Success Stories

ML Core and Predictive Modeling

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ML Core and Predictive Modeling

BKOAI’s ML Core streamlines the development of real-time predictive models through an accessible automated machine-learning workflow

Goals

Simplify predictive-model development through automated machine-learning workflows.
Make real-time predictions accessible through an operator-facing interface.
Tailor predictive models to plant-specific operating conditions.
Support operational decisions with accessible model outputs.

Challenges

Fragmented industrial data requires preparation before it can support model development.
Generic machine-learning models may not capture plant-specific operating behavior.
Preparing data and configuring predictive models requires time and specialized expertise.
Model outputs must be accessible within operators’ existing workflows to support their use.

Solutions

Use template-driven contextualization to organize asset data consistently for analysis.
Integrate predictive models with platforms such as Seeq and AVEVA PI to make outputs accessible.
Tune machine-learning models using plant-specific characteristics and historical data.
Provide an operator-facing interface for viewing predictions and model outputs.

Results

Provided predictive alerts to support proactive operator responses.
Made model outputs accessible through integrated dashboards.
Standardized asset analytics through reusable templates.