Inline Process Monitoring · Physical AI

See Inside Your Process.
Reduce Losses.
Increase Yield.

Real-time spatial visibility inside reactors, columns, and pipes — without stopping production. Built for process engineers who need to understand what's actually happening, not just what wall sensors imply.

  • Detect instabilities and off-spec conditions before they escalate
  • End cycles when the process is complete — not when a timer expires
  • Build the data foundation for AI-driven process optimization
Request a Pilot See Applications
quantropIQ inline process sensor installed in industrial reactor piping
1,000+frames per second
256–10kpixels across cross-section
24/7inline, no production stop
IP&EXhazardous area certified
🔬 Active pilots in chemical & biotech
⚙️ EX & IP certified sensor hardware
🏭 Designed for continuous production environments
🤝 6–12 week pilot at your site
The Problem

Most Process Control Is Based on What You Can't See

Pressure, temperature, and flow sensors report what's happening at a single point — or at the pipe wall. But the physics that determines yield, quality, and stability happens inside the process: how phases distribute, how mixing propagates, where instabilities form.

Without spatial visibility, engineers manage by proxy. The result: conservative setpoints, late detection of off-spec conditions, over-engineered safety margins, and process knowledge that stays in people's heads rather than in systems that can learn.

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Single-point blind spots

Wall sensors miss the spatial dynamics that determine mixing quality, phase distribution, and reaction uniformity across the cross-section.

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Late fault detection

Foaming, channeling, and phase instabilities are invisible until they reach product quality — by which point intervention is already costly.

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Conservative cycle times

Without knowing the actual process state, operators run longer than necessary — wasting energy and capacity on every batch.

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Knowledge that doesn't scale

Process expertise stays with individuals. It cannot be encoded, transferred across sites, or used to train the next generation of controls.

Use Cases

Where It Makes the Biggest Difference

Inline spatial monitoring solves fundamentally different problems depending on the process. Select your context.

Biotech · Pharma · Food & Beverage

Fermentation & Bioprocessing

Current limitation

Gas distribution, bubble size, and mixing uniformity in fermenters cannot be measured in real time. A dissolved oxygen probe gives one value — but the spatial reality across a large vessel is often far from uniform. Yield variability persists batch to batch without a clear root cause.

The quantropIQ sensor resolves the full fermenter cross-section at over 1,000 frames per second — capturing gas holdup distribution, bubble dynamics, and mixing progression continuously. Engineers can verify whether aeration and agitation are actually reaching the entire culture volume.

What becomes possible

End cycles when the process is genuinely complete. Detect foam formation before it reaches critical levels. Identify the spatial root cause of batch-to-batch yield variation.

Discuss a Fermentation Pilot

Current limitation Dissolved oxygen probes

Single-point DO sensors report a local value. Gas distribution in large bioreactors is inherently non-uniform — one reading does not represent the bulk state of the culture.

What the sensor provides Spatial gas holdup maps

Every cross-sectional frame shows the actual distribution of gas bubbles across the vessel diameter at millisecond resolution. Aeration uniformity becomes measured, not assumed.

Application Foam detection & prevention

Foam formation appears as a characteristic change in the phase distribution signature — before it reaches dangerous levels. Antifoam is dosed reactively, not preventively.

Application Fermentation endpoint detection

The physical state of the broth in the final fermentation stage has a measurable spatial signature. Endpoint is determined by process state — not elapsed time.

Chemicals · Petrochemicals · Refining

Distillation & Separation

Current limitation

Flooding, weeping, and liquid maldistribution are detected through indirect indicators: pressure drop, temperature profiles, product quality. By the time these register, the column is already underperforming — and recovery takes time and energy.

Inline sensors mounted at column cross-sections provide direct measurement of liquid and vapor distribution, holdup dynamics, and flow regime transitions. Flooding onset becomes an observable event, not an inferred one.

