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  • 1.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Aslanidou, Ioanna
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Axelsson, Jakob
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Hatvani, Leo
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Olsson, Anders
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Schwede, Sebastian
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Sjödin, Carina
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dilemmas in designing e-learning experiences for professionals2021In: Proceedings of the European Conference on e-Learning, ECEL, 2021, p. 10-17Conference paper (Refereed)
    Abstract [en]

    The aims of this research are to enhance industry-university collaboration and to design learning experiences connecting the research front to practitioners. We present an empirical study with a qualitative approach involving teachers who gathered data from newly developed advanced level courses in artificial intelligence, energy, environmental, and systems engineering. The study is part of FutureE, an academic development project over 3 years involving 12 courses. The project, as well as this study, is part of a cross-disciplinary collaboration effort. Empirical data comes from course evaluations, course analysis, teacher workshops, and semi-structured interviews with selected students, who are also professionals. This paper will discuss course design and course implementation by presenting dilemmas and paradoxes. Flexibility is key for the completion of studies while working. Academia needs to develop new ways to offer flexible education for students from a professional context, but still fulfil high quality standards and regulations as an academic institution. Student-to-student interactions are often suggested as necessary for qualified learning, and students support this idea but will often not commit to it during courses. Other dilemmas are micro-sized learning versus vast knowledge, flexibility versus deadlines as motivating factors, and feedback hunger versus hesitation to share work. Furthermore, we present the challenges of providing equivalent online experience to practical in-person labs. On a structural level, dilemmas appear in the communication between university management and teachers. These dilemmas are often the result of a culture designed for traditional campus education. We suggest a user-oriented approach to solve these dilemmas, which involves changes in teacher roles, culture, and processes. The findings will be relevant for teachers designing and running courses aiming to attract professionals. They will also be relevant for university management, building a strategy for lifelong e-learning based on co-creation with industry.

  • 2.
    Aslanidou, Ioanna
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Rahman, Moksadur
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Micro Gas Turbines in the Future Smart Energy System: Fleet Monitoring, Diagnostics, and System Level Requirements2021In: Frontiers in Mechanical Engineering, E-ISSN 2297-3079, Vol. 7, article id 676853Article, review/survey (Refereed)
    Abstract [en]

    The energy generation landscape is changing, pushed by stricter regulations for emissions control and green energy generation. The limitations of renewable energy sources, however, require flexible energy production sources to supplement them. Micro gas turbine based combined heat and power plants, which are used for domestic applications, can fill this gap if they become more reliable. This can be achieved with the use of an engine monitoring and diagnostics system: real-time engine condition monitoring and fault diagnostics results in reduced operating and maintenance costs and increased component and engine life. In order to allow the step change in the connection of small engines to the grid, a fleet monitoring system for micro gas turbines is required. A proposed framework combines a physics-based model and a data-driven model with machine learning capabilities for predicting system behavior, and includes a purpose-developed diagnostic tool for anomaly detection and classification for a multitude of engines. The framework has been implemented on a fleet of micro gas turbines and some of the lessons learned from the demonstration of the concept as well as key takeaways from the general literature are presented in this paper. The extension of fleet monitoring to optimal operation and production planning in relation to the needs of the grid will allow the micro gas turbines to fit in the future green energy system, connect to the grid, and trade in the energy market. The requirements on the system level for the widespread use of micro gas turbines in the energy system are addressed in the paper. A review of the current solutions in fleet monitoring and diagnostics, generally developed for larger engines, is included, with an outlook into a sustainable future.

  • 3.
    Aslanidou, Ioanna
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Fentaye, Amare Desalegn
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Development of web-based short courses on control, diagnostics, and instrumentation2020In: Proceedings of the ASME Turbo Expo 2020, Sep 21-25, 2020, article id v006t08a004Conference paper (Refereed)
    Abstract [en]

    As a consequence of globalization and advances in digital tools, synchronous or asynchronous distance courses are becoming an integral part of universities’ educational offers. The design of an online course introduces more challenges compared to a traditional on campus course with face to face lectures. This is true especially for engineering subjects where problem or project-based courses may be preferred to stimulate critical thinking and engage the learners with real-life problems. However, realizing this with distance learning implies that a similar study pace should be kept by the learners involved. This may not be easy, since individual pace is often a motivation for choosing a distance course. Student engagement in group projects, collaborations, and the proper design of examination tasks are only some of the challenges in designing a distance course for an engineering program. 

    A series of web-based courses on measurement techniques, control, and diagnostics were developed and delivered to groups of learners. Each course comprised short modules covering key points of the subject and aimed at getting learners to understand both the fundamental concepts that they do not typically learn or understand in the respective base courses and to build on that knowledge to reach a more advanced cognitive level. 

    The experience obtained in the courses on what strategies worked better or worse for the learners is presented in this paper. A comparison between the courses provides an interesting outlook on how the learners reacted to slightly different requirements and incentives in each course. The results from the evaluation of the courses are also used as a base for discussion.

    The background and availability of the learners is closely linked to how a course should be designed to optimally fit the learning group, without compromising on the achievement of the learning outcomes. This series of courses is a good example of continuous professional development courses in the field of control, diagnostics, and instrumentation (CDI), and brings with it a number of challenges and opportunities for the development of online courses. 

  • 4.
    Aslanidou, Ioanna
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Pontika, E.
    Aristotle University of Thessaloniki, Thessaloniki, Greece.
    Zimmerman, Nathan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kalfas, A. I.
    Aristotle University of Thessaloniki, Thessaloniki, Greece.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Teaching gas turbine technology to undergraduate students in Sweden2018In: Proceedings of the ASME Turbo Expo, American Society of Mechanical Engineers (ASME) , 2018, Vol. 6Conference paper (Refereed)
    Abstract [en]

    This paper addresses the teaching of gas turbine technology in a third-year undergraduate course in Sweden and the challenges encountered. The improvements noted in the reaction of the students and the achievement of the learning outcomes is discussed. The course, aimed at students with a broad academic education on energy, is focused on gas turbines, covering topics from cycle studies and performance calculations to detailed design of turbomachinery components. It also includes economic aspects during the operation of heat and power generation systems and addresses combined cycles as well as hybrid energy systems with fuel cells. The course structure comprises lectures from academics and industrial experts, study visits, and a comprehensive assignment. With the inclusion of all of these aspects in the course, the students find it rewarding despite the significant challenges encountered. An important contribution to the education of the students is giving them the chance, stimulation, and support to complete an assignment on gas turbine design. Particular attention is given on striking a balance between helping them find the solution to the design problem and encouraging them to think on their own. Feedback received from the students highlighted some of the challenges and has given directions for improvements in the structure of the course, particularly with regards to the course assignment. This year, an application developed for a mobile phone in the Aristotle University of Thessaloniki for the calculation of engine performance will be introduced in the course. The app will have a supporting role during discussions and presentations in the classroom and help the students better understand gas turbine operation. This is also expected to reduce the workload of the students for the assignment and spike their interest.

  • 5.
    Aslanidou, Ioanna
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Rahman, Moksadur
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Oostveen, Mark
    Micro Turbine Technology bv, Eindhoven, Netherlands.
    Olsson, Tomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. RISE SICS, Västerås, Sweden.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Towards an Integrated Approach for Micro Gas Turbine Fleet Monitoring, Control and Diagnostics2018Conference 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.

