Adaptive Robust Control of Biomass Fuel Co-Combustion Process

The share of biomass in energy production is constantly growing. This is caused by environmental and industry standards and EU guidelines. Biomass is used in the process of co-firing in large power plants and industrial installations. In the existing power stations, biomass is milled and burned simultaneously with coal. However, low-emission combustion techniques, including biomass co-combustion, have some negative side effects that can be split into two categories. The direct effects influence the process control stability, whereas the indirect ones on combustion installations via increased corrosion or boiler slagging. The effects can be minimised using additional information about the process. The proper combustion diagnosis as well as an appropriate, robust control system ought to be applied. The chapter is devoted to the analysis of modern, robust control techniques for complex power engineering applications

OPTOELEKTRONICZNE SYSTEMY W ZASTOSOWANIACH DIAGNOSTYCZNYCH I POMIAROWYCH

The article presents brief survey of optical diagnostic methods used especially in the case of pulverized coal combustion process as well as coal – biomass blends, that was developed in Institute of Electronic and Information Technology. Lublin University of Technology. Special attention was paid to the methods that can be applied in harsh conditions.W artykule dokonano krótkiego przeglądu optycznych metod diagnostyki procesów przemysłowych ze szczególnym uwzględnieniem procesu spalania pyłu węglowego oraz mieszanin z udziałem biomasy. Zaprezentowane rozwiązania są wynikiem szeregu badań prowadzonych w Instytucie Elektronik i Technik Informacyjnych Politechniki Lubelskiej. Szczególną uwagę poświęcono rozwijanym metodom, które mogą być stosowane także w warunkach przemysłowych

ИНТЕЛЛЕКТУАЛЬНЫЙ числовым программным ДЛЯ MIMD-компьютер

For most scientific and engineering problems simulated on computers the solving of problems of the computational mathematics with approximately given initial data constitutes an intermediate or a final stage. Basic problems of the computational mathematics include the investigating and solving of linear algebraic systems, evaluating of eigenvalues and eigenvectors of matrices, the solving of systems of non-linear equations, numerical integration of initial- value problems for systems of ordinary differential equations.Для більшості наукових та інженерних задач моделювання на ЕОМ рішення задач обчислювальної математики з наближено заданими вихідними даними складає проміжний або остаточний етап. Основні проблеми обчислювальної математики відносяться дослідження і рішення лінійних алгебраїчних систем оцінки власних значень і власних векторів матриць, рішення систем нелінійних рівнянь, чисельного інтегрування початково задач для систем звичайних диференціальних рівнянь.Для большинства научных и инженерных задач моделирования на ЭВМ решение задач вычислительной математики с приближенно заданным исходным данным составляет промежуточный или окончательный этап. Основные проблемы вычислительной математики относятся исследования и решения линейных алгебраических систем оценки собственных значений и собственных векторов матриц, решение систем нелинейных уравнений, численного интегрирования начально задач для систем обыкновенных дифференциальных уравнений

PORÓWNANIE WYBRANYCH METOD WYZNACZANIA OBSZARU PŁOMIENIA W WIZYJNYM SYSTEMIE DIAGNOSTYCZNYM

This paper presents comparison edge detection method of combustion pulverized coal. Compared method are: Canny edge detection operator, level set method and Chan-Vese active contour method. Experimental results show that edges extracted with method based on Chan-Vese active contour model gives good result.W pracy przedstawiono porównanie wybranych metod wykrywania krawędzi dla obrazów spalania pyłu węglowego. Porównano metodę gradientową Canny’ego z metodą zbiorów poziomicowych oraz metodą opartą o model konturu aktywnego Chan-Vese. Wyniki badań pokazują, że metoda korzystająca z modelu Chan-Vese dobrze odwzorowała brzeg obszaru

Approaches to evaluating the quality of masking noise interference

The paper discusses the characteristics of spatial electromagnetic noise generators, as well as the formation of a broadband noise signal. A number of well-known methods for assessing the quality of masking noise interference and the approaches used in them have been described. Approaches to the measurement of masking noise were also determined in assessing their quality. In conclusion, additional methods are proposed for assessing the quality of masking noises, such as searching for correlation of noise in different frequency sub-bands and using statistical and (or) graphical methods (tests) for randomness

Diagnostyka procesu spalania pyłu węglowego wykorzystująca analizę obrazu

Tyt. z nagłówka.Bibliogr. s. 528.Dostępny również w formie drukowanej

Flame Image Processing and Classification Using a Pre-Trained VGG16 Model in Combustion Diagnosis

Nowadays, despite a negative impact on the natural environment, coal combustion is still a significant energy source. One way to minimize the adverse side effects is sophisticated combustion technologies, such as, e.g., staged combustion, co-combustion with biomass, and oxy-combustion. Maintaining the combustion process at its optimal state, considering the emission of harmful substances, safe operation, and costs requires immediate information about the process. Flame image is a primary source of data which proper processing make keeping the combustion at desired conditions, possible. The paper presents a method combining flame image processing with a deep convolutional neural network (DCNN) that ensures high accuracy of identifying undesired combustion states. The method is based on the adaptive selection of the gamma correction coefficient (G) in the flame segmentation process. It uses the empirically determined relationship between the G coefficient and the average intensity of the R image component. The pre-trained VGG16 model for classification was used. It provided accuracy in detecting particular combustion states on the ranging from 82 to 98%. High accuracy and fast processing time make the proposed method possible to apply in the real systems

Flame Image Processing and Classification Using a Pre-Trained VGG16 Model in Combustion Diagnosis

Nowadays, despite a negative impact on the natural environment, coal combustion is still a significant energy source. One way to minimize the adverse side effects is sophisticated combustion technologies, such as, e.g., staged combustion, co-combustion with biomass, and oxy-combustion. Maintaining the combustion process at its optimal state, considering the emission of harmful substances, safe operation, and costs requires immediate information about the process. Flame image is a primary source of data which proper processing make keeping the combustion at desired conditions, possible. The paper presents a method combining flame image processing with a deep convolutional neural network (DCNN) that ensures high accuracy of identifying undesired combustion states. The method is based on the adaptive selection of the gamma correction coefficient (G) in the flame segmentation process. It uses the empirically determined relationship between the G coefficient and the average intensity of the R image component. The pre-trained VGG16 model for classification was used. It provided accuracy in detecting particular combustion states on the ranging from 82 to 98%. High accuracy and fast processing time make the proposed method possible to apply in the real systems