Population Characteristics of Prawns in Natural and Selective Stocking System

present work deals with the various aspects of population characteristics of penaeus indicus ,Metapenaeus dobsoni and metapenaeus monoceros during their nursery phase in tidal ponds and adjacent backwaters.Importance of the present study is to suggest scientific basis for the management of penaeid resources in tidal ponds and backwaters based on their biological characteristics to ensure better yield.Seasonal closure of fishing will be effective in improving the size of the shrimp at harvest.Hydrology of tidal ponds varied with location, but showed a common seasonal pattem.Seasonal variation in temperature was very small. It fluctuated between 27.5 to 32.3°C in tidalponds and 26.9 to 29.9°C in open backwaters.Improvement of nursery habitats with due consideration for biological requirements of the resource will ensure better growth, survival and abundance of the stock.The recruitment, growth and emigration data of prawns from their nurseries can be used successfully for fishery forecasting. projecting juvenile growth forward through time, it is possible to establish, which cohort contributes to offshore fishery each year. So, by interpreting the recruitment and growth data of species in their nurseries with offshore catch data, fishery can be forecasted successfully.

Impact of Climatic Variability and Drought on Crop Yields in Kerala

In the present investigation, the impacts of the variability of the climatic parameters on the yields of major crops grown in the State are analyzed. In particular, the effects of rainfall variability on the water balances of the different regions in the State have been studied. Through this analysis the drought climatology of the region has been studied along with an overview of the climatic shifts involved in individual years. The relationship between weather parameters and crop yields over the State has been analyzed with case studies of two crops- coconut and paddy. Crop-weather models for forecasting coconut and paddy yields have been developed, which could be used for planning purposes

Development of Zero Water Exchange Shrimp Culture System Integrated With Bioremediation of Detritus and Ammonia- Nitrogen

Aquaculture is one of the fastest growing food sectors in the world. Amongst the various branches of aquaculture, shrimp culture has expanded rapidly across the globe because of its faster growth rate, short culture period, high export value and demand in the International market. Indian shrimp farming has experienced phenomenal development over the decades due to its excellent commercial viability. Farmers have adopted a number of innovative technologies to improve the production and to maximize the returns per unit area. The culture methods adopted can be classified in to extensive, modified extensive and semi intensive based on the management strategies adopted in terms of pond size, stocking density, feeding and environmental control. In all these systems water exchanges through the natural tidal effects, or pump fed either from creek or from estuaries is a common practice. In all the cases, the systems are prone to epizootics due to the pathogen introduction through the incoming water, either brought by vectors, reservoir hosts, infected tissue debris and free pathogens themselves. In this scenario, measures to prevent the introduction of pathogen have become a necessity to protect the crop from the onslaught of diseases as well as to prevent the discharge of waste water in to the culture environment.The present thesis deals with Standardization of bioremediation technology for zero water exchange shrimp culture system

Spatio-temporal variation of microphytoplankton in the upwelling system of the south-eastern Arabian Sea during the summer monsoon of 2009

The phytoplankton standing crop was assessed in detail along the South Eastern Arabian Sea (SEAS) during the different phases of coastal upwelling in 2009.During phase 1 intense upwelling was observed along the southern transects (8◦N and 8.5◦N). The maximum chlorophyll a concentration (22.7 mg m −3) was observed in the coastal waters off Thiruvananthapuram (8.5◦N). Further north there was no signature of upwelling, with extensive Trichodesmium erythraeum blooms. Diatoms dominated in these upwelling regions with the centric diatom Chaetoceros curvisetus being the dominant species along the 8◦N transect. Along the 8.5◦N transect pennate diatoms like Nitzschia seriata and Pseudo-nitzschia sp. dominated. During phase 2, upwelling of varying intensity was observed throughout the study area with maximum chlorophyll a concentrations along the 9◦N transect (25 mg m−3) with Chaetoceros curvisetus as the dominant phytoplankton. Along the 8.5◦N transect pennate diatoms during phase 1 were replaced by centric diatoms like Chaetoceros sp. The presence of solitary pennate diatoms Amphora sp. and Navicula sp. were significant in the waters off Kochi. Upwelling was waning during phase 3 and was confined to the coastal waters of the southern transects with the highest chlorophyll a concentration of 11.2 mg m−3. Along with diatoms, dinoflagellate cell densities increased in phases 2 and 3. In the northern transects (9◦N and 10◦N) the proportion of dinoflagellates was comparatively higher and was represented mainly by Protoperidinium spp., Ceratium spp. and Dinophysis spp.

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