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  • 6.
    Campana, Pietro Elia
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zhang, Yang
    Stridh, Bengt
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Flexibility Services Provided by Building Thermal Inertia2018Conference paper (Refereed)
  • 7.
    Cuneo, A.
    et al.
    Thermochemical Power Group, Università di Genova, Via Montallegro 1, Genova, Italy.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Tucker, D.
    U.S. DOE National Energy Technology Laboratory, 3610 Collins Ferry Rd., Morgantown, WV, United States.
    Sorce, A.
    Thermochemical Power Group, Università di Genova, Via Montallegro 1, Genova, Italy.
    ECONOMICAL OPTIMIZATION OF A HYBRID SYSTEM GAS TURBINE SIZE WITH SOFC STACK DEGRADATION2017In: EFC - Proc. Eur. Fuel Cell Piero Lunghi Conf., ENEA , 2017, p. 117-118Conference paper (Refereed)
    Abstract [en]

    The coupling of a pressurized solid oxide fuel cell (SOFC) and a gas turbine has been proven to result in extremely high efficiency and reduced emissions. The presence of the gas turbine can improve system durability compared to a standalone SOFC, because the turbomachinery can supply additional power as the fuel cell degrades to meet the power request. Since performance degradation is an obstacles to SOFC systems commercialization, the optimization of the hybrid system to mitigate SOFC degradation effects is of great interest. In this work, an optimization approach was used to innovatively study the effect of gas turbine size on system durability for a 400 kW fuel cell stack. A larger turbine allowed a bigger reduction in SOFC power before replacing the stack, but increased the initial capital investment and decreased the initial turbine efficiency. Thus, the power ratio between SOFC and gas turbine significantly influenced system economic results.

  • 8.
    Cuneo, A.
    et al.
    Thermochemical Power Group, Università di Genova, Italy.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Tucker, D.
    U.S. DOE National Energy Technology Laboratory, Morgantown, United States.
    Sorce, A.
    Thermochemical Power Group, Università di Genova, Italy.
    Gas turbine size optimization in a hybrid system considering SOFC degradation2018In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 230, p. 855-864Article in journal (Refereed)
    Abstract [en]

    The coupling of a pressurized solid oxide fuel cell (SOFC) and a gas turbine has been proven to result in extremely high efficiency and reduced emissions. The presence of the gas turbine can improve system durability compared to a standalone SOFC, because the turbomachinery can supply additional power as the fuel cell degrades to meet the power request. Since performance degradation is an obstacles to SOFC systems commercialization, the optimization of the hybrid system to mitigate SOFC degradation effects is of great interest. In this work, an optimization approach was used to innovatively study the effect of gas turbine size on system durability for a 400 kW fuel cell stack. A larger turbine allowed a bigger reduction in SOFC power before replacing the stack, but increased the initial capital investment and decreased the initial turbine efficiency. Thus, the power ratio between SOFC and gas turbine significantly influenced system economic results.

  • 9.
    Diamantidou, Eirini
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kalfas, A.
    Department of Mechanical Engineering Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece.
    Mission-Level Design Studies for Efficient Hybrid-Electric Regional Aircraft Concepts2024In: International Journal of Gas Turbine, Propulsion and Power Systems, ISSN 1882-5079, Vol. 15, no 1, p. 48-56Article in journal (Refereed)
    Abstract [en]

    This work delves into the design and operation of a series/parallel partial hybrid-electric architecture for regional aircraft. Employing a comprehensive approach, this study leverages mission-level analysis to optimize a 19-passenger hybrid-electric aircraft. The conceptual design framework employed is based on the OpenConcept library, and a systematic computational scheme is developed to effectively investigate the concept’s performance, utilizing the supplied and shaft power ratios. Through the examination of three distinct mission ranges and consideration of two technological scenarios, this work offers valuable insights. For the longest mission, an aircraft design optimization problem is posed, and a 23% reduction in total energy consumption is achieved for the optimistic technological scenario. On the other hand, the focus shifts to optimize the power management for shorter missions, where a 26% and a 32% reduction in energy consumption are achieved for the typical and short missions. The results highlight the potential of hybrid-electric propulsion for regional aircraft.

  • 10.
    Fentaye, Amare Desalegn
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aircraft engine performance monitoring and diagnostics based on deep convolutional neural networks2021In: Machines, E-ISSN 2075-1702, Vol. 9, no 12, article id 337Article in journal (Refereed)
    Abstract [en]

    The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains, including object recognition and computer vision. Recently, there has been a rapid rise in the use of convolutional neural networks for rotating machinery diagnostics inspired by their powerful feature learning and classification capability. However, the application in the field of gas turbine diagnostics is still limited. This paper presents a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. The trend-monitoring system was employed to capture performance changes due to degradation, establish a new baseline when it is needed, and generatefault signatures. The fault detection and isolation system was trained to step-by-step detect and classify gas path faults to the component level using fault signatures obtained from the physics part. The performance of the method proposed was evaluated based on different fault scenarios for a three-shaft turbofan engine, under significant measurement noise to ensure model robustness. Two comparative assessments were also carried out: with a single convolutional-neural-network-architecture-based fault classification method and with a deep long short-term memory-assisted fault detection and isolation method. The results obtained revealed the performance of the proposed method to detect and isolate multiple gas path faults with over 96% accuracy. Moreover, sharing diagnostic tasks with modular architectures is seen as relevant to significantly enhance diagnostic accuracy.

  • 11.
    Fentaye, Amare Desalegn
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Discrimination of rapid and gradual deterioration for an enhanced gas turbine life-cycle monitoring and diagnostics2021In: International Journal of Prognostics and Health Management, E-ISSN 2153-2648, Vol. 12, no 3, p. 1-16Article in journal (Refereed)
    Abstract [en]

    Advanced engine health monitoring and diagnostic systems greatly benefit users helping them avoid potentially expensive and time-consuming repairs by proactively identifying shifts in engine performance trends and proposing optimal maintenance decisions. Engine health deterioration can manifest itself in terms of rapid and gradual performance deviations. The former is due to a fault event that results in a short-term performance shift and is usually concentrated in a single component. Whereas the latter implies a gradual performance loss that develops slowly and simultaneously in all engine components over their lifetime due to wear and tear. An effective engine lifecycle monitoring and diagnostic system is therefore required to be capable of discriminating these two deterioration mechanisms followed by isolating and identifying the rapid fault accurately. In the proposed solution, this diagnostic problem is addressed through a combination of adaptive gas path analysis and artificial neural networks. The gas path analysis is applied to predict performance trends in the form of isentropic efficiency and flow capacity residuals that provide preliminary information about the deterioration type. Sets of neural network modules are trained to filter out noise in the measurements, discriminate rapid and gradual faults, and identify the nature of the root cause, in an integrated manner with the gas path analysis. The performance of the proposed integrated method has been demonstrated and validated based on performance data obtained from a three-shaft turbofan engine. The improvement achieved by the combined approach over the gas path analysis technique alone would strengthen the relevance and long-term impact of our proposed method in the gas turbine industry. © 2021, Prognostics and Health Management Society. All rights reserved.

