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New Methods for Adaptation to Degeneration in Process Models for Process Industries
Mälardalen University, School of Sustainable Development of Society and Technology. (PRO)
Mälardalen University, School of Business, Society and Engineering. (MERO)ORCID iD: 0000-0001-8191-4901
Mälardalen University, School of Sustainable Development of Society and Technology. (PRO)ORCID iD: 0000-0002-7233-6916
2009 (English)In: Chemical Product and Process Modeling, Vol. Vol. 4, no : Iss. 1, p. , Article 25.-Article in journal (Refereed) Published
Abstract [en]

The implementation of model-based control and diagnostics suffer strongly from the fact that models deteriorate as a function of process and sensor deterioration. Also, changes in the raw material (i.e. wood) may occur and often the process control is not addressing these variations in reality. It is thus vital for the model system to be robust in the sense that it is transparent and easy for the operator to maintain. Robustness is essential in many parts of the system, including measurement, process model validation, the ability of the model to adapt to changes in the process, optimization algorithms, and of course the model itself. In this paper, we first show three real-life applications of the utilization of models for diagnostics and control. Thereafter conditions for on-line adaptation of the models are discussed. The challenges when designing such a system are in achieving operator confidence, filtering of misleading measured data, adaptation of process parameters when the process parameters change, and combining validation of measurements and process models. These challenges are met by using a combination of physical and statistical models and methods based on them such as model predictive control (MPC) and parameter estimation. The model should be maintained by a qualified engineer who should be able to explain the system to the operator so that it is understood and confidence can be maintained.

Place, publisher, year, edition, pages
Berkley Electronic Press , 2009. Vol. Vol. 4, no : Iss. 1, p. , Article 25.-
Keywords [en]
simulation, model based control, MPC, power boiler
National Category
Chemical Process Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-8011DOI: 10.2202/1934-2659.1127Scopus ID: 2-s2.0-77954559066OAI: oai:DiVA.org:mdh-8011DiVA, id: diva2:293134
Projects
Värmeforskproject on model based control of boilerAvailable from: 2010-02-10 Created: 2010-02-10 Last updated: 2015-11-12Bibliographically approved
In thesis
1. Process Modeling of Combustion and Digesters for On-line Applications
Open this publication in new window or tab >>Process Modeling of Combustion and Digesters for On-line Applications
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The use of biomass has increased in recent years due to the efforts to reduce the high emissions of greenhouse gases, primarily carbon dioxide from combustion of fossil fuels. At the same time industrial processes have become more complex because of increased production rates and profitability requirements. A higher degree of automation is needed when the processes are too complex to be handled manually. There is a need to find modeling strategies that can automatically handle the challenges that the conversion of biomass in an industrial process entails, such as operational changes, decreasing component and overall system efficiency, drifting sensors, etc. The objective of this thesis is to develop a methodology for on-line applications in industrial processes. Dynamic process models have been developed for continuous digesters and boilers. Process models have been evaluated for their use in continuous industrial process. Applications that have been studied are monitoring and diagnostics, advanced control and decision support. The process models are designed for on-line simulations. The results shows that the use of mathematical simulation models can improve the use of both process data and process understanding, to achieve improved diagnostics, advanced control and process optimization. In the two examples of industrial processes covered in this thesis, we can see that similar types of models can be used for completely different types of processes, such as pulp digesters and boilers. It also demonstrates the ability to combine soft sensors and hard sensors with physical models to take the information to a higher level of utilization.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2015
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 194
National Category
Energy Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-29462 (URN)978-91-7485-244-8 (ISBN)
Public defence
2015-12-15, R2-025, Mälardalens högskola, Västerås, 08:15 (English)
Opponent
Supervisors
Available from: 2015-11-12 Created: 2015-11-11 Last updated: 2015-11-25Bibliographically approved

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Avelin, AndersDahlquist, Erik

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