BTT: Online Process Monitoring Using Historical Process Data Manuel Martin – Lubrizol EU Process Data Scientist

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BTT: Online Process Monitoring using Historical Process Data Manuel Martin – Lubrizol EU Process Data Scientist

Agenda

. About Lubrizol . Use case

. The pains . Tangible benefits

. The concept . Intangible benefits

. The infrastructure . Next steps

. The tool

About Lubrizol

Lubrizol is a global specialty chemical company

We combine market insights with chemistry and application capabilities to deliver valuable solutions to customers and their end-users in the global transportation, industrial and consumer markets.

Lubrizol is part of Berkshire Hathaway – Warren Buffett, Chairman and CEO

Businesses

LUBRIZOL LUBRIZOL ADVANCED ADDITIVES MATERIALS

The global leader in performance A leader in polymer-based additives for automotive and technology for consumer and industrial lubrication industrial applications

Geographic Footprint

The Pain: process variability

The Journey From Uni to Multivariate

Multivariate

Semi - multivariate Looks at all variables and their relationships at the same time Looks at more than one variable Univariate Promotes full understanding of Operators infer the most the process Looks at variables separately prominent internal relationships Allows predicting final batch Does not promote understanding “Softly” relates process variability characteristics of internal relationships to batch quality

Does not relate process variability to final batch quality

Short Term Solution

Use what we already have to do as much as we can with it

Fast and simple to develop and Open enough to be standardized Achieve management buy-in, deploy, and to maintain across the company deliver quick tangible results

Can be used as a training tool Help the operators drive the Make life easier, help eliminate when final MV tool is available batches in real-time non-value added tasks

Why we didn’t use the Golden-batch

ONE good batch vs MANY Does not promote link between good batches process with final batch results

Does not allow online prediction of final batch quality Looks at the process variables separately

Does not promote detailed Does not address the fact that process understanding at the natural process variation exists plant level

Concept analogy: the human voice

Hello! Hello! Hello!

Hello!

The Concept: the “Voice” of the Process

ReferenceAAcceptance set of both batches bandOK andmay is KOcalculatedbe references removed based and batches on to all CTQsimplify tagsis selected viewas configured to the operator

Individual batches Model of acceptable variation Band of acceptable variation

The Solution

. BTT – Batch Trending Tool

. MS Excel VBA + PI datalink module I BTT . Event Frames generated from DCS ISA S88 recipe structure

. In non-batch managed units, the event frames can derived using embedded logic

The Data Infrastructure BTT

Excel VBA PI DataLink Event Frames

Process DCS for visualization Local servers at most sites Other analytics / stats tools

Solution: Main Interface

Use Case: Latex production

Avoid use of “last batch” as guide for “next batch” Latex production in manual and semi-auto batch reactors

Automatically generate batch Products designed to include report – paperless plant adjustment step at end – still some quality variability

Delay investments in sold-out units by improving their performance

This Photo by Unknown Author is licensed under CC BY-NC-ND Tangible benefits

Significantly increased process stability in CTQ process variables.

Before After

Tangible Benefits Variability down by 56% with no impact to average quality

Box-and-Whisker Plot

7900 Improved before-adjustment variability – cycle time reduction 6900

5900 Significantly improved units performance – delay capital

4900 investments Viscosity before adjustment Viscosity before

3900 1 Before BTT 2 After BTT Enables long standing projects with potential benefits 0.5 - 1 MM€

Set a solid ground to support the Intangible Benefits future

Improves knowledge and Simplifies and rationalizes the understanding of the processes at operators job little cost

Operators heavily involved – Lots of new improvement ideas empowerment and recognition generated

Freed up time for value added tasks

Next steps: the Future

Switch to MV and move to use as a training aid

Improve maintenance model Prepare transition to new MV toolset being developed

Identify new use cases and Collect feedback for other sites potential benefits and processes

BTT: Online Process Monitoring using Historical Process Data

CHALLENGE SOLUTION RESULTS

Reduce batch-to-batch variability in Online batch monitoring tool developed Batch-to-batch variability of CTQ manual & semi-auto units impacting cycle applying the basic MV concepts variables significantly reduced time and plant capacity

• MS Excel + PI Datalink • Before adjustment quality improved by 56%, significant opportunities • Serve as an intermediate step in the • Combines cont. variables + event frames journey to MV identified • Implemented concept of band of allowable • Operators motivated and contributing • Get the operators familiarized with variation to further development the MV concepts to come • Started demonstrating benefits of approach, • Set solid ground for further supports next step deployment and future move to MV

Thank you

Manuel Martin EU Process Data Analytics Specialist

[email protected] www.linkedin.com/in/mmartin11

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