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Rahman, M., Avelin, A. & Kyprianidis, K. (2019). An Approach for Feedforward Model Predictive Control of Continuous Pulp Digesters. Processes, 7(9), 602-622
Open this publication in new window or tab >>An Approach for Feedforward Model Predictive Control of Continuous Pulp Digesters
2019 (English)In: Processes, ISSN 2227 9717, Vol. 7, no 9, p. 602-622Article in journal (Refereed) Published
Abstract [en]

Kappa number variability at the continuous digester outlet is a major concern for pulp and paper mills. It is evident that the aforementioned variability is strongly linked to the feedstock wood properties, particularly lignin content. Online measurement of lignin content utilizing near-infrared spectroscopy at the inlet of the digester is paving the way for tighter control of the blow-line Kappa number. In this paper, an innovative approach of feedforwarding the lignin content to a model predictive controller was investigated with the help of modeling and simulation studies. For this purpose, a physics-based modeling library for continuous pulp digesters was developed and validated. Finally, model predictive control approaches with and without feedforwarding the lignin measurement were evaluated against current industrial control and proportional-integral-derivative (PID) schemes. 

Keywords
pulp and paper; Kappa number; pulp digester; modeling; feedforward; predictive control
National Category
Energy Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-45217 (URN)10.3390/pr7090602 (DOI)000489121800055 ()2-s2.0-85072222936 (Scopus ID)
Projects
FUDIPO
Available from: 2019-09-16 Created: 2019-09-16 Last updated: 2019-10-24Bibliographically approved
Rahman, M. (2019). Towards a learning system for process and energy industry: Enabling optimal control, diagnostics and decision support. (Licentiate dissertation). Västerås: Mälardalen University
Open this publication in new window or tab >>Towards a learning system for process and energy industry: Enabling optimal control, diagnostics and decision support
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Driven by intense competition, increasing operational cost and strict environmental regulations, the modern process and energy industry needs to find the best possible way to adapt to maintain profitability. Optimization of control and operation of the industrial systems is essential to satisfy the contradicting objectives of improving product quality and process efficiency while reducing production cost and plant downtime. Use of optimization not only improves the control and monitoring of assets but also offers better coordination among different assets. Thus, it can lead to considerable savings in energy and resource consumption, and consequently offer a reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks that can be integrated with the existing industrial automation platforms to benefit from optimal control and operation. The main focus of this dissertation is the use of different process models, soft sensors and optimization techniques to improve the control, diagnostics and decision support for the process and energy industry. A generic architecture for an optimal control, diagnostics and decision support system, referred to here as a learning system, is proposed. The research is centred around an investigation of different components of the proposed learning system. Two very different case studies within the energy-intensive pulp and paper industry and the promising micro-combined heat and power (CHP) industry are selected to demonstrate the learning system. One of the main challenges in this research arises from the marked differences between the case studies in terms of size, functions, quantity and structure of the existing automation systems. Typically, only a few pulp digesters are found in a Kraft pulping mill, but there may be hundreds of units in a micro-CHP fleet. The main argument behind the selection of these two case studies is that if the proposed learning system architecture can be adapted for these significantly different cases, it can be adapted for many other energy and process industrial cases. Within the scope of this thesis, mathematical modelling, model adaptation, model predictive control and diagnostics methods are studied for continuous pulp digesters, whereas mathematical modelling, model adaptation and diagnostics techniques are explored for the micro-CHP fleet.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2019. p. 178
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 282
Keywords
Learning system, Supervisory system, Pulp and paper, Micro gas turbine, Process modelling, Model-based control, Diagnostics, Decision support, Anomaly detection, Fault detection
National Category
Energy Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-45219 (URN)978-91-7485-438-1 (ISBN)
Presentation
2019-10-30, Pi, Mälardalen University, Västerås, 13:00 (English)
Opponent
Supervisors
Projects
FUDIPO – FUture DIrections for Process industry Optimization
Funder
EU, Horizon 2020, 723523
Available from: 2019-09-16 Created: 2019-09-16 Last updated: 2019-09-24Bibliographically approved
Rahman, M., Avelin, A., Kyprianidis, K., Jansson, J. & Dahlquist, E. (2018). Model based Control and Diagnostics strategies for a Continuous Pulp Digester. In: Paper Conference and Trade Show, PaperCon 2018: . Paper presented at TAPPI Paper Conference and Trade Show, PaperCon 2018; Charlotte; United States; 15 April 2018 through 18 April 2018; Code 143482 (pp. 136-147). , 1
Open this publication in new window or tab >>Model based Control and Diagnostics strategies for a Continuous Pulp Digester
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2018 (English)In: Paper Conference and Trade Show, PaperCon 2018, 2018, Vol. 1, p. 136-147Conference paper, Published paper (Refereed)
Abstract [en]

Kappa number, which essentially indicates the amount of lignin left in the pulp after cooking, is the most important physical quantity linked to the quality and economics of a Kraft-pulp mill. Controlling the Kappa number is a difficult task mainly due to the naturally varying feedstock, significant residence time, insufficient measurements and complex nature of the delignification process. Moreover, faults such as screen clogging, hang-ups and channeling in the process often occur and increase the operational costs considerably. In this work, the possibility of feedforwarding the lignin content of incoming wood chips, by a near-infrared spectroscopic measurement of one of the major process disturbances, to a model predictive controller, is investigated by means of modeling and simulation studies. Additionally, a simple Bayesian network based diagnostics approach is proposed to detect the continuous digester faults.