  • 12.
    Fentaye, Amare Desalegn
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Rahman, Moksadur
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Stenfelt, Mikael
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Hybrid model-based and data-driven diagnostic algorithm for gas turbine engines2020In: Proceedings of the ASME Turbo Expo, American Society of Mechanical Engineers (ASME) , 2020Conference paper (Refereed)
    Abstract [en]

    Data-driven algorithms require large and comprehensive training samples in order to provide reliable diagnostic solutions. However, in many gas turbine applications, it is hard to find fault data due to proprietary and liability issues. Operational data samples obtained from end-users through collaboration projects do not represent fault conditions sufficiently and are not labeled either. Conversely, model-based methods have some accuracy deficiencies due to measurement uncertainty and model smearing effects when the number of gas path components to be assessed is large. The present paper integrates physics-based and data-driven approaches aiming to overcome this limitation. In the proposed method, an adaptive gas path analysis (AGPA) is used to correct measurement data against the ambient condition variations and normalize. Fault signatures drawn from the AGPA are used to assess the health status of the case engine through a Bayesian network (BN) based fault diagnostic algorithm. The performance of the proposed technique is evaluated based on five different gas path component faults of a three-shaft turbofan engine, namely intermediate-pressure compressor fouling (IPCF), high-pressure compressor fouling (HPCF), high-pressure turbine erosion (HPTE), intermediate-pressure turbine erosion (IPTE), and low-pressure turbine erosion (LPTE). Robustness of the method under measurement uncertainty has also been tested using noise-contaminated data. Moreover, the fault diagnostic effectiveness of the BN algorithm on different number and type of measurements is also examined based on three different sensor groups. The test results verify the effectiveness of the proposed method to diagnose single gas path component faults correctly even under a significant noise level and different instrumentation suites. This enables to accommodate measurement suite inconsistencies between engines of the same type. The proposed method can further be used to support the gas turbine maintenance decision-making process when coupled with overall Engine Health Management (EHM) systems.

  • 13.
    Ferrari, M. L.
    et al.
    University of Genoa, Genova, Italy.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Pressurized SOFC system fuelled by biogas: Control approaches and degradation impact2020In: Proceedings of the ASME Turbo Expo, American Society of Mechanical Engineers (ASME) , 2020, Vol. 143, article id 4048653Conference paper (Refereed)
    Abstract [en]

    This paper shows control approaches for managing a pressurized Solid Oxide Fuel Cell (SOFC) system fuelled by biogas. This is an advanced solution to integrate the high efficiency benefits of a pressurized SOFC with a renewable source. The operative conditions of these analyses are based on the matching with an emulator rig including a T100 machine for tests in cyber-physical mode (a real-time model including components emulated in the rig, operating in parallel with the experimental facility and used to manage some properties in the plant, such as the turbine outlet temperature set-point and the air flow injected in the anodic circuit). The T100 machine is a microturbine able to produce a nominal electric power output of 100 kW. So, the paper presents a real-time model including the fuel cell, the off-gas burner, and the recirculation lines. Although the microturbine components are planned to be evaluated with the hardware devices, the model includes also the T100 expander for machine control reasons, as detailed presented in the devoted section. The simulations shown in this paper regard the assessment of an innovative control tool based on the Model Predictive Control (MPC) technology. This controller and an additional tool based on the coupling of MPC and PID approaches were assessed against the application of Proportional Integral Derivative (PID) controllers. The control targets consider both steady-state (e.g. high efficiency solutions) and dynamic aspects (stress smoothing in the cell). Moreover, different control solutions are presented to operate the system during fuel cell degradation. The results include the system response to load variations, and SOFC voltage decrease. Special attention is devoted to the fuel cell system constraints, such as temperature and time-dependent thermal gradient. Considering the simulations including SOFC degradation, the MPC was able to decrease the thermal stress, but it was not able to compensate the degradation. On the other hand, the tool based on the coupling of the MPC and the PID approaches produced the best results in terms of set-point matching, and SOFC thermal stress containment.

  • 14.
    Ferrari, M. L.
    et al.
    Thermochemical Power Group (TPG), University of Genoa, Italy.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Pressurized SOFC System Fuelled by Biogas: Control Approaches and Degradation Impact2021In: Journal of engineering for gas turbines and power, ISSN 0742-4795, E-ISSN 1528-8919, Vol. 143, no 6, article id 4048653Article in journal (Refereed)
    Abstract [en]

    This paper shows control approaches for managing a pressurized solid oxide fuel cell (SOFC) system fuelled by biogas. This is an advanced solution to integrate the high efficiency benefits of a pressurized SOFC with a renewable source. The operative conditions of these analyses are based on the matching with an emulator rig including a T100 machine for tests in cyber-physical mode. So, this paper presents a real-time model including the fuel cell, the off-gas burner (OFB), and the recirculation lines. Although the microturbine components are planned to be evaluated with the hardware devices, the model includes also the T100 expander for machine control reasons. The simulations shown in this paper regard the assessment of an innovative control tool based on the model predictive control (MPC) technology. This controller and an additional tool based on the coupling of MPC and proportional integral derivative (PID) approaches were assessed against the application of PID controllers. The control targets consider both steady-state and dynamic aspects. Moreover, different control solutions are presented to operate the system during fuel cell degradation. The results include the system response to load variations, and SOFC voltage decrease. Considering the simulations including SOFC degradation, the MPC was able to decrease the thermal stress, but it was not able to compensate the degradation. On the other hand, the tool based on the coupling of the MPC and the PID approaches produced the best results in terms of set-point matching, and SOFC thermal stress containment.

  • 15.
    Ivan, Heidi Lynn
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Exploring the effects of faults on the performance of a biological wastewater treatment process2024In: Water Science and Technology, ISSN 0273-1223, E-ISSN 1996-9732Article in journal (Refereed)
    Abstract [en]

    To prioritise which faults should be detected in a biological wastewater treatment process, and with what level of urgency, it is necessary to understand the effect that they have on the process. Using the Benchmark Simulation Model No. 1 and 2. (BSM1 and BSM2), several process and sensor faults were considered and their impacts on various cost, quality, and controller performance evaluation metrics analysed. Both the cost of treating the wastewater and the quality of the effluent were impacted in varying degrees of severity by the faults tested. The most influential faults in both models were decreases to autotrophic and heterotrophic growth rates, decreases to the heterotrophic death rate, and the inhabitation fault. It was shown that only larger fault sizes were significant, and the required speed of detection is dependent on the fault profile. Prioritising detection of the most influential faults was shown to have significant effects on monitoring requirements for fault detection and the subsequent complexity required of a fault detection system. A valuable takeaway was the similarity of results from BSM1 and BSM2; the consistency of the influential process faults suggests that systems that can be described by these models are likely affected by the same faults.

  • 16.
    Mantel, Luca
    et al.
    Univ Genoa, TPG DIME, Via Montallegro 1, I-16145 Genoa, Italy..
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ferrari, Mario Luigi
    Univ Genoa, TPG DIME, Via Montallegro 1, I-16145 Genoa, Italy..
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    A Degradation Diagnosis Method for Gas Turbine-Fuel Cell Hybrid Systems Using Bayesian Networks2021In: Journal of engineering for gas turbines and power, ISSN 0742-4795, E-ISSN 1528-8919, Vol. 143, no 5, article id 054502Article in journal (Refereed)
    Abstract [en]

    This paper aims to develop and test Bayesian belief network-based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor, and fuel cell (FC) in a hybrid system based on different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks are generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks (BBNs) to fuel cell-gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in a gas turbine, fuel cell and sensors in a fuel cell-gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady-state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.

