APACT’21 On-line Conference: Industrial Strength Analytics

Join Brian Rohrback, president of Infometrix, for his talk on Industrial Strength Analytics Friday April 23rd at 13:00 BST.

The conference is free to attend and is open to all. Register at: https://apact.co.uk/

Industrial Strength Analytics

As scientists, we need to be focused on specific aspects of technology in order to make advances in our fields. Success in the lab is key, but if we want to reap the benefits, the tasks that come next are every bit as critical as the initial innovation. In order for the work to be useful in the manufacturing sector, the analytics need to be industrialized and often the peripheral portions of this hardening process are as complex as the discovery process and it takes time, often more than Phase I. The example here is to outline the steps required to optimize the use of optical spectroscopy in manufacturing quality control.

ISA Virtual Conference: Rethinking Calibration for Process Spectrometers

ISA 2021 Virtual ConferenceJoin Brian Rohrback at the 2021 ISA Analysis Division Virtual Conference

March 23, 2021 at 12:00 ET

Register and be ready to take part in these in-depth discussions at www.isa.org/ad

 

Rethinking Calibration for Process Spectrometers

The talk focuses 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. The result is a standardized procedure for doing calibrations that adheres to best practices, archives all data and models with easy access in mind, and delivers models in any format.

One Month to IFPAC-2021 – February 28th – March 5th

An IFPAC Digital Event – Register Today

 

 

 

 

 

The dynamic program benefits experienced professionals and the next generation of leaders from the pharmaceutical, biotechnology, generic, chemical, petrochemical, and related industries. Expanded Tracks on Continuous Manufacturing, Real Time Analytics, Biotechnology, Data & Knowledge Management, Industry 4.0, Rapid Response Manufacturing, and Various Analytical Technologies. View the program and register at www.IFPACglobal.org.

 

2020 AIChE Spring Meeting and 16th Global Congress on Process Safety

 2020 AIChE Spring Meeting and 16th Global Congress on Process Safety
Aug 19, 2020
Virtual Meeting

See abstract below for presentation at the 2020 AIChE Spring Meeting. Join us or contact us for more information.

 

Harnessing Big Data Approaches and AI in the Chemical Processing Industry
Brian Rohrback – Infometrix

The term 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 the chemical, petrochemical, and petroleum industries. To accomplish anything in the Big Data 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, neuro-computing, and machine learning. In order for industry to achieve the goals that this form of AI promises, we need to approach the issues with more than just words.

This is a summary of a multi-company, multi-industry, hydrocarbon processing consortium, established seven 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. The idea is 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 are in hand. The analyzers in place, optical spectrometers in particular, represent the low-hanging fruit.

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.