Abstract
Abstract Correct interpretation of measured process variables is essential for the evaluation, diagnosis and control of batch operations. The extraction, though, of the ‘pivotal’ temporal features from operating data is not a trivial task for two reasons: the localization in time of the operating features, and the multi-scale character of operating trends. This paper introduces an integrated systematic approach, based on the wavelet decomposition of batch records of operating variables, which accomplishes the following goals: (1) It extracts the temporal features of the process trends at various time-scales. (2) It generalizes the multi-scale description of operating variables, by identifying the common features among many records of these operating variables. (3) It establishes relationships among the temporal patterns of operating variables, thus leading to pattern-based diagnosis and control of batch operations. Data from an industrial batch fermentor are used to illustrate the ideas of the proposed approach and its value for evaluating batch operations.
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