Monitoring near burner slag deposition with a hybrid neural network system

Abstract

This paper is concerned with the development of a system to detect and monitor slag growth in the near burner region in a pulverized-fuel (pf) fired combustion rig. These slag deposits are commonly known as 'eyebrows' and can markedly affect the stability of the burner. The study thus involved a series of experiments with two different coals over a range of burner conditions using a 150 kW pf burner fitted with simulated eyebrows. These simulated eyebrows consisted of annular refractory inserts mounted immediately in front of the original burner quarl. Data obtained by monitoring the infra-red radiation and sound emitted by the flame were processed to yield time and frequency-domain features, which were then used to train and test a hybrid neural network. This hybrid 'intelligent' system was based on self organizing map and radial-basis-function neural networks. This system was able to classify different sized eyebrows with a success rate of at least 99.5%. Consequently, it is possible not only to detect the presence of an eyebrow by monitoring the flame, but also the network can provide an estimate of the size of the deposit, over a reasonably large range of conditions.

Soft-computing models for soot-blowing optimization in coal-fired utility boilers

“…Artificial Neural Networks (ANN) have shown their versatility to model the behavior of complex systems in a wide number of scientific and commercial areas, including energy systems and fouling phenomena [18][19][20]. The main advantage of ANN is that it does not need any mathematical model, since it learns from historical data to recognize non-evident relations and patterns in a set of input-output variables, without any prior assumption about their nature.…”

An intelligent monitoring system for the detection of slag deposition on a pulverized coal fired burner

“…In the previous study [6], the monitoring system was tested with Cerrejon and Daw Mill coals with two different-sized burner eyebrows. Consequently, this paper is concerned first of all with the performance of the system over a wider range of eyebrow geometries.…”

“…The present authors have described the initial development of the intelligent monitoring system in previous publications [6,7]. These involved a series of experiments with a 150 kW pulverized coal-fired burner fitted with two different sized, symmetrically shaped, artificial eyebrows.…”

Determination of High Temperature Corrosion Rates of Steam Boiler Evaporators Using Continuous Measurements of Flue Gas Composition and Neural Networks

“…In particular, their fault tolerance for noisy data (typical of boiler data), their abilities to learn from examples, and their generalization properties, have provided great potential in many application areas related to complex combustion processes [24]. They have been used successfully in diverse applications, ranging from modelling coal/biomass devolatilization [25] or rate of char oxidation [26], through predicting quality of fuel blends [27] to predicting the emissions of dioxins from waste power plants [28], identifying slagging in the vicinity of burners [29], or soot-blowing optimization [30]. ANNs were also used to predict the corrosion behavior of metal alloys [31], corrosion fatigue crack growth rates [32], or the atmospheric corrosion behavior of steel and zinc [33].…”