Process monitoring and modelling
Operator support systems
Process manufacturing is increasingly being driven by market forces, customer needs and perceptions, resulting in more and more complex multi-product manufacturing technologies. The increasing automation and tighter quality constraints related to these processes make the operator’s job more and more difficult. This makes decision support systems for the operator more important than ever before. Traditional OSS focuses only on specific tasks, which are performed. In case of complex processes the design of integrated information system is extremely important.
The huge amount of data recorded by modern production systems definitely have the potential to provide information for product and process design, monitoring and control. This project proposes soft-computing (SC)-based approaches for the extraction of knowledge from the historical data of production.
The proposed data warehouse based Operator Support System makes possible linking complex and isolated production units based on the integration of the heterogeneous information collected from the production units of a complex production process. The developed OSS is based on a data warehouse designed by following the proposed focus on process data warehouse design approach, which means stronger focus on the material and information flow through the entire enterprise. The resulted OSS follows the process through the organization instead of focusing separate tasks of the isolated process units. For human-computer interaction front-end tools have been worked out where exploratory data analysis and advanced multivariate statistical models are applied to extract the most informative features of the operation of the technology. The concept is illustrated by an industrial case study, where the OSSis designed for the monitoring and control of a high-density polyethylene plant.
F. P. Pach, B. Feil, S. Nemeth, P. Arva, J. Abonyi, Process data warehousing based operator support system for complex production technologies, IEEE Transactions on Systems, Man and Cybernetics, Part A, Special Issue on 'Advances in Heterogeneous and Complex System Integration', pp. 136-153. (2006)
B. Feil., J. Abonyi, S. Nemeth, P. Arva, Monitoring process transitions by Kalman filtering and Time Series segmentation, Computers & Chemical Engineering, Preliminary Accepted, 2004, IF: 0.784
J. Abonyi, B. Feil, S. Nemeth, P. Arva, Modified gath-geva clustering for fuzzy segmentation of multivariate Time Series, Fuzzy Sets and Systems, Data Mining Special Issue, 2005, 149 39-56, IF 0.55, (MATLAB implementation)
B. Feil, J. Abonyi, P. Pach, S. Nemeth, P. Arva, M. Nemeth, G. Nagy, Semi-mechanistic models for state-estimation – Soft sensor for polymer melt index prediction, Lecture Notes in Computer Science, 2004, Volume 3070, 1111 – 1117, 2004, IF: 0.515
J. Abonyi, B. Feil, S. Nemeth, P. Arva, Fuzzy clustering based Time Series segmentation, Lecture Notes in Computer Science. Vol. 2810, 75-84, 2003, IF: 0.515,
J. Abonyi, S. Nemeth, Cs. Vincze, P. Arva, Process analysis and product quality estimation by Self-Organizing maps with an application to polyethylene production, Computers in Industry, Special Issue on Soft Computing in Industrial Applications, 52 (3): 221-234 DEC, 2003, IF: 0.602
J. Abonyi, P. Arva, S. Nemeth, Cs. Vincze, B. Bodolai, Zs. Dobosné Horváth, G. Nagy, M. Németh, Operator support system for multi product processes - Application to polyethylene production, European Symposium on Computer Aided Process Engineering -13, 347-352, (ESCAPE-13 Lappeenranta, Finland, June 1-4, 2003), A. Kraslawski, I. Turunen (Eds.), Computer-Aided Chemical Engineering, Vol. 14, Elsevier, 2003
Process data warehouse
Process data warehousing supports efficiently the monitoring and analysis of complex multi-product technologies. Main steps and conditions of a novel process information system were developed to monitor and analyze complex multi-product technologies.
In order to extract the relevant information both exploratory analysis and data mining methodologies were elaborated.
A two-level structure of the developed information system was determined, in which the process data warehouse is the central element of the second, analytical level. It contains non-violate, consistent and pre-processed data and works independently from other databases at the first, operating level. Analyzer tools and applications connected to the data warehouse were also developed. The developed process information system was evaluated on a complex, multi-product polypropylene technology. The evaluation has proved that the system works efficiently and the various statistical analyzes and data mining methods mine the historical process data effectively.
F. P. Pach, B. Feil, S. Nemeth, P. Arva and J. Abonyi, Process data warehousing based operator support system for complex production technologies, IEEE Transactions on Systems Man and Cybernetics, Part A: Systems and Humans}, Special Issue on Emerging Approaches to Integrating Distributed, Heterogenous and Complex Systems, 2006, Volume 36, Number 1, 136-153
Self-Organizing Maps in Process Monitoring and Control
For the effective application of Self-Organizing Maps in model based control a control-related framework have been worked out. This framework is based on the following generating elements: Identification, Adaptation, Inversion, and Linearization. By means of these building blocks, inverse model based and model predictive (MPC) controllers were generated.
J. Abonyi, S. Nemeth, Cs. Vincze, P. Arva, Process analysis and product quality estimation by Self-Organizing maps with an application to polyethylene production, Computers in Industry, Special Issue on Soft Computing in Industrial Applications, Accepted, 2003,