GCC 2024 – Machine Learning for Spectroscopy Calibration

GCC 2024Venue:
October 15-16, 2024
Moody Gardens Convention Center, Galveston, Texas 77554

Presented by: Brian Rohrback – Infometrix, Inc.

Abstract Number: 119

Abstract: Artificial intelligence and machine learning are inevitable results of the work driven by the consumer side of our economy. The question is not whether it will impact refining and chemical plant operation, but how soon and how long it will take for the benefits to outstrip the costs.  The goal is to provide practical guidance for making progress in this complicated set of fields. Machine Learning is critical to interpreting output from any type of spectrometer and improves the flow of information providing a significant leg up for process understanding. The key is to fully automate spectroscopic calibration. Gulf Coast Conference 2024

GCC 2024 – Eliminating Retention Time Variability in Chromatography

GCC 2024Venue:
October 15-16, 2024
Moody Gardens Convention Center, Galveston, Texas 77554

Presented by: Brian Rohrback – Infometrix, Inc.

Abstract Number: 120

Abstract: The heartbeat of the process environment is in the data we collect, but we are not always efficient in translating our data streams into actionable information. For many applications, gas chromatography is the richest source of hydrocarbon process information and is the cheapest, most adaptable, and most reliable technology available.  We can use technology borrowed from other fields to provide more consistent and objective GC results, automate translation of the raw traces into real-time information streams, and create databases that can be used across plant sites or even across industries. Gulf Coast Conference 2024

Industrial Grade Chemometrics: from Laboratory to Process Implementation – APACT 2024 Program

Venue: April 24, 2024, 8:30am – 9:10am Hyatt Regency Hotel, New Brunswick, NJ Industrial Grade Chemometrics: from Laboratory to Process Implementation Presented by: Brian Rohrback, President, Infometrix, Inc. Abstract: The use of multivariate statistics, whether termed chemometrics or machine learning or (fill in the blank), is critical for industry to move from human-centered processing to more automated, objectively reliable processes. In the laboratory world, we seek to discover new routes for solving problems that will give us better solutions than previous methods. And this largely university-based effort is critical for enabling improvements in product manufacturing and the quality control process that accompanies it. But, given academia’s limited access to commercial application samples, most chemometrics publications deal only with the method development side of a given problem. If we are going to benefit a manufacturing organization, an industry, or especially society, the emphasis needs to be on the shift from this laboratory origin to a solid process implementation. Anything useful we devise must ultimately get used; if we fail to integrate proven technology into the day-to-day, the development process is simply an expense. The experience through the now five decades of implementing multivariate solutions has identified some steps, some barriers, and some low-hanging fruit. How do we succeed in pushing new technology the last mile? Register at APACT 2024.