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Improvement of energy efficiency, reduction of operating costs, and reduction of harmful emissions released into the atmosphere are issues of major concern in modern energy plants. While air emissions have to be restricted due to tightening environmental legislation, at the same time it is ever more important to be able to respond quickly to any changes in the load demand or fuel quality. As unpredictability increases with changing fuel quality and more complex operational strategies, undesired phenomena such as increased emission release rates may become more likely. Therefore, it is crucial that emission monitoring systems are able to adapt to varying conditions, and advanced methodologies are needed for monitoring and decision-support. In this paper a novel approach for advanced monitoring of emissions in CFB (circulating fluidized bed) boilers is described. In this approach a model based on SOM (self-organizing maps) is updated regularly to respond to the prevailing condition of the boiler. After creating each model a new set of measurements is input to the system, and the current state of the process is determined using vector distance calculation. Finally, the system evaluates the current condition and may alert if a preset limit defined for each emission component is exceeded. [ABSTRACT FROM AUTHOR]

Abstract: Efforts to reduce harmful emissions and the increasing demands for combustion efficiency have generated a number of challenges for power plants. Changes in the operation of a combustion process, for example, can induce fluctuations that have unexpected consequences such as an increased level of emissions. Despite the importance of these changes, their impact and relevance are often ignored in analyses of industrial process data due to the complexity of these phenomena. It seems that the behavioral evolution of a process could be understood more easily by monitoring the transition of the process from one characteristic state to another. We demonstrate here that the self-organizing map (SOM) provides an efficient method for revealing the most characteristic features of input data, making it a powerful tool for discovering general phenomena and visualizing the behavior and evolution of a combustion process. In this approach the process data are analyzed using a SOM and K-means clustering to create subsets representing the separate process states in the boiler. A trajectory analysis is then performed to indicate fluctuations in the process and their implications. The results show that process fluctuations can significantly affect the levels of nitrogen oxides released. The method enables efficient diagnosis, and provides a clear illustration of the evolution of the process and an applicable means of defining the best path for achieving a more efficient process that produces less emissions. [Copyright &y& Elsevier]

The number of people in a certain place in desired time period is one of the main questions in many monitoring and management applications. Such information is needed also in tourism which has been one of most growing business areas in recent years and it have become a more important area of the service sector. Thus effective and sustainable management is needed. Regional visitor monitoring provides general tools for managers and decision-makers to handle multidimensional growth and the development of tourism business. Furthermore, predictive information is a key for solving many environmentally related daily problems like minimizing raw material loss. In this study, we present continuous approach for monitoring and predicting regional visitor attendance levels using inferred data and self-organizing map. The data used in study contained variables describing regional mobile telecommunication events, weather conditions, traffic density, restaurant sales and the use of accommodation facilities. The self-organizing map (SOM) was used to integrate the variables into a combined regional attendance index and multilayer perceptron was applied to predict the number of visitors. The proposed method was tested and an online modelling system created using real data gathered from the recreational area of Tahko. In general, the results showed that the method is suitable for describing a real regional situation and seasonal variations in visitor attendance levels. Moreover, the results indicated that mobile telecommunication data improve the modelling of daily visitors at a more detailed level. Presented online modelling system were applied, during the research project, to optimize number of operating taxi vehicles, plan opening hours and times of grocery shop and reschedule the collection of municipal wastes. [ABSTRACT FROM AUTHOR]

Evaluation of online information on operating conditions is necessary when reducing air emissions in energy plants. In this respect, automated monitoring and control are of primary concern, particularly in biomass combustion. As monitoring of emissions in power plants is ever more challenging because of low-grade fuels and fuel mixtures, new monitoring applications are needed to extract essential information from the large amount of measurement data. The management of emissions in energy boilers lacks economically efficient, fast, and competent computational systems that could support decision-making regarding the improvement of emission efficiency. In this paper, a novel emission monitoring platform based on the self-organizing map method is presented. The system is capable, not only of visualizing the prevailing status of the process and detecting problem situations (i.e. increased emission release rates), but also of analyzing these situations automatically and presenting factors potentially affecting them. The system is demonstrated using measurement data from an industrial circulating fluidized bed boiler fired by forest residue as the primary fuel and coal as the supporting fuel. [ABSTRACT FROM PUBLISHER]

Abstract: There are several challenges confronting energy production nowadays, such as increasing the efficiency of combustion processes and at the same time reducing harmful emissions. The latter, however, often necessitates process improvement, which requires knowledge of the behavior of the process. It is therefore important to develop and implement novel methods for process diagnostics that can respond to the challenges of modern-day energy plants. In this study the formation of nitrogen oxides (NOx) in a circulating fluidized bed (CFB) boiler is modeled by using artificial neural networks (ANN). In the approach used, the process data are first arranged using self-organizing maps (SOM) and k-means clustering to create subsets representing the separate process states in the boiler, including load increase and load decrease situations and conditions of high or low boiler load. After the determination of these process states, variable selection based on multilayer perceptrons (MLP) is performed to obtain information on the factors affecting the formation of NOx in those states. The results show that this approach provides a useful way of monitoring a combustion process. [ABSTRACT FROM AUTHOR]