EAS 2021 – ChemMLometrics. Perform. Repeat. , Brian Rohrback

EAS 2021 ChemMLometrics. Perform.Repeat. Join Brian Rohrback for his talk on ChemMLometrics. Perform. Repeat., November 16th at 10:15AM.

ChemMLometrics. Perform. Repeat.

Doing a one-off research project can be very satisfying, but in industry the money is made when the results of such a project can be placed into routine quality assessment.  There is much discussion today revolving around machine learning and some of this discussion has invaded the space that has been occupied by chemometrics over the last few decades. There is a large overlap as these techniques are applied in instruments aimed at process quality control.  To a large extent, any differentiation will simply be a question of the jargon chosen. An on-line Stanford University class on machine learning covers Principal Components Analysis, Principal Components Regression, Partial Least Squares, K-Nearest Neighbor, and other mainstays of the chemometrics toolbox.  However, there are techniques that are new in the machine learning realm that can be employed for targeted tasks, improving the traditional chemometrics regime of supervised learning.  The key is to narrow the focus for each component in a system and to understand the extent to which you can control the inputs.  This talk highlights examples in optical spectroscopy calibration particularly for the identification of outliers, and in chromatography for both signal processing and the management of chromatographic libraries. The emphasis is to highlight both the similarities and the uniqueness of machine learning and chemometrics and show how they combine to form robust solutions for industry.

EAS 2021 – Automating Calibrations for Optical Spectroscopy

EAS 2021 Automating Calibrations for Optical Spectroscopy Join Brian Rohrback for his talk on Automating Calibrations for Optical Spectroscopy, November 16th at 1:30PM.

Automating Calibrations for Optical Spectroscopy

This talk represents a summary of a multi-industry consortium established eight years ago to re-evaluate how the calibration process in optical spectroscopy could be managed more efficiently.  The idea is to enable a shift from current practices to approaches that take better advantage of the computational power and some newer concepts supplied by research into machine learning algorithms.  The result is a solution that is not disruptive of any legacy instruments or software already in place and lessens the workload rather than laying on additional requirements. The approach uses all readily available components and can be assembled easily for any specified purpose.  The use of commercial components reduces the cost of deployment and assembling pieces in a plug-and-play manner minimizes the impact of any previous selections of hardware and software.

Gulf Coast Conference 2021 – Managing Calibrations for Optical Spectroscopy

GCC 2021 event

GCC 2021

Join Brian Rohrback on 10/12/2021 at the Orchid Room, 9:40 AM – 10:10 AM.

Abstract:
A multi-industry consortium got together 8 years ago to rethink how calibrations need to be performed for spectroscopy instruments and analyzers. The priority was on solutions that are non-disruptive, fully utilize legacy systems, and lessen the workload rather than layer on additional requirements. The result is a foolproof, simple approach that can be used with any type of brand of spectrometer, any type or brand of chemometrics, and ensures robust and reliable calibrations. For more information, email info@infometrix.com.

ISA Webinar – Practical AI: In Search of Dynamic, Autonomous Process Analytics

ISA 2021 webinar postingJoin Brian Rohrback, President of Infometrix on Feb. 25th at 1:00pm ET.

Free Webinar: Process Control & Instrumentation Series

 

 

Practical AI: In Search of Dynamic, Autonomous Process Analytics

The application of the concepts behind artificial intelligence and machine learning mandates a systematic approach to extracting information from multiple, byte-dense data sources. Effective extraction of this information leads to improvements in decision making at all levels of the chemical, petrochemical, and petroleum industries. To accomplish anything in the AI space, we need to combine traditional approaches in statistics, database organization, pattern recognition, and chemometrics with some newer concepts tied to better understanding of data mining, neurocomputing, and machine learning. This is an introduction to a practical approach to deploying AI and how a multi-company, multi-industry, hydrocarbon processing consortium, established eight years ago to re-evaluate how the calibration process for sensors and analyzers could be managed more efficiently. The focus spans optical spectrometers, chromatographs, and process sensors, independently and in combination, with a shift from current practices to approaches that take advantage of the computational power at our fingertips.

Dr. Rohrback’s expertise covers the integration of multivariate data processing for process analyzers and laboratory instruments catering to routine quality analysis. Prior to his current position, he worked for Cities Services Oil Company, now Occidental Petroleum, with industry positions including research scientist managing the chromatography group, an exploration geologist, and manager of planning/budget for EAME. He holds a B.S. in chemistry, a Ph.D. in organic geochemistry, and an MBA. His 50-year span of published works include topics in petroleum exploration, chemical plant optimization, clinical and pharmaceutical diagnostics, informatics, pattern recognition and multivariate analysis.

Efficient Calibration Process and Big Data

View latest talks on Big Data and Calibration Process Efficiency.

 

 

 

 

Harnessing Big Data – AiChE 2020

Big Data implies a systematic approach to extracting information from multiple, byte-dense data sources. Effective extraction of this information leads to improvements in decision making at all levels of industry. Here, we combine traditional approaches in statistics, database organization, pattern recognition, and chemometrics with some newer concepts tied to data mining, neurocomputing, and machine learning. The cost is low and the benefits are high.

The Multivariate Process Paradigm – SciX 2020

This is a summary of a chemical processing consortium, established eight years ago to re-evaluate how the calibration process for sensors and analyzers could be managed more efficiently. The focus is on optical spectrometers to enable a shift from current practices to approaches that take advantage of the computational power at our fingertips. It was critical to prioritize solutions that are non-disruptive, utilize legacy systems, and lessen the workload rather than layer on additional requirements. The result is a choice of tools available to consume the data and generate actionable, process-specific information.