Oxy-fuel coal combustion, together with carbon capture and storage or utilization, is a set of technologies allowing to burn coal without emitting globe warming CO 2. As it is expected that oxy-fuel combustion may be used for a retrofit of existing boilers, development of a novel oxy-burners is very important step. It is expected that these burners will be able to sustain stable flame in oxy-fuel conditions, but also, for start-up and emergency reasons, in conventional, air conditions. The most cost effective way of achieving dual-mode boilers is to introduce dual-mode burners. Numerical simulations allow investigation of new designs and technologies at a relatively low cost, but for the results to be trustworthy they need to be validated. This paper proposes a workflow for design, modeling, and validation of dual-mode burners by combining experimental investigation and numerical simulations. Experiments are performed with semi-industrial scale burners in 0.5 MW t test facility for flame investigation. Novel CFD model based on ANSYS FLUENT solver, with special consideration of coal combustion process, especially regarding devolatilization, ignition, gaseous and surface reactions, NO x formation, and radiation was suggested. The main model feature is its ability to simulate pulverized coal combustion under different combusting atmospheres, and thus is suitable for both air and oxy-fuel combustion simulations. Using the proposed methodology two designs of pulverized coal burners have been investigated both experimentally and numerically giving consistent results. The improved burner design proved to be a more flexible device, achieving stable ignition and combustion during both combustion regimes: conventional in air and oxy-fuel in a mixture of O 2 and CO 2 (representing dry recycled flue gas with high CO 2 content). The proposed framework is expected to be of use for further improvement of multi-mode pulverized fuel swirl burners but can be also used for independent designs evaluation.

The use of low-emission combustion techniques in pulverized coal-fired (PC) boilers are usually associated with the formation of a reduced-gas atmosphere near evaporator walls. This increases the risk of high temperature (low oxygen) corrosion processes in coal-fired boilers. The identification of the dynamics and the locations of these processes, and minimizing negative consequences are essential for power plant operation. This paper presents the diagnostic system for determining corrosion risks, based on continuous measurements of flue gas composition in the boundary layer of the combustion chamber, and artificial intelligence techniques. Experience from the implementation of these measurements on the OP-230 hard coal-fired boiler, to identify the corrosion hazard of one of the evaporator walls, has been thoroughly described. The results obtained indicate that the continuous controlling of the concentrations of O2 and CO near the water wall, in combination with the use of neural networks, allows for the forecasting of the corrosion rate of the evaporator. The correlation between flue gas composition and corrosion rate has been demonstrated. At the same time, the analysis of the possibilities of significantly simplifying the measurement system by using neural networks was carried out.

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Abstract. Slagging issues present in pulverized steam boilers very often lead to heat transfer problems, corrosion and not planned outages of boilers which increase the cost of energy production and decrease the efficiency of energy production. Slagging especially occurs in regions with reductive atmospheres which nowadays are very common due to very strict limitations in NOx emissions. Moreover alternative fuels like biomass which are also used in combustion systems from two decades in order to decrease CO2 emissions also usually increase the risk of slagging. Thus the prediction of slagging properties of fuels is not the minor issue which can be neglected before purchasing or mixing of fuels. This however is rather difficult to estimate and even commonly known standard laboratory methods like fusion temperature determination or special indexers calculated on the basis of proximate and ultimate analyses, very often have no reasonable correlation to real boiler fuel behaviour. In this paper the method of determination of slagging properties of solid fuels based on laboratory investigation and artificial neural networks were presented. A fuel data base with over 40 fuels was created. Neural networks simulations were carried out in order to predict the beginning temperature and intensity of slagging. Reasonable results were obtained for some of tested neural networks, especially for hybrid feedforward networks with PCA technique. Consequently neural network model will be used in Common Intelligent Boiler Operation Platform (CIBOP) being elaborated within CERUBIS research project for two BP-1150 and BB-1150 steam boilers. The model among others enables proper fuel selection in order to minimize slagging risk.