Acoustic Structure Interaction For Floating Structure Under Moving Load

The present thesis concentrates largely on sound radiation from floating structure due to moving load

Reducing Vibrio load in Artemia nauplii usingantimicrobial photodynamic therapy: a promising strategy to reduce antibiotic application in shrimp larviculture

We propose antimicrobial photodynamic therapy (aPDT) as an alternative strategy to reduce the use of antibiotics in shrimp larviculture systems. The growth of a multiple antibiotic resistant Vibrio harveyi strain was effectively controlled by treating the cells with Rose Bengal and photosensitizing for 30 min using a halogen lamp. This resulted in the death of > 50% of the cells within the first 10 min of exposure and the 50% reduction in the cell wall integrity after 30 min could be attributed to the destruction of outer membrane protein of V. harveyi by reactive oxygen intermediates produced during the photosensitization. Further, mesocosm experiments with V. harveyi and Artemia nauplii demonstrated that in 30 min, the aPDT could kill 78.9% and 91.2% of heterotrophic bacterial and Vibrio population respectively. In conclusion, the study demonstrated that aPDT with its rapid action and as yet unreported resistance development possibilities could be a propitious strategy to reduce the use of antibiotics in shrimp larviculture systems and thereby, avoid their hazardous effects on human health and the ecosystem at large.

Structural, magnetic and electrical properties of the sol-gel prepared Li0.5Fe2.5O4 fine particles

Fine particles of lithium ferrite were synthesized by the sol-gel method. By subsequent heat treatment at different temperatures, lithium ferrites of different grain sizes were prepared. A structural characterization of all the samples was conducted by the x-ray diffraction technique. A grain size of around 12 nm was observed for Li0.5Fe2.5O4 obtained through the sol-gel method. Magnetic properties of lithium ferrite nanoparticles with grain size ranging from 12 to 32 nm were studied. Magnetization measurements showed that Li0.5Fe2.5O4 fine particles exhibit a deviation from the predicted magnetic behaviour. The as-prepared sample of lithium ferrite showed a maximum saturation magnetization of 75 emu g−1. Variation of coercivity is attributed to the transition from multi-domain to single domain nature. Dielectric permittivity and ac conductivity of all the samples were evaluated as a function of frequency, temperature and grain size. Variation of permittivity and ac conductivity with frequency reveals that the dispersion is due to the Maxwell–Wagner type interfacial polarization

Finite size effects on the electrical properties of sol–gel synthesized CoFe2O4 powders: deviation from Maxwell–Wagner theory and evidence of surface polarization effects

Fine particles of cobalt ferrite were synthesized by the sol–gel method. Subsequent heat treatment at different temperatures yielded cobalt ferrites having different grain sizes. X-ray diffraction studies were carried out to elucidate the structure of all the samples. Dielectric permittivity and ac conductivity of all the samples were evaluated as a function of frequency, temperature and grain size. The variation of permittivity and ac conductivity with frequency reveals that the dispersion is due to Maxwell–Wagner type interfacial polarization in general, with a noted variation from the expected behaviour for the cold synthesized samples. High permittivity and conductivity for small grains were explained on the basis of the correlated barrier-hopping model

On the optical and electrical properties of rf and a.c. plasma polymerized aniline thin films

Polyaniline is a widely studied conducting polymer and is a useful material in its bulk and thin film form for many applications, because of its excellent optical and electrical properties. Pristine and iodine doped polyaniline thin films were prepared by a.c. and rf plasma polymerization techniques separately for the comparison of their optical and electrical properties. Doping of iodine was effected in situ. The structural properties of these films were evaluated by FTIR spectroscopy and the optical band gap was estimated from UV-vis-NIR measurements. Comparative studies on the structural, optical and electrical properties of a.c. and rf polymerization are presented here. It has been found that the optical band gap of the polyaniline thin films prepared by rf and a.c. plasma polymerization techniques differ considerably and the band gap is further reduced by in situ doping of iodine. The electrical conductivity measurements on these films show a higher value of electrical conductivity in the case of rf plasma polymerized thin films when compared to the a.c. plasma polymerized films. Also, it is found that the iodine doping enhanced conductivity of the polymer thin films considerably. The results are compared and correlated and have been explained with respect to the different structures adopted under these two preparation techniques

