Model Predictive Control (MPC)

Advanced multivariable controllers that use dynamic process models to predict future behavior and optimize every control move — within your operating constraints.

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CONTROLLER // SPEC

VARIABLESMANIPULATED · CONTROLLED · DISTURBANCE

HORIZONMINUTES TO HOURS, PER UNIT DYNAMICS

CONSTRAINTSHARD AND SOFT LIMITS, PRIORITIZED

OBJECTIVETHROUGHPUT · YIELD · ENERGY · STABILITY

INTERFACEOPC UA TO THE DCS, OPERATOR ON/OFF

// What it is

How Model Predictive Control works for your plant

Model Predictive Control uses a dynamic model of your process to predict how it will respond over the coming minutes to hours. Each cycle it computes the optimal set of control moves that respects every constraint — then does it again, continuously pushing the unit toward its best feasible operating point.

PREDICTION HORIZON // CV-1

CONSTRAINTS RESPECTEDMOVES OPTIMIZED EVERY CYCLE

// How it works

From data to running system

STEP 01IdentifyDynamic behavior captured from step tests and historian data.

STEP 02ModelMultivariable dynamic model built and reviewed.

STEP 03CommissionController tuned with constraints and operator limits.

STEP 04OptimizeClosed-loop moves computed every cycle.

// Industries

Where it applies

“Every cycle the controller asks one question: what is the best feasible move right now?”

SMHCODERS · CONTROL

// Benefits

What it changes

MPC-02 // IMPACT BOARD

Stabilized plant operationOK Increased throughputOK Improved yieldOK Improved energy efficiencyOK

FROM THE FIELD

MPC does not replace your operators. It gives them a unit that holds steady while they run the plant.

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// FAQ

Common questions

Does MPC replace our DCS?+

No. MPC sits above your existing control layer and writes setpoints to it. Operators keep full authority, including one-click handback to manual.

What data do we need to start?+

Most engagements start with 6–24 months of historian, lab or document data. We audit coverage in the first week and tell you plainly if it is not enough — before you commit.

How long until we see results?+

A proof of concept on your historical data typically lands in 4–8 weeks. Production deployment follows once the KPI target is met and your team signs off.

Where does it run, and who sees our data?+

On your infrastructure — plant-side servers, your Azure or AWS tenancy, or an air-gapped network. Data never leaves your boundary, and nothing is used to train third-party models.

Who owns the result?+

You do. Handover includes source code, documentation, training sessions and a retraining plan.