What becomes possible

Detect flooding 2–5 minutes before product deviation registers. Optimize reboiler duty to actual internal column state. Reduce reflux ratio conservatism based on real holdup data.

Discuss a Distillation Pilot

Current limitation Pressure drop monitoring

dP across a column tray gives one scalar value. It cannot distinguish flooding from weeping, or identify which section is maldistributed.

What the sensor provides Tray-level liquid holdup

The spatial distribution of liquid across a column cross-section — resolving radial non-uniformities, weeping zones, and entrainment patterns that dP cannot distinguish.

Application Flooding prediction

Flooding onset has a characteristic spatial signature — liquid holdup distribution changes measurably before column performance degrades. Predictive alerts become physically grounded.

Application Energy optimization

When actual internal column state is known, reboiler and reflux parameters are adjusted to the efficiency boundary — not a conservative margin from it.

Chemicals · Energy · Materials

Multiphase Flow Detection

Current limitation

Flow regime determines mass transfer, reaction rate, and mixing efficiency — but is not directly measured. It is inferred from process conditions. Slug, bubble, and stratified regimes have fundamentally different mass transfer characteristics that existing sensors cannot distinguish in real time.

The sensor resolves the full cross-sectional phase distribution at millisecond resolution — providing direct, continuous identification of flow regime, local phase fractions, bubble dynamics, and interfacial area estimates.

What becomes possible

Control gas injection to maintain optimal flow regime. Detect regime transitions before they affect conversion. Measure interfacial area as a continuous process variable rather than a design assumption.

Discuss a Multiphase Pilot

Current limitation Indirect regime inference

Flow regime is estimated from superficial velocities using flow maps — derived from lab data, rarely valid for actual operating fluids and geometries.

What the sensor provides Direct flow regime ID

Real-time classification of bubble, slug, churn, annular, and stratified flow from the spatial phase distribution at 1,000+ frames per second.

Application Gas injection optimization

With flow regime continuously identified, gas injection rate is closed-loop controlled to maintain the target regime — not fixed at a conservative design point.

Application Phase fraction measurement

Local and cross-sectional average phase fractions are directly measured — not inferred. Relevant for separator control, mass balance closure, and reaction conversion monitoring.

Specialty Chemicals · Fine Chemistry · Polymers

Chemical & Fine Chemistry Process Control

Current limitation

Mixing uniformity, concentration gradients, and phase distribution directly determine selectivity, yield, and product quality. Yet these parameters are inaccessible to conventional inline sensors — leaving engineers to rely on end-of-batch analytics and conservative operating margins.

Inline spatial sensing transforms these hidden variables into real-time observables. Mixing completion, concentration homogeneity, and reactive phase distribution become continuously measurable — enabling tighter process control and informed scale-up decisions.

What becomes possible

End mixing phases when homogeneity is actually reached. Detect segregation before it affects selectivity. Build process fingerprints that accelerate scale-up from lab to plant.

Discuss a Chemical Process Pilot

Current limitation Sampling and offline analytics

Concentration and phase distribution are measured by taking samples and running offline analysis — hours later, at a single point. The process has already moved on.

What the sensor provides Inline spatial concentration

The spatial distribution of conductivity and permittivity across the reactor cross-section correlates directly with concentration and phase composition — continuously, without sampling.

Application Mixing endpoint detection

Spatial homogeneity is directly observable. Mixing is declared complete when the cross-sectional distribution is genuinely uniform — not after a fixed time or arbitrary sample check.

Application Scale-up fingerprinting

Each run generates a physical state signature. Comparing signatures across scales identifies conditions that must be preserved — reducing scale-up iterations and de-risking technology transfer.

Operational Impact

What Changes When Processes Become Visible

Spatial process visibility unlocks improvements across every dimension of plant operation — from energy and yield to maintenance and knowledge transfer.

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Lower Energy per Batch

Spatial data reveals over-mixing, excess aeration, and heat distribution inefficiencies — allowing adjustment to what the physics actually requires.

