Austin, Texas, already a hotbed for technology as recently recognized by Forbes magazine, will host a gathering of chemical engineering professionals later this month. The AIChE is hosting the 2015 Spring Meeting and 11th Global Congress on Process Safety here on April 26-30.
If you’re planning to attend, make sure to catch some of the “Big Data” sessions to see how this data is being applied in our world of process instrumentation and automation. I’ll highlight two sessions featuring members of our Emerson team here in Austin.
On Monday, April 27 at 3:30pm, Emerson’s Mark Nixon, Terry Blevins, Willy Wojsznis and John Caldwell will present, Industrial Big Data Vision and Solutions. Here’s an excerpt from the session abstract:
The process industries adopt many Big Data approaches that are applied in other industries however the Big Data implementation for Process Industries is distinctive in that it sets specific requirements for Big Data infrastructure, learning algorithms including data analytics, and presenting the results.
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The presentation will address the basic components of Big Data pipeline for the process industry: hardware and software infrastructure, data streaming, data preprocessing and data learning techniques.
The core of data learning is Data Analytics (DA) which has proven its effectiveness in process fault detection and quality prediction both for batch and continuous processes. The real prospects are that Big Data based on DA will be among the leading directions for improving process effectiveness. DA requires a significant departure from the traditional thinking about how process control is implemented. Instead of the deterministic and tangible world of signals and devices, there is an abstract realm of statistical indexes, correlation factors and matrix operations. This puts a significant strain on the control systems’ developers, engineering companies, process operation and maintenance personnel. The presentation will address these major challenges for professionals working on Big Data for the process industry.
Mark, Terry, and Willy will also present Tuesday, April 28 at 2pm on Embedding Continuous Data Analytic in Industry Big Data Applications. Here is the abstract:
Embedded data analytics is a core component of big data applications in the process industries and can be used as the initial approach for big data applications and to test data-driven techniques before moving to the much larger big data approach.
An integrated approach toward analytic functionality design provides an easy-to-use set of tools for analytic model development, verification and subsequent download to the DCS controller for on-line operation. The model development and download for on-line operation is streamlined into clearly defined and easily executed steps:
On-line analytic modeling monitors faults in the process operations and predicts product quality. The results are presented in a web based interface and can also be used to enhance an existing alarming system. The predicted property quality can be used in designing the control.
The continuous data analytic design has been tested on simulated data and in the field. An analytic application’s PLS model was used to predict heavy components in a distillation column. When the results were compared with an online analyzer, the analytic modeling demonstrated several advantages: more reliable operation, less demanding maintenance, and integrated fault monitoring that prevented the use of the prediction results when a significant fault was detected in the process operation. Process engineers greatly appreciated the ability to identify abnormal distillation column operation and the potential for including this functionality into an existing alarming system.
The field trial facilitated the development of an iterative procedure for analytic model improvement. Similar results were obtained from a polibutene unit analytic model development and an on-line test. Specific to this process is the product quality (viscosity) which can be set on two distinct levels, depending on the manufacturing needs. Two alternative approaches were explored: one using a state parameter associated with viscosity and the other using two models for Low and High viscosity values. In the majority of tests two separate analytic models performed better. The primary problem with the multistate analytic model was identifying a process parameter that was defined in the analytic model as a state parameter that correlates well with quality level.
I hope you’re able to catch the presentations. If not, I’m sure they’ll have more to share at the Emerson Exchange conference in Denver this fall. You can also connect and interact with other control system analytics experts in the DeltaV groups in the Emerson Exchange 365 community.
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