  • 17.
    Mantelli, L.
    et al.
    TPG, University of Genoa, Genoa, Italy.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ferrari, M. L.
    TPG, University of Genoa, Genoa, Italy.
    A degradation diagnosis method for gas turbine - fuel cell hybrid systems using Bayesian networks2020In: Proceedings of the ASME Turbo Expo, American Society of Mechanical Engineers (ASME) , 2020Conference paper (Refereed)
    Abstract [en]

    During the last decades there has been a rise of awareness regarding the necessity to increase energy systems efficiency and reduce carbon emissions. These goals could be partially achieved through a greater use of gas turbine - solid oxide fuel cell hybrid systems to generate both electric power and heat. However, this kind of systems are known to be delicate, especially due to the fragility of the cell, which could be permanently damaged if its temperature and pressure levels exceed their operative limits. This could be caused by degradation of a component in the system (e.g. the turbomachinery), but also by some sensor fault which leads to a wrong control action. To be considered commercially competitive, these systems must guarantee high reliability and their maintenance costs must be minimized. Thus, it is necessary to integrate these plants with an automated diagnosis system capable to detect degradation levels of the many components (e.g. turbomachinery and fuel cell stack) in order to plan properly the maintenance operations, and also to recognize a sensor fault. This task can be very challenging due to the high complexity of the system and the interactions between its components. Another difficulty is related to the lack of sensors, which is common on commercial power plants, and makes harder the identification of faults in the system. This paper aims to develop and test Bayesian belief network based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor and fuel cell in a hybrid system on the basis of different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks is generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks to fuel cell - gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in gas turbine, fuel cell and sensors in a fuel cell - gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.

  • 18.
    Marais, Heidi Lynn
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ivan, Jean-Paul A.
    Örebro University, Sweden.
    Nordlander, Eva
    Detectability of Fault Signatures in a Wastewater Treatment Process2022In: Proceedings of The First SIMS EUROSIM Conference on Modelling and Simulation, SIMS EUROSIM 2021, and 62nd International Conference of Scandinavian Simulation Society, SIMS 2021 / [ed] Esko Juuso, Bernt Lie, Erik Dahlquist and Jari Ruuska, 2022, p. 418-423Conference paper (Refereed)
    Abstract [en]

    In a wastewater treatment plant reliable fault detection is an integral component of process supervision and ensuring safe operation of the process. Detecting and isolating process faults requires that sensors in the process can be used to uniquely identify such faults. However, sensors in the wastewater treatment process operate in hostile environments and often require expensive equipment and maintenance. This work addresses this problem by identifying a minimal set of sensors which can detect and isolate these faults in the Benchmark Simulation Model No. 1.Residual-based fault signatures are used to determine this sensor set using a graph-based approach; these fault signatures can be used in future work developing fault detection methods. It is recommended that further work investigate what sizes of faults are critical to detect based on their potential effects on the process, as well as ways to select an optimal sensor set from multiple valid configurations.

  • 19.
    Marais, Heidi Lynn
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Odlare, Monica
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Comparing statistical process control charts for fault detection in wastewater treatment2022In: Water Science and Technology, ISSN 0273-1223, E-ISSN 1996-9732, Vol. 85, no 4, p. 1250-1262Article in journal (Refereed)
    Abstract [en]

    Fault detection is an important part of process supervision, especially in processes where there are strict requirements on the process outputs like in wastewater treatment. Statistical control charts such as Shewhart charts, cumulative sum (CUSUM) charts, and exponentially weighted moving average (EWMA) charts are common univariate fault detection methods. These methods have different strengths and weaknesses that are dependent on the characteristics of the fault. To account for this the methods in their base forms were tested with drift and bias sensor faults of different sizes to determine the overall performance of each method. Additionally, the faults were detected using two different sensors in the system to see how the presence of active process control influenced fault detectability. The EWMA method performed best for both fault types, specifically the drift faults, with a low false alarm rate and good detection time in comparison to the other methods. It was shown that decreasing the detection time can effectively reduce excess energy consumption caused by sensor faults. Additionally, it was shown that monitoring a manipulated variable has advantages over monitoring a controlled variable as setpoint tracking hides faults on controlled variables; lower missed detection rates are observed using manipulated variables.

  • 20.
    Marzi, E.
    et al.
    Department of Engineering and Architecture, University of Parma, Parma, Italy.
    Morini, M.
    Department of Engineering and Architecture, University of Parma, Parma, Italy.
    Saletti, C.
    Department of Engineering and Architecture, University of Parma, Parma, Italy.
    Vouros, Stavros
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Gambarotta, A.
    Department of Engineering and Architecture, University of Parma, Parma, Italy.
    Power-to-Gas for energy system flexibility under uncertainty in demand, production and price2023In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 284, article id 129212Article in journal (Refereed)
    Abstract [en]

    The growing penetration of non-programmable renewable energy sources and the consequent fluctuations in energy prices and availability lead to the need to enhance energy system flexibility and synergies between different energy vectors. This can be reached through sector integration. Among the most relevant technologies used for this purpose, Power-to-Gas systems allow excess renewable electricity to be converted directly into fuels that can be then stored or used. A smart energy system, however, which includes these innovative solutions, requires intelligent management methods to optimize its operation. This work investigates the operational strategy of energy systems integrated with Power-to-Gas solutions for seasonal storage, by developing an optimization model for the system, formulated as Mixed-Integer Linear Programming problem. The algorithm tackles the uncertain nature of future disturbances, such as energy needs, generation and price using two-stage stochastic programming. The algorithm is tested on grid-connected and 100% renewable energy supply case studies. The novel stochastic algorithm allows a more robust optimization compared to a deterministic optimization, and system management is ensured under several future disturbances realization. Furthermore, the integration of Power-to-Gas solutions warrants the energy security of the energy systems and acts as a buffer to forestall unpredictable behavior of the disturbances.

  • 21.
    Rahman, Moksadur
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Fentaye, Amare Desalegn
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aslanidou, Ioanna
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    A Framework for Learning System for Complex Industrial Processes2020In: 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.

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  • 22.
    Rahman, Moksadur
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Xin, Zhao
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Diagnostics-Oriented Modelling of Micro Gas Turbines for Fleet Monitoring and Maintenance Optimization2018In: Processes, ISSN 2227-9717, Vol. 6, no 11Article in journal (Refereed)
    Abstract [en]

    The market for the small-scale micro gas turbine is expected to grow rapidly in the coming years. Especially, utilization of commercial off-the-shelf components is rapidly reducing the cost of ownership and maintenance, which is paving the way for vast adoption of such units. However, to meet the high-reliability requirements of power generators, there is an acute need of a real-time monitoring system that will be able to detect faults and performance degradation, and thus allow preventive maintenance of these units to decrease downtime. In this paper, a micro gas turbine based combined heat and power system is modelled and used for development of physics-based diagnostic approaches. Different diagnostic schemes for performance monitoring of micro gas turbines are investigated.

