Harnessing Big Data Approaches and AI in the Chemical Processing Industry

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, neurocomputing, 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.