The Aug 2019 Infometrix eNews was recently published. Included in this newsletter:
-Ai-Metrix and the Potential Economic Impact of its Role in Gasoline Blending Operations
-Tech Tip: Validating Chemometric Models
-After Further Review…
Click on image to view the full paper.
Rethinking Calibration for Process Spectrometers
Will Warkentin, Chevron
Brian Rohrback, Infometrix
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.
Best Practices, Calibration, Cloud Computing, Database, Optical Spectroscopy, PLS, Process Control
Autonomous Calibration and the Impact on Process Analysis and Control – The use of multivariate analysis in processing spectroscopic data is far from new; the processing of NIR data in petroleum refineries dates to the early 1980s and in the food industry well before that. Although the computers have improved in performance leading to speed ups in the calibration process, the procedures being followed have not changed significantly since the 1980s. Intriguingly, we have made decisions on the corporate level that work against each other. We are installing more spectrometers and at the same time we are reducing staffing for spectrometer calibration and maintenance. A change in approach is mandated. In the spirit of automation, there are tools from the general statistics realm that can be applied to simplify the work involved in optimizing a calibration through selection of samples. Robust techniques require some set-up of parameters, but once the parameters are established for an application, they are often usable in every other instance of that application. The result is a one-pass means of selecting optimal samples for a calibration problem and in turn simplifies the assignment of model rank. This approach is completely independent of hardware configuration and can be used with any software – plus it solves two problems: It is a selection process that can be completely automated; and It is objective and does not rely on the relative skill of a specific analyst. The ultimate goal is to integrate spectroscopic measurements in a process setting with the same simplicity-of-effort with which we install temperature sensors. Presented by Brian Rohrback, Infometrix, Inc.
Re-engineering Calibration for Process Spectrometers – This is a report of a multi-company, multi-industry, hydrocarbon processing consortium, established six years ago, to re-evaluate how the calibration process for analyzers could be managed more efficiently. The first focus was optical spectroscopy, an increasingly important source of process chemistry knowledge due to its advantage of speed, sensitivity, and simple safety requirements. As one of very few analyzer technologies that can measure chemistry, spectroscopy has become a workhorse in the chemical, petrochemical, and petroleum industries. But, even as the number of optical systems is continuing to increase, companies have been decreasing the number of employees who are tasked with their management. As a result, a paradigm shift is required for industry to adapt to a higher workload combined with changes in the fitness level and longevity of the technicians responsible for installation, calibration, and maintenance. 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 performance does not match legacy standards, the system dials itself in or calls for help. The systematic approach discussed is not constrained by the brand of hardware or by the software vendor and, as such, the approach can be used to manage any new or any in-place system. Presented by Brian Rohrback, Infometrix, Inc.
Multivariate Analysis for Inferentials and Analyzers This course examines a series of algorithmic approaches with the goal of streamlining multivariate model construction to make the analyzers significantly more robust when put into routine practice. This class will benefit those who want to better understand the tools available to build qualitative and quantitative inferentials for their process. It will also benefit technicians who perform both routine and irregular maintenance of chromatographic and spectroscopic instruments in both process and laboratory settings. Managing pressure, temperature, flow and level as an ensemble rather than as independent measurements to track will be highlighted. Attention will also be aimed at optical spectrometers, in particular near infrared and Raman. Chromatographic applications will be discussed with the purpose of simplifying maintenance. These topics are designed to cover the best practices of multivariate technology and will prepare participants for tackling a vast array of problems. This course is useful for any chemist or engineer who is concerned about optimizing their analytical methods to get the most out of their process operations. This course will be presented by Brian Rohrback, president of Infometrix, Inc.