  • 23.
    Rossi, I.
    et al.
    University of Genoa, Genova, Italy.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Traverso, A.
    University of Genoa, Genova, Italy.
    Advanced Control for Clusters of SOFC/Gas Turbine Hybrid Systems2018In: Journal of engineering for gas turbines and power, ISSN 0742-4795, E-ISSN 1528-8919, Vol. 140, no 5, article id 051703Article in journal (Refereed)
    Abstract [en]

    The use of model predictive control (MPC) in advanced power systems can be advantageous in controlling highly coupled variables and optimizing system operations. Solid oxide fuel cell/gas turbine (SOFC/GT) hybrids are an example where advanced control techniques can be effectively applied. For example, to manage load distribution among several identical generation units characterized by different temperature distributions due to different degradation paths of the fuel cell stacks. When implementing an MPC, a critical aspect is the trade-off between model accuracy and simplicity, the latter related to a fast computational time. In this work, a hybrid physical and numerical approach was used to reduce the number of states necessary to describe such complex target system. The reduced number of states in the model and the simple framework allow real-time performance and potential extension to a wide range of power plants for industrial application, at the expense of accuracy losses, discussed in the paper. 

  • 24.
    Stenfelt, Mikael
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    AUTOMATIC GAS TURBINE MATCHING SCHEME ADAPTATION FOR ROBUST GPA DIAGNOSTICS2019In: Proceedings of the ASME Turbo ExpoVolume 6, 2019, 2019, Vol. 6Conference paper (Refereed)
    Abstract [en]

    When performing gas turbine diagnostics using Gas Path Analysis (GPA), a convenient way of extracting the degradations is by feeding the measured data from a gas turbine to a well-tuned gas turbine performance code, which in turn calculates the deltas on the chosen health parameters matching the measured inputs. For this, a set of measured parameters must be matched with suitable health parameters, such as deltas on compressor and turbine efficiency and flow capacity.

    In aero engines, the number of sensors are in general limited due to cost and weight constraints and only the necessary sensors for safe engine operation are available. Some important sensors may have redundancy in case of a sensor loss but it is far from certain that this applies to all sensors available.

    If a sensor malfunctions by giving false or no values, the functions using the sensor will be negatively affected in some way causing them to either synthesize a fictive measurement, changing operating scheme, going into a degraded operating mode or shutting down parts or the whole process. If an onboard diagnostic algorithm fails due to sensor faults it will lead to a decrease in flight safety, thus there is a need for a robust system.

    This paper presents a strategy for automatic modifications of the gas turbine diagnostic matching scheme when sensors malfunction to ensure a robust function. When a sensor fault is detected and classified as malfunctioning, the gas turbine matching scheme is modified according to predefined rules. If possible, a redundant measurement replaces the faulty measurement. If not, the matching scheme will be modified by determining if any health parameters cannot be derived by the functional set of measurements and remove the least valuable health parameter while maintaining a working matching scheme for the remaining health parameters.

  • 25.
    Taha, Mohammed
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Lundvall, Nick
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Salman, Awais
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Vouros, Stavros
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Techno-economic evaluation of hydrogen production for airport hubs2024In: Energy Proceedings, Scanditale AB , 2024, Vol. 45Conference paper (Refereed)
    Abstract [en]

    Hydrogen is considered one of the most promising alternative fuels for aviation, which can be used to power aircraft and airport ground services. Onsite hydrogen production from renewables can be suitable for small- size airports, while the larger size airports can be supplied through transportation either from dedicated green hydrogen production plants or other sources of hydrogen. This paper presents a study of two hydrogen supply scenarios, one taking the small airport of Stockholm Skavsta as a case study for in-house hydrogen production. The second is evaluating offshore green hydrogen supply to the large size airport of Arlanda. The in-house hydrogen production evaluates 18 scenarios covering all possible scenarios for alkaline, PEM, and solid oxide electrolysis as production means and compressed, cryo- compressed, and liquid gas as storage, with power supply from grid and grid plus in-house solar system. The optimum production and storage facility size is determined in association with the levelized cost and carbon emissions for each scenario. For the large-size airport, the study evaluates the hydrogen supply from offshore production facilities transported as compressed, cryo-compressed, or liquid gas via offshore pipeline and onshore pipeline, Offshore pipeline and truck, Ship and onshore pipeline, or Ship and truck. The results showed the levelized cost to be between 2.93-2.44 Euro/kg H2 in the case of in-house production. Compressed hydrogen offshore and onshore pipeline is the least cost for Arlanda airport hydrogen supply. This paper demonstrates a direction for aviation sector decarbonization and establishes a pathway for airports' in-house hydrogen production and outsourced hydrogen supply.

  • 26.
    Zaccaria, Valentina
    et al.
    U.S. Department of Energy, US.
    Branum, Zachary
    Arizona State University, USA.
    Tucker, David
    U.S. Department of Energy, US.
    Fuel Cell Temperature Control with a Pre-Combustor in SOFC Gas Turbine Hybrids during Load Changes2017In: Journal of electrochemical energy conversion and storage, ISSN 2381-6872, Vol. 14, p. 031006-031014Article in journal (Refereed)
    Abstract [en]

    The use of high temperature fuel cells, such as Solid Oxide Fuel Cells (SOFCs), for power generation is considered a very efficient and clean solution to conservation of energy resources. When the SOFC is coupled with a gas turbine, the global system efficiency can go beyond 70% on natural gas LHV. However, durability of the ceramic material and system operability can be significantly penalized by thermal stresses due to temperature fluctuations and non-even temperature distributions. Thermal management of the cell during load following is therefore essential.The purpose of this work was to develop and test a pre-combustor model for real-time applications in hardware-based simulations, and to implement a control strategy to keep constant cathode inlet temperature during different operative conditions. The real-time model of the pre-combustor was incorporated into the existing SOFC model and tested in a hybrid system facility, where a physical gas turbine and hardware components were coupled with a cyber-physical fuel cell for flexible, accurate, and cost-reduced simulations.The control of the fuel flow to the pre-combustor was proven to be effective in maintaining a constant cathode inlet temperature during a step change in fuel cell load. With a 20 A load variation, the maximum temperature deviation from the nominal value was below 0.3% (3K). Temperature gradients along the cell were maintained below 10 K/cm. An efficiency analysis was performed in order to evaluate the impact of the pre-combustor on the overall system efficiency.

  • 27.
    Zaccaria, Valentina
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Cuneo, Alessandra
    University of Genoa, Italy.
    Sorce, Alessandro
    University of Genoa, Italy.
    Influence of multiple degrading components on fuel cell gas turbine hybrid systems lifetime2018In: Proceedings of GPPS Forum 18 Global Power and Propulsion Society Zurich, 10th-12th January 2018, 2018Conference paper (Refereed)
    Abstract [en]

    Energy system reliability and operational cost depend highly on the performance degradation experienced by system components. In complex systems, degradation of each single component affects matching and interactions of different system parts. Gas turbine fuel cell hybrid systems combine two different technologies to produce power with an extremely high conversion efficiency. Severe performance decay over time currently limits high temperature fuel cells lifetime; although at a different rate, gas turbine engines also experience gradual deterioration phenomena such as erosion, corrosion, and creep. This work aims at evaluating, for the first time, the complex performance interaction between degrading components in a hybrid system. The effect of deterioration in gas turbine pressure ratio and efficiency on fuel cell performance was analyzed, and at the same time, the impact of the degrading fuel cell thermal output on turbine blade aging was modeled to estimate a remaining useful lifetime.

