Using a Full Factorial Experiment to Optimize the Performance of an Industrial Chemical Process

Objective: The aim of this work is to show the application of the Full Factorial Experiment in a Chemical Industry Process. Theoretical framework: The design of experiments is defined as a set of statistical techniques applied to planning, conducting, analyzing and interpreting controlled tests, seeking to find (define) the factors that influence the values of a parameter or a group of parameters. Its basic principle makes it possible to vary all the levels of all the discrete or continuous variables (called factors) at once, for each experiment, in a programmed and rational way (Antônio et al., 2025). Method: Experimental data was taken from a chemical industry in the south of the state of Rio de Janeiro and treated with a full factorial experiment. Final Considerations: It can be concluded that full factorial experiments are a very viable option for optimizing the performance of industrial processes. In this specific case of the plant, it can be concluded that the significant Factors are A (Temperature) and B (Concentration) and the Interaction AC (Temperature x Catalyst). Furthermore, in the AC Interaction, the best result is obtained when using a temperature of 180 oC and Catalyst B, and for Main Effect B (Concentration) the best result is obtained when using a concentration of 20 mg/L. Implications of the research: The cases of using a Full Factorial Experiment are multiplying in the scientific literature and are proving to be highly effective for dealing with data in which the assumptions of Normality are confirmed. Originality/value: Despite being well-known statistical tools, the Full Factorial Experiment is widely used and can bring innovations to its application, as in the case of the company in question.