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Process plant control systems capture measurements of thousands of variables as often as every few seconds. These add to a continuously lengthening time-series for each measurement which began when the plant was first started. This data has enormous potential, which is largely untapped by the conventional analysis tools available to most process and control engineers. Fashionable “Big Data” approaches are challenged by process plant data and have limited application for busy engineers since many of the assumptions and simplifications destroy the richness of process data. Geometric Process Control (GPC) – a technology unique to us here at PPCL – avoids these pitfalls and provides engineers with graphical tools to work with datasets spanning their entire plant and create low-cost, equation-free predictive models to develop new process understanding quickly and easily.

This webinar, presented by Dr Alan Mahoney in May 2019, demonstrates our unique approach to analysis on a process with a medium size dataset spanning the process from feed to product, 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 explore their data fully and make discoveries that they couldn’t before. We also cover process stewardship, using your discoveries to achieve quality targets and operational excellence long into the future.

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