  • 28.
    Zaccaria, Valentina
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dik, Andreas
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Bitén, Nikolas
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aslanidou, Ioanna
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Conceptual Design of a 3-Shaft Turbofan Engine with Reduced Fuel Consumption for 20252017In: Energy Procedia / [ed] Elsevier, 2017Conference paper (Refereed)
    Abstract [en]

    In the past decade, aircraft fuel burn has been continually decreased, mainly by improving thermal and propulsion efficiencies with consequent decrement in specific fuel consumption. In view of future emission specifications, the requirements for SFC in the forthcoming years are expected to become more stringent. In this paper, a preliminary design of a turbofan engine for entry in service in 2025 was performed. The design of a baseline 2010 EIS engine was improved according to 2025 specifications. A thermodynamic analysis was carried out to select optimal jet velocity ratio, pressure ratio, and temperatures with the goal of minimizing specific fuel consumption. A gas path layout was generated and an aerodynamic analysis was performed to optimize the engine stage by stage design. The optimization resulted in a 3-shaft turbofan jet engine with a 21% increase in fan diameter, a 2.2% increment in engine length, and a fuel burn improvement of 11% compared to the baseline engine, mainly due to an increment in propulsive efficiency. A sensitivity analysis was also conducted to highlight what the focus of technology development should be.

  • 29.
    Zaccaria, Valentina
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Fentaye, Amare Desalegn
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Assessment of Dynamic Bayesian Models for Gas Turbine Diagnostics, Part 1: Prior Probability Analysis2021In: Machines, E-ISSN 2075-1702, Vol. 9, no 11, article id 298Article in journal (Refereed)
    Abstract [en]

    The reliability and cost-effectiveness of energy conversion in gas turbine systems are strongly dependent on an accurate diagnosis of possible process and sensor anomalies. Because data collected from a gas turbine system for diagnosis are inherently uncertain due to measurement noise and errors, probabilistic methods offer a promising tool for this problem. In particular, dynamic Bayesian networks present numerous advantages. In this work, two Bayesian networks were developed for compressor fouling and turbine erosion diagnostics. Different prior probability distributions were compared to determine the benefits of a dynamic, first-order hierarchical Markov model over a static prior probability and one dependent only on time. The influence of data uncertainty and scatter was analyzed by testing the diagnostics models on simulated fleet data. It was shown that the condition-based hierarchical model resulted in the best accuracy, and the benefit was more significant for data with higher overlap between states (i.e., for compressor fouling). The improvement with the proposed dynamic Bayesian network was 8 percentage points (in classification accuracy) for compressor fouling and 5 points for turbine erosion compared with the static network.

  • 30.
    Zaccaria, Valentina
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Fentaye, Amare Desalegn
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Assessment of dynamic bayesian models for gas turbine diagnostics, part 2: Discrimination of gradual degradation and rapid faults2021In: Machines, E-ISSN 2075-1702, Vol. 9, no 12, article id 308Article in journal (Refereed)
    Abstract [en]

    There are many challenges that an effective diagnostic system must overcome for successful fault diagnosis in gas turbines. Among others, it has to be robust to engine-to-engine variations in the fleet, it has to discriminate between gradual deterioration and abrupt faults, and it has to identify sensor faults correctly and be robust in case of such faults. To combine their benefits and overcome their limitations, two diagnostic methods were integrated in this work to form a multi-layer system. An adaptive performance model was used to track gradual deterioration and detect rapid or abrupt anomalies, while a series of static and dynamic Bayesian networks were integrated to identify component degradation, component abrupt faults, and sensor faults. The proposed approach was tested on synthetic data and field data from a single-shaft gas turbine of 50 MW class. The results showed that the approach could give acceptable accuracy in the isolation and identification of multiple faults, with 99% detection and isolation accuracy and 1% maximum error in the identified fault magnitude. The approach was also proven robust to sensor faults, by replacing the faulty signal with an estimated value that had only 3% error compared to the real measurement.

  • 31.
    Zaccaria, Valentina
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Fentaye, Amare Desalegn
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    BAYESIAN INFORMATION FUSION FOR GAS TURBINES DIAGNOSTICS AND PROGNOSTICS2023In: Proc. ASME Turbo Expo, American Society of Mechanical Engineers (ASME) , 2023Conference paper (Refereed)
    Abstract [en]

    Prognosis, or the forecasting of remaining operational life of a component, is a fundamental step for predictive maintenance of turbomachines. While diagnostics gives important information on the current conditions of the engine, it is through prognostics that a suitable maintenance interval can be determined, which is critical to minimize costs. However, mature prognostic models are still lacking in industry, which still heavily relies on human experience or generic statistical quantifications. Predicting future conditions is very challenging due to many factors that introduce significant uncertainty, including unknown future machine operations, interaction between multiple faults, and inherent errors in diagnostic and prognostic models. Given the importance to quantify this uncertainty and its impact on operational decisions, this work presents an information fusion approach for gas turbine prognostics. Condition monitoring performed by a Bayesian network is fused with a particle filter for prognosis of gas turbine degradation, and the effect of diagnostic models uncertainty on the prognosis are estimated through probabilistic analysis. Gradual and rapid degradation are simulated on a gas turbine performance model and the impact of sensor noise and initial conditions for the particle filter estimation are assessed. This work demonstrates that the combination of Bayesian networks and particle filters can give good results for short-term prognosis.

  • 32.
    Zaccaria, Valentina
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Fentaye, Amare Desalegn
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Stenfelt, Mikael
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. SAAB Aeronautic, Linköping, Sweden.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Probabilistic model for aero-engines fleet condition monitoring2020In: Aerospace, E-ISSN 2226-4310, Vol. 7, no 6, article id 66Article in journal (Refereed)
    Abstract [en]

    Since aeronautic transportation is responsible for a rising share of polluting emissions, it is of primary importance to minimize the fuel consumption any time during operations. From this perspective, continuous monitoring of engine performance is essential to implement proper corrective actions and avoid excessive fuel consumption due to engine deterioration. This requires, however, automated systems for diagnostics and decision support, which should be able to handle large amounts of data and ensure reliability in all the multiple conditions the engines of a fleet can be found in. In particular, the proposed solution should be robust to engine-to-engine deviations and dierent sensors availability scenarios. In this paper, a probabilistic Bayesian network for fault detection and identification is applied to a fleet of engines, simulated by an adaptive performance model. The combination of the performance model and the Bayesian network is also studied and compared to the probabilistic model only. The benefit in the suggested hybrid approach is identified as up to 50% higher accuracy. Sensors unavailability due to manufacturing constraints or sensor faults reduce the accuracy of the physics-based method, whereas the Bayesian model is less aected.

