A new species of Tetraselmis, T. indica Arora & Anil, was isolated from nanoplankton collected fr... more A new species of Tetraselmis, T. indica Arora & Anil, was isolated from nanoplankton collected from salt pans in Goa (India) and is described based on morphological, ultrastructural, 18S rRNA gene sequence and genome size data. The species is characterized by a distinct eyespot, rectangular nucleus, a large number of Golgi bodies, two types of flagellar pit hairs and a characteristic type of cell division. In nature, the species was found in a wide range of temperatures (48°C down to 28°C) and salinities, from hypersaline (up to 350 psu) down to marine (c. 35 psu) conditions. Phylogenetic analysis based on 18S rDNA sequence data showed that T. indica is most closely related to unidentified Tetraselmis strains from a salt lake in North America.
Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; IGTI Scholar Award, 2001
ABSTRACT Data normalization for gas turbines is necessary for comparison of test data collected a... more ABSTRACT Data normalization for gas turbines is necessary for comparison of test data collected at various environmental conditions. The normalization procedure is regulated by the ISO-standard. In this study, a single Artificial Neural Network is used to model the performance of a simple gas turbine (VT600) using measured data at various environmental and operational conditions. Consequently, engine performance maps covering a wide range of operational and environmental conditions have been generated. Comparison of the normalized/experimental data, results provided by thermodynamic models using heat and mass balance programs and results generated by the Artificial Neural Network (ANN) model shows a high level of consistency. The study presented here was performed as a pilot study, to investigate the applicability of an ANN model for data normalization applied to an Evaporative Gas Turbine (EvGT), since the ISO-standard normalization procedure is not applicable to the EvGT plant. Results of this work show that Artificial Neural Networks are powerful tools for performance prediction as well as generation of accurate power plant model of an specific simple gas turbine, and that data normalization can easily and accurately be carried out by using these performance maps.
Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; IGTI Scholar Award, 2001
ABSTRACT Modelling and data-normalization of a gas turbine process, called Evaporative Gas Turbin... more ABSTRACT Modelling and data-normalization of a gas turbine process, called Evaporative Gas Turbine (EvGT) is studied here. The most important factor to achieve a high level of accuracy during the data normalization, is the consideration of changes in thermodynamic properties of the working medium at different environmental conditions. Performance of the EvGT, which is working with a mixture of air and steam, is strongly affected by the changes in the environmental conditions. When the properties of the working fluid such as the water content are continuously changing, the normalization process using conventional techniques becomes very difficult if not impossible. In this study, measured data from the worlds’ first Evaporative Gas Turbine at Lund University in Sweden have been used for generation of an empirical model by a single Artificial Neural Network system. Performance maps generated by ANN have been successfully used for data normalization and performance prediction of the Evaporative Gas Turbine. ANN predicted values are compared with experimental results, not used during the training, where very good correlation was observed.
Accurate and defendable taxonomic identification of microalgae strains is vital for culture colle... more Accurate and defendable taxonomic identification of microalgae strains is vital for culture collections, industry and academia; particularly when addressing issues of intellectual property. We demonstrate the remarkable effectiveness of Matrix Assisted Laser Desorption Ionisation Time of Flight Mass Spectrometry (MALDI-TOF-MS) biotyping to deliver rapid and accurate strain separation, even in situations where standard molecular tools prove ineffective. Highly distinctive MALDI spectra were obtained for thirty two biotechnologically interesting Dunaliella strains plus strains of Arthrospira, Chlorella, Isochrysis, Tetraselmis and a range of culturable co-occurring bacteria. Spectra were directly compared with genomic DNA sequences (internal transcribed spacer, ITS). Within individual Dunaliella isolates MALDI discriminated between strains with identical ITS sequences, thereby emphasising and enhancing knowledge of the diversity within microalgae culture collections. Further, MALDI sp...
2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), 2012
The floating raft system is more and more used in ship powerplant for the purpose of reducing vib... more The floating raft system is more and more used in ship powerplant for the purpose of reducing vibration and noise; it is that the multi-equipments are installed on the same vibration isolating system with the double layer isolators and a middle raft frame. The optimization design on float raft system is significant for advancing the design level and enhancing the
ABSTRACT Floating offshore structures, particularly floating oil production, storage and offloadi... more ABSTRACT Floating offshore structures, particularly floating oil production, storage and offloading systems (FPSOs) are still in great demand, both in small and large reservoirs, for deployment in deep water. The prediction of such vessels’ responses to her environmental loading over her lifetime is now often undertaken using response-based design methodology, although the approach is still in its early stages of development. Determining the vessel’s responses to hydrodynamic loads induced by long term sea environments is essential for implementing this approach effectively. However, it is often not practical to perform a complete simulation for every 3-hour period of environmental data being considered. Therefore, an Artificial Neural Networks (ANN) modelling technique has been developed for the prediction of FPSO’s responses to arbitrary wind, wave and current loads that alleviates this problem. Comparison of results obtained from a conventional mathematical model with those of the ANN-based technique for the case of a 200,000 tdw tanker demonstrates that the approach can successfully predict the vessel’s responses due to arbitrary loads.
