Iron and Steel Division - Experimental Planning for Rapid Determination of Optimum Process Conditions

Fractional replication of factorial design, a general method for planning experimentation and for analysis of data obtained, is described as applied to a flotation investigation. This method leads to determination of optimum process conditions with minimum experimental effort. Its advantages over simple factorial design are demonstrated. A METHOD for planning experimentation and for analyzing the data secured will be demonstrated. This method, termed fractional replication of factorial design, employs a relatively small number of individual experiments to determine which of a large number of process variables are controlling, to determine which combination of levels of these variables is most likely to produce optimum results, and also to predict what results will be obtained with a particular combination of conditions not already tested. Although the general method is not new, having been developed by Finney in 1945,' the extent to which it can improve the effectiveness of applied research generally has not been recognized by metallurgists. The fractional replication procedure is particularly useful in flotation experimentation and an example from such an investigation will be used in the paper. However, it has equal value in any investigation in which similar experimental difficulties are encountered. In developing a flotation process for a particular mineral separation, the investigator is inevitably confronted with the following difficulties: 1—There are a large number of potentially important process variables. 2—Results of individual experiments are not highly reproducible, due in part to the difficulty in precisely controlling all the variables. 3—Considerable effort is expended in conducting individual experiments. 4—There are practical limits on the number of individual experiments which can be made. In situations of just such a type, modern statistical methods of planning experimentation and analyzing data have their greatest value. Applications of one such technique, called factorial design, to problems of this type have been described by Dorenfeld and others.'-' The simple factorial design is an efficient procedure when the investigator hopes to provide a comprehensive understanding of the effects and interrelationships of a small number of variables over a limited range. In applied research, this is seldom the main objective. Rather, the investigator usually wishes to determine which of the many potentially important variables are in fact controlling, which levels of the controlling variables will provide opti- mum metallurgical results, and what these results will be at optimum conditions. Interest in detailed trends is limited to the controlling variables and to levels in the region of optimum conditions. Simple factorial design has serious deficiencies for such objectives and is not the most efficient method of experimental design. Deficiencies of Factorial Design In a simple factorial design, an experiment must be made at every possible combination of each level of every variable, once these have been chosen and the levels of each to be included have been decided upon. As the number of variables or levels of each increases, the experimental program quickly reaches prohibitive size. For example, an investigation of only four variables, each at four levels, requires 256 individual experiments. Often upon completion of such an extensive program, it is found that one or more of the variables has metallurgically unimportant effects or that a poor estimate has been made as to the appropriate range of levels to be investigated. The result is that only a small proportion of the data obtained falls in the range of real metallurgical interest. Indeed, it frequently can be anticipated that certain combinations of levels of variables will not produce results of interest, but they still must be included so that the essential balance, or orthogonality, of the design will be retained. It may be true that factorial design will provide the greatest amount of information from a given number of experiments, but it does not necessarily follow that it will lead to the desired information with a minimum number of experiments. Much of the information provided may be of little value. Advantages of Fractional Replication The disadvantages of simple factorial design are overcome to a great extent by a modification known as fractional replication. This is a technique for sampling systematically the potential data of a full factorial experiment, that is, the data which would have been obtained if the complete factorially designed program had been completed. Only a fraction of the total array of experiments is made, but the experiments are chosen in such a way that the important advantages of factorial design and the accompanying analysis of variance are retained. The data obtained from the first group of experiments are used to determine which of several variables are controlling and which levels of these variables are most likely to produce the desired result. Unimportant variables and levels of variables then may be