(52a) Industrial Big Data Vision and Solutions | AIChE

(52a) Industrial Big Data Vision and Solutions


Blevins, T., Emerson Process Management

Big Data is a ground breaking technology for many industries. Every industry is faced with the challenge of how to implement technology to achieve maximum benefits.

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.

The process industry’s Big Data infrastructure uses networking and database structures that have many common features with existing applications. In addition the infrastructure also includes structures such as lab data, NIR data, and spectral data; items that are not normally incorporated into control systems. Big Data treats all of this normally unincorporated data as unstructured. Schema information is combined with unstructured data to allow a wide range of analytics to be performed both off-line and on-line.

Process industry data includes interdependent time series data that requires efficient streaming, storage, and call-up.  For meaningful analytics to be performed, real-time analysis must be combined with data context. Calculations are performed through the stream processing and calculation engine. Common Big Data techniques applied to the process industries require extensive adaptation or redevelopment, typically outside of the control system itself.

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.


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