Ascend Performance Materials - Why We Use Seeq in Anomaly Detection at Ascend

Yele Soyombo, Data Science Manager, Ascend Performance Materials | Ezgi Gumusbas, Data Scientist,  Ascend Performance Materials

Ascend’s AI/ML team prioritizes anomaly detection use cases performed at scale, which presented problems for historically used traditional modeling and deep learning models. Seeq provided the flexibility to scale the same built-in anomaly detection model, while making minor tweaks to tailor to the asset or data available. In this presentation, we’ll share how we’re using Seeq ML to do anomaly detection, at scale, and present the results to engineering teams in Seeq’s friendly user interface.

Plan, Learn, Adapt - Rollout Strategies for a Diverse Organization

Fariha Imami, Manufacturing Intelligence Project Leader, PQ

At PQ, no sites are alike. Differences in data maturity and resourcing add extra layers of complexity. We didn’t find one recipe that fits all. Instead, we found a new mindset of agility and continuous improvement, enabling us to leverage existing resources and apply our learnings to deliver better results at each iteration.

Conneqt 2024 – Chemicals

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    19:29

    Plan, Learn, Adapt – Rollout Strategies for a Diverse Organization

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    18:32

    Distillation Tower Fouling Prediction: Anomaly Detection & Causality Analysis Using Process Health Solution (PHS)

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    15:55

    Monitoring Flows for Wastewater Management

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    16:10

    Navigating a Grassroots Deployment Strategy

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    13:49

    Empowering Remote Monitoring Teams

Eli Lilly and Company- Adoption and Deployment Strategy for Rollout of Seeq at Eli Lilly and Company

Speaker: Wilfred Mascarenhas, Sr. Director for Data and Analytics – Eli Lilly & Company

Conneqt 2023 – Pharmaceuticals

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    19:10

    Eli Lilly and Company- Adoption and Deployment Strategy for Rollout of Seeq at Eli Lilly and Company

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    20:58

    Genentech, Mettler Toledo – Data Analysis for an Online Water Bioburden Analyzer

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    23:31

    Bristol Myers Squibb – Cell Culture Offline Sample Monitoring

Ascend Performance Materials - Why We Use Seeq in Anomaly Detection at Ascend

Yele Soyombo, Data Science Manager, Ascend Performance Materials | Ezgi Gumusbas, Data Scientist,  Ascend Performance Materials

Ascend’s AI/ML team prioritizes anomaly detection use cases performed at scale, which presented problems for historically used traditional modeling and deep learning models. Seeq provided the flexibility to scale the same built-in anomaly detection model, while making minor tweaks to tailor to the asset or data available. In this presentation, we’ll share how we’re using Seeq ML to do anomaly detection, at scale, and present the results to engineering teams in Seeq’s friendly user interface.

Conneqt 2023 – Chemicals

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    18:09

    Ascend Performance Materials – Why We Use Seeq in Anomaly Detection at Ascend

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    20:57

    Seeq Deployment at Scale – Cargill

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    17:14

    Chemours – Seeq Deployment at Chemours

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    17:03

    Honeywell UOP – A Sustainable Future with Seeq

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    15:35

    Multi-Product Best Run Rate

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    15:05

    Leveraging IF97 Property Data to Optimize Batch Cycle Time

Eli Lilly and Company- Adoption and Deployment Strategy for Rollout of Seeq at Eli Lilly and Company

Speaker: Wilfred Mascarenhas, Sr. Director for Data and Analytics – Eli Lilly & Company

Conneqt 2022 – Pharmaceuticals

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    19:10

    Eli Lilly and Company- Adoption and Deployment Strategy for Rollout of Seeq at Eli Lilly and Company

  • 2

    20:58

    Genentech, Mettler Toledo – Data Analysis for an Online Water Bioburden Analyzer

  • 3

    23:31

    Bristol Myers Squibb – Cell Culture Offline Sample Monitoring

Devon Energy - Using Seeq at Scale for Data & Analytics Integration

Speaker: Don Morrison, Real Time Systems Architect, Devon Energy

Conneqt 2022 – Oil & Gas

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    15:52

    Devon Energy – Using Seeq at Scale for Data & Analytics Integration

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    21:09

    Parkland Refinery – Self Service Analytics for Processing of Hydrocarbons

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    13:20

    Western Midstream – Pinpointing Pressure Drop Issues in Gas Plants