Investigations on the electrical and structural properties of polyaniline doped with camphor sulphonic acid

Polyaniline is chemically synthesised and doped with camphor sulphonic acid. FTIR studies carried out on these samples indicate that the aromatic rings are retained after polymerisation. The percentage of crystallinity for polyaniline doped with camphor sulphonic acid has been estimated from the X-ray diffraction studies and is around 56% with respect to polyaniline emeraldine base. The change in dielectric permittivity with respect to temperature and frequency is explained on the basis of interfacial polarisation. AC conductivity is evaluated from the observed dielectric permittivity. The values of AC and DC conductivity and activation energy are calculated. The activation energy values suggested that the hopping conduction is the prominent conduction mechanism in this system.

Structural and electrical studies on tetrameric cobalt phthalocyanine and polyaniline composites

Polyaniline and oligomeric cobalt phthalocyanine are blended in different proportions by chemical methods. These blends are characterised by spectroscopic methods and dielectric measurements. Dielectric studies on the conducting polymer blends are carried out in the frequency range of 100 kHz to 5MHz from room temperature (300 K) to 373 K. Dielectric permittivity and dielectric loss of these blends are explained on the basis of interfacial polarisation. From the dielectric permittivity studies, ac conductivity of the samples were calculated and the results are correlated. In order to understand the exact conduction mechanism of the samples, dc electrical conductivity of the blends is carried out in the temperature range of 70–300 K. By applying Mott’s theory, it is found that the conducting polymer composites obey a 3D variable range hopping mechanism. The values of Mott’s temperature (T0), density of states at the Fermi energy (N(EF)), range of hopping (R) and hopping energy (W) for the composites are calculated and presented

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year

Assessment of Energy Conservation in Indian Cement Industry and Forecasting of Co2 Emissions

Cement industry ranks 2nd in energy consumption among the industries in India. It is one of the major emitter of CO2, due to combustion of fossil fuel and calcination process. As the huge amount of CO2 emissions cause severe environment problems, the efficient and effective utilization of energy is a major concern in Indian cement industry. The main objective of the research work is to assess the energy cosumption and energy conservation of the Indian cement industry and to predict future trends in cement production and reduction of CO2 emissions. In order to achieve this objective, a detailed energy and exergy analysis of a typical cement plant in Kerala was carried out. The data on fuel usage, electricity consumption, amount of clinker and cement production were also collected from a few selected cement industries in India for the period 2001 - 2010 and the CO2 emissions were estimated. A complete decomposition method was used for the analysis of change in CO2 emissions during the period 2001 - 2010 by categorising the cement industries according to the specific thermal energy consumption. A basic forecasting model for the cement production trend was developed by using the system dynamic approach and the model was validated with the data collected from the selected cement industries. The cement production and CO2 emissions from the industries were also predicted with the base year as 2010. The sensitivity analysis of the forecasting model was conducted and found satisfactory. The model was then modified for the total cement production in India to predict the cement production and CO2 emissions for the next 21 years under three different scenarios. The parmeters that influence CO2 emissions like population and GDP growth rate, demand of cement and its production, clinker consumption and energy utilization are incorporated in these scenarios. The existing growth rate of the population and cement production in the year 2010 were used in the baseline scenario. In the scenario-1 (S1) the growth rate of population was assumed to be gradually decreasing and finally reach zero by the year 2030, while in scenario-2 (S2) a faster decline in the growth rate was assumed such that zero growth rate is achieved in the year 2020. The mitigation strategiesfor the reduction of CO2 emissions from the cement production were identified and analyzed in the energy management scenarioThe energy and exergy analysis of the raw mill of the cement plant revealed that the exergy utilization was worse than energy utilization. The energy analysis of the kiln system showed that around 38% of heat energy is wasted through exhaust gases of the preheater and cooler of the kiln sysetm. This could be recovered by the waste heat recovery system. A secondary insulation shell was also recommended for the kiln in the plant in order to prevent heat loss and enhance the efficiency of the plant. The decomposition analysis of the change in CO2 emissions during 2001- 2010 showed that the activity effect was the main factor for CO2 emissions for the cement industries since it is directly dependent on economic growth of the country. The forecasting model showed that 15.22% and 29.44% of CO2 emissions reduction can be achieved by the year 2030 in scenario- (S1) and scenario-2 (S2) respectively. In analysing the energy management scenario, it was assumed that 25% of electrical energy supply to the cement plants is replaced by renewable energy. The analysis revealed that the recovery of waste heat and the use of renewable energy could lead to decline in CO2 emissions 7.1% for baseline scenario, 10.9 % in scenario-1 (S1) and 11.16% in scenario-2 (S2) in 2030. The combined scenario considering population stabilization by the year 2020, 25% of contribution from renewable energy sources of the cement industry and 38% thermal energy from the waste heat streams shows that CO2 emissions from Indian cement industry could be reduced by nearly 37% in the year 2030. This would reduce a substantial level of greenhouse gas load to the environment. The cement industry will remain one of the critical sectors for India to meet its CO2 emissions reduction target. India’s cement production will continue to grow in the near future due to its GDP growth. The control of population, improvement in plant efficiency and use of renewable energy are the important options for the mitigation of CO2 emissions from Indian cement industries