  • 33.
    Zaccaria, Valentina
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ferrari, Mario
    University of Genoa, Italy.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Adaptive Control of Micro Gas Turbine for Engine Degradation Compensation2020In: Journal of engineering for gas turbines and power, ISSN 0742-4795, E-ISSN 1528-8919, Vol. 142, no 4, article id GTP-19-1400Article in journal (Refereed)
    Abstract [en]

    Microgas turbine (MGT) engines in the range of 1–100 kW are playing a key role in distributed generation applications, due to the high reliability and quick load following that favor their integration with intermittent renewable sources. Micro-combined heat and power (CHP) systems based on gas turbine technology are obtaining a higher share in the market and are aiming at reducing the costs and increasing energy conversion efficiency. An effective control of system operating parameters during the whole engine lifetime is essential to maintain desired performance and at the same time guarantee safe operations. Because of the necessity to reduce the costs, fewer sensors are usually available than in standard industrial gas turbines, limiting the choice of control parameters. This aspect is aggravated by engine aging and deterioration phenomena that change operating performance from the expected one. In this situation, a control architecture designed for healthy operations may not be adequate anymore, because the relationship between measured parameters and unmeasured variables (e.g., turbine inlet temperature (TIT) or efficiency) varies depending on the level of engine deterioration. In this work, an adaptive control scheme is proposed to compensate the effects of engine degradation over the lifetime. Component degradation level is monitored by a diagnostic tool that estimates performance variations from the available measurements; then, the information on the gas turbine health condition is used by an observer-based model predictive controller to maintain the machine in a safe range of operation and limit the reduction in system efficiency.

  • 34.
    Zaccaria, Valentina
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ferrari, Mario L.
    Univ Genoa, TPG, Genoa, Italy..
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    ADAPTIVE CONTROL OF MICRO GAS TURBINE FOR ENGINE DEGRADATION COMPENSATION2019In: PROCEEDINGS OF THE ASME TURBO EXPO: TURBOMACHINERY TECHNICAL CONFERENCE AND EXPOSITION, 2019, VOL 3, AMER SOC MECHANICAL ENGINEERS , 2019Conference paper (Refereed)
    Abstract [en]

    Micro gas turbine engines in the range of 1-100 kW are playing a key role in distributed generation applications, due to the high reliability and quick load following that favor their integration with intermittent renewable sources. Micro-CHP systems based on gas turbine technology are obtaining a higher share in the market and are aiming at reducing the costs and increasing energy conversion efficiency. An effective control of system operating parameters during the whole engine lifetime is essential to maintain desired performance and at the same time guarantee safe operations. Because of the necessity to reduce the costs, fewer sensors are usually available than in standard industrial gas turbines, limiting the choice of control parameters. This aspect is aggravated by engine aging and deterioration phenomena that change operating performance from the expected one. In this situation, a control architecture designed for healthy operations may not be adequate anymore, because the relationship between measured parameters and unmeasured variables (e.g. turbine inlet temperature or efficiency) varies depending on the level of engine deterioration. In this work, an adaptive control scheme is proposed to compensate the effects of engine degradation over the lifetime. Component degradation level is monitored by a diagnostic tool that estimates performance variations from available measurements; then, the information on the gas turbine health condition is used by an observer-based model predictive controller to maintain the machine in a safe range of operation and limit the reduction in system efficiency.

  • 35.
    Zaccaria, Valentina
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Rahman, Moksadur
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aslanidou, Ioanna
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    A review of information fusion methodsfor gas turbine diagnostics2019In: Sustainability, E-ISSN 2071-1050, Vol. 11, no 22, article id 6202Article, review/survey (Refereed)
    Abstract [en]

    The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed. 

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  • 36.
    Zaccaria, Valentina
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Stenfelt, Mikael
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aslanidou, Ioanna
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Fleet monitoring and diagnostics framework based on digital twin of aero-engines2018In: Proceedings of the ASME Turbo Expo, American Society of Mechanical Engineers (ASME) , 2018, Vol. 6Conference paper (Refereed)
    Abstract [en]

    Monitoring aircraft performance in a fleet is fundamental to ensure optimal operation and promptly detect anomalies that can increase fuel consumption or compromise flight safety. Accurate failure detection and life prediction methods also result in reduced maintenance costs. The major challenges in fleet monitoring are the great amount of collected data that need to be processed and the variability between engines of the fleet, which requires adaptive models. In this paper, a framework for monitoring, diagnostics, and health management of a fleet of aircrafts is proposed. The framework consists of a multi-level approach: starting from thresholds exceedance monitoring, problematic engines are isolated, on which a fault detection system is then applied. Different methods for fault isolation, identification, and quantification are presented and compared, and the related challenges and opportunities are discussed. This conceptual strategy is tested on fleet data generated through a performance model of a turbofan engine, considering engine-to-engine and flight-to-flight variations and uncertainties in sensor measurements. Limitations of physics-based methods and machine learning techniques are investigated and the needs for fleet diagnostics are highlighted. 

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  • 37.
    Zaccaria, Valentina
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Stenfelt, Mikael
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Sjunnesson, Anna
    Siemens Industrial Turbomachinery AB, Sweden.
    Andreas, Hansson
    Siemens Industrial Turbomachinery AB, Sweden.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    A MODEL-BASED SOLUTION FOR GAS TURBINE DIAGNOSTICS: SIMULATIONS AND EXPERIMENTAL VERIFICATION2019In: Proceedings of the ASME Turbo ExpoVolume 6, 2019, 2019, article id GT2019-90858Conference paper (Refereed)
    Abstract [en]

    Prompt detection of incipient faults and accurate monitoring of engine deterioration are key aspects for ensuring safe operations and planning a timely maintenance. Modern computing capabilities allow for more and more complex tools for engine monitoring and diagnostics. Nevertheless, an underlying physics-based approach is often preferable, because not only the “what” but also the “why” can be identified, providing an effective decision support tool to the service engineer. In this work, a physics-based adaptive model is used to evaluate performance deltas and correct the data to reference conditions (gas turbine load and ambient conditions), while a data-driven correlation algorithm identifies the most likely matches within a fault signatures database. Possible faults are ordered from the highest correlation in the decision support system and the most likely fault can be selected based on the number of occurrences and the associated correlation. Gradual engine degradation can also be monitored by displaying performance deltas trends during time. The diagnostics tool was tested on a validated performance model of a single-shaft industrial gas turbine and subsequently on experimental data. This paper presents the diagnostics system structure, the model adaptation scheme, and the results obtained from simulated and real fault data. Accurate fault isolation and severity identification were achieved in all cases, demonstrating the tool capability for decision support system.

  • 38.
    Zaccaria, Valentina
    et al.
    University of Genova, Italy.
    Traverso, Alberto
    University of Genova, Italy.
    Tucker, David
    U.S. Department of Energy, US.
    Advanced gas turbine hybrid power systems to improve SOFC economic viability2017In: Journal of the Global Power and Propulsion Society, E-ISSN 2515-3080, Vol. 1, p. 28-40Article in journal (Refereed)
    Abstract [en]

    Coupling a solid oxide fuel cell (SOFC) with a gas turbineprovides a substantial increment in system efficiency comparedto the separate technologies, which can potentiallyintroduce economic benefits and favor an early market penetrationof fuel cells. Currently, the economic viability of suchsystems is limited by fuel cell short lifetime due to a progressiveperformance degradation that leads to cell failure.Mitigating these phenomena would have a significant impacton system economic feasibility. In this study, the lifetime of astandalone, atmospheric SOFC system was compared to apressurized SOFC gas turbine hybrid and an economic analysiswas performed. In both cases, the power production wasrequired to be constant over time, with significantly differentresults for the two systems in terms of fuel cell operating life,system efficiency, and economic return. In the hybrid system,an extended fuel cell lifetime is achieved while maintaininghigh system efficiency and improving economic performance.In this work, the optimal power density was determined forthe standalone fuel cell in order to have the best economicperformance. Nevertheless, the hybrid system showed bettereconomic performance, and it was less affected by the stackcost.

