Mineral Processing Simulation System and Simulator

The inclusion of optimization procedures in mineral processing simulators can assist in achievement major benefits in terms of plant efficiency and profitability. A simulator is generally thought of a tool for predicting the response of a system to changes in process operating conditions or feed inputs. This may be referred to as the forward use of a simulator. By and large, simulations allow the study of a system through manipulation of a mathematical model that represents it. Then a simulator can be employed to solve the reverse problem of identification of the operating conditions or input that produce a response. This use is defined as goal seeking.

The key elements required for model based control are good on line models, accurate estimations procedures based on precise measurements and inexpensive distributed control hardware to handle stabilizing and optimizing levels of calculation. One of the most important steps in process analysis and simulation using models is parameter estimation. Normally, these parameters are estimated based on experimental data which are inconsistent from a material balance point of view due to unreliable instrument readings, sampling errors and laboratory inaccuracies. The errors encountered in these experimental data are most the time directly reflected in the quality of the model parameter estimates from the data.

For a judicious choice to be made on the basis of simulations the direct costs of alternatives must be known, along with the indirect costs and benefits. Costs and benefits must be synthesized in a way that allows comparisons to be made. The more complete objective function that is used to discriminate between and rank alternatives, the better the final decision will be.

Simulation is base on mathematical models which has been derived after an exhaustive study of any equipment or process. For example mathematical models for grinding classification circuits have been proposed by many researches and are now widely accepted as simulation tool, however, some process engineers and practitioners trend to employ empirical design relationships that are provided by the manufacturers for the sizing and selection of tumbling mills and classifiers.

An interesting where simulation is applied is a flotation process. The incentive for increasing recovery by as little as one per cent is very important when flotation plants handle large tons of ore. However, when circuit re-arrangements are proposed is very difficult to obtain conclusive evidence of so small an increase experimentally because of errors in assay and flow determinations and variations of ore composition. An alternative method to this problem is the use of simulation methods. These methods enable the metallurgist to compare accurately numerous control strategies. Nevertheless, the conclusions reached by simulation are very sensitive to the accuracy with which the system can be described by a mathematical model. So, the best approach is a combination of simulation and experimental studies with each contributing towards a better understanding of the process.

It is necessary to point out that a simulation model can be used effectively for design and control. For design purposes it can be used to predict the performance of a large plant from laboratory and pilot plant data. It can be used for control purposes to assess the utility of any proposed operating modification to an existing plant, and it can be used to determine optimal control settings for best operating performance.