Dynamic modelling and simulation of a power gas turbine using artificial neural network: A comparative study
| dc.contributor.author | Attamah, Chikaodili Stephinie | |
| dc.date.accessioned | 2025-02-26T10:36:28Z | |
| dc.date.available | 2025-02-26T10:36:28Z | |
| dc.date.issued | 2022-12 | |
| dc.description | The thesis contains tables and figures | |
| dc.description.abstract | The dynamic modelling and simulation of a power gas turbine by comparing three forms of Artificial Neural Network was adopted given the complexities of the physics and mathematical based turbine models. Layer Recurrent Neural Network (Layrecnet), Feed Forward Back Propagation (FF BPP) Network and Non Linear Autoregressive Network with Exogenous Input (NARX) were selected for the dynamic modelling of the turbine. The start up data was trained with these networks and Multiple Input Multiple Output (MIMO) and Multiple Input Single Output (MISO) models were developed for the machine. Furthermore; the selected models were validated with operational data from the turbine similar in manouver to the data adopted for modelling. It is observed that “Layrecnet” has the least Mean Squared Error (MSE) of 1.12 and Mean of Absolute Percentage Error (MAPE) of 0.7310 in the MIMO model while “FF BPP” network comes a close second with MSE of 1.74 and MAPE of 1.4249. “LayRecNet” MIMO and MISO models were used to simulate the start-up of the gas turbine because it ranked the highest among the three networks with the use of MSE and MAPE error performance metrics. However; the “FF BPP” network also performed well as it had the best performance for the Turbine Outlet Temperature MISO model with MSE of 0.296 and MAPE of 0.495. The research showed that the “Layrecnet” Network is a better tool for dynamic time series modelling as the network had the least MSE and MAPE with FF BPP coming a close second, while the much acclaimed NARX Network is the least performing network. It was shown that neural networks can be considered a reliable alternative to conventional mathematical driven techniques. Therefore, by using the developed tool, an optimization of the plant operation and maintenance is rendered possible. | |
| dc.identifier.citation | Attamah, C. S. ( 2022 ). Dynamic modelling and simulation of a power gas turbine using artificial neural network: A comparative study (Unpublished Master's Thesis). Federal University of Technology. Owerri, Nigeria | |
| dc.identifier.uri | https://repository.futo.edu.ng/handle/20.500.14562/1652 | |
| dc.language.iso | en | |
| dc.publisher | Federal University of Technology, Owerri | |
| dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.subject | Gas turbine | |
| dc.subject | neural networks | |
| dc.subject | blackbox models | |
| dc.subject | start-up phase | |
| dc.subject | load | |
| dc.subject | simulation | |
| dc.subject | plant | |
| dc.subject | manouver | |
| dc.subject | Department of Chemical Engineering | |
| dc.title | Dynamic modelling and simulation of a power gas turbine using artificial neural network: A comparative study | |
| dc.type | Master’s Thesis |