A chance-constrained programming approach for a zinc hydrometallurgy blending problem under uncertainty

Considering the low qualified rate in the blending of zinc concentrate caused by uncertainty of zinc concentrate composition in individual warehouses, we proposed a nonlinear stochastic optimization framework to handle uncertainties in the zinc concentrate blending problem. First, we obtained the distribution characteristics of uncertainty of zinc concentrate. Second, to minimize the pessimistic value of the objective function, we transformed the nonlinear stochastic optimization model into a nonlinear MiniMin chance-constrained programming model, which was easy to handle. Then, we used a hybrid intelligent optimization algorithm to solve the problem effectively. Finally, we verified the validity of the method using the actual production data of a zinc smelter's batching process. Compared with the deterministic optimization method, the model not only gave the probability that each component of the mixed zinc concentrate met the requirements but also achieved a higher qualified rate under the same conditions.