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Sigmafine: Sigmafine: Providing Reconciled Data to the BusinessProviding Reconciled Data to the Business
Tom Hosea
OSIsoft, Houston, TX
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A Simple Problem, Complicated by Reality
Production Management/ Loss ControlProduction Management/ Loss Control
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The Fog of DataThe Fog of Data
Typical large petrochemical complex or refinery can be polling 100,000 points, once per minute.
This corresponds to about 200 Gbytes per year.
(It will be 200,000 points or more in five years) – Data does not balance
– Used (Misused) by multiple groups
– Inconsistent and incompatible conclusions
– How do you find the data you need?
– How do you analyze it?
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Typical RefineryTypical Refinery
A refinery is consistently shows an average mass balance of 97.5%, i.e. the products plus fuel consumed plus known losses is only 97.5% of the crude plus intermediates purchased
– Is the refinery paying for crude not received?
– Is the refinery not being paid for all products?
– Is there theft of product or leakage or evaporation?
– Is more fuel being burned than estimated? Flared?
– Is there excessive off spec product being recycled?
2.5% losses on a 200,000 BPD refinery are worth $300,000 per DAY or $110 million per year.
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What is Data Reconciliation?What is Data Reconciliation?
A statistical method of resolving detected errors according to pre-specified rules and tolerances
Distributes errors across a system
Reports on and explains errors
Includes:
– Data Validation
– Systematic Detection of Gross Errors (e.g. missing measurements, mis-specified movement, etc)The K
ey to P
roducti
on Management
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The Issues with Data ValidationThe Issues with Data Validation
Too much data
– Thousands of data points
Too many sources
– Lab systems, DCS, manual entry
Too many interactions
– Transfers, flows, measurements
Not enough time…
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SigmafineSigmafine
A product that
enables data reconciliation
and validation
for any industrial process.
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Information
Typical Scenario Without ValidationTypical Scenario Without Validation
Some sort of local balance
Some arbitrary and subjective corrections
No agreement on data
Difficult to detect measurement errors
?Fog
Data
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Validation with Sigmafine Validation with Sigmafine
A unique balance, valid for the whole operation
Systematic and objective corrections
Agreement on balanced data
Easier to detect measurement problems
InformationData
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Data Reconciliation ChallengesData Reconciliation Challenges
RefiningRefining Metals & MiningMetals & Mining ChemicalsChemicals
Many ProductsMany Products X X
Lineup ChangesLineup Changes X
Flows & TransfersFlows & Transfers X X
Model Size (elements)Model Size (elements) 5000 1000 1000
Model ComplexityModel Complexity High High Low
RedundancyRedundancy High Low Low
Analyzer countsAnalyzer counts Low High Medium
Unmeasurable FeedstocksUnmeasurable Feedstocks X
Material Acct per ElementMaterial Acct per Element X
Componenet BalancesComponenet Balances X
Stoichiometric BalanceStoichiometric Balance X
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How to solve these problemsUse Sigmafine to…How to solve these problemsUse Sigmafine to…
1. Build and configure a model (once)
2. Run the model using the appropriate analysis rules (frequently)
3. Analyze results (frequently)
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Sigmafine ToolsSigmafine Tools
Data References– A component that reads, writes and executes
calculations
Analysis Rules– Provides model analysis for balances, composition
tracking or gross error detection
Data Loader– Imports data elements of many formats to create
cases or transfers
Visualization/Analysis tools– ProcessBook, AF Excel Add-in, RtReports
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Benefits by IndustryBenefits by Industry
Refining– Transfers provide the basis of model receipts, shipments and movements– Automatic Inventory calculations– Composition Tracking of stored products– Refining specific calculations – gross to net conversion
Chemical– Mass and Component balance– Configurable reaction constraints– Meter Compensation – gas and liquid– Inventory calculations
Metals and Mining– Component balance of materials not typically measured– Independent solvability of components– Independent accuracies of measurements– Efficient system management of sparse measurements
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ConclusionsConclusions
Sigmafine can be applied to any industry
Validated data is available to make better business decisions
No process model is required to derive value from Sigmafine
The use of data references does not require a model
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Good Data for Good Business DecisionsGood Data for Good Business Decisions
“You can't manage what you can't control, and you can't control what you don't measure.”
Tom DeMarco
Sigmafine increases confidence in what you measure and provides estimates of what you don’t measure, helping you to make better business decisions
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Thank YouThank You
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