Dynamic simulation predicts steam consumption in unpredictable paper mill application

Langerbrugge mill used simulation analysis to make sure the boiler and steam system could remain stable even during the biggest disruption: a turbine trip.


Stora Enso’s Langerbrugge paper mill decided to install a new CFB boiler and a condensing turbine back in 2008. Pöyry was chosen as the consultant for the project. A vital part of Pöyry’s engineering is a dynamic simulation of the steam network, which wasStora Enso’s Langerbrugge (Belgium) paper mill decided to install a new CFB (circulating fluidized bed) boiler and a condensing turbine back in 2008. Pöyry was chosen as the consultant for the project, and assigned the task of engineering a dynamic simulation of the steam network, which would be used in the process of designing the new installation. This was a particularly complex project in that the mill had previously purchased steam from an outside supplier and this was a major change to generating and controlling its own steam supply. The underlying idea of using dynamic simulation to assist with the design process is that control specialists take part in the entire engineering cycle, ensuring that once the plant is started, the controls and process will be capable of handling all process disturbances anticipated, such as paper machine web breaks and turbine trips. This approach proved to be very successful in Langerbrugge, and this discussion explains how the process dynamics part of the engineering was carried out.

Dynamic vs. static simulations

In spite of the variety of design tools available today, it seems that much power plant design work is carried out using only static simulations, such as heat-balance calculations. While these are useful, static simulations typically assume that power-plant operating conditions, such as steam consumption, are completely stable at the given operation point. However, day-to-day power plant operation is filled with different kinds of disturbance situations. Since dynamic simulations have historically been very expensive to carry out, disturbances have not been tested during engineering. Even today, many elements of the control strategy are pieced together only during the commissioning, through trial and error.

Pöyry developed a dynamic steam-net simulator, Modysim, 10 years ago. The most important feature of Modysim is that the models are simple enough to carry out cost-efficient simulations, but detailed enough to provide accurate results. So far Modysim has been used in over 40 projects, and it has proven to produce accurate results within just a few days once the modeling process has begun. 

After Modysim had been used successfully in separate steam-network optimization projects for several years, in 2007, Pöyry decided to modernize its whole power-plant engineering process by adding Modysim simulation in all power-plant engineering phases. Stora Enso Langerbrugge was among the first ones to use this new approach in full.

Dynamic simulation gives a lot of input to process dimensioning, but pays particular attention to power plant control configuration. In Pöyry’s approach, the control specialists who carry out the simulation tests also supervise turbine and boiler control configuration and give assistance during commissioning, ensuring that the controls work properly from the first moment when the equipment is started. Experience has shown that if the recommendations obtained from the simulation have been followed correctly, steam-network control commissioning is typically over in just a few weeks instead of months.

Dealing with turbine trips

Before the new boiler project, a significant part of the Langerbrugge mill’s process steam was purchased from a nearby utility. After the new boiler was installed, the steam pipe to the utility was completely cut off. Since the mill’s process steam pressure had been controlled with a valve at the point where the utility supply came into the mill, the whole steam network control concept had to be redesigned.

Even while the new power-plant concept was still under development, it was obvious that a turbine trip would cause challenges for the operation. Therefore, Stora Enso was very keen on seeing how well Pöyry and the Modysim simulation of the steam network could check the process dimensioning and control behavior during a turbine trip.

The existing pressure control scheme was developed by Pöyry in 2003, so the Langerbrugge staff was already familiar with Pöyry’s method of building an integrated control scheme and combining independent control systems. The purpose of the dynamic simulation was not only to check the process dimensioning, but also to connect the new and existing controls together. 

Figure 1. Langerbrugge’s old BFB plant and new CFB plant comprise a somewhat complex steam network, with many different process components affecting each other via the net. Courtesy: Pöyry

Process dimensioning checks

Dynamic simulation provides a way of checking process dimensioning. Typically, valve capacities, actuator stroke times, and accumulator volume are checked by feeding process disturbances, such as paper machine web breaks, into the model. Naturally, in the Langerbrugge case, the existing power-plant process could not be altered. However, at the new CFB plant, a turbine trip provided a lot of challenge.

Initially the bypass to the turbine condenser was to be connected from the HP (high-pressure) header, mainly due to the fact that, for cost reasons, the turbine bypass valve was initially dimensioned only for the minimum load of the new condensing turbine, not for the maximum load as might be expected. However, a more cost-efficient way would be to connect the bypass from the LP (low-pressure) header. The question was whether the steam network could handle a turbine trip in this way.

The capacities and opening times of the turbine bypass, turbine condenser bypass, and CFB start valves were studied as critical elements of the process.

