Disturbances that spread plant-wide in a chemical process pose challenges to maintenance staff. Connections within the plant and the presence of multiple causal paths mean it is not straightforward to locate the root disturbance because the effects can propagate and be detected elsewhere. Measurementbased methods use quantitative process history to generate hypotheses about the root cause, while a separate strand of work in the literature has used causal maps and digraphs. It has been reported that both approaches can give spurious solutions, however. The idea behind this article is to reduce the number of spurious solutions by combining basic and readily-available information about the connectivity of the process with the results from causal measurement-based analysis. Connectivity information is captured from an XML description of the process schematic that complies with the CAEX schema. The capabilities of the approach and its potential for future development are discussed.

No abstract

It is common practice in nonlinear regression situations to use asymptotic linear approximations of the model functions to construct parameter inference regions; such approximations may turn out to be a poor representation of the true underlying surfaces, especially for highly nonlinear situations and small sample sizes. For this reason, experimental designs based on these approximations could well be moderately noninformative. We present a new method for optimal experimental design for improving parametric precision while taking account of curvature in multiresponse nonlinear structured dynamic models. We base the curvature measures in the multiresponse case on the Box-Draper estimation criterion through use of the generalized leastsquares model conditioned on the maximum likelihood estimate of the variance-covariance matrix for the responses. Curvature measures commensurate with those found in the literature are used for the generalized least-squares model in the neighborhood of the parameter point estimates. The problem of designing dynamic experiments is cast as an optimal control problem that enables the calculation of a fixed number of optimal sampling points, experiment duration, fixed and variable external control profiles, and initial conditions of a dynamic experiment subject to general constraints on inputs and outputs. We illustrate the experimental design concepts with a relatively simple but pedagogical example of the dynamic modeling of the fermentation of baker's yeast.

This article describes how isolation and diagnosis of the root cause of a plant-wide disturbance is enhanced when process connectivity is considered alongside the results of data-driven analysis. A prototype software has been designed and implemented which, when given an electronic process schematic of a plant and results from a data-driven analysis of process measurements, allows the user to pose queries about the plant and to find root causes of plant-wide disturbances. The plant topology information is written in XML according to the Computer Aided Engineering Exchange (CAEX) schema.