A process historian is a time-series database that collects, compresses and stores data from plant control systems, such as sensor readings, setpoints and alarms, so it can be trended, analyzed and retrieved later.
A historian connects to the DCS, PLCs or SCADA system and records values by tag. A tag is one measured or calculated point, such as a pump discharge pressure. Each value is stored with a timestamp and often a quality flag. Collection runs continuously, often at one-second resolution or faster.
Plants generate large volumes of such data, so historians compress it. A common method stores a new value only when it deviates from the previous trend by more than a set band. Retrieval tools interpolate between stored points and compute aggregates like hourly averages. Access is typically through OPC HDA, SQL or REST interfaces. Some sites run a local historian per unit and an enterprise historian that aggregates selected tags for the business.
Two limits matter for analytics. First, compression settings chosen years ago may have discarded the small variations a model needs. Second, historians store measurements, not context: which product was running, which batch, which shift. That context lives in MES, LIMS or paper logs and must be joined in. Historians are the usual training data source for soft sensors, anomaly detection and predictive maintenance models.
Questions about Process historian
What is a tag in a process historian?
A tag is one measured or calculated point, such as a pump discharge pressure. The historian records values by tag, each with a timestamp and often a quality flag.
Is historian data ready to train an AI model?
Not always. Compression settings chosen years ago may have discarded the small variations a model needs. A historian also stores measurements without context such as product, batch or shift. That context lives in MES, LIMS or paper logs and has to be joined in.
How does a process historian compress data?
A common method stores a new value only when it deviates from the previous trend by more than a set band. Retrieval tools then interpolate between the stored points and compute aggregates such as hourly averages.
Process historian in real plants
- KBR and Seeq: Analysts at the KBR Innovation Studio Bengaluru will use Seeq to investigate anomalies flagged by Orbital in KBR's INSITE platform.
- PBF Energy and Seeq: PBF Energy uses Seeq for real-time visibility of energy efficiency across six refineries, with reusable projects on fuel firing and steam systems.
- International Paper and Seeq: International Paper uses Seeq as part of its analytics ecosystem, supporting 15 mills, 300+ users and 48 data source connections.
- ConocoPhillips and HighByte: ConocoPhillips uses HighByte Intelligence Hub to standardize OT data from SCADA and several historians across business units and deliver it to Snowflake in a common data model.
- TotalEnergies and Emerson: TotalEnergies will roll out Emerson's AspenTech Inmation data fabric across its industrial sites worldwide over a two-year period, together with Emerson advanced process control, as a base for AI use cases.
Vendors linked to Process historian
Related terms
DCS (distributed control system)
A DCS (distributed control system) is a plant control system in which control functions are spread across many autonomous controllers near the equipment, linked by a network to central operator stations.
SCADA
SCADA (supervisory control and data acquisition) is a computerized system that gathers data from remote or distributed equipment and lets operators monitor and control it.
MES (manufacturing execution system)
An MES (manufacturing execution system) is software that manages and records production on the shop floor.
Anomaly detection
Anomaly detection is the identification of observations or patterns that deviate significantly from normal behavior in data.
Soft sensor
A soft sensor, or virtual sensor, is software that estimates a quantity that is hard or costly to measure directly by calculating it from other measurements that are available in real time.