BTT: Online Process Monitoring Using Historical Process Data - OSIsoft

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BTT: Online Process Monitoring usingHistorical Process DataManuel Martin – LubrizolEU Process Data Scientist#PIWorld 2018 OSIsoft, LLC1

Agenda About Lubrizol Use case The pains Tangible benefits The concept Intangible benefits The infrastructure Next steps The tool#PIWorld 2018 OSIsoft, LLC2

About LubrizolLubrizol is a global specialtychemical companyWe combine market insights with chemistry and applicationcapabilities to deliver valuable solutions to customers and theirend-users in the global transportation, industrial and consumermarkets.Lubrizol is part of Berkshire Hathaway – Warren Buffett, Chairman and CEO#PIWorld 2018 OSIsoft, LLC3

BusinessesLUBRIZOLADDITIVESLUBRIZOL ADVANCEDMATERIALSThe global leader in performanceadditives for automotive andindustrial lubricationA leader in polymer-basedtechnology for consumer andindustrial applications#PIWorld 2018 OSIsoft, LLC4

Geographic Footprint#PIWorld 2018 OSIsoft, LLC5

The Pain: process variability#PIWorld 2018 OSIsoft, LLC6

The Journey From Uni to MultivariateMultivariateSemi - multivariateUnivariateLooks at more than one variableLooks at variables separatelyOperators infer the mostprominent internal relationshipsDoes not promote understandingof internal relationships“Softly” relates process variabilityto batch qualityLooks at all variables and theirrelationships at the same timePromotes full understanding ofthe processAllows predicting final batchcharacteristicsDoes not relate processvariability to final batch quality#PIWorld 2018 OSIsoft, LLC7

Short Term SolutionUse what we already have to do as much as we can with itFast and simple to develop anddeploy, and to maintainOpen enough to be standardizedacross the companyAchieve management buy-in,deliver quick tangible resultsCan be used as a training toolwhen final MV tool is availableHelp the operators drive thebatches in real-timeMake life easier, help eliminatenon-value added tasks#PIWorld 2018 OSIsoft, LLC8

Why we didn’t use the Golden-batchONE good batch vs MANYgood batchesLooks at the processvariables separatelyDoes not promote detailedprocess understanding at theplant levelDoes not promote link betweenprocess with final batch resultsDoes not allow online predictionof final batch qualityDoes not address the fact thatnatural process variation exists#PIWorld 2018 OSIsoft, LLC9

Concept analogy: the human voiceHello!Hello!Hello!Hello!#PIWorld 2018 OSIsoft, LLC10

The Concept: the “Voice” of the ProcessAAcceptanceset of bothbandOK toallbereferencesremovedandCTQtagsis selectedas configuredsimplifyviewto theoperatorIndividual batchesModel of acceptable variationBand of acceptable variation#PIWorld 2018 OSIsoft, LLC11

The Solution BTT – Batch Trending Tool MS Excel VBA PI datalink module Event Frames generated from DCS ISA S88recipe structureIBTT In non-batch managed units, the event framescan derived using embedded logic#PIWorld 2018 OSIsoft, LLC12

The Data InfrastructureBTTExcel VBAPI DataLinkEvent FramesProcessDCS forvisualizationLocalservers atmost sitesOther analytics /stats tools#PIWorld 2018 OSIsoft, LLC13

Solution: Main Interface#PIWorld 2018 OSIsoft, LLC14

Use Case: Latex productionLatex production in manual andsemi-auto batch reactorsProducts designed to includeadjustment step at end – still somequality variabilityAvoid use of “last batch” as guidefor “next batch”Automatically generate batchreport – paperless plantDelay investments in sold-out unitsby improving their performance#PIWorld 2018 OSIsoft, LLC15

Tangible benefitsSignificantly increased process stability in CTQ process variables.BeforeAfter#PIWorld 2018 OSIsoft, LLC16

Tangible BenefitsVariability down by 56% with noimpact to average qualityBox-and-Whisker PlotImproved before-adjustmentvariability – cycle time reductionViscosity before adjustment790069005900Significantly improved unitsperformance – delay capitalinvestments490039001 Before BTT2 After BTTEnables long standing projects withpotential benefits 0.5 - 1 MM #PIWorld 2018 OSIsoft, LLC17

Intangible BenefitsSimplifies and rationalizes theoperators jobOperators heavily involved –empowerment and recognitionSet a solid ground to support thefutureImproves knowledge andunderstanding of the processes atlittle costLots of new improvement ideasgeneratedFreed up time for value addedtasks#PIWorld 2018 OSIsoft, LLC18

Next steps: the FutureSwitch to MV and move to use asa training aidImprove maintenance modelPrepare transition to new MVtoolset being developedIdentify new use cases andpotential benefitsCollect feedback for other sitesand processes#PIWorld 2018 OSIsoft, LLC19

BTT: Online Process Monitoringusing Historical Process DataCHALLENGEReduce batch-to-batch variability inmanual & semi-auto units impacting cycletime and plant capacity Serve as an intermediate step in thejourney to MV Get the operators familiarized withthe MV concepts to comeSOLUTIONRESULTSOnline batch monitoring tool developedapplying the basic MV conceptsBatch-to-batch variability of CTQvariables significantly reduced MS Excel PI Datalink Before adjustment quality improvedby 56%, significant opportunitiesidentified Operators motivated and contributingto further development Set solid ground for furtherdeployment and future move to MV Combines cont. variables event frames Implemented concept of band of allowablevariation Started demonstrating benefits of approach,supports next step#PIWorld 2018 OSIsoft, LLC20

Thank youManuel MartinEU Process Data Analytics om/in/mmartin11#PIWorld 2018 OSIsoft, LLC21

Questions?Please wait forthe microphoneState yourname & companyPlease rate this sessionin the mobile app!Search“OSIsoft” inyour app store#PIWorld 2018 OSIsoft, LLC22

#PIWorld 2018 OSIsoft, LLC23

BTT: Online Process Monitoring using Historical Process Data 20 Reduce batch-to-batch variability in manual & semi-auto units impacting cycle time and plant capacity Online batch monitoring tool developed applying the basic MV concepts Batch-to-batch variability of CTQ variables significantly reduced MS Excel PI Datalink

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