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A Framework for Learning System for Complex Industrial Processes
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. (SOFIA Research group)ORCID iD: 0000-0003-3610-4680
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. (SOFIA Research group)
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. (SOFIA Research group)ORCID iD: 0000-0001-6101-2863
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-2978-6217
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2020 (English)In: AI and Learning Systems - Industrial Applications and Future Directions / [ed] Konstantinos Kyprianidis and Erik Dahlquist, IntechOpen , 2020, 1, p. 29-Chapter in book (Refereed)
Abstract [en]

Due to the intense price-based global competition, rising operating cost, rapidly changing economic conditions and stringent environmental regulations, modern process and energy industries are confronting unprecedented challenges to maintain profitability. Therefore, improving the product quality and process efficiency while reducing the production cost and plant downtime are matters of utmost importance. These objectives are somewhat counteracting, and to satisfy them, optimal operation and control of the plant components are essential. 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 extensive savings in the energy and resource consumption, and consequently offer 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. In this chapter, a generic learning system architecture is presented that can be retrofitted to existing automation platforms of different industrial plants. The architecture offers flexibility and modularity, so that relevant functionalities can be selected for a specific plant on an as-needed basis. Various functionalities such as soft-sensors, outputs prediction, model adaptation, control optimization, anomaly detection, diagnostics and decision supports are discussed in detail.

Place, publisher, year, edition, pages
IntechOpen , 2020, 1. p. 29-
Keywords [en]
learning system, soft-sensors, model predictive control, fault detection, isolation and identification, information fusion
National Category
Engineering and Technology Control Engineering
Research subject
Energy- and Environmental Engineering; Energy- and Environmental Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-53498DOI: 10.5772/intechopen.92899ISBN: 978-1-78985-878-5 (print)OAI: oai:DiVA.org:mdh-53498DiVA, id: diva2:1529923
Projects
FUDIPO
Funder
EU, Horizon 2020, 723523Available from: 2021-02-19 Created: 2021-02-19 Last updated: 2022-11-08Bibliographically approved
In thesis
1. On a learning system for industrial automation: Model-based control and diagnostics for decision support
Open this publication in new window or tab >>On a learning system for industrial automation: Model-based control and diagnostics for decision support
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Access to energy is fundamental to economic and technological advancement. Hence, the more the world develops, the greater the demand for energy becomes. Evidently, the production and consumption of energy alone account for more than 80% of global anthropogenic greenhouse gas (GHG) emissions. There is broad scientific consensus that efficiency improvements in energy production and consumption must come first on the path to reducing global GHG emissions. As the largest producer and consumer of energy, the industrial sector faces tremendous challenges due to stringent environmental regulations, intense price-based global competition, rising operating costs and rapidly changing economic conditions. Therefore, increasing energy and resource efficiency while improving throughput and asset reliability is a matter of utmost importance. Satisfying such demanding objectives requires an optimal operation, control and monitoring of plant assets and processes. This is one of the main driving forces behind developing digital solutions, methods, and frameworks that can be integrated with old and new industrial automation platforms. The main focus of this dissertation is to investigate frameworks, process models, soft sensors, control optimization, and diagnostic techniques to improve the operation, control, and monitoring of industrial plants and processes. In this thesis, a generic architecture for control optimization, 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, one representing large-scale assets and another representing a fleet of small-scale assets, are considered to demonstrate the genericness of the proposed system architecture. In this thesis, a very energy-intensive chemical pulping process represents the case study of large-scale assets, and a micro gas turbine (MGT) fleet for distributed heat and power generation represent the case study of a fleet of small-scale assets. 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 MGT 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, then it can be adapted for many other industrial applications as well. 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 MGT fleet. Due to the naturally varying wood quality, significant residence time, insufficient measurements, and complexity of pulping reactions, modelling and controlling a continuous pulp digester is a challenging task. Moreover, process abnormalities due to non-ideal flow in the digester often occur that considerably affect the pulp quality. Within this dissertation, variation of wood-chip quality is identified as one of the main process disturbances. Thereafter, a feedforward model predictive control (MPC) approach is explored by feedforwarding the lignin content of the wood chips to the controller. The result shows that the disturbance rejection and tracking performance of the feedforward MPC are superior to other alternatives, like Proportional–integral–derivative (PID), MPC, and current industrial control. When it comes to diagnostics, a literature gap is identified in the area of modelling digester faults. Hence, the well-known Purdue model, a widely used dynamic model of the digester, is extended to simulate process faults like screen-clogging, hangups, and channelling. The findings suggest that both hangups and channelling considerably affect the pulp quality at the blowline. The impact of channelling is prominent on reaction temperature compared to hangups, while hangups change the residence time of the wood chips significantly. Subsequently, a hybrid diagnostics scheme for pulp digester, combining a physical model and a Bayesian network (BN), is demonstrated. Overall, the results show that fault type and severity can be estimated with acceptable accuracy even in presence of noise. Enabling remote fleet diagnostics is expected to foster the commercialization of distributed micro-combined heat and power (micro-CHP) generators, i.e., MGTs. Even though the modelling and diagnostics of large-scale gas turbines are well researched, studies targeting MGT are limited. In this thesis, a physical model of a commercial MGT system is developed. Subsequently, a hybrid scheme by combining a physics-based gas path analysis with a data-driven approach is used to enable MGT diagnostics. The proposed scheme was tested by simulating case studies corresponding to single and multiple faults. Furthermore, sensitivity studies are performed for different measurement uncertainties (i.e., sensor noise and bias) to evaluate the robustness of the scheme against measurement uncertainties. The findings show that the proposed diagnostics approach performs satisfactorily even under measurement uncertainties. To sum up, the increased availability of data and higher computing power is fostering the development of accurate process models and algorithms necessary for optimal operation, control, and monitoring of industrial processes. With the emergence of new measurement techniques, it is possible to leverage productivity and quality with tighter control of key process parameters. Additionally, studying the underlying mechanism of process degradation and developing diagnostics methods by incorporating these can lead to significant economic benefits. Having said that, to tap the full potential of these digital solutions, an integrated framework like that presented in this thesis, i.e., a learning system is essential.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2022
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 354
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
Engineering and Technology Energy Systems
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-56514 (URN)978-91-7485-537-1 (ISBN)
Public defence
2022-01-21, Gamma och digitalt via Zoom, Mälardalens högskola, Västerås, 09:15 (English)
Opponent
Supervisors
Projects
FUDIPO
Available from: 2021-11-15 Created: 2021-11-12 Last updated: 2022-01-12Bibliographically approved

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Rahman, MoksadurFentaye, Amare DesalegnZaccaria, ValentinaAslanidou, IoannaDahlquist, ErikKyprianidis, Konstantinos

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