Radiation measurement based on spectral emissions in industrial flames

Soot propensity by image magnification and artificial intelligence

Particulate matter emission into the atmosphere is a massive-scale problem. Fossil fuel combustion is an important source of this kind of pollution. The knowledge of total suspended particle (TSP) emissions is the first step for TSP control. The formation of TSP emissions is poorly understood; therefore new approaches for TSP emissions source modelling are required. TSP modelling is a multi-variable non-linear problem that would only require basic information on boiler operation. This work reports the development of a non-linear model for TSP emissions estimation from an industrial boiler based on a one-layer neural network. Expansion polynomial basic functions combined with an orthogonal least-square and model structure selection approach were used for modelling. The model required five independent boiler variables for TSP emissions estimation. Data from the data acquisition system of a 350 MW industrial boiler were used for model development and validation. The results show that polynomial expansion basic functions are an excellent approach to solve modelling problems related to complex non-linear systems in the industry.

Total Suspended Particle Emissions Modelling in an Industrial Boiler

The furnace process is very important in boiler operation, and furnace pressure works as an important parameter in furnace process. Therefore, there is a need to analyze and monitor the pressure signal in furnace. However, little work has been conducted on the relationship with the pressure sequence and boiler’s load under different working conditions. Since pressure sequence contains complex information, it demands feature extraction methods from multi-aspect consideration. In this paper, fuzzy c-means analysis method based on weighted validity index (VFCM) has been proposed for the working condition classification based on feature extraction. To deal with the fluctuating and time-varying pressure sequence, feature extraction is taken as nonlinear analysis based on entropy theory. Three kinds of entropy values, extracted from pressure sequence in time-frequency domain, are studied as the clustering objects for work condition classification. Weighted validity index, taking the close and separation degree into consideration, is calculated on the base of Silhouette index and Krzanowski-Lai index to obtain the optimal clustering number. Each time FCM runs, the weighted validity index evaluates the clustering result and the optimal clustering number will be obtained when it reaches the maximum value. Four datasets from UCI Machine Learning Repository are presented to certify the effectiveness in VFCM. Pressure sequences got from a 300 MW boiler are then taken for case study. The result of the pressure sequence case study with an error rate of 0.5332% shows the valuable information on boiler’s load and pressure sequence in furnace. The relationship between boiler’s load and entropy values extracted from pressure sequence is proposed. Moreover, the method can be considered to be a reference method for data mining in other fluctuating and time-varying sequences.

Evolved clustering analysis of 300 MW boiler furnace pressure sequence based on entropy characterization

The pulverised coal and its mixture with biomass are one of the most popular fuels in industrial energy. To ensure, on one hand, minimal greenhouse gas emission and, on the other hand, maximum ener...

F. Hernandez-Rosales

Papers

On-line flame signal time series analysis for oil-fired burner optimization