A detailed methodology to design the size of solar field for a parabolic trough plant is not explicitly available in open literature, particularly if thermal storage and hybridization are also considered, as most of the papers present a gross overview. This paper gives a procedure to determine the annual electricity generated for a parabolic trough based solar plant of a given rated capacity (1-50 MWe), at a chosen location & given hourly annual solar input, specified hours of thermal energy storage using a two-tank molten salt system and specified fraction of hybridization using natural gas. In this methodology losses due to shut down or cloud cover are also covered. The size of the solar field is optimized for the maximum annual solar to electric conversion efficiency using the concept of solar multiple (ratio of actual aperture area to the reference aperture area needed to get rated power output at maximum solar input). This procedure is validated with the existing parabolic trough plants (Solar Energy Generating Systems VI and Solana Generating Station) and it was found that the annual electrical energy generated by the plant matches reasonably well.Jodhpur, in India, was considered as a location for the case study and the results are presented to understand the influence of thermal storage and hybridization for a given capacity of the plant. The results for various combinations of thermal storage hours and fraction of hybridization used with respect to plant capacity, solar multiple, annual plant efficiency etc. have been discussed in detail. It is observed from the results that, under design conditions, the reference aperture area per MW decreases as plant capacity increases and reaches a limiting value asymptotically at a capacity of 50 MW. The optimized size of the solar field, with respect to annual efficiency, is found to be 1.4 and 2.3 times the size under design conditions for zero and six hours thermal storage respectively. The benefit of hybridization is high for lower solar multiples.
The annual consumption of petroleum products in India was about 221 million metric tons in 2015. Of this, 84% was imported. The Indian industrial sector accounts for about 16%-20% of the total fuel consumption for thermal energy for different heating applications in the temperature range of 50°C-250°C. Solar collectors can meet these temperature requirements and offer the possibility to mitigate the consumption of oil. This study highlights the fact that conversion efficiency from solar energy is much higher for process heating than for electricity generation and that process heating applications constitute a significant share of industrial energy consumption. In this paper, a methodology has been developed to estimate the potential for integration of solar collectors for process heating. The methodology employs process operating temperatures to select the type of solar collectors. The size of the solar field is estimated taking into account the thermal heat loads, working fluid and temperatures of these processes, the efficiency of the chosen solar collectors, location-based solar irradiance and capacity utilization of the solar collectors. The proposed methodology has been validated with a software tool called System Advisor Model (SAM). The techno-economic analysis will indicate the viability of solar systems for integration in industries. Therefore, the consociated parameters on economic (capital cost, fuel oil savings, monetary benefits), financial (Payback periods, Rates of Returns) and environmental (Carbon savings) are estimated. Further, the methodology has been applied to select Indian industries to verify its potential quantitatively. The industries selected include Textile, Pulp & Paper, Dairy, Leather and Automobile. Processwise energy demands are considered while estimating the potential as the fuel requirement offset by solar energy in terms of absolute fuel oil savings, monetary benefits and carbon savings. The other economic and financial parameters mentioned above were estimated to verify the capability and present the market position of solar systems. Further, sensitivity analyses have been performed with respect to solar energy penetration and fuel oil prices to address the viability of integration of solar energy for process heating.
In Fast Breeder Reactor (FBR), shutdown system is envisaged by Safety and Control Rod Acceleration Movement by using (SCRAM) signals. These SCRAM signals are realized with redundant triplicate sensors, which are made available at different locations of reactor. In this case sensors should be in healthy condition to run the reactor in trouble free manner. To know the health status of sensors a monitoring system is necessary. For this purpose, discordance supervision system is envisaged, to monitor the discordance among the SCRAM signal sensors and generate the alarm when discordance occurs. If discordance occurs, the sensor data validation is necessary to justify the discordance. The sensor data validation by knowledge based approach is simple and reliable. The discordance data is obtained from SCRAM signals. To validate these sensors data value, a neural network based approach is used. The proposed technique is used the data obtained from the coolant temperature monitoring system and relevant application is reported in this paper. The results of this investigation are discussed in this paper.
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