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Fewer Off-Spec Batches

Instabilities and concentration deviations are visible before they affect product quality — allowing intervention before the batch is at risk.

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Higher & Consistent Yield

Uniform concentration fields and controlled reaction kinetics across the full cross-section — not inferred from a single point measurement.

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Shorter Cycle Times

Cycles end when the process is actually complete — not when a conservative timer expires. More batches per shift with the same assets.

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More Stable Operation

Flow regime changes, foaming precursors, and instability signatures become early-warning signals — replacing reactive firefighting with proactive control.

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Predictive Maintenance

Fouling and equipment degradation appear as changes in spatial flow signatures — maintenance based on actual condition, not fixed schedules.

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Faster Product Development

Process fingerprints from existing plants transfer to new scales and sites — cutting scale-up iterations and reducing time-to-market for new formulations.

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Scalable Process Knowledge

Process behavior encoded in physical models — not held tacitly by individuals. Transferable, reproducible, and progressively automated over time.

How It Works

The Technology Stack

Five integrated layers from hardware sensing to autonomous process control — each building on the previous, designed for continuous industrial operation from day one.

quantropIQ inline process sensor cross-section measurement
256–10kpixels across pipe cross-section
1,000+frames per second
IP & EXcertified (with plant operators)
24/7continuous inline operation

The sensor mounts via standard industrial flange — no flow interruption, no sampling, no modification of the process vessel. A measurement grid spans the full cross-section, resolving spatial distribution of phases, concentrations, and flow structures at millisecond resolution.

At 1,000+ frames per second, the rapid cross-sectional sequence creates an effective 3D picture of flow structures — without the complexity or cost of volumetric scanning methods.

L1
Physical Sensing

Direct Access to Hidden Physics

Conductivity and permittivity captured simultaneously across the full cross-section. Output is a space-time field where gas holdup, mixing quality, and phase instabilities are directly observable.

  • Flange-mounted, inline, no production stop
  • Conductivity + capacitance modes — covers conductive and non-conductive systems
L2
Data & Compute Infrastructure

Real-Time, On-Premise, No Cloud Dependency

Embedded GPU compute per sensor. Multiple sensors aggregated and synchronized with auxiliary signals. Deterministic low-latency processing — fully on-premise.

L3
Physical State Space

Process Fingerprints, Not Just Numbers

Each moment is represented as a physical fingerprint — a structured spatial signature capturing gradients, flow patterns, and dynamic changes. Similar states produce similar fingerprints; deviations are measurable distances from a known baseline.

L4
Physics-Informed Models

Understanding Why, Not Just What

Physics-Informed Neural Networks (PINNs) combine measurement data with physical equations — enabling predictions, what-if analysis, and causal interpretation that purely data-driven models cannot provide.

L5
Applications

From Visibility to Control to Autonomy

Stability assurance, energy optimization, autonomous closed-loop control, scale-up acceleration, and cross-plant learning — each building on the physical understanding established in L1–L4.

Not All Sensors Are Equal — Especially for AI

Physical AI requires dense, spatially resolved, high-speed data. Most conventional measurement technologies cannot deliver this combination.

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Point & Path Sensors
Pressure, temperature, Coriolis, DO probes
Spatial resolutionNone — single point or path-average
Temporal resolution1–100 readings/sec
What they seeSymptoms at the wall — not internal dynamics
AI-ready?No — too sparse for physical learning
⚠️
Advanced Tomography
EIT/ECT, X-ray & gamma densitometry
Spatial resolutionSome cross-sectional detail, but coarse
Temporal resolution10–100 frames/sec — misses fast dynamics
What they seeBulk phase distribution — limited spatial detail
AI-ready?Partial — temporal resolution limits usefulness
✅
quantropIQ Process Sensor
Spatial resolution + real-time speed, inline 24/7
Spatial resolution256–10,000 pixels across the pipe cross-section
Temporal resolution1,000+ frames/sec — resolves fast dynamics
What it seesActual internal process state in real time
AI-ready?Yes — spatial + temporal structure for Physical AI
Pilot Program

From First Contact to Working Sensor in 6–12 Weeks

A structured, low-friction entry point. We configure for your process, install at your site, and deliver results you can evaluate before any long-term commitment.

