Multiobjective Pareto Optimization of an Industrial Straight Grate Iron Ore Induration Process Using an Evolutionary Algorithm

Multiobjective optimization of an industrial straight grate iron ore induration process is carried out in this study using an evolutionary algorithm. A simultaneous maximization of throughput and pellet quality indices like cold compression strength (CCS) and Tumbler index (TI) is adopted for this purpose, which leads to an improved optimal control of the induration process as compared to the conventional practice of controlling the process based on burn-through point (BTP) temperature. Discretized pressure and temperature profiles, grate speed, and bed height are used as decision variables whereas the bounds on CCS, abrasion index (AI), maximum pellet temperature, and BTP temperature are treated as constraints. The optimization results show that it may be possible to achieve significant improvement in the throughput with similar TI values and without violating any operational constraints. Commonality among decision variables corresponding to various Pareto optimal (PO) solutions obtained as a result of this multiobjective optimization study helps in unveiling the embedded relationship amongst them, which, in turn, can reveal the operating principles of running the process in an optimal fashion. The methodology is quite generic in nature and can be adopted for similar processes. The results of this optimization exercise can be used as a set of operating target points for the underlying model based predictive control algorithms to control and optimize the process.