Monitorização, modelação e melhoria de processos químicos : abordagem multiescala baseada em dados

Abstract

Processes going on in modern chemical processing plants are typically very complex, and this complexity is also present in collected data, which contain the cumulative effect of many underlying phenomena and disturbances, presenting different patterns in the time/frequency domain. Such characteristics motivate the development and application of data-driven multiscale approaches to process analysis, with the ability of selectively analyzing the information contained at different scales, but, even in these cases, there is a number of additional complicating features that can make the analysis not being completely successful. Missing and multirate data structures are two representatives of the difficulties that can be found, to which we can add multiresolution data structures, among others. On the other hand, some additional requisites should be considered when performing such an analysis, in particular the incorporation of all available knowledge about data, namely data uncertainty information. In this context, this thesis addresses the problem of developing frameworks that are able to perform the required multiscale decomposition analysis while coping with the complex features present in industrial data and, simultaneously, considering measurement uncertainty information. These frameworks are proven to be useful in conducting data analysis in these circumstances, representing conveniently data and the associated uncertainties at the different relevant resolution levels, being also instrumental for selecting the proper scales for conducting data analysis. In line with efforts described in the last paragraph and to further explore the information processed by such frameworks, the integration of uncertainty information on common single-scale data analysis tasks is also addressed. We propose developments in this regard in the fields of multivariate linear regression, multivariate statistical process control and process optimization. The second part of this thesis is oriented towards the development of intrinsically multiscale approaches, where two such methodologies are presented in the field of process monitoring, the first aiming to detect changes in the multiscale characteristics of profiles, while the second is focused on analysing patterns evolving in the time domain.