Abstract
With the increase in available data and the stricter control requirements for mineral processes, the development of automated methods for data processing and model creation are becoming increasingly important. In this paper, the application of data quality assessment methods for the development of semirigorous and empirical models of a primary milling circuit in a platinum concentrator plant is investigated to determine their validity and how best to handle multivariate input data. The data set used consists of both routine operating data and planned step tests. Applying the data quality assessment method to this data set, it was seen that selecting the appropriate subset of variables for multivariate assessment was difficult. However, it was shown that it was possible to identify regions of sufficient value for modeling. Using the identified data, it was possible to fit empirical linear models and a semirigorous nonlinear model. As expected, models obtained from the routine operating data were, in general, worse than those obtained from the planned step tests. However, using the models obtained from routine operating data as the initial seed models for the automated advanced process control methods would be extremely helpful. Therefore, it can be concluded that the data quality assessment method was able to extract and identify regions sufficient and acceptable for modeling.
Highlights
As the importance of data increases and increasingly large amounts of data are collected by industrial processes [1,2], there is a growing need to develop methods that can automate the processing of the data
data quality assessment (DQA) indicates that a section of data consisting of a little over 19 days of data was suitable for identification
The fitting routine defines cases consisting of a set of manipulated variables (MVs) and controlled variables (CVs)
Summary
As the importance of data increases and increasingly large amounts of data are collected by industrial processes [1,2], there is a growing need to develop methods that can automate the processing of the data. Much of the data are directly used without any consideration of its quality. In the context of modeling, this means that if the data provided are useless, the resulting analysis will be useless. One of the most important applications of data is for understanding the process, that is, developing models that describe the behaviour of the system for different situations [4]. The key metric for determining the quality of the data is persistent excitation, which measures how much of the system has been excited [5,6]. Operating companies who are involved in MPC projects often ask the question “why can we not use all the historical data that we have gathered for our process to derive the models”? Operating companies who are involved in MPC projects often ask the question “why can we not use all the historical data that we have gathered for our process to derive the models”? This is a valid question, and the standard answers are listed below
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