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To optimize the electricity distribution in an electrical grid and integrate power plants with renewable and heat storage energy systems, with a focus on improving energy efficiency while reducing economic costs and emissions, an artificial intelligence method is applied for power plant control and operation monitoring. The effective use of an artificial intelligence method in a power plant can be achieved by implementing real-time digital twins specifically for the most crucial devices, such as power transformers and electric power generators, whose operation and reliability strongly depend on the energy demands and the temperature distribution. However, the development of a power transformer digital twin is based on a complex numerical model that requires high computational demands and large amounts of data for its enhancement. Furthermore, the realtime behaviour of both devices must be considered. Therefore, the main aim of this work is to introduce a hybrid reduced-order model for a large-scale gas-cooled electric power generator and power transformer as the real-time digital twin for a control system. This hybrid approach integrates data gathered from in-field measurements with developed three-dimensional coupled numerical models that can monitor and predict the hot-spot status of both devices at part load, nominal load and overload conditions under different ambient temperatures. The results confirmed the robustness and accuracy of the hybrid reduced-order model within +/- 8.0 K for all output temperatures due to the accurate predictions of the three-dimensional numerical models within +/- 5.0 K.
Celem pracy była ocena wpływu aktywacji za pomocą plazmy wysokoczęstotliwościowej na strukturę chemiczną i właściwości sorpcyjne biowęgla otrzymanego z wysłodzin browarnianych (BSG). Próbki poddano najpierw pirolizie w temperaturze 540C przez 30 minut w atmosferze azotowej, a następnie aktywacji plazmowej (HFPT – High Frequency Plasma Treatment) przez 1 i 5 minut. Analiza widm FTIR wykazała znaczne zmiany strukturalne materiału – zanik pasm charakterystycznych dla ligniny, celulozy i hemicelulozy wskazuje na postępującą dekarboksylację i aromatyzację biocharu. W próbkach po 5 minutach aktywacji nie zaobserwowano żadnych wyraźnych pików, co potwierdza wysokie przetworzenie lignocelulozowej struktury. Właściwościsorpcyjne oceniono na podstawie dopasowania danych do modeli izoterm Langmuira i Freundlicha. Uzyskane rezultaty wykazały, że biochar po 1 minucie aktywacji charakteryzuje się większą heterogenicznością powierzchni, natomiast materiał po 5 minutach aktywacji wykazuje wyraźnie wyższą stałą Langmuira (KL = 52,8), co wskazuje na silniejsze powinowactwo sorbatu. Otrzymane wyniki potwierdzają, że aktywacja plazmowa CO₂ stanowi skuteczną metodę modyfikacji biocharu pod kątem zastosowań sorpcyjnych.
This paper explores the use of sophisticated, predictive AI algorithms for monitoring and optimizing industrial installations of CFB power plant. The effectiveness of the system was shown by applying it to a circulating fluidized bed (CFB 1300) power unit. A customized optimization algorithm was developed to manage the oxygen distribution within the combustion chamber. Implementing the developed control system methodology adjusted the fuel distribution, which in turn impacted the overall performance of the boiler. The approach was evaluated under different boiler operating scenarios, including simulated fuel line malfunctions. The devised methodology enables a reduction of approximately 17% in oxygen distribution imbalance within the combustion chamber when a failure was detected. Furthermore, the optimization algorithms facilitate a seamless adjustment in fuel loads, maintaining the necessary oxygen and temperature distribution at the control plane. Moreover, the capabilities of this system were demonstrated for the automatic identification of malfunctions within two crucial parts of the power unit. The initial issue pertains to a fault in the internal phase insulator of the block transformer, and the second occurrence involved the proactive identification of a membrane wall leak over 13 h before its failure.
Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments.
Performances of a novel hybrid electrostatic filtration system HYBRYDA+ have been investigated in the paper. The semi-industrial scale hybrid system, of a flow rate of 6000 Nm3/h, comprised of conventional electrostatic precipitator, kinematic electrostatic agglomerator and bag filter. The agglomeration process in this unipolar agglomerator was due to the collision between larger particles and smaller ones in the AC electric field. The electrostatic precipitator and kinematic electrostatic agglomerator, which were used upstream of the bag filter resulted in higher collection efficiency of the system than a bag filter operating alone, and the frequency of bag filter regeneration has been reduced. The period of bag filter regeneration without electrostatic agglomerator was about 8 min, but it increased more than 20 times, to 165 min, when the electrostatic precipitator and agglomerator were in operation. The collection efficiency of this system was >99.998%. The combination of electrostatic precipitator and agglomerator mitigate also the problem of bag filter clogging by submicron particles, which are agglomerated with larger particles (>5 µm) in the process of agglomeration.