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  • 39.
    Zaccaria, Valentina
    et al.
    U.S. Department of Energy, United States.
    Tucker, David
    U.S. Department of Energy, United States.
    Traverso, Alberto
    University of Genoa, Italy.
    Transfer function development for SOFC/GT hybrid systems control using cold air bypass2016In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 165, p. 695-706Article in journal (Refereed)
    Abstract [en]

    Fuel cell gas turbine hybrids present significant challenges in terms of system control because of the couplingof different time-scale phenomena. Hence, the importance of studying the integrated systemdynamics is critical. With the aim of safe operability and efficiency optimization, the cold air bypass valvewas considered an important actuator since it affects several key parameters and can be very effective incontrolling compressor surge. Two different tests were conducted using a cyber-physical approach. TheHybrid Performance (HyPer) facility couples gas turbine equipment with a cyber physical solid oxide fuelcell in which the hardware is driven by a numerical fuel cell model operating in real time. The tests wereperformed moving the cold air valve from the nominal position of 40% with a step of 15% up and down,while the system was in open loop, i.e. no control on turbine speed or inlet temperature. The effect of thevalve change on the system was analyzed and transfer functions were developed for several importantvariables such as cathode mass flow, total pressure drop and surge margin. Transfer functions can showthe response time of different system variables, and are used to characterize the dynamic response of theintegrated system. Opening the valve resulted in an immediate positive impact on pressure drop andsurge margin. A valve change also significantly affected fuel cell temperature, demonstrating that the coldair bypass can be used for thermal management of the cell.

  • 40.
    Zlatkovikj, Milan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Dynamic model for large scale hot water storage tank2023In: Energy proceedings, ISSN 2965, Vol. 30, p. 1-6Article in journal (Refereed)
    Abstract [en]

    Due to the growing share of intermittent renewable energy sources (RES), the requirement for flexibility in the energy system is increasing to balance the generation and demand of electricity. It has been well recognized that Combined heat and power plants (CHPs) can contribute towards improved flexibility in the energy system. Thermal energy storage (TES), using hot water as working fluid, is a commonly integrated in CHPs, which allows for decoupling of heat and electricity generation. It has been verified that proper control of the operation of TES can improve the flexibility provided by CHP. The development of advanced control system relies on accurate dynamic modeling of TES. In this work, a one-dimension (1D) dynamic model for large scale TES is developed in Dymola, based on mass and energy balances. It is validated against the operational data from a real CHP plant. Results show that the model can capture the dynamic variation in the operation of the TES energy content with maximum deviations of 6.5% from the maximum value.

  • 41.
    Zlatkovikj, Milan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aslanidou, Ioanna
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Development of feed-forward model predictive control for applications in biomass bubbling fluidized bed boilers2022In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 115, p. 167-180Article in journal (Refereed)
    Abstract [en]

    In order to accommodate more intermittent renewable energy sources, biomass fueled combined heat and power plants (bio-CHPs) can contribute towards sustainable and flexible energy systems. However, the varying properties of biomass, such as moisture contents and heating values, can clearly affect the combustion in boilers, which further affects the flexibility provided by bio-CHPs. In order to achieve better control, this paper proposes a feed-forward model predictive controller (FF MPC) to handle the variation of biomass properties. A dynamic model was built in Dymola to simulate the performance of a bubbling fluidized bed boiler, which was validated against the real operation data. Based on the simulation, the key manipulated variables were optimized for the given controlled variables. The advantages of the proposed FF MPC were demonstrated through comparisons with proportional- integral (PI), FF PI and MPC. The results of FF MPC show the best performance, such as the lowest magnitude of fluctuations for 3 outputs (thermal load, steam and fluidized bed temperature), and the most stable operation. Consequently, FF MPC can potentially increase the electricity generation and further lead to an economic benefit. Using one week in winter as an example, compared to PI, FF PI and MPC, FF MPC can generate more electricity and improve revenues by 14.77 MWh/590 =C, 4.1 MWh/164 =C and 5.03 MWh/211.2 =C respectively.

  • 42.
    Zlatkovikj, Milan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Aslanidou, Ioanna
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Kyprianidis, Konstantinos
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Simulation study for comparison of control structures for BFB biomass boiler2020Conference paper (Other academic)
  • 43.
    Zlatkovikj, Milan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Influence of fuel properties on the performance of the feed forward model predictive control (FF MPC) for biomass boilers2023In: Energy Proceedings, ISSN 2004-2965, Vol. 32, p. 1-6Article in journal (Refereed)
    Abstract [en]

    The growing share of renewable energy sources drives the need for increased flexibility in the energy systems. The flexibility provision from thermal plants is limited by the boiler’s thermal inertia as a bottleneck. Advanced controllers, such as model predictive control (MPC), have been identified as potential flexibility enablers. Fuel properties are crucial input for controllers. This work investigated the feasibility of using the properties obtained online by using near infrared spectroscopy based soft sensor to further improve the control performance. The performance of the existing proportional integral (PI) controller is compared with those of 2 feed forward (FF) MPC controllers. Both FF MPCs have significant improvement compared to PI controller and the FF MPC based on the full elemental composition shows the best performance due to more complete fuel information. There is a potential for revenues improvement with advanced control up to 1050 euros for one operation day.

  • 44.
    Zlatkovikj, Milan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zaccaria, Valentina
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Influence of the transient operation of a large-scale thermal energy storage system on the flexibility provided by CHP plants2023In: e-Prime - Advances in Electrical Engineering, Electronics and Energy, ISSN 2772-6711, Vol. 4, article id 100160Article in journal (Refereed)
    Abstract [en]

    Among many technical options to improve the flexibility in combined heat and power (CHP) plants, thermal energy storage (TES) has attracted the most attention with its high applicability and benefit. Previous studies normally adopted a simplified approach for modelling a TES system, which assumes the optimized charged or discharged rate of heat can always be realized within the rated capacity. However, this may yield unfeasible results as the charging and discharging rates are dependent on the dynamic status of a TES, such as the state of charge (SOC) and water temperature, and the water flowrate for charging and discharging. In order to consider the transient operation of a TES, a 1D dynamic model was developed and validated against measured data from a real CHP plant. To analyze the dynamic performance of a TES, two key performance indicators (KPI), the maximum charging/discharging rate (C/D-ratemax) and the constant maximum charging/discharging rate that can be maintained constantly for one hour (CC/CD-ratemax) were employed. By doing simulations, it has been found that the CC/CD-ratemax was lower than the C/D-ratemax for most given SOCs of the studied TES. The developed model was also used to examine the optimized operation of a TES for providing flexibility. Some unfeasible results have been identified, as the optimized hourly charging/discharging rates were constrained by the CC/CD-ratemax. Therefore, it is of great importance to integrate a detailed dynamic model when optimizing the dispatch of electricity and heat for a CHP plant. 

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