Dynamic simulation and results

When a dynamic simulation model is running, results come as precise dynamic curves where one can easily see if the selected capacities, actuator speeds, and component connections are enough for the steam network to survive the selected situations. In this case the selected situations were a paper machine web break and new condensing turbine trip.  

Figure 2: Partial Modysim model of the Langerbrugge mill

The simulation results indicated several operational facts:

  • During normal operation, the new condensing turbine was able to control low pressure during the worst disturbances, such as web breaks.
  • The bypass to the turbine condenser could be connected to the low-pressure header instead of the high-pressure network, resulting in a more cost-efficient solution.
  • To make the power plant survive a turbine trip, the turbine and condenser bypass valves had to be opened very quickly, and the start-up valve had to be equipped with a fast pneumatic actuator.
  • The turbine control system needed some modifications to function well with the existing power plant. 

Figure 3: Some turbine trip Modysim simulation curves, without and with fast HP blow-out.

Specification for steam network controls

One interesting fact about steam network simulations has emerged from a growing number of projects. While the process itself has an effect on the results, experience suggests that at least half of the phenomena seen on the curves, especially disturbance magnitude and behavior, come from how the controls have been configured and tuned. Therefore, regardless of how good the simulation results are, they are meaningless if the control configuration and tuning parameters developed during the simulations are not implemented. The most important deliverable from the dynamic simulations is therefore a specification for a steam network control configuration where all modifications and decisions are explained in the form of a steam network control strategy. This control strategy acts as a basis for all control configuration elements, such as the turbine pressure control.

The analysis suggested two very important requirements for the controls:

First, all pressure controllers should use only one pressure transmitter. This makes it possible to avoid measurement errors between different control loops, integrate functions of all pieces of equipment, and stabilize the steam network.

Second, the turbine pressure control algorithms had to be modified. Turbine pressure controllers now use external pressure signals, and turbine valve interaction was changed.

The mill decided to follow these recommendations from the simulation results, and the design was altered accordingly.


Pöyry assembled a team comprising engineers from the DCS supplier, the turbine and boiler builders, and mill personnel, which worked in close cooperation. Since there are many control systems that need to interact properly, it is important that everyone interprets the results and implements the specifications in the same and correct way. This can be achieved most easily by good communication between all parties involved. 

Figure 4: Implementation time schedule

After initial testing, the new loops were turned on and tuned one-by-one according to a predetermined procedure. Pöyry also held training sessions for mill personnel to clarify the new steam network control concept. Initial parameters for the controllers were obtained from the simulator model, which sped up the fine-tuning process.


The start-up of the new equipment went smoothly. The predetermined implementation order was carried out, and step-by-step, the existing connection to the nearby utility was separated and the new equipment was turned on. After start-up and fine-tuning, the steam network behaved just as it did in the simulator. It was also clear that if the control strategy had not been modified according to the simulation results, any turbine trip would have also caused trips in the CFB and LP header. 

Figure 5: PM break: Modysim simulation results vs. actual trend curves after fine tuningFigure 5: PM break: Modysim simulation results vs. actual trend curves after fine tuning

PM break: Modysim simulation results vs. actual trend curves after fine tuning 

Figure 6: TG trip: Modysim simulation results vs. actual trend curves after fine tuningFigure 6: TG trip: Modysim simulation results vs. actual trend curves after fine tuning

TG trip: Modysim simulation results vs. actual trend curves after fine tuning


Stora Enso realized a number of practical benefits from the project:

  • The worst types of disturbances that can take place during new power plant operation had already been tested at the beginning of the engineering phase, so they didn’t have to be learned the hard way.
  • Process and automation engineering needs were supported during the entire power plant control strategy design process.
  • The modifications that were required for the DCS and in the turbine control system were specified at an early stage instead of fixing them by trial and error during commissioning.
  • The steam network control system developed during the simulations worked just as predicted from initial start-up, providing very good pressure stability, maintaining ±0.05 bar during normal operation and ±0.1 bar during upsets.
  • Steam network control commissioning was over in just a few weeks instead of several months.
  • Back-pressure power generation from the turbines is maximized during operation, because pressure remains stable and as low as possible under all circumstances. 

Hans Boghaert is energy manager for Stora Enso. Jarno Nyman is a power plant controls advisor and Mikael Maasalo is a senior power plant controls advisor for Pöyry Finland. 

Key concepts

  • Process simulators can characterize a new facility even before construction
  • Simulation results can suggest specific configurations and equipment choices to ensure desired operating characteristics

Go online

• Find information about Stora Enso’s forest products at www.storaenso.com

• Learn more about Pöyry at www.poyry.com

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