Minerals Beneficiation - The Use of Curvilinear Multiple correlation Analysis in Computer Simulation of Complex Models

This paper presents a general discussion of the utility of the statistical technique known as multiple correlation, and gives three specific examples of its application. The first demonstrates the most simple form, or straight-line, multiple correlation. The other two demonstrate multiple correlation analysis in best-fitting third-degree parabolas. With the increasing availability of digital computers, the method becomes entirely feasible where heretofore its use was impractical. The advantages of the method are discussed, and curves are presented which were developed by computerizing the regression equations developed in the examples. The method described in this paper is for use with complex systems, and the examples given are complex metallurgical systems. This subject falls under the general classification of cybernetics, which is defined' as "the science of control and communication, in the animal and the machine." Machine, in this case, can include chemical and physical processes which take place within machines and processing equipment of all kinds. The classical approach to scientific investigation has been the construction of simple models and the changing of variables one at a time. This method is wholly inadequate for obtaining useful information from the complex systems of modern technology including many of our flotation and hydrometallurgical processes. Cybernetics offers the method by which complex systems which have been implemented as an art in the past can now be analyzed with all the discipline of a science. Mathematical techniques for achieving multiple correlation analysis have decided advantages over some of the other statistical methods.2-4 Multiple correlation does not require a series of experiments that have been rigidly planned as do many of the other statistical methods. For this reason, it can be used effectively for interpretation of information from op- erating plants, where planned experiments are impractical, as well as for laboratory experiments. First-degree multiple correlation has been used for many years, and was useful, but limited to linear or nearly linear relationships. The technique described in this paper provides a method for analyzing systems which may be non-linear. This particular technique is multiple correlation analysis in best-fitting third-degree parabolas. It is quite possible that lack of reference to the technique has been due to the overwhelming mass of computations it requires even for relatively simple problems. With the advent of computers, however, its use has become entirely feasible. For the benefit of those who may not be familiar with the subject, it might be well to pause at this point to discuss multiple correlation analysis — what it is and what it achieves. To go back to the old familiar fitting of a set of points with a best-fitting straight line by the least squares procedure, it is conceded that this a good method where there is only one independent variable influencing the dependent variable under consideration, and providing that the relationship is linear. In most fields of investigation, however, there are usually a number of factors exerting important influence, and they frequently bear relationships with the dependent variable other than straight line. Under these conditions, it is easy to see how misleading a simple correlation study might be. Multiple correlation can be used for any number of variables. Within the experience of the writer, this technique has been used to establish regression equations for determination of carbon rate, burden permeability, and production rate on iron blast furnaces, production rate on open hearth steel furnaces, life of molds for steel ingots, rolled steel tensile strength and various defects in rolled products, the performance of ore dressing equipment, and optimization of extractive metallurgical process variables. The discovery of mathematical correlation does not prove, per se, true cause-and-effect relationship, but simply establishes a hypothesis. If the regression equation can be used to make successful predictions of what will happen with future arrays of independent variables, then one can feel reasonably assured that true cause-and-effect relationships have been discovered.