Entwicklung eines Windleistungprognosemodells zur Verbesserung der Kraftwerkseinsatzplanung

Die Maßnahmen zur Förderung der Windenergie in Deutschland haben wichtige Anstöße zur technologischen Weiterentwicklung geliefert und die Grundlagen für den enormen Anlagenzubau geschaffen. Die installierte Windleistung hat heute eine beachtliche Größenordnung erreicht und ein weiteres Wachstum in ähnlichen Dimensionen ist auch für die nächsten Jahre zu erwarten. Die aus Wind erzeugte elektrische Leistung deckt bereits heute in einigen Netzbereichen die Netzlast zu Schwachlastzeiten. Dies zeigt, dass die Windenergie ein nicht mehr zu vernachlässigender Faktor in der elektrischen Energieversorgung geworden ist. Im Rahmen der Kraftwerkseinsatzplanung sind Betrag und Verlauf der Windleistung des folgenden Tages mittlerweile zu wichtigen und zugleich schwierig zu bestimmenden Variablen geworden. Starke Schwankungen und falsche Prognosen der Windstromeinspeisung verursachen zusätzlichen Bedarf an Regel- und Ausgleichsleistung durch die Systemführung. Das im Rahmen dieser Arbeit entwickelte Prognosemodell liefert die zu erwartenden Windleistungen an 16 repräsentativen Windparks bzw. Gruppen von Windparks für bis zu 48 Stunden im Voraus. Aufgrund von prognostizierten Wetterdaten des deutschen Wetterdienstes (DWD) werden die Leistungen der einzelnen Windparks mit Hilfe von künstlichen neuronalen Netzen (KNN) berechnet. Diese Methode hat gegenüber physikalischen Verfahren den Vorteil, dass der komplexe Zusammenhang zwischen Wettergeschehen und Windparkleistung nicht aufwendig analysiert und detailliert mathematisch beschrieben werden muss, sondern anhand von Daten aus der Vergangenheit von den KNN gelernt wird. Das Prognosemodell besteht aus zwei Modulen. Mit dem ersten wird, basierend auf den meteorologischen Vorhersagen des DWD, eine Prognose für den Folgetag erstellt. Das zweite Modul bezieht die online gemessenen Leistungsdaten der repräsentativen Windparks mit ein, um die ursprüngliche Folgetagsprognose zu verbessern und eine sehr genaue Kurzzeitprognose für die nächsten drei bis sechs Stunden zu berechnen. Mit den Ergebnissen der Prognosemodule für die repräsentativen Standorte wird dann über ein Transformationsmodell, dem so genannten Online-Modell, die Gesamteinspeisung in einem größeren Gebiet berechnet. Das Prognoseverfahren hat seine besonderen Vorzüge in der Genauigkeit, den geringen Rechenzeiten und den niedrigen Betriebskosten, da durch die Verwendung des bereits implementierten Online-Modells nur eine geringe Anzahl von Vorhersage- und Messstandorten benötigt wird. Das hier vorgestellte Prognosemodell wurde ursprünglich für die E.ON-Netz GmbH entwickelt und optimiert und ist dort seit Juli 2001 im Einsatz. Es lässt sich jedoch auch leicht an andere Gebiete anpassen. Benötigt werden dazu nur die Messdaten der Leistung ausgewählter repräsentativer Windparks sowie die dazu gehörenden Wettervorhersagen, um die KNN entsprechend zu trainieren.

