Make the Most of Historical Process Data

The past can provide critical insights for the future.

By Dane Overfield, Exele Information Systems, Inc.

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Sometimes, regulatory or industry requirements mandate implementation of a data historian. In such cases, their strict stipulations may narrow your solution choices.

Regulatory requirements for your process, particularly for industries such as pharmaceuticals, may demand that you provide data history as well as may mandate that you perform extensive certification of your data historization and presentation tools — thoroughly testing and documenting accuracy of data retrieval, storage and playback. This may lock you into current software versions because making any changes to the system, including upgrading software to the latest version, may require that you recertify upgraded components. Due to these strict requirements, some companies implement “uncertified” installations that can be frequently upgraded and modified, with the understanding that data contained in the system are also uncertified and therefore can’t be used when making process or production decisions.

Requirements for continuous emissions monitoring systems (CEMS) have steadily expanded to more geographical areas and industries. CEMS involves collection, storage and reporting of data related to emissions such as NOx, SOx and CO2. In some of the stricter reporting areas, data must be electronically reported daily to the supervising district. In many cases there’s oversight and validation of the implemented solution, requirements for long-term and accurate storage of emissions data, rules and requirements for calculating and reporting data, and heavy financial fines for incomplete, missing or inaccurate reporting.

Unfortunately, the complex and varying rules make purchasing an “out of the box” CEMS solution difficult.

Selecting a Solution
Although you may be able to in-house build a custom data-archiving solution, you may quickly find that it can’t deliver more than basic required functionality without a significant investment of time, money and resources.

Fortunately, over the past few decades, companies and products that focus on data process data historization have emerged. Their product maturity and breadth of features make custom solutions difficult, if not impossible, to justify. These products support efficient data collection for hundreds of data sources (programmable logic controllers (PLC), distributed control systems (DCS), supervisory control and data acquisition (SCADA) systems and OPC servers), provide efficient storage and backup of historized data, offer value-added functionality such as aggregation, and serve data to clients through a rich set of tools including trends, displays and reports. Scalability makes a core set of functionality available to all customers regardless of the number of archived data points. The number of archived data points and client products used typically determines price.

Even though your initial data historization project may involve a single data source, you should consider sources that may be added in the future. You currently may own some type of data historization solution through your existing DCS or SCADA system; these solutions typically concentrate on the historization of their own data, though. As your solution expands, the ability to support the collection of process data from multiple data sources will become increasingly important. Different DCSs, PLCs and SCADA systems may be deployed across your company, so selecting a historization solution that will support all of them is crucial.

Data collection is performed by a “data interface” or “data collection” program for each specific data source. These data interfaces usually are written using software drivers from the data source vendor to poll the source for new data values. The new values along with the collection time are sent to the data archive for historization. An increasing number of vendors are making their process data available through OPC, so the “OPC data collection” interface has become the most popular method for process data collection.

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