Two-phase optimization methodology for the design of mineral flotation plants, including multispecies and bank or cell models

Froth flotation processes are carried out in flotation cells that are grouped into banks, and these banks are interconnected, forming a flotation circuit. A literature review shows the existence of papers related to flotation circuit design based on mathematical programming. However, due to the complexity of solving the mathematical model in most of the work, it is considered that a small number of species is present in the feed to the circuit, which differs from practice. In addition, simple bank models are generally used. This paper presents a methodology for designing mineral concentration circuits that overcomes the problems mentioned. It allows the use of more suitable cell or bank models and the inclusion of several species. The methodology is based on two phases. The first phase identifies the set of optimal structures using discrete values of stage recoveries, solving several mixed integer linear programming (MILP) problems. In the second phase, the optimal design for each of the structures obtained in the previous phase is determined using a suitable model for the recovery at each cell or bank, which results in a mixed integer nonlinear programming (MINLP) model. The design of a copper concentration plant with eight species and the design of a zinc concentration plant with three species and five size fractions by species are used to validate the proposed methodology. The structure of the cells in the rougher and cleaner banks deliver structures that are novel.

[1]  D. S. Yan,et al.  Mineral Processing Design and Operation: An Introduction , 2006 .

[2]  Markus A. Reuter,et al.  The use of linear programming in the optimal design of flotation circuits incorporating regrind mills , 1990 .

[3]  S. Banisi,et al.  Optimisation of the performance of flotation circuits using a genetic algorithm orientated by process-based rules , 2011 .

[4]  Edelmira D. Gálvez,et al.  State of the art in the conceptual design of flotation circuits , 2009 .

[5]  Edelmira D. Gálvez,et al.  The effects of stage recovery uncertainty in the performance of concentration circuits , 2015 .

[6]  Edelmira D. Gálvez,et al.  Approximate recovery values for each stage are sufficient to select the concentration circuit structures , 2015 .

[7]  Aaron Noble,et al.  Value-based objective functions applied to circuit analysis , 2015 .

[8]  Edelmira D. Gálvez,et al.  Effect of the objective function in the design of concentration plants , 2014 .

[9]  James A. Finch,et al.  Optimizing flotation bank performance by recovery profiling , 2011 .

[10]  Markus A. Reuter,et al.  Optimal design of mineral separation circuits by use of linear programming , 1988 .

[11]  Edelmira D. Gálvez,et al.  A MILP model for design of flotation circuits with bank/column and regrind/no regrind selection , 2006 .

[12]  Juan Yianatos,et al.  Short-cut method for flotation rates modelling of industrial flotation banks , 2006 .

[13]  J. Franzidis,et al.  Studies on impeller type, impeller speed and air flow rate in an industrial scale flotation cell. Part 4: Effect of bubble surface area flux on flotation performance☆ , 1997 .

[14]  Edelmira D. Gálvez,et al.  Mineral Concentration Plants Design Using Rigorous Models , 2016 .

[15]  James A. Finch,et al.  An Overview of Optimizing Strategies for Flotation Banks , 2012 .

[16]  Luis A. Cisternas,et al.  On the synthesis of inorganic chemical and metallurgical processes, review and extension , 1999 .

[17]  Luis A. Cisternas,et al.  Solution strategies to the stochastic design of mineral flotation plants , 2015 .

[18]  Edelmira D. Gálvez,et al.  Modeling of grinding and classification circuits as applied to the design of flotation processes , 2009, Comput. Chem. Eng..

[19]  Gianni Schena,et al.  A method for a financially efficient design of cell-based flotation circuits , 1996 .

[20]  Ivana Jovanović,et al.  Modelling Of Flotation Processes By Classical Mathematical Methods – A Review , 2015 .

[21]  Chandan Guria,et al.  Multi-objective optimal synthesis and design of froth flotation circuits for mineral processing using the Jumping gene adaptation of genetic algorithm , 2005 .

[22]  Emmanuel Manlapig,et al.  The empirical prediction of bubble surface area flux in mechanical flotation cells from cell design and operating data , 1999 .

[23]  Stephen J. Neethling,et al.  Determining flotation circuit layout using genetic algorithms with pulp and froth models , 2013 .

[24]  Edelmira D. Gálvez,et al.  Methodology for process analysis and design with multiple objectives under uncertainty: Application to flotation circuits , 2013 .

[25]  P. C. Kapur,et al.  Optimal-Suboptimal Synthesis and Design of Flotation Circuits , 1974 .

[26]  Santosh K. Gupta,et al.  Simultaneous optimization of the performance of flotation circuits and their simplification using the jumping gene adaptations of genetic algorithm , 2005 .

[27]  Jon C. Yingling Parameter and configuration optimization of flotation circuits, part I. A review of prior work , 1993 .