Erneuerbare Energien im Verbund mit Hochleistungs-Bleiakkumulatoren zur Bereitstellung von Spitzenleistung - am Beispiel von Windkraftwerken im 20 kV-Versorgungsnetz der CEGEDEL / Luxemburg -

Die wachsende Weltbevölkerung bedingt einen höheren Energiebedarf, dies jedoch unter der Beachtung der nachhaltigen Entwicklung. Die derzeitige zentrale Versorgung mit elektrischer Energie wird durch wenige Erzeugungsanlagen auf der Basis von fossilen Primärenergieträgern und Kernenergie bestimmt, die die räumlich verteilten Verbraucher zuverlässig und wirtschaftlich über ein strukturiertes Versorgungssystem beliefert. In den Elektrizitätsversorgungsnetzen sind keine nennenswerten Speicherkapazitäten vorhanden, deshalb muss die von den Verbrauchern angeforderte Energie resp. Leistung jederzeit von den Kraftwerken gedeckt werden. Bedingt durch die Liberalisierung der Energiemärkte und die geforderte Verringerung der Energieabhängigkeit Luxemburgs, unterliegt die Versorgung einem Wandel hin zu mehr Energieeffizienz und erhöhter Nutzung der dargebotsabhängigen Energiequellen. Die Speicherung der aus der Windkraft erzeugten elektrischen Energie, wird in den Hochleistungs-Bleiakkumulatoren, errichtet im ländlichen Raum in der Nähe der Windkraftwerke, eingespeichert. Die zeitversetzte Einspeisung dieser gespeicherten elektrischen Energie in Form von veredelter elektrischer Leistung während den Lastspitzen in das 20 kV-Versorgungsnetz der CEGEDEL stellt die Innovation in der luxemburgischen Elektrizitätsversorgung dar. Die Betrachtungen beschränken sich somit auf die regionale, relativ kleinräumige Einbindung der Windkraft in die elektrische Energieversorgung des Großherzogtums Luxemburg. Die Integration der Windkraft im Regionalbereich wird in den Vordergrund der Untersuchung gerückt. Überregionale Ausgleichseffekte durch Hochspannungsleitungen der 230/400 kV-Systeme werden außer Acht gelassen. Durch die verbrauchernahe Bereitstellung von elektrischer Spitzenleistung vermindern sich ebenfalls die Übertragungskosten aus den entfernten Spitzenlastkraftwerken, der Ausbau von Kraftwerkskapazitäten kann in die Zukunft verschoben werden. Die Emission von Treibhausgasen in thermischen Kraftwerken wird zum Teil reduziert. Die Berechnungen der Wirtschaftlichkeit von Hybridanlagen, zusammengesetzt aus den Windkraftwerken und den Hochleistungs-Bleiakkumulatoren bringen weitere Informationen zum Einsatz dieser dezentralen Speichern, als Partner der nachhaltigen Energieversorgung im ländlichen Raum. Die untersuchte Einspeisung von erneuerbarer Spitzenleistung lässt sich auch in die Entwicklungsländer übertragen, welche nicht über zentrale Kraftwerkskapazitäten und Verteilungsnetze verfügen.

Short-term effects of human urine fertiliser and wood ash on soil pH and electrical conductivity

The fertiliser value of human urine has been examined on several crops, yet little is known about its effects on key soil properties of agronomic significance. This study investigated temporal soil salinization potential of human urine fertiliser (HUF). It further looked at combined effects of human urine and wood ash (WA) on soil pH, urine-NH_3 volatilisation, soil electrical conductivity (EC), and basic cation contents of two Acrisols (Adenta and Toje series) from the coastal savannah zone of Ghana. The experiment was a factorial design conducted in the laboratory for 12 weeks. The results indicated an increase in soil pH by 1.2 units for Adenta series and 1 unit for Toje series after one week of HUF application followed by a decline by about 2 pH units for both soil types after twelve weeks. This was attributed to nitrification of ammonium to nitrate leading to acidification. The EC otherwise increased with HUF application creating slightly saline conditions in Toje series and non-saline conditions in Adenta series. When WA was applied with HUF, both soil pH and EC increased. In contrast, the HUF alone slightly salinized Toje series, but both soils remained non-saline whenWA and HUF were applied together. The application ofWA resulted in two-fold increase in Ca, Mg, K, and Na content compared to HUF alone. Hence, WA is a promising amendment of acid soils and could reduce the effect of soluble salts in human urine fertilizer, which is likely to cause soil salinity.

