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)

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. The automationX APC Optimizer 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

• Model-based optimization solutions for the chemical and mechanical process industry with a focus on pulp/paper and  environmental industries (biodiesel, biogas, wastewater)
• Team with experienced process engineers in the core market segments
• Potential analyzes together with plant operators. Development of KPIs and optimization goals derived from them
• Development of simulation models using KDD
• Integration of simulation models in model- based control concepts for optimized closed loop operation (24/7)
• Life cycle management for optimal system support
• Transparent implementation via automationX APC optimization platform (no black box)
  • Open system environment with access to all system information
  • Standard Industry platform
  • Standard interfaces to existing – Customer DCS

Identification of technical relationships from existing, mostly large databases. In the difference to data mining, KDD as an overall process also includes the preparation of the data and the evaluation of the results.

• Provision of background knowledge for the specific process area
• Definition of goals for knowledge development
• Data selection
• Data cleaning
• Data reduction (e.g. through transformations)
• Selection of a model in which the findings can be represented (empirical, hybrid, physical)
• Data mining, the real data analysis
• Interpretation and validation of findings (knowledgement)

The AutomationX MPC solutions in two waste paper processing plants are a further step in Steinbeis Papers endeavour to steady increase plant efficiency and improve quality consistency.
The cooperation in the projects and afterwards was and is excellent.

Michael Hunold
Head of New Processes / Steinbeis Papier

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.

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.

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.

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).

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.

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.

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.

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.