APC

INCREASES THE EFFICIENCY OF YOUR PLANTS

The goal of every operator is to exploit the full potential of his or her own manufacturing plant. We provide you with the support you need to avoid investment in costly refurbishments. Intelligent model-based simulation/control enables you to break new ground.

Requirement

Our solutions range from the complete digitization of individual components to plant-wide production optimization. Our products include defined guaranteed values that we achieve with the latest technology available! Our ongoing advances are the result of intensive research and development in this area.

Result

Optimal production settings at any given time—which is sustainable and efficient. As a result of the careful use of resources, reduced energy needs and lower environmental impact, APC solutions are an investment in the future. Increase your competitive edge and secure your production site in the long-term.

Software

APC modules

Our tools create a digital twin of your facilities. High end automationX eMPC is used for completed process steps and automationX ePO for plant-wide process and production optimization.

Model Predictive Control

eModelPredictiveControl solutions optimize specific stages in a process. We use the classic advanced process control approach. Optimization of individual steps in the process that takes into account all conditions such as quality and production permits a great amount of savings (additives, raw materials, etc.) and a maximization of the required quality.

  • Reduced variances
  • Optimal set points
  • Maximum process stability
  • Minimized operator interventions
  • Reduced use of raw materials
  • Minimized rejects
  • Best possible quality
  • Adaptive model correction
  • Robust process models (24/7)
  • Soft sensors (replacement for or plausibility monitoring of physical measurements)
Process Optimizer

The eProcessOptimizer enables completely interconnected process optimization throughout all the process steps. In contrast to Standard systems, hybrid physical and historical models that are interconnected with all the required systems (ERP, PLS, QCS, etc.) combine time and energy optimization (Balanced Manufacturing―BaMa) aXBaMa Toolchain makes simulation and optimization of the entire plant possible. Our tools are based on hybrid discrete event system mechanisms (hybrid DEVS) with which you can link together a great number of different models in order to describe your plants.

  • Production coordination—optimized production sequence
  • Energy efficient production (cost optimization)
  • Preparation of process and storage for unplanned events (optimal time for your maintenance))
  • Comparison of scenarios to support production decisions
  • Real-time variance analysis
  • Quality and material tracking
  • Balancing of the most important process flows
  • Simulation and optimization of the entire plant using empirical and/or physical models
  • Identification of production limits (bottlenecks)
  • Interfaces to ERP, DCS, PLC and QCS systems
  • Automated and personalized reports

 

Accomplishments

The way to optimal performance

Acquisition

Group level data acquisition with standardized ioT mechanisms—made possible by interfaces to all required process levels (horizontal and vertical). Separate validation processes allow safe processing of collected process data.

Analysis

We analyze your processes and find relationships between parameters related to production and quality. Based on these findings, we create mathematical models that are empirical and/or physical. Missing measurements and laboratory analysis are provided by aX soft sensors.

Optimization

Model predictive solutions enable the optimization of production and quality and the minimization of costs. Process modeling and simulation deepen understanding of the process. They recognize critical process steps and bottlenecks and can effectively counteract them.

Information

Model-based simulation and optimization solutions employ a digital twin of your facility. This copy, which includes an online interface to the production process, supplies a variety of additional information—from basic OEE to optimized maintenance intervals.

Solutions

From experience

Optimized coating color preparation | Reduced refiner costs | Optimal bleaching results | Reduced raw materials | Integrated management of rejects | Maximized deinking yield | Batch-optimized biodiesel production | Production plan optimization | Higher level ash control  | Training simulators | Steam network optimization | Reduced steam consumption of a paper machine

Operator Training Simulator

Training simulators make up an entire area of production including utilities and secondary facilities. The visualization includes graphics, control blocks and features of the actual process (models). Simulators greatly increase the efficiency of employee training and professional development. Such an installation is built upon a variety of mathematical relationships based on physical and empirical models.