Thermal Impacts on Building Integrated Photovoltaic (BIPV) (Electrical, Thermal and Mechanical Characteristics)

Ein Drittel des weltweiten gesamten Energiebedarfs wird durch Gebäude verbraucht. Um diesen Energiebedarf teilweise zu decken, den erheblichen Energieverbrauch zu reduzieren und weiterhin andere Gebäudefunktionen beizubehalten, ist Gebäudeintegrierte Photovoltaik (BIPV) eine der am besten geeigneten Lösungen für die Gebäudenanwendung. Im Bezug auf eine Vielzahl von Gestalltungsmöglichkeiten, sind die Randbedingungen der BIPV-Anwendungen eindeutig anders im Vergleich zu Standard-PV-Anwendungen, insbesondere bezüglich der Betriebstemperatur. Bisher gab es nicht viele Informationen zu den relevanten thermischen Auswirkungen auf die entsprechenden elektrischen Eigenschaften zusammen mit thermischen und mechanischen relevanten Gebäudenfunktionen. Die meisten Hersteller übernehmen diese Eigenschaften von entsprechenden PV-Modulen und konventionellen Bauprodukten Normen, die zur ungenauen System- und Gebäudeplanungen führen. Deshalb ist die Untersuchung des thermischen Einflusses auf elektrische, thermische sowie mechanische Eigenschaften das Hauptziel der vorliegenden Arbeit. Zunächst wird das Temperatur-Model mit dem Power-Balance-Konzept erstellt. Unter Berücksichtigung der variablen Installationsmöglichkeiten und Konfigurationen des Moduls wird das Model auf Basis dynamischer und stationär Eigenschaften entwickelt. Im Hinblick auf die dynamische Simulation können der Energieertrag und Leistung zusammen mit der thermischen Gebäudesimulation in Echtzeit simuliert werden. Für stationäre Simulationen können die relevanten Gebäudefunktionen von BIPV-Modulen sowohl im Sommer als auch im Winter simuliert werden. Basierend auf unterschiedlichen thermischen und mechanischen Last-Szenarien wurde darüber hinaus das mechanische Model zusammen mit Variationen von Belastungsdauer, Montagesystem und Verkapselungsmaterialien entwickelt. Um die Temperatur- und Mechanik-Modelle zu validieren, wurden die verschiedenen Prüfeinrichtungen zusammen mit neuen Testmethoden entwickelt. Bei Verwendung der Prüfanlage „PV variable mounting system“ und „mechanical testing equipment“ werden zudem die verschiedenen Szenarien von Montagesystemen, Modul-Konfigurationen und mechanischen Belastungen emuliert. Mit der neuen Testmethode „back-bias current concept“ können zum einen die solare Einstrahlung und bestimmte Betriebstemperaturen eingestellt werden. Darüber hinaus wurden mit den eingangs erwähnten validierten Modellen das jeweilige elektrische, thermische und mechanische Verhalten auf andere Konfigurationen bewertet. Zum Abschluss wird die Anwendung von Software-Tools bei PV-Herstellern im Hinblick auf die entsprechenden Modellentwicklungen thematisiert.

Modelling the Somantic Electrical Response of Hippocampal Pyramidal Neurons

A modeling study of hippocampal pyramidal neurons is described. This study is based on simulations using HIPPO, a program which simulates the somatic electrical activity of these cells. HIPPO is based on a) descriptions of eleven non-linear conductances that have been either reported for this class of cell in the literature or postulated in the present study, and b) an approximation of the electrotonic structure of the cell that is derived in this thesis, based on data for the linear properties of these cells. HIPPO is used a) to integrate empirical data from a variety of sources on the electrical characteristics of this type of cell, b) to investigate the functional significance of the various elements that underly the electrical behavior, and c) to provide a tool for the electrophysiologist to supplement direct observation of these cells and provide a method of testing speculations regarding parameters that are not accessible.