This thesis is concerned with the implementation of Artificial Neural Networks (ANNs) to monitor and control chain grate stoker-fired coal boilers with a view to improving the combustion efficiency whilst minimising pollutant emissions. A novel Neural Network Based Controller (NNBC) was developed following a comprehensive set of experiments carried out on a stoker test facility at the Coal Research Establishment (CRE) Ltd., before being evaluated on an industrial chain grate stoker at Her Majesty's Prison Garth, Leyland. The NNBC mimicked the actions of an expert boiler operator, by providing 'near optimum' settings of coal feed and air flow, as well as taking into account the correct 'staging' sequence of these parameters during load following conditions, before subsequently fine tuning the combustion air under quasi steady- state conditions. Test results from the on-line implementation of the NNBC on both chain grate stoker plants have demonstrated that improved transient and steady state combustion conditions were attained without having any adverse effect on the pollutant emissions nor the integrity of the appliances. A novel combustion monitoring system was also developed during the course of the work that can be used to infer the stability of combustion on the fire bed, following a pilot study of the 'flame front' movement during boiler load changes on the stoker test facility at CRE. This novel low-cost flame front monitor was rigorously tested on the industrial stoker plant, and long hours of successful on-line operation were achieved. It was also demonstrated with the use of ANNs, that the data gathered from the novel flame front monitor can be processed to yield evidence concerning movement of the ignition plane over a short period of time (several minutes). The prototype controller and flame front monitor would thus provide both stoker manufacturers and users with a means of meeting future legislative limits on pollutant emissions as indicated by the European Commission, as well as improving the combustion efficiency of this type of coal firing equipment Finally, ANNs were also used as a simplistic means to represent the complex coal combustion process on the bed of the stoker test facility whilst burning a particular type of coal. The resultant 'black-box' models of the combustion derivatives were able to represent the dynamics of the process and delivered accurate one-step ahead predictions over a wide range of unseen data. The work demonstrated the complex functional mapping capability of ANNs and also addressed the deficiencies in mathematical modelling of the coal combustion process on fixed grate, as indicated in the literature.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:297838 |
| Date | January 1999 |
| Creators | Chong, Alex Zyh Siong |
| Publisher | University of South Wales |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://pure.southwales.ac.uk/en/studentthesis/the-monitoring-and-control-of-stokerfired-boiler-plant-by-neural-networks(edc913a9-3dc2-4159-ac89-9b04aef8465b).html |
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