Seeq, a leading provider of advanced analytics solutions for process manufacturing companies, is expanding support for the chemicals industry and is currently working with over 30 of the top chemical companies in the world to help them leverage their process data to improve processes and profitability. Seeq will have employees presenting use cases at several of the leading chemical events this fall.
Julianne Wagoner, an Analytical Engineer at Seeq, will present Leveraging Advanced Analytics to Improve Production Outcomes at the Chem Show in New York on October 24th from 10:55-11:15 am. This presentation will feature use cases highlighting best practices in maintenance, repair, and overhaul as well as optimizing process efficiency, and will discuss solutions to common processing problems.
The AIChE Annual Meeting in Orlando will feature Seeq’s VP of Analytics Engineering Lisa Graham, PE and PhD, who will be presenting Big Data Impact on the Future of Manufacturing on November 11th at 9:20am. This presentation will focus on the opportunity for innovation in chemical and pharmaceutical manufacturing processes through the integration of human intuition and experience with big data, machine learning, and other software innovations.
Brian Parsonnet, Seeq’s CTO, will present at Advances at Process Automation & Control 2019 in Manchester, UK on November 20th from 9:00-9:50 am. His plenary session, titled Addressing Practical Challenges of Process Analytics, will cover the opportunity for innovation in chemical manufacturing processes through the integration of human intuition and experience with big data, machine learning, and other software innovations.
In chemical production facilities, evolution and agility are the keys to success because change in regulations, energy cost, and raw materials is the one constant. Dealing with change requires collaborative, rapid insights into the data and systems impacting production efficiency and bottom-line results. Chemical companies use Seeq to achieve success with practical solutions for optimizing energy use and plant emissions, reducing cycle times and improving batch repeatability, identifying and remediating batch exceptions, and improving yields while maintaining quality.
Seeq builds on machine learning and big data technologies to speed time to insight for diagnostic, descriptive, and predictive analytics. “The rapid pace of Seeq innovation is leading to bigger customer engagements and even faster insights and impact from production data,” says Steve Sliwa, CEO and Co-founder of Seeq Corporation. “We are growing the organization quickly to keep up with customer needs in training, use case development, and core feature development.”