Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Online boiler convective heat exchanger monitoring: a comparison of soft sensing and data-driven approaches
Online monitoring supports plant reliability and performance management by providing real time information about the condition of equipment. However, the intricate geometries and harsh operating environment of coal fired power plant boilers inhibit the ability to do online measurements of all proces...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Mechanical Engineering
2019
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613197543931904 |
|---|---|
| access_status_str | Open Access |
| author | Prinsloo, Gerto |
| author2 | Rousseau, Pieter |
| author_browse | Prinsloo, Gerto Rousseau, Pieter |
| author_facet | Rousseau, Pieter Prinsloo, Gerto |
| author_sort | Prinsloo, Gerto |
| collection | Thesis |
| description | Online monitoring supports plant reliability and performance management by providing real time information about the condition of equipment. However, the intricate geometries and harsh operating environment of coal fired power plant boilers inhibit the ability to do online measurements of all process related variables. A low-cost alternative lies in the possibility of using knowledge about boiler operation to extract information about its condition from standard online process measurements. This approach is evaluated with the aim of enhancing online condition monitoring of a boiler’s convective pass heat exchanger network by respectively using a soft sensor and a data-driven method. The soft sensor approach is based on a one-dimensional thermofluid process model which takes measurements as inputs and calculates unmeasured variables as outputs. The model is calibrated based on design information. The data-driven method is one developed specifically in this study to identify unique fault signatures in measurement data to detect and quantify changes in unmeasured variables. The fault signatures are initially constructed using the calibrated one-dimensional thermofluid process model. The benefits and limitations of these methods are compared at the hand of a case study boiler. The case study boiler has five convective heat exchanger stages, each composed of four separate legs. The data-driven method estimates the average conduction thermal resistance of individual heat exchanger legs and the flue gas temperature at the inlet to the convective pass. In addition to this, the soft sensor estimates the average fluid variables for individual legs throughout the convective pass and therefore provides information better suited for condition prognosis. The methods are tested using real plant measurements recorded during a period which contained load changes and on-load heat exchanger cleaning events. The cleaning event provides some basis for validating the results because the qualitative changes of some unmeasured monitored variables expected during this event are known. The relative changes detected by both methods are closely correlated. The data-driven method is computationally less expensive and easily implementable across different software platforms once the fault signatures have been obtained. Fault signatures are easily trainable once the model has been developed. The soft sensors require the continuous use of the modelling software and will therefore be subject to licencing constraints. Both methods offer the possibility to enhance the monitoring resolution of modern boilers without the need to install any additional measurements. Implementation of these monitoring frameworks can provide a simple and low-cost contribution to optimized boiler performance and reliability management. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/30038 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:18.917Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Department of Mechanical Engineering |
| publisherStr | Department of Mechanical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/30038 Online boiler convective heat exchanger monitoring: a comparison of soft sensing and data-driven approaches Prinsloo, Gerto Rousseau, Pieter Gosai, Priyesh coal fired power plant boiler, heat exchanger network, online monitoring, soft sensor, data-driven f Online monitoring supports plant reliability and performance management by providing real time information about the condition of equipment. However, the intricate geometries and harsh operating environment of coal fired power plant boilers inhibit the ability to do online measurements of all process related variables. A low-cost alternative lies in the possibility of using knowledge about boiler operation to extract information about its condition from standard online process measurements. This approach is evaluated with the aim of enhancing online condition monitoring of a boiler’s convective pass heat exchanger network by respectively using a soft sensor and a data-driven method. The soft sensor approach is based on a one-dimensional thermofluid process model which takes measurements as inputs and calculates unmeasured variables as outputs. The model is calibrated based on design information. The data-driven method is one developed specifically in this study to identify unique fault signatures in measurement data to detect and quantify changes in unmeasured variables. The fault signatures are initially constructed using the calibrated one-dimensional thermofluid process model. The benefits and limitations of these methods are compared at the hand of a case study boiler. The case study boiler has five convective heat exchanger stages, each composed of four separate legs. The data-driven method estimates the average conduction thermal resistance of individual heat exchanger legs and the flue gas temperature at the inlet to the convective pass. In addition to this, the soft sensor estimates the average fluid variables for individual legs throughout the convective pass and therefore provides information better suited for condition prognosis. The methods are tested using real plant measurements recorded during a period which contained load changes and on-load heat exchanger cleaning events. The cleaning event provides some basis for validating the results because the qualitative changes of some unmeasured monitored variables expected during this event are known. The relative changes detected by both methods are closely correlated. The data-driven method is computationally less expensive and easily implementable across different software platforms once the fault signatures have been obtained. Fault signatures are easily trainable once the model has been developed. The soft sensors require the continuous use of the modelling software and will therefore be subject to licencing constraints. Both methods offer the possibility to enhance the monitoring resolution of modern boilers without the need to install any additional measurements. Implementation of these monitoring frameworks can provide a simple and low-cost contribution to optimized boiler performance and reliability management. 2019-05-10T11:52:44Z 2019-05-10T11:52:44Z 2018 2019-05-07T11:15:30Z Master Thesis Masters MSc http://hdl.handle.net/11427/30038 eng application/pdf Department of Mechanical Engineering Faculty of Engineering and the Built Environment |
| spellingShingle | coal fired power plant boiler, heat exchanger network, online monitoring, soft sensor, data-driven f Prinsloo, Gerto Online boiler convective heat exchanger monitoring: a comparison of soft sensing and data-driven approaches |
| thesis_degree_str | Master's |
| title | Online boiler convective heat exchanger monitoring: a comparison of soft sensing and data-driven approaches |
| title_full | Online boiler convective heat exchanger monitoring: a comparison of soft sensing and data-driven approaches |
| title_fullStr | Online boiler convective heat exchanger monitoring: a comparison of soft sensing and data-driven approaches |
| title_full_unstemmed | Online boiler convective heat exchanger monitoring: a comparison of soft sensing and data-driven approaches |
| title_short | Online boiler convective heat exchanger monitoring: a comparison of soft sensing and data-driven approaches |
| title_sort | online boiler convective heat exchanger monitoring a comparison of soft sensing and data driven approaches |
| topic | coal fired power plant boiler, heat exchanger network, online monitoring, soft sensor, data-driven f |
| url | http://hdl.handle.net/11427/30038 |
| work_keys_str_mv | AT prinsloogerto onlineboilerconvectiveheatexchangermonitoringacomparisonofsoftsensinganddatadrivenapproaches |