Concluding the previously described need for integration, the main motivation of the thesis is to create an integrated framework, which exploits all three components in Fig. 1.7 i.e. whereto data mining, modeling and simulation, experimentation tools can be incorporated. To achieve model integrity, the existing models should be reapplied, the non-existing models created, and all the models connected in an appropriate way. If it is possible to collect suciently large amount of data from the process, Knowledge Discovery in Databases (KDD) technique can be applied to extract information focused on the maintenance or control operation problems to make the production more ecient [152].
As I suggest in Figure 1.8, the information ow of such integrated method-ologies should be centered around a process data warehouse in a process im-provement cycle. Sources come from available process data, current process knowledge (rules, constraints, etc.) and an integrated global model of prod-ucts, process and process control. As these information are collected in the data warehouse, data mining tools, modeling and experimentation tools can be applied to aid the improvement of the process while extracting further knowledge.
This study concentrates on integrated handling of models. It touches the topic of data analysis but data mining tool development as part of the inte-grated framework was a key topic in the PhD study of one of my colleagues, Ferenc Péter Pach, entitled as "Rule-based knowledge discovery for process data
Process data warehouse Process data
Initial Process knowledge
Models
Data mining tools
Simulation &
experimentation tools
Knowledge extraction Process Improvement
Figure 1.8: An integrated framework for process improvement.
mining", thus it is out of the scope of this thesis.
Concluding all the sources, tools and possibilities, four levels of integrity can be distinguished as stairs to a thoroughly integrated information environment:
Level 1: Data integrity. Connecting all data sources vertically and horizontally in the company into a data system (database, data mart, data universe, data warehouse), from future business plan data through schedule order data to production/logistics/laboratory measurement data. Reporting functions attached to these basic information sources can alone guide decisions in every level of the company.
Level 2: Model integrity. Connecting multi-scale models to data sources leads to large information base extension, experiments on models and model outputs support (among others) business decisions, estimation of current and future eciency and optimization asset utilization.
Level 3: Knowledge integrity. Taking the initial (process, business, etc.) knowl-edge connected to the central information base and expanding it by deeper understanding extracted by simulation or data mining while re-solving generated contradictions leads to knowledge integrity in the sys-tem.
Level 4: Information integrity. Having all the three levels in a systematically
structured way results in an extended, multi-level Enterprize Resource Planning (ERP) system.
As such integration level exceeds the scope of a PhD thesis, commercial Enterprize Resource Planning systems cover only parts of such a exible, in-tegrated information system and even for small companies it would take years to connect their islands into "a knowledge continent", the current thesis tries to collect elements from every aspect of the previously described environment.
Consequently, the main goals of the thesis are the following:
1. Develop an integrated model based simulator where process model is integrated with control model, which can eectively serve as a base for process improvements and is connected to a data warehouse.
2. Improve experimentation for model parameter estimation by a novel ex-periment design technique.
3. Develop an easy-to-use but ecient data analysis tool for stored and simulated data.
All these developments and the process data warehouse, which they are centered around, were created within the research project of the Cooperative Research Centre of Chemical Engineering Institute entitled as "Optimization of multi-product continuous technologies" with implementation at the Polypropy-lene plant of Tisza Chemical Group Plc., Hungary.
According to the motivations and main goals explained above, the thesis is structured as follows:
Chapter 2 describes an integrated process simulator development for a poly-merization process with applications to product quality and operating cost es-timations, while Chapter 3 presents a novel segmentation based data analysis tool to be able to analyze data queried from or transferred to the data ware-house. As shown previously, process data and the simulator models are linked together through experimentation, hence a genetic algorithm based novel ex-periment design scheme was developed, which is detailed in Chapter 4. Finally, all the theses of the study are summarized in Chapter 5.
Chapter 2
Integrated modeling and
simulation of processes and control systems
Approaches to fulll customers' expectations and market demand in the chem-ical industry are under continuous development. In the near future communi-cation between design, manufacturing, marketing and management should be centered on modeling and simulation, which could integrate the whole product and process development chains, process units and subdivisions of the com-pany. Solutions to this topic often set aside one or more component from product, process and control models. As a novel know-how, an information system methodology is introduced in this chapter with a structure that in-tegrates models of these components with process Data Warehouse. In this methodology, integration means information source, location, application and time integrity. It supports complex engineering tasks related to analysis of sys-tem performance, process optimization, operator training syssys-tems (OTS), de-cision support systems, reverse engineering or software sensors (soft-sensors).
