In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.
The pyrolysis of eucalyptus wood was carried out in a batch reactor to optimize the yield of bio-oil.Effect of various parameters like feed（particle） size,temperature,presence of catalyst and heating rate on the yield of bio-oil was investigated.The optimum conditions for high yield of bio-oil are for the particle size 2 mm~5 mm（average l/d=12.84/2.03 mm） at 450 ℃ in high heating rate.The reaction kinetics and the quality of bio-oil produced are independent of the presence of different catalysts like mordenite,kaoline clay,fly ash and silica alumina.The physical properties like odour,colour,PH,viscosity,heating value were determined.The FT-IR analysis of bio-oil indicates the presence of different functional groups such as monomeric alcohol,phenol,ketones,aldehydes,carboxylic acid,amines,and nitro compounds.The composition of the bio-oil at different conditions was analyzed using GC-MS and found that the components are temperature dependent but independent of catalysts used.
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