A new species of Tetraselmis, T. indica Arora & Anil, was isolated from nanoplankton collected fr... more A new species of Tetraselmis, T. indica Arora & Anil, was isolated from nanoplankton collected from salt pans in Goa (India) and is described based on morphological, ultrastructural, 18S rRNA gene sequence and genome size data. The species is characterized by a distinct eyespot, rectangular nucleus, a large number of Golgi bodies, two types of flagellar pit hairs and a characteristic type of cell division. In nature, the species was found in a wide range of temperatures (48°C down to 28°C) and salinities, from hypersaline (up to 350 psu) down to marine (c. 35 psu) conditions. Phylogenetic analysis based on 18S rDNA sequence data showed that T. indica is most closely related to unidentified Tetraselmis strains from a salt lake in North America.
Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; IGTI Scholar Award, 2001
ABSTRACT Data normalization for gas turbines is necessary for comparison of test data collected a... more ABSTRACT Data normalization for gas turbines is necessary for comparison of test data collected at various environmental conditions. The normalization procedure is regulated by the ISO-standard. In this study, a single Artificial Neural Network is used to model the performance of a simple gas turbine (VT600) using measured data at various environmental and operational conditions. Consequently, engine performance maps covering a wide range of operational and environmental conditions have been generated. Comparison of the normalized/experimental data, results provided by thermodynamic models using heat and mass balance programs and results generated by the Artificial Neural Network (ANN) model shows a high level of consistency. The study presented here was performed as a pilot study, to investigate the applicability of an ANN model for data normalization applied to an Evaporative Gas Turbine (EvGT), since the ISO-standard normalization procedure is not applicable to the EvGT plant. Results of this work show that Artificial Neural Networks are powerful tools for performance prediction as well as generation of accurate power plant model of an specific simple gas turbine, and that data normalization can easily and accurately be carried out by using these performance maps.
Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; IGTI Scholar Award, 2001
ABSTRACT Modelling and data-normalization of a gas turbine process, called Evaporative Gas Turbin... more ABSTRACT Modelling and data-normalization of a gas turbine process, called Evaporative Gas Turbine (EvGT) is studied here. The most important factor to achieve a high level of accuracy during the data normalization, is the consideration of changes in thermodynamic properties of the working medium at different environmental conditions. Performance of the EvGT, which is working with a mixture of air and steam, is strongly affected by the changes in the environmental conditions. When the properties of the working fluid such as the water content are continuously changing, the normalization process using conventional techniques becomes very difficult if not impossible. In this study, measured data from the worlds’ first Evaporative Gas Turbine at Lund University in Sweden have been used for generation of an empirical model by a single Artificial Neural Network system. Performance maps generated by ANN have been successfully used for data normalization and performance prediction of the Evaporative Gas Turbine. ANN predicted values are compared with experimental results, not used during the training, where very good correlation was observed.
Accurate and defendable taxonomic identification of microalgae strains is vital for culture colle... more Accurate and defendable taxonomic identification of microalgae strains is vital for culture collections, industry and academia; particularly when addressing issues of intellectual property. We demonstrate the remarkable effectiveness of Matrix Assisted Laser Desorption Ionisation Time of Flight Mass Spectrometry (MALDI-TOF-MS) biotyping to deliver rapid and accurate strain separation, even in situations where standard molecular tools prove ineffective. Highly distinctive MALDI spectra were obtained for thirty two biotechnologically interesting Dunaliella strains plus strains of Arthrospira, Chlorella, Isochrysis, Tetraselmis and a range of culturable co-occurring bacteria. Spectra were directly compared with genomic DNA sequences (internal transcribed spacer, ITS). Within individual Dunaliella isolates MALDI discriminated between strains with identical ITS sequences, thereby emphasising and enhancing knowledge of the diversity within microalgae culture collections. Further, MALDI sp...
2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), 2012
The floating raft system is more and more used in ship powerplant for the purpose of reducing vib... more The floating raft system is more and more used in ship powerplant for the purpose of reducing vibration and noise; it is that the multi-equipments are installed on the same vibration isolating system with the double layer isolators and a middle raft frame. The optimization design on float raft system is significant for advancing the design level and enhancing the
ABSTRACT Floating offshore structures, particularly floating oil production, storage and offloadi... more ABSTRACT Floating offshore structures, particularly floating oil production, storage and offloading systems (FPSOs) are still in great demand, both in small and large reservoirs, for deployment in deep water. The prediction of such vessels’ responses to her environmental loading over her lifetime is now often undertaken using response-based design methodology, although the approach is still in its early stages of development. Determining the vessel’s responses to hydrodynamic loads induced by long term sea environments is essential for implementing this approach effectively. However, it is often not practical to perform a complete simulation for every 3-hour period of environmental data being considered. Therefore, an Artificial Neural Networks (ANN) modelling technique has been developed for the prediction of FPSO’s responses to arbitrary wind, wave and current loads that alleviates this problem. Comparison of results obtained from a conventional mathematical model with those of the ANN-based technique for the case of a 200,000 tdw tanker demonstrates that the approach can successfully predict the vessel’s responses due to arbitrary loads.
Uploads