Application of multi-disciplinary optimization architectures in mineral processing simulations

Optimization is a pivotal point in distinguishing the competitiveness of industries that are developing, designing and operating various products and processes. Mineral processing is an industry which operates various sub-processes and produces one or several products. The sub-processes involved are dynamic in nature and differ in the discipline of operation. These dynamic sub-processes are sequentially integrated forming a mineral processing system. Currently, the developed simulations for the mineral processing systems have the potential to be used to design, operate and control mineral processing plants to an increased extent, but need broader optimization strategies to integrate multiple sub-processes involved. The scope of this research is to demonstrate application of multi-disciplinary optimization (MDO) architectures into a mineral processing simulation. A simulation study consisting of two sub-processes of comminution and classification circuits to produce aggregate products is used to demonstrate the application of MDO architectures. The MDO architectures are compared based on problem formulation, computational resources required and validity of the results. The optimization results using MDO architectures can be used to illustrate trade-offs between different sub-processes within the considered scope. The application of MDO architectures can facilitate the linking mathematical models of various disciplines such as comminution, and liberation in mineral processing simulation.

[1]  Daniel Sbarbaro,et al.  Advanced Control and Supervision of Mineral Processing Plants , 2010 .

[2]  Gauti Asbjörnsson,et al.  Modelling and dynamic simulation of gradual performance deterioration of a crushing circuit – Including time dependence and wear , 2012 .

[3]  Per Svedensten,et al.  Improving Yield and Shape in a Crushing Plant , 2009 .

[4]  Erik Hulthén,et al.  Algorithm for dynamic cone crusher control , 2009 .

[5]  Gauti Asbjörnsson,et al.  State of the Art in Application of Optimization Theory in Minerals Processing , 2017 .

[6]  Magnus Bengtsson.  Quality-Driven Production of Aggregates in Crushing Plants , 2009 .

[7]  Gauti Asbjörnsson.  Crushing Plant Dynamics , 2015 .

[8]  Per Svedensten,et al.  Crushing plant optimisation by means of a genetic evolutionary algorithm , 2005 .

[9]  Panos Y. Papalambros,et al.  Principles of Optimal Design: Modeling and Computation , 1988 .

[10]  Gauti Asbjörnsson,et al.  Tuning of real-time algorithm for crushing plants using a dynamic crushing plant simulator , 2012 .

[11]  JiGuan G. Lin.  Analysis and Enhancement of Collaborative Optimization for Multidisciplinary Design , 2004 .

[12]  Gauti Asbjörnsson,et al.  Modelling of Discrete Downtime in Continuous Crushing Operation , 2015 .

[13]  K. J. Reid.  Derivation of an equation for classifier-reduced performance curves , 1971 .

[14]  John E. Dennis,et al.  Problem Formulation for Multidisciplinary Optimization , 1994, SIAM J. Optim..

[15]  Ilan Kroo,et al.  Implementation and Performance Issues in Collaborative Optimization , 1996 .

[16]  T. G. Callcott,et al.  A matrix analysis of processes involving particle assemblies , 1956, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[17]  L. A. Schmit,et al.  Structural synthesis - Its genesis and development , 1981 .

[18]  Gauti Asbjörnsson,et al.  Towards dynamical profit optimization of comminution circuits , 2017 .

[19]  Joaquim R. R. A. Martins,et al.  Multidisciplinary design optimization: A survey of architectures , 2013 .

[20]  R. A. Bearman,et al.  A novel approach to circuit synthesis in mineral processing , 1997 .

[21]  Xiang Li,et al.  Geometric analysis of collaborative optimization , 2008 .

[22]  Erik Hulthén,et al.  Real-time algorithm for cone crusher control with two variables , 2011 .

[23]  Raphael T. Haftka,et al.  Optimization of Flexible Wing Structures Subject to Strength and Induced Drag Constraints , 1977 .

[24]  Luigi Barone,et al.  Maximising overall value in plant design , 2006 .