Join us at PEFTEC 2022. This year it will be hybrid and access to the exhibition and all of the technical sessions is free of charge.
Keynote speaker, Brian Rohrback of Infometrix, Inc. will present his talk on Streamlining the Use of Chemometrics
June 8th at 13:15 CEST
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 distinguish between vision and hallucination and to provide some practical guidance for making progress in this complicated set of fields. There are three categories of measurements that provide us with the data that will form the basis for any interpretation system: single-purpose sensors, chromatographs, and spectrometers. Multivariate analysis can be used in all three categories and, in fact, is critical to interpreting output from any type of spectrometer. We can easily demonstrate that the use of multivariate analysis for each of the three groupings, even these data sources assembled together, gives faster response, improved flow of information derived from these data, and a significant leg up for process understanding. This information is available and is nearly cost-free.
Join Brian Rohrback on 5/2/22 at 9:10 AM PDT for the CPAC Spring Meeting in Seattle.
Industry Case Study: Routine Quality Assessment – Similarities and Uniqueness of machine learning and Chemometrics and how they combine to form robust
solutions. For more information, email firstname.lastname@example.org.
We miss in person relationships and live experiences that are not available with virtual and hybrid meetings. Virtual platforms have their issues which makes us desire more for in person gatherings. We’re still not back to normal but we are easing our way back. Hope you feel the same and will join us at these upcoming events to share and engage in a valuable discussion.
Upcoming Events in 2022
CPAC, Seattle, WA, May 2-3
Routine Quality Assessment – Similarities and uniqueness of machine learning and chemometrics and how they combine to form robust solutions.
PEFTEC, Rotterdam, June 8-9
Streamlining the Use of Chemometrics – Faster response, improved flow of information and a significant process understanding nearly cost-free.
IFPAC, Bethesda, MD, June 12-15
Agile Process Analytics – Combining technical tools to augment or replace tasks that consume brainpower for timely response and greater profits with future goals of optimization with automated spectroscopy calibration.
SciX, Kentucky, Oct 2-7
Optimizing Spectroscopy Performance – Lessening the workload with automation of models and maintaining them quickly and easily for robust, reliable, and timely calibrations.
Join Brian Rohrback of Infometrix, Inc. for his talk on Agile Process Analytics, June 14, 1:05pm EST.
Agile Process Analytics
Application knowledge and chemometrics play a vital role in the processing of all types of multivariate data into application-specific information and has been doing so for at least 50 years. There has been a not-so-subtle shift in thinking as we integrate basic concepts and the occasional hallucination in the data mining, artificial intelligence, machine learning worlds. The target is to identify combinations of our technical tools to augment or replace tasks that consume brainpower where timely response is valued, and profits are at risk. The biggest focus of chemometrics has been in the calibration of optical spectrometers. It is worth considering the subtasks:
- Optimizing the instrument settings for a given application;
- Optimizing the method parameters – preprocessing, transformations, wavelength ranges;
- Handling of calibration transfer; and
- Optimizing models for inliers and rank in pursuit of routine processing and adjusting to changes in ingredients and unit operation.
The first two tasks are a set-once method development and the third may be generic across all applications. This paper tackles subtask 4 with a project that combined 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. The future goal is to automate spectroscopy calibrations such that it is possible to have instrument systems tune themselves.
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.