Management of filler and rejects in paper machines

Our ePM solution enables all types of paper to be produced at the best cost and quick and precise changes between the paper grades being produced. Significant savings are achieved by maximizing the amount of filler introduced via the rejects or the fresh filler according to availability. Seamless alternating between quality and cost-effective modes of operation enables flexible adjustment to customer and product requirements.

The continual adjustment of fresh filler, amounts of rejects and chemical agents reduces the variance in the ash content of the base paper as well as variances in the retention system. This permits higher target values for ash in all grades of paper depending on what is physically possible. The innovative hybrid eMPC technology makes it possible to reproduce discrete process conditions and constant dynamic process behavior in a common process model and thus to critically improve control behavior.

Integrated management of rejects results in optimal control of rejects as well as a considerable easing of the burden on the operating team.

Project goals/accomplishments Increase in control accuracy of ash > 70%
Variance reduction in ash > 50%
   
Implementation period 6 months  
   
Amortization  < 6 months  

 

Maximized deinking yield

Implementation of an eDIP solution in the deinking plant provides the basis for a process management procedure that immediately reacts with appropriate measures to any fluctuations in raw materials and variations in the process, stabilizes quality, reduces costs and increases yield.

A higher level optimizer controls the individual steps (drum pulping, preflotation, postflotation, bleaching) as well as the process steps at all levels. This allows cost-effective DIP material preparation while meeting the defined quality goals for ash content and brightness.

Project goals/accomplishments

Maximized yield > 1%
Variance reduction in ash/whiteness > 35%
Reduced chemical use > 10%

   
Implementation period 8 months 
   
Amortization < 6 months 

 

PROCESS COORDINATION BETWEEN DIP AND PM

Changes in quality on the PM (brightness and/or ash) result in automatic adjustment of the mode of operation at the DIP. Missing online POPE brightness sensors are provided by soft sensors (virtual sensor). The DIP target values are adjusted to the required PM quality as cost-effectively as possible so that grade management becomes obsolete. Timely adjustment of the DIP in the event of PM quality changes by the ePO process model can be expanded by the coupling of the PM production planning data. The result is the adjustment of the mode of operation in the deinking plant.

Project goals/accomplishments

     DIP

  • Variance reduction in brightness > 50%
  • Variance reduction in ash > 25%
  • Yield maximization > 1 %
  • Reduction in bleaching chemicals> 8%

PM

  • Variance reduction in ash > 35%
  • Maximization of fresh filler (chalk)
  • Automatic management of rejects

ePO

  • No grade management at the deinking plant
  • Easing of the burden on the operators
  • Better quality/independent of operators
  • More stability on the paper machine
   
Implementation period     9 months 
   
Amortization    < 6 months 

 

VIRTUAL STRENGTH SENSOR REDUCES STARCH USE

Surface starch is a quick way to compensate for deficits and fluctuations in strength introduced into the system by raw materials (waste paper) and the process. The high price of starch is the reason why paper manufacturers try to use as little of it as possible. Soft sensors for target values such as SCT and CMT permit online control of the pre- and postdilution of starch. Model-based controllers calculate the optimal setpoints in real-time using virtual sensors. The lab values provided cyclically allow ongoing calibration of the modelled soft sensors. The considerable savings in starch use is achieved with closed loop operation (24/7).

Project goals/accomplishments

Savings in starch use > 5%
Variance reduction in quality > 20%

   
Implementation period 6 months 
   
Amortization < 8 months 

 

Stabilized pulp bleaching

The ePULP system monitors the effect of the bleaching agent minute by minute and continually adjusts the use of chemicals so that more stable process conditions (e.g., pH number) and a clearly lower variance in end quality are achieved. As a result, the operating team does not have to deal with any changes in the process and can concentrate exclusively on operations.

Avoiding excessive quality immediately results in the use of fewer chemicals. Since there is less variance, the set point and the average value of brightness finished pulp are reduced by several tenths of a percent and the total amount of bleaching agent is optimally distributed between all the steps. Not only does this ease the burden on the operating team, but also significant economic success is demonstrated and the environmental impact is reduced as a result of lower consumption of bleaching chemicals.

