title: Predicting Steam Turbine Power Generation: A Comparison of Long Short-Term Memory and Willans Line Model

authors: Pasandideh, Mostafa

abstract: This study focuses on using machine learning techniques to accurately predict the generated power in a two-stage back-pressure steam turbine used in the paper production industry. In order to accurately predict power production by a steam turbine, it is crucial to consider the time dependence of the input data. For this purpose, the long-short-term memory (LSTM) approach is employed. Correlation analysis is performed to select parameters with a correlation coefficient greater than 0.8. Initially, nine inputs are considered, and the study showcases the superior performance of the LSTM method, with an accuracy rate of 0.47. Further refinement is conducted by reducing the inputs to four based on correlation analysis, resulting in an improved accuracy rate of 0.39. The comparison between the LSTM method and the Willans line model evaluates the efficacy of the former in predicting production power. The root mean square error (RMSE) evaluation parameter is used to assess the accuracy of the prediction algorithm used for the generator’s production power. By highlighting the importance of selecting appropriate machine learning techniques, high-quality input data, and utilising correlation analysis for input refinement, this work demonstrates a valuable approach to accurately estimating and predicting power production in the energy industry.

text: Predicting Steam Turbine Power Generation: A Comparison of Long Short-Term Memory and Willans Line Model This study focuses on using machine learning techniques to accurately predict the generated power in a two-stage back-pressure steam turbine used in the paper production industry. In order to accurately predict power production by a steam turbine, it is crucial to consider the time dependence of the input data. For this purpose, the long-short-term memory (LSTM) approach is employed. Correlation analysis is performed to select parameters with a correlation coefficient greater than 0.8. Initially, nine inputs are considered, and the study showcases the superior performance of the LSTM method, with an accuracy rate of 0.47. Further refinement is conducted by reducing the inputs to four based on correlation analysis, resulting in an improved accuracy rate of 0.39. The comparison between the LSTM method and the Willans line model evaluates the efficacy of the former in predicting production power. The root mean square error (RMSE) evaluation parameter is used to assess the accuracy of the prediction algorithm used for the generator’s production power. By highlighting the importance of selecting appropriate machine learning techniques, high-quality input data, and utilising correlation analysis for input refinement, this work demonstrates a valuable approach to accurately estimating and predicting power production in the energy industry.

year: 2024-01-10

title: Book Review: Cycles of disadvantage?

authors: Barnes, Jo

subjects: life cycle poverty

abstract: Review of Scott Boggess, Mary Corcoran and Stephen P Jenkins. Cycles of disadvantages?

text: Book Review: Cycles of disadvantage? Review of Scott Boggess, Mary Corcoran and Stephen P Jenkins. Cycles of disadvantages?

year: 2000

title: “Seeing” Kāpō Māori: Making visible the experiences of Kāpō Māori during and after COVID-19

authors: Masters-Awatere, Bridgette

abstract: A scoping project focused on the intersection of Māori and the framing of vision impairments (disability) within the context of a pandemic response (in this instance COVID-19). Specifically, we were interested in the experiences of kāpō Māori during and post COVID-19 in order to centre the cultural and health needs of kāpō Māori in a culturally responsive manner. We highlight the aspirations that kāpō Māori have for themselves and their whānau, and to identify the facilitators and barriers that kāpō Māori and their whānau face in achieving these

text: “Seeing” Kāpō Māori: Making visible the experiences of Kāpō Māori during and after COVID-19 A scoping project focused on the intersection of Māori and the framing of vision impairments (disability) within the context of a pandemic response (in this instance COVID-19). Specifically, we were interested in the experiences of kāpō Māori during and post COVID-19 in order to centre the cultural and health needs of kāpō Māori in a culturally responsive manner. We highlight the aspirations that kāpō Māori have for themselves and their whānau, and to identify the facilitators and barriers that kāpō Māori and their whānau face in achieving these

year: 2021