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.

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.