Cloud Computing

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IFPAC 2024 – Chemometrics in the Cloud

IFPAC 2024Venue:
March 3-6, 2024
Bethesda North Marriott Hotel and Conference Center

Presented by:
Brian Rohrback, Ph.D., MBA, President, Infometrix, Inc.

Abstract:

In the spirit of automation, there are cloud-based tools from both the chemometrics and the general statistics realms that can be applied to simplify the work involved in optimizing a calibration. Robust statistical techniques require some set-up of parameters, but once established for an application, they are often usable in every other instance of that application. The result is a one-pass automated means of selecting optimal samples for a calibration problem and, in turn, simplifies and automates the assignment of model rank. In the end case, this means that a spectrometer can essentially become an appliance; take it out of the box, plug it in, and enjoy. The capability exists to have a spectrometer self-tune and adapt to a specific application, then keep the spectrometer in appropriate calibration completely through closed-loop control. Automation of best practices needs to include how to match laboratory reference data to spectral data, an unbiased approach to selecting validation samples, an optimal mechanism for model construction, establishing standards for quality reports, tracking model performance over time, handling process or ingredient transitions, and much more.

Register at www.IFPACglobal.org/attendee-registration.

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ISA 2020 – Rethinking Calibration for Process Spectrometers II

The Long Beach Convention Center
Long Beach, CA
1:30pm, April 27th

 

Brian Rohrback
Infometrix, Inc.
Will Warkentin
Chevron Richmond Refinery

 

KEYWORDS
Best Practices, Calibration, Cloud Computing, Database, Gasoline Blending, Optical Spectroscopy, PLS, Process Control

ABSTRACT
Optical spectroscopy is a favored technology to measure chemistry and is ubiquitous in the hydrocarbon processing industry. In a previous paper, we focused on a generic, machine-learning approach that addressed the primary bottlenecks of mustering data, automating analyzer calibration, and tracking data and model performance over time. The gain in efficiency has been considerable, and the fact that the approach does not disturb any of the legacy (i.e., no changes or alterations to any analyzer or software in place) made deployment simple.

We also standardized a procedure for doing calibrations that, adheres to best practices, archives all data and models, provides ease of access, and delivers the models in any format. What remains is to assess the speed of processing and the quality of the models. To that end a series of calibration experts were tasked with model optimization, restricting the work to selecting the proper samples to include in the computation and setting the number of factors in PLS.  The amount of time and the quality of the models were then compared.  The automated system performed the work in minutes rather than hours and the quality of the predictions at least matched the best experts and performed significantly better than the average expert.  The conclusion is that there is a large amount of recoverable giveaway that can be avoided through automation of this process and the consistency it brings to the PLS model construction.

INTRODUCTION
There is a lot of mundane work tied to the assembly of spectra and laboratory reference values to enable quality calibration work.  There is also insufficient guidance when it comes to the model construction task.  How much time should be spent on this task?  How to best assess whether a spectrum-reference pair is an outlier or not? How many cycles of regression-sample elimination make sense? Where do we switch over from improving the model by adding PLS factors to overfitting and incorporating destabilizing noise?

For more information or the full paper, contact us.

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Rethinking Calibration for Process Spectrometers

Click on image to view the full paper.

 

 

Title:
Rethinking Calibration for Process Spectrometers

Authors:
Will Warkentin, Chevron
Brian Rohrback, Infometrix

Abstract:
Optical spectroscopy is a great source of process chemistry knowledge. It has the advantage of speed, sensitivity, and simple safety requirements. As one of very few analyzer technologies that can measure chemistry, it has become a workhorse in the hydrocarbon processing industry. What if we could put a spectroscopy system in place and have it handle the application and communicate results as soon as it is turned on? Then, if predictions do not match legacy standards, the system dials itself in or calls for help. And, we are not constrained on either the hardware or the software front. In this paper, we address the primary bottleneck of mustering data, automating analyzer calibration, and tracking data and model performance over time.

Keywords:
Best Practices, Calibration, Cloud Computing, Database, Optical Spectroscopy, PLS, Process Control