On design of experiments in continuous processes

Sammanfattning: Design of Experiments (DoE) includes powerful methods, such as factorial designs, to help maximize the information output from conducted experiments while minimizing the experimental work required for statistically significant results. The benefits of using DoE in industry are thoroughly described in the literature although the actual use of the methods in industry is far from being pervasive. Continuous processes, frequently found in the process industry, highlight special issues that are typically not addressed in the DoE literature. The overall objective of this research is to increase the knowledge of DoE in continuous processes. More specifically, the aims of this research are [1] to identify, explore, and describe potential problems that can occur when planning, conducting, and analyzing experiments in continuous processes, and [2] to propose methods of analysis that help the experimenter in continuous processes tackle some of the identified problems.This research has focused on developing analysis procedures adapted for experiments in continuous processes using a combination of existing DoE methods and methods from the related fields: multivariate statistical methods and time series analysis. The work uses real industrial data as well as simulations. The method is dominated by the study of the practical use of DoE methods and the developed analysis procedures using an industrial case - the LKAB Experimental Blast Furnace plant.The results are presented in six appended papers. Paper A provides a tentative overview of special considerations that the experimenter needs to consider in the planning phase of an experiment in a continuous process. Examples of important experimental complications further discussed in the papers are: their multivariate nature, their dynamic characteristics, the need for randomization restrictions due to experimental costs, the need for process control during experimentation, and the time series nature of the responses. Paper B develops a method to analyze factorial experiments with randomization restrictions using principal components combined with analysis of variance. Paper C shows how the use of the multivariate projection method principal component analysis can reduce the monitoring problem for a process with many and correlated variables. Paper D focuses on the dynamic characteristic of continuous processes and presents a method to determine the transistion time between experimental runs combining principal components and transfer function-noise models and/or intervention analysis. Paper E further addresses the time series aspects of responses from continuous processes and illustrates and compares different methods to analyze two-level factorials with time series responses to estimate location effects. In particular, Paper E shows how multiple interventions with autoregressive integrated moving average models for the noise can be used to effectively analyze experiments in continuous processes. Paper F develops a Bayesian procedure, adapted from Box and Meyer (1986), to calculate posterior probabilities of active effects for unreplicated twolevel factorials, successively considering the sparsity, hierarchy, and heredity principles.