whole life-cycle of the plant would require a well-defined and uniform data structure behind it to incorporate both design and operational data and to communicate with process design tools, automation systems, data repositories, and so forth.

Updated: October 11, 2015 — 12:05 am