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Data-driven methods for batch data analysis - A critical overview and mapping on the complexity scale
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DOI：10.1016/j.compchemeng.2019.01.014.png)
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Abstract
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More than two decades have passed since the first holistic data-driven approaches for batch data analysis (BDA) were published. The emphasis was on multivariate statistical process monitoring and quality prediction. In the subsequent years, more methods were proposed, with varying degrees of success and acceptance by practitioners. A detailed and comprehensive analysis of these contributions reveals different complexity levels, both in terms of the degrees of freedom used for setting up the techniques (modeling complexity) and the expertise/training required by practitioners to autonomously apply them in concrete real world applications (implementation complexity). Both dimensions decisively contribute to the impact of a given proposal in industry. In this paper, we present a structured overview of BDA methodologies and analyze them from both perspectives. As a corollary, we map the BDA methods into the complexity scale, and elaborate how it can be used for selecting a suitable method for a given task. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Batch data analysis
Complexity
Parsimony
Process monitoring
Quality prediction
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