1
Week 1–2

Process Alignment

We understand your process, equipment geometry, and measurement objectives — and define what success looks like for your team.

2
Week 3–4

Sensor Configuration & Preparation

Sensor configured to your pipe geometry, fluid system, and operating conditions. EX/IP scope aligned with your site requirements.

3
Week 5–6

Installation & Commissioning

Flange-based installation into your live process — no production stop. Sensor commissioned and baseline established with your process engineers on-site.

4
Week 7–10

Live Monitoring & Analysis

Continuous data collection during normal operation. Weekly sessions with your team translating spatial sensor data into process insights.

5
Week 11–12

Results & Decision

Structured review: what was visible, what insights were generated, and what operational improvements are quantifiable. Clear basis for evaluating full deployment.

What the Pilot Includes

A complete working deployment — not a demo. We bring the sensor, configure it for your process, and work alongside your team for the full duration.

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Hardware tailored to your geometry

Sensor configured to your pipe diameter, fluid system, and pressure/temperature range.

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Integration with your data infrastructure

Sensor data streams alongside your existing process historian or DCS — no standalone island.

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Engineering support throughout

Our team works with yours during installation, commissioning, and the full monitoring period.

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Quantified results report

A structured findings document — what was measured, what it means, what it's worth to your operation.

Request a Pilot

We respond within 1 business day. No commitment required to start the conversation.

Industries

Wherever Multiphase Flow Matters

The platform applies wherever the gap between what's happening inside a process and what conventional sensors report has an economic consequence.

⚗️

Specialty & Fine Chemicals

Reactive mixing control, concentration uniformity, selectivity optimization, and scale-up fingerprinting for complex multi-step synthesis routes.

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Biotechnology

Spatial aeration monitoring, foam detection, mixing uniformity, and fermentation endpoint determination across large-scale bioreactors.

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Pharmaceuticals

Inline process understanding for regulated manufacturing — batch consistency, contamination precursor detection, and PAT integration.

🥤

Food & Beverage

Mixing homogeneity, phase transition monitoring, emulsion stability, and batch-to-batch quality consistency for complex liquid food processes.

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Petrochemicals & Refining

Distillation column optimization, separator control, two-phase flow monitoring, and energy efficiency improvement across continuous processes.

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Polymers & Materials

Crystallization monitoring, phase transition detection, and flow regime identification in polymerization and materials processing reactors.

Why Trust It

Built on Physics, Validated in the Field

The measurement principle and sensor technology are grounded in peer-reviewed research in multiphase flow measurement — packaged as a production-ready industrial system.

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Deep Research Foundation

The spatial measurement approach builds on peer-reviewed science in multiphase flow tomography — with a measurement principle and calibration methodology that is physically interpretable and not a black box.

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Industrial-Grade Hardware

Designed for continuous operation from the start: flange-mounted, IP and EX certified (developed jointly with plant operators), embedded compute, and compatible with industrial pressure and temperature envelopes.

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Active Pilots, Not Vaporware

The system is currently deployed in pilot projects with chemical and biotech manufacturers. We work alongside process engineering teams — not around them — and results are shared transparently throughout.

Current status: Prototype and pilot phase — sensor hardware is production-ready and actively deployed. AI optimization layers (L4–L5) are in development alongside pilot data collection.

Get Started

Ready to See Inside Your Process?

Whether you're a process engineer looking to solve a specific measurement problem, or a plant manager evaluating new monitoring technology — we'd like to understand your process and show you what's possible.

Request a Pilot contact@quantropiq.com