방법 비교

	슬래그 염기도	전해 채취(Electrowinning)	수축핵 모델
분야	광산공학	광산공학	광산공학
계열	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	1950	1890	1976
창시자	Pyrometallurgical Practice	Industrial Electrometallurgy Practice	Szekely, Evans, and Sohn
유형	Slag composition parameter for controlling roast/smelt conditions	Electrochemical metal extraction and purification	Reaction kinetics model for solid-fluid reactions
원전	Barnes, J. F., Edwards, C. C., & Sims, R. L. (2010). Copper smelting and refining: pyrometallurgical fundamentals. JOM, 52(12), 38-43.	Habashi, F. (2011). Electrometallurgy: principles, processes and materials. Metallurgical Transactions, 29(7), 1569-1589.	Szekely, J., Evans, J. W., & Sohn, H. Y. (1976). Gas-solid reactions. Academic Press, New York.
별칭	Basicity Index, Slag Chemistry Parameter	Electrodeposition, Electrolytic Extraction	Shrinking Unreacted Core Model, SCM, Leaching Kinetics Model
관련	3	3	3
요약	Slag basicity is a measure of the composition of slag formed during smelting and roasting operations. It is typically expressed as the ratio of basic oxides (CaO, MgO) to acidic oxides (SiO2). Basicity controls slag fluidity, viscosity, and reactivity, directly affecting metal recovery, processing temperature, and product quality. It is a critical parameter in copper, nickel, and lead smelting.	Electrowinning is an electrochemical process that extracts and refines metals from dilute leaching solutions by passing electric current through an electrolytic cell. Metal ions migrate to the cathode (negative electrode) and are reduced to pure metal, while impurities remain in solution. This process is essential for copper, zinc, cobalt, nickel, and gold refining, producing metals of exceptional purity.	The Shrinking Core Model, formalized by Szekely, Evans, and Sohn in 1976, describes the kinetics of chemical reactions between solid ore particles and surrounding fluids (leaching solutions, roasting gases). As the reaction proceeds from the particle surface inward, an unreacted core shrinks while products accumulate in a product layer. The model enables prediction of leaching times and optimization of hydrometallurgical processes.