A Multivariate Process Analysis on a Paper Production Process

Abstract: A big challenge in managing large scale industry processes, like the ones in the paper and pulp industry, is to reduce the amount of downtime and reduce sources of product quality variability to a minimum, while staying cost effective. To accomplish this the key is to understand the complex nature of the processes variables, and to quantify the causal relationships between them and the product quality together with the amount of output. Paper and pulp industry processes consist mainly of chemical processes and the relatively low cost of sensors today enables collection of huge amounts of data, both variables and observations on frequent time intervals. These masses of data usually come with the intrinsic problem of multicollinearity which requires efficient multivari- ate statistical tools for the extraction of useful insights among the noise. One goal in this multivariate situation is to breakthrough the noise and find a relatively small subset of variables that are important, that is, variable selection. The purpose with this master thesis is to help SCA Obbola, a large paper manu- facturer that have had a variable production output, to come up with conclusions that can help them ensure a long term high production quantity and quality. We apply different variable selection approaches that have proven successful in the literature. The results that we get are of mixed success, but we manage to find both variables that SCA Obbola knows affect specific response variables, but also variables that they find interesting for further investigation.