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.
Branden Poulsen Ph.D., P.E., Sr. Process Engineer, Chobani
Chobani’s Twin Falls facility is one of the largest yogurt production plants in the world, producing up to 1.5 million lbs/day of dairy products. Learn how the Wastewater Operations team is using Seeq daily to monitor and predict flows to inform leadership teams and reduce city permit violations.
Troy Ness, Sr. Manufacturing Technology Specialist, 3M Company
3M took a grassroots approach to Seeq adoption, developing a thriving user community from the ground up that is now delivering value throughout the organization. They achieved this transformation by thoughtfully engaging and training users, carefully communicating the value to essential stakeholders, and effectively navigating technical and business obstacles. Join the co-leader of this transformation as he breaks down the challenges they faced throughout this rollout and the key strategies used to overcome them.
Liberty Barney, Data Scientist, Ascend Performance Materials
Distillation tower fouling plagued Ascend’s operations forcing an unplanned 11 day downtime. The team used Seeq coupled with Seeq ML to identify key fouling contributors. This work has allowed Ascend to operationalize an ML model to provide a 60 day lead time in maintenance planning and propose operational optimization to extend run time between cleaning events.
Dr. Sami Bahroun, Head of Industrial Data Analytics & Advanced Automation, Syensqo
Speakers: Chris Hamlin, Director of Capability
Development & Krista Novstrup, Principal Analytics Engineer, Seeq
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.
John Wood, Manufacturing Effectiveness Director, Chemours | Kris McQuay, Automation & Process Control Consultant, Chemours
Engineers at Chemours had been looking at process data the same way for decade when we realized that our process data analytics toolset was stagnant, not keeping pace with the evolving technology market. So, in 2022, we made the decision to pilot and then deploy Seeq enterprise-wide. This presentation covers our experiences, learnings and a few use cases that helped us build the justification for purchase and enable the roll-out.
Chad Bjorkland, Director Software Engineering, Honeywell UOP | Louis Lattanzio, Digital Innovation and Implementation Leader, Honeywell UOP
As a technology licensor in the Petrochemical and Refining industry we see and feel the struggles of our customers trying to create a sustainable future. We are uniquely positioned and structured to address the struggles of our customers. However, we face challenges analyzing the exorbitant about of information coming in from a variety of sources. To solve this issue, we provided our Service group access to advanced analytics tools like Seeq to structure and interpret the vast amounts of information being thrown at them. With technology templates and automated deployments, they are enabled to innovate and create novel solutions that can reshape the future of our customers Refining and Petrochemicals processes.
