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: Phylogeny and taxonomy of the Ophiostoma piceae complex and the Dutch elm disease fungi

authors: Harrington, Thomas C.

subjects: Bluestain

abstract: The Ophiostoma piceae complex forms a monophyletic group of insect-dispersed pyrenomycetes with synnemata (Pesotum) and micronematous (Sporothrix) synanamorphs. Other species of Ophios-toma outside of the O. piceae complex that form syn-nemata lack the Sporothrix state. The nine recognized species within the 0. piceae complex are delimited by synnema morphology, growth rate at 32 C, mating reactions and sequences of the internal transcribed spacer (ITS) region of the rDNA operon. Phyloge-netic analysis of the ITS region suggests two major clades in the complex, one that causes bluestain in primarily coniferous hosts and the other on primarily hardwood hosts. In the coniferous group are O. pi-ceae, O. canum, O. floccosum and the recently de-scribed O. setosum (anamorph Pesotum cupulatum sp. nov.). In the hardwood group are O. querci, O. caton-ianum, and the Dutch elm disease fungi: O. ulmi, O. novo-ulmi and O. himal-ulmi. Restriction fragment length polymorphisms of the ITS region are shown to be a convenient diagnostic tool for delimiting these species.

text: Phylogeny and taxonomy of the Ophiostoma piceae complex and the Dutch elm disease fungi The Ophiostoma piceae complex forms a monophyletic group of insect-dispersed pyrenomycetes with synnemata (Pesotum) and micronematous (Sporothrix) synanamorphs. Other species of Ophios-toma outside of the O. piceae complex that form syn-nemata lack the Sporothrix state. The nine recognized species within the 0. piceae complex are delimited by synnema morphology, growth rate at 32 C, mating reactions and sequences of the internal transcribed spacer (ITS) region of the rDNA operon. Phyloge-netic analysis of the ITS region suggests two major clades in the complex, one that causes bluestain in primarily coniferous hosts and the other on primarily hardwood hosts. In the coniferous group are O. pi-ceae, O. canum, O. floccosum and the recently de-scribed O. setosum (anamorph Pesotum cupulatum sp. nov.). In the hardwood group are O. querci, O. caton-ianum, and the Dutch elm disease fungi: O. ulmi, O. novo-ulmi and O. himal-ulmi. Restriction fragment length polymorphisms of the ITS region are shown to be a convenient diagnostic tool for delimiting these species.

year: 2001

title: The Māori language revitalisation agenda and Te Whakapiki Reo and Te Whakapiki i te Reo: Meeting the needs of teachers

authors: Kahukura, Hine

subjects: Te reo Māori

abstract: Since 1994, and under contract to the New Zealand Ministry of Education, the University of Waikato has provided teacher professional development programmes in the context of kaupapa matauranga Māori. Because these programmes are designed to play a role in the Māori language revitalisation agenda as it relates to the teaching and learning of te reo Māori in school contexts, we believe that it is important to share our experiences with others. In this article, we discuss the context in which these programmes are delivered, some of the ways in which they have changed over time, and what we have learned from them.

text: The Māori language revitalisation agenda and Te Whakapiki Reo and Te Whakapiki i te Reo: Meeting the needs of teachers Since 1994, and under contract to the New Zealand Ministry of Education, the University of Waikato has provided teacher professional development programmes in the context of kaupapa matauranga Māori. Because these programmes are designed to play a role in the Māori language revitalisation agenda as it relates to the teaching and learning of te reo Māori in school contexts, we believe that it is important to share our experiences with others. In this article, we discuss the context in which these programmes are delivered, some of the ways in which they have changed over time, and what we have learned from them.

year: 2005