National Category
Engineering and Technology Energy Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-41297 (URN)2-s2.0-85060374344 (DOI)2-s2.0-85060374344 (Scopus ID)9781510871892 (ISBN)
Conference
TAPPI Paper Conference and Trade Show, PaperCon 2018; Charlotte; United States; 15 April 2018 through 18 April 2018; Code 143482
Projects
FUDIPO
Available from: 2018-11-07 Created: 2018-11-07 Last updated: 2019-10-01Bibliographically approved
Aslanidou, I., Zaccaria, V., Rahman, M., Oostveen, M., Olsson, T. & Kyprianidis, K. (2018). Towards an Integrated Approach for Micro Gas Turbine Fleet Monitoring, Control and Diagnostics. In: : . Paper presented at Global Power and Propulsion Forum 2018, Zurich, Switzerland.
Open this publication in new window or tab >>Towards an Integrated Approach for Micro Gas Turbine Fleet Monitoring, Control and Diagnostics
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2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Real-time engine condition monitoring and fault diagnostics results in reduced operating and maintenance costs and increased component and engine life. Prediction of faults can change the maintenance model of a system from a fixed maintenance interval to a condition based maintenance interval, further decreasing the total cost of ownership of a system. Technologies developed for engine health monitoring and advanced diagnostic capabilities are generally developed for larger gas turbines, and generally focus on a single system; no solutions are publicly available for engine fleets. This paper presents a concept for fleet monitoring finely tuned to the specific needs of micro gas turbines. The proposed framework includes a physics-based model and a data-driven model with machine learning capabilities for predicting system behaviour, combined with a diagnostic tool for anomaly detection and classification. The integrated system will develop advanced diagnostics and condition monitoring for gas turbines with a power output under 100 kW.

National Category
Aerospace Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-43169 (URN)
Conference
Global Power and Propulsion Forum 2018, Zurich, Switzerland
Available from: 2019-04-21 Created: 2019-04-21 Last updated: 2019-06-03Bibliographically approved
Rahman, M., Avelin, A., Kyprianidis, K. & Dahlquist, E. (2017). An Approach For Feedforward Model Predictive Control For Pulp and Paper Applications: Challenges And The Way Forward. In: Paper Conference and Trade Show, PaperCon 2017: Renew, Rethink, Redefine the Future, Volume 3. Paper presented at PaperCon 2017, April 23 - 26, 2017 Minneapolis, Minnesota, USA (pp. 1441-1450). TAPPI Press, 10
Open this publication in new window or tab >>An Approach For Feedforward Model Predictive Control For Pulp and Paper Applications: Challenges And The Way Forward
2017 (English)In: Paper Conference and Trade Show, PaperCon 2017: Renew, Rethink, Redefine the Future, Volume 3, TAPPI Press, 2017, Vol. 10, p. 1441-1450Conference paper, Published paper (Refereed)
Abstract [en]

Due to the naturally varying feedstock, significant residence time, insufficient measurements and complex nature of the delignification process, producing pulp with consistent quality i.e. stable kappa number with sufficiently high yield is a challenging task that requires multi-variable process control. A wide variety of control structures, ranging from classical concepts like cascade control, feedforward, ratio control, and parallel control to more modern concepts like model-based predictive control, is used in pulp and paper industries all over the world. In this paper, a survey of model-based predictive control will be presented along with the control challenges that lie within the chemical pulping process. The potential of this control concept for overcoming the aforementioned technical challenges will also be discussed in the second part of the paper. Particular focus will be given on the use of near-infrared spectroscopy based soft-sensors coupled with dynamic process models as an enabler for feedforward model-based predictive control. Overall, the proposed control concept is expected to significantly improve process performance, in the presence of measurement noise and various complex chemical process uncertainties common in pulp and paper applications.

Place, publisher, year, edition, pages
TAPPI Press, 2017
Keywords
Pulp and paper, Pump Digester, MPC, Advanced control, Process modelling, NIR
National Category
Mechanical Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-35839 (URN)2-s2.0-85041499426 (Scopus ID)9781510847286 (ISBN)
Conference
PaperCon 2017, April 23 - 26, 2017 Minneapolis, Minnesota, USA
Projects
FUDIPO
Funder
EU, Horizon 2020
Available from: 2017-06-19 Created: 2017-06-19 Last updated: 2019-09-16Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3610-4680

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