Project goals/accomplishments Reduced chemical use > 10%
Variance reduction in white finished stock > 35%
   
Implementation period 6 months  
   
Amortization < 8 months  

 

Increased refiner efficiency

eTMP ensures that a refiner is operated at the best cost. Process models of every refiner step ensure a local optimum is achieved. Missing online measurements of quality are provided by soft sensors. A higher level optimization module takes care of load shifting and thus targeted energy savings. As a result, much better exploitation of energy efficient components is possible. Computing cycles at one minute intervals permit continual adjustment of the required set points. In addition, the daily energy price forecast is taken into account during production. Intelligent optimization algorithms exploit tank levels skillfully to operate the refiners at the lowest energy costs.

Project goals/accomplishments

Savings in electrical energy > 5%
Variance reduction in quality (shives) > 25%

   
Implementation period 10 months  
   
Amortization < 6 months  

 

Reduced drying energy

Around 70% of the total energy required by the paper industry is used for drying processes. This is reason enough to invest in reducing steam consumption. eDRY manages this without any need for intervention by your operating team. Hybrid process models (empirical and physical) that include all secondary processes reproduce the drying process, thereby enabling virtual representation of the actual paper machine. Closed loop operation of the actual paper machine optimizes steam consumption in the dryer group. The key to success is the calculated dry content after the press section.
Using the ePM solution, steam consumption is reduced considerably without exceeding the quality limits.

Project goals/accomplishments

Reduced steam consumption > 6%

   
Implementation period 10 months 
   
Amortization < 6 months   

 

STEAM NETWORK OPTIMIZATION

Control of steam network is a complex task. The breaks on Paper machines and heating sequences in the digester house are significant disturbances for steam suppling system. The pressure in the network together with boiler performance is currently controlled more or less manually.
Combination of physical simulation with advanced optimization strategies is the only tool to solve this complex control task efficiently in full range of possible operational conditions including process disturbances. The simulator takes care of all relevant steam network equipment. The control strategy will be discussed, proved and tuned with the offline simulator. The unknown steam consumption in the mill will be simulated by random steam flow near to frequency changes of the real one.
The automationX eNET system will use all available steam equipment, like steam accumulator, feed water tank, condenser,  CFBB boiler aso.  to stabilize steam pressure. The efficiency of the control strategy will be tested offline by simulated breaks, heating steam sequence in the digester house and random changes in the steam flow simulating unmeasured steam consumption in the mill. The control system uses predictive steam flow information from digester house and available scheduled production information.
This enables to stabilize steam network significantly, reduce steam loss (blow-down) and increase power generation. The eNET simulator/controller will be connected via AutomationX control software to the existing DCS. In closed loop operation it predicts progress in the steam network 30 - 60 minutes ahead, applying optimal remote set points to stabilize the steam net according the given objectives . The simulator will be synchronized each 10 second with current situation in the mill. This brings transparency into the control task and makes the control algorithm ready to handle any situation in the steam network. Beside this, the system gives the operator a unique tool to observe and control the steam network because not measured process values are calculated and can be even displayed on operator screen in the same way as the measured ones.

Project goals/accomplishments

Reduction Usage of Backup boiler for steam production > 50%
Reduction Blow off steam reduction > 50%

   
Implementation period 10 months 
   
Amortization < 6 months   

 

Sappi

ePM | ePULP | eMPC

LEIPA

eDIP

Norske Skog

ePM

Stora Enso

eTMP | ePM | eDIP | eDRY

Steinbeis

ePM | eDIP | ePO

Borealis

eOTS

SCA

eMPC-Wirbelschichtkessel

GAWtechnology

ePO-Streichfarbenoptimierung

Holmen Paper

eDIP

Smurfit Kappa

ePM

Heinzel Pulp

eMPC

Biodiesel International | BDI

eMPC

MPREIS

aXBaMA