Advanced Control and Supervision of Mineral Processing by Daniel Sbárbaro

By Daniel Sbárbaro

Advanced keep watch over and Supervision of Mineral Processing Plants describes using dynamic types of mineral processing apparatus within the layout of keep watch over, facts reconciliation and soft-sensing schemes; via examples, it illustrates instruments integrating simulation and keep watch over method layout for comminuting circuits and flotation columns. assurance is given to the layout of soppy sensors in line with both single-point measurements or extra complicated measurements like photographs. matters touching on info reconciliation and its employment within the production of tool structure and fault analysis are surveyed. In attention of the frequent use of disbursed keep an eye on and knowledge administration platforms in mineral processing, the publication describes the systems and toolkits on hand for imposing such systems.

Applications of the recommendations defined in genuine crops are used to focus on their merits; info for the entire examples, including aiding MATLAB® code are available at www.springer.com/978-1-84996-105-9.

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Example text

There is no unique way to select the set of variables X that characterize process states. The structure of the models and constraints describing a process behavior depends upon variable selection. This will have an impact on the observation or data reconciliation method, although the resulting values of reconciled states should not rest upon the problem formulation, if consistent information processing methods are used. Similarly, the measured values Y of the Z process variables used as input to the reconciliation procedure may or may not be raw measurements of the process states X.

Unfortunately, in metallurgical processes, the knowledge is frequently fuzzy and less accurate than in mechanical and chemical industries. 1 summarizes the concept under discussion in this chapter. The core of a data reconciliation procedure is a mathematical algorithm that can be called either an observer, or an estimator, or a filter. , the observation of the variables upon which the process behavior and performance are qualified. It is an estimator in the sense that it estimates numerical values of state variables which may not necessarily be measured or measurable.

Because of the conflict generated by the uncertainties e and ε , these equations generate residual values that are not zero but functions of e and ε . These residuals can be used to detect measurement biases or abnormal deviations to mass and energy conservation laws. 12. 9. 30 degenerates into two limit reconciliation problems when e or ε are assumed to have null values: the steady-state case and the node imbalance case. Steady-state data reconciliation. The SSR case is obtained by setting Vε to zero, thus removing the second term of the criterion.

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