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Govind Ballabh Pant University of Agriculture and Technology, Pantnagar

After independence, development of the rural sector was considered the primary concern of the Government of India. In 1949, with the appointment of the Radhakrishnan University Education Commission, imparting of agricultural education through the setting up of rural universities became the focal point. Later, in 1954 an Indo-American team led by Dr. K.R. Damle, the Vice-President of ICAR, was constituted that arrived at the idea of establishing a Rural University on the land-grant pattern of USA. As a consequence a contract between the Government of India, the Technical Cooperation Mission and some land-grant universities of USA, was signed to promote agricultural education in the country. The US universities included the universities of Tennessee, the Ohio State University, the Kansas State University, The University of Illinois, the Pennsylvania State University and the University of Missouri. The task of assisting Uttar Pradesh in establishing an agricultural university was assigned to the University of Illinois which signed a contract in 1959 to establish an agricultural University in the State. Dean, H.W. Hannah, of the University of Illinois prepared a blueprint for a Rural University to be set up at the Tarai State Farm in the district Nainital, UP. In the initial stage the University of Illinois also offered the services of its scientists and teachers. Thus, in 1960, the first agricultural university of India, UP Agricultural University, came into being by an Act of legislation, UP Act XI-V of 1958. The Act was later amended under UP Universities Re-enactment and Amendment Act 1972 and the University was rechristened as Govind Ballabh Pant University of Agriculture and Technology keeping in view the contributions of Pt. Govind Ballabh Pant, the then Chief Minister of UP. The University was dedicated to the Nation by the first Prime Minister of India Pt Jawaharlal Nehru on 17 November 1960. The G.B. Pant University is a symbol of successful partnership between India and the United States. The establishment of this university brought about a revolution in agricultural education, research and extension. It paved the way for setting up of 31 other agricultural universities in the country.

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  • ThesisItemOpen Access
    Optimization and Artificial Neural Network (ANN) modeling of oil expression from enzyme treated Jatropha curcas L. (Ratanjot) on a hydraulic press
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2007-06) Durgesh Nandini; Pandey, R.K.
    Rapid urbanization, agricultural mechanization and increase in vehicular population enhance demand for fuel. So meeting the energy requirements in a sustainable manner is a major challenge. Among the many tree species which can yield oil as a source of energy in the form of bio-diesel, Jatropha curcas L. (Ratanjot) has been found most suitable due to its various favorable attributes like hardy nature, short gestation period, high oil recovery and quality of oil Study was conducted to optimize and to develop Artificial Neural Network (ANN) models for oil expression from Jatropha curcas L. (Ratanjot) on a hydraulic press. Experiments were planned using full factorial design in two phases. In the first phase of experimental design, three levels of husk percentage, five levels of pressure and five levels of holding time were taken as independent variables. In a similar way five levels of enzyme concentration, five levels of pressure and five levels of holding time were taken as independent parameters in the second phase of experiments. Line curves, surface plots and iso-oleum curves were developed to show the effect of independent parameters on oil expression. Empirical mathematical models representing oil expression in terms of single and multiple responses of process parameters were developed using SPSS software. Optimization of variables was performed by partial differentiation of multiple regression equation with respect to each variable and then solving the coefficient matrix on MATLAB software. In order to have a better prediction of unseen input conditions within the experimental range Artificial Neural Network (ANN) modeling of oil expression process was carried out using back propagation algorithm and MATLAB software. Enzymatic treatment substantially enhances oil expression from 87% with hydraulic pressing alone to 91% for hydraulic pressing with enzymatic treatment. The optimum conditions of husk percentage, pressure and holding time for maximum oil recovery were obtained as 87.40%, 45.63 MPa and 27.09 min respectively. Optimum conditions of enzyme concentration, pressure and holding time for maximum oil recovery were obtained as 110.73mg/100 g dry matter, 43.83 MPa and 17.42 min respectively. Optimum architecture of ANN for training at different husk percentages was found to be two hidden layers with 8 and 11 nodes in first and second hidden layer while that for samples at different enzyme concentrations was found to be two hidden layers with 9 and 11 nodes in first and second hidden layer respectively. Both in case of training and testing results of output predicted by ANN architecture shows good agreement with experimental values.