V. Cortés

Influence of plate spacing and ash resistivity on the efficiency of electrostatic precipitators

Abstract This paper contributes to the knowledge of the behaviour of electrostatic precipitators in pulverized-coal power plants. The information was obtained through real tests carried out on different types of coal with real flue gases in a pilot precipitator installed at a pulverized-coal power plant. In particular, the influence of different plate spacings, the characteristics of the ash generated by each type of coal and the mass load of ash in the gas flow are considered. The tests carried out lead to conclusions with respect to the most advantageous arrangement of the plates for each case. Conclusions with respect to other variables that affect the efficiency, such as gas velocity, can also be drawn.

OPTICOM: Advanced automatic monitoring system of local combustion conditions for improving boiler performance in PC power plants ☆

Abstract Improving combustion in pulverised coal power stations offers the greatest potential for economic savings. However, optimisation of these facilities is normally limited by an inability to adequately monitor operational conditions. This paper presents the OPTICOM technology, a novel monitoring approach for characterising local combustion conditions, that will contribute to avoid the mentioned limitation concerning monitoring of operational conditions. OPTICOM technology allows one to obtain temperature and gas concentration (O2, CO, NOx, SO2) profiles that exist within the most critical regions of the furnace. These measurements provide a local evaluation of the combustion process, which allows the operator to consider the unit as a set of small virtual units, each consisting of a single burner, that can therefore be individually optimised. Adequate adjustment of each of these ‘smaller facilities’ brings about overall optimisation of the boiler.

Modelling of pulverized coal combustion with respect to fly ash particle size distribution

Abstract An approach is made to predict fly ash production and characteristics on the basis of: experimental information from a power plant tracking programme; and the results of a pulverized coal combustion model. Conclusions presented in this paper do not agree completely with previous experimental results obtained under laboratory simulated combustion conditions, or with generally accepted hypotheses about fly ash particle size configuration processes, showing the critical influence of individual particles temperature evolution on the final ash size distribution. The validity of a char fragmentation model, used extensively in fly ash size estimation, is also investigated. It is found that the presence of unburned particles in industrial fly ash must always be taken into account. It is also proved that model application must be restricted to a small number of cases that satisfy the global assumptions.

Study of the Use of Bag Filters in Hot Gas Filtration Applications, Pilot Plant Experiences

A complete experimental campaign has been carried out in a hot gas filtration test facility so as to test different types of bag filters. The facility is designed to operate under a wide range of conditions, thus providing an excellent tool for the investigation of hot gas filtration applications for the conventional and advanced electrical power generation industry such as IGCC, PFBC or fuel cell technologies.

Ozone forecasting in the urban area of Seville using artificial neural network technology

In this paper a short-term ambient ozone model based on artificial neural networks is presented. The method has been successfully applied in the city of Seville (Spain) to forecast ozone emission concentration typically one day in advance. The applied methodology is based on the following steps: I.The development of an exhaustive measurement campaign in order to evaluate ozone pollution in the city during 1999 summer, autumn and winter (partially) providing a consistent data series of ozone and meteorological variables. 2.A screening of the most relevant variables affecting ozone concentration to obtain accurate forecasting capabilities based on a reduced number of easy-to-measure parameters: traffic, meteorology and features of the area (sheet width, building height, etc.). 3.A neural network approach based on a backpropagation algorithm is adopted to provide both spatial and temporal predictions. In the first case, estimated ozone concentration can be obtained in any area of the city without direct measurements. In the second case, it is possible to predict future ozone concentration levels in the same locations based on weather and traffic flows forecasting.