This chapter presents a realization of the proposed methodology framework introduced in Section 1.4. As part of a R&D project, the proposed method-ology was implemented as an information system prototype in an operating polymerization plant in Hungary.
2.1 An integrated framework
A real advantage to the basic integration concept explained in Section 1.4 is that the proposed methodology integrates not only a system model but also a multi-scale (basic and advanced) control model into the framework thus both can be analyzed and improved simultaneously. This section provides a view how control models can be incorporated into the system and the components of such a framework, which will be implemented in the later sections.
For an integrated framework described in Chapter 1, there is a recent inno-vation on this topic at Bayer Technology Services [153], but from the scope of process-product-control, the control system part is unnoticed. Honeywell Inc.
is also a leader in the process development area, but its eorts focus on process and control, product related models are present only at simplied level. Ob-viously, the solutions are rather system-specic but a systematic methodology can be generally applied to handle the complexity and get relevant knowledge.
The developed model components for such an integrated information sys-tem are shown in Figure 2.1. It shows the structure of the proposed process analysis methodology for the development of a complex system/process. This structure supposes that there are a DCS (with data storage functions) and a process computer in the system, so a process Data Warehouse integrated into the framework can be created and all the developed tools are centered around this data warehouse.
- Process Data Warehouse. The data stored by DCS denitely have the potential to provide information for product and process design, monitor-ing and control. However, these data have limited access in time on the process control computers, since they are archived retrospectively, and can be unreliable because of measurement failure or inconsistent storage.
Process Data Warehouse is a data analysis-decision support and infor-mation process unit, which operates separately from the databases of the DCS. It is an information environment in contrast to the data transfer-oriented environment, which contains trusted, processed and collected data for historic data analysis. The data collected into DW directly provide input for dierent data mining, statistical tools, like classica-tion, clustering, association rules, etc., and visualization techniques, e.g.
quantile-quantile plots, box plots, histograms, etc. Besides these tools and techniques, DW indirectly creates a basis for optimization and
sys-Distributed
Process Control Computer
OP’s
Data mining tools Model of
Reactor
Performance analysis Optimization techniques sSP’s, sOP’s
Figure 2.1: the integrated methodology of process analysis for a complex, DCS regulated process
tem performance analysis techniques through a process simulator of the process and its advanced local control system, since models can be vali-dated based on historic data stored in DW.
- Process model. It is an integrated application of laboratory kinetics, ther-modynamics, transport phenomena and experiments with plant scale-up parameters embedded into dierent process unit models. Therefore, a multi-scale model, whose complexity depends on the current technol-ogy/process. Its parts can be achieved by rst principle, black-box or semi-mechanistic (hybrid) modeling approaches.
- Product model i.e. inferential model. Models that are attached to process models, hence in many applications they are not considered separately from them, but inferential product models are rather closely related to product attributes than to process models. For example, if the process model denes the composition of a reactor liquid phase output stream, a possible product model can estimate boiling curve of the output mixture.
They can also be modeled by dierent approaches for proper estimation
of property relationships.
- Process Control model. It uses the designed structure of regulatory pro-cess control system, information about the controlled and the perturbed variables, possible states, and operation ranges. In case of a complex system, usually distributed control system (DCS) assures locally the se-cure and safe operating of the technology. It is extended by an advanced model based process control computer (Process Computer) that calcu-lates among others the operation set points (OP's) to DCS.
- (Graphical) Interface. It handles the input-output connections between process-product model, control model, data warehouse and the user.
For particular analysis, the applicability of the integrated model depends on the applied components. It can be a soft-sensor (e.g. product quality calcu-lation), process monitoring (e.g. state estimation and visualization), reasoning or reverse engineering tool (e.g. production parameter estimation), operator training/qualication (e.g. state transition optimization, product classica-tion) or decision support system application (new bias point, recipes or catalyst system investigation).