MODELING AND OPTIMIZATION OF ZINC RECOVERY FROM ENYIGBA SPHALERITE IN A BINARY SOLUTION OF ACETIC ACID AND HYDROGEN PEROXIDE

Authors : Ikechukwu A NNANWUBE, Judith N UDEAJA, Okechukwu D ONUKWULI

Pages : 589-601

View : 38 | Download : 10

Publication Date : 2021-06-01

Article Type : Research Paper

Abstract :This work focused on the modeling and optimization of zinc recovery from sphalerite in a binary solution of acetic acid and hydrogen peroxide. The sphalerite sample was characterized using X-ray fluorescence insert ignore into journalissuearticles values(XRF);, X-ray diffraction insert ignore into journalissuearticles values(XRD); and Scanning electron micrograph insert ignore into journalissuearticles values(SEM);. The result revealed that the ore exists as zinc sulphide insert ignore into journalissuearticles values(ZnS);. Levenberg-Marquardt insert ignore into journalissuearticles values(LM); back-propagation algorithm was employed for artificial neural network insert ignore into journalissuearticles values(ANN); modeling while central composite rotatable design insert ignore into journalissuearticles values(CCRD); was deployed for response surface methodology insert ignore into journalissuearticles values(RSM); modeling. RSM modeling gave optimum conditions of 90oC leaching temperature, 6M acid concentration, 540 rpm stirring rate, 120 minutes leaching time and 6M hydrogen peroxide concentration; at which about 89.91% zinc was recovered. Comparison of the two modeling techniques revealed that ANN insert ignore into journalissuearticles values(root mean square error, RMSE = 0.530, absolute average deviation, AAD = 0.681, coefficient of determination = 0.996); gave better predictions than RSM insert ignore into journalissuearticles values(root mean square error, RMSE = 0.755, absolute average deviation, AAD = 0.841, coefficient of determination = 0.991);. Hence, ANN demonstrated higher predictive capability than RSM.
Keywords : Sphalerite, acetic acid, hydrogen peroxide, optimization, artificial neural network, response surface methodology

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