Process identification from plant data

In this paper the feed preparation process of the copper flash smelter at Outokumpu Harjavalta plant is studied from a control theoretic perspective. The aim of the study is to identify a dynamic process model from experimental data and to compare different model structures. A sampling campaign was arranged to provide data for identification. The process was modelled with an adaptive ARX model and with a blending tanks model where the process units were modelled as first-order systems. A Kalman filter was used to estimate the process state. The Kalman filter was the most efficient algorithm for predicting the process output and it has been successfully used online to the process. 

No plant-specific identification number was available for this facility. The wastewater from Plant B contains pollutants from both metals processing and finishing operations. It is treated by precipitation-settling followed by filtration with a rapid sand filter. A clarifier is used to remove much of the solids load. Table 5.14 summarizes the data on pollutant removal efficiency at Plant B. 

The correct interpretation of measured process data is essential for the satisfactory execution of many computer-aided, intelligent decision support systems that modern processing plants require. In supervisory control, detection and diagnosis of faults, adaptive control, product quality control, and recovery from large operational deviations, determining the mapping from process trends to operational conditions is the pivotal task. Plant operators skilled in the extraction of real-time patterns of process data and the identification of distinguishing features in process trends, can form a mental model on the operational status and its anticipated evolution in time. 

For continuous process systems, empirical models are used most often for control system development and implementation. Model predictive control strategies often make use of linear input-output models, developed through empirical identification steps conducted on the actual plant. Linear input-output models are obtained from a fit to input-output data from this plant. For batch processes such as autoclave curing, however, the time-dependent nature of these processes—and the extreme state variations that occur during them—prevent use of these models. Hence, one must use a nonlinear process model, obtained through a nonlinear regression technique for fitting data from many batch runs. 

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