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Process plants generate continuous time-series data for thousands of variables. This data has enormous potential, untapped today because of the limitations of process engineers’ conventional analysis tools. Fashionable “Big Data” approaches are challenged by process plant data and have limited applications for busy process and control engineers. Predictive Analytics, a time-consuming method which requires a data scientist to interpret the results, provides generalized answers through simplifications which can destroy much of the richness of the data. Geometric Process Control (GPC) – a technology developed here at Process Plant Computing Ltd – avoids these pitfalls and provides engineers with graphical tools to work with datasets spanning their entire plant which create low-cost predictive models without equations to develop new process understanding quickly and easily.

This webinar from October 2018 starts with analysis of a process with 750 variables over a year at 10-minute intervals. We discuss how to approach big datasets and explore them visually, using operating envelopes and finding interactions between variables. Covering the entire process including incoming analyses through processing conditions to final quality variables, KPIs and performance variables, GPC enables engineers to fully explore their data and make discoveries that they can’t now. We also demonstrate real-time online GPC models that provide ongoing enforcement of your discoveries for achieving quality targets and operational excellence long after you have moved on to your next project.

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