Conventional sensors tell you something happened. quantropIQ tells you where, when, and why — resolving spatial dynamics that point sensors cannot reach.
Temperature, pressure, and flow probes measure at a single point or along a path. They report averages. But industrial processes — mixing, phase separation, fermentation, distillation — are inherently spatial. The critical events happen across a cross-section, not at the wall.
The result: engineers operate on incomplete information. Yield variability, off-spec batches, and suboptimal energy use persist because the spatial root cause remains invisible.
A probe at the wall does not represent conditions at the center. Non-uniform distributions — the norm in large vessels — go undetected.
Offline sampling and slow-responding probes mean process deviations are detected late — after the damage is done.
Conventional data tells you a batch failed. It cannot tell you where in the vessel the failure originated or why.
Full cross-sectional distribution visible at 1,000+ frames per second — not a point average, but the actual spatial state.
Phase boundaries, mixing gradients, and flow instabilities resolved in real time, continuously, without interrupting production.
Root-cause attribution becomes possible: spatial data shows where a problem originates, not just that one occurred.
Process models trained on dense physical data generalize across operating conditions — and improve with each deployment.
End conditions based on actual process state, not conservative time estimates — tighter cycles, less waste.
Gas distribution, bubble size, and mixing uniformity cannot be measured in real time. A dissolved oxygen probe yields one value — 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 continuously — capturing gas holdup distribution, bubble dynamics, and mixing progression. Engineers verify whether aeration and agitation are reaching the entire culture volume.
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.
Column flooding develops gradually — but conventional instruments detect it only after it occurs, through pressure drops or product quality deviations. Intervention at that point means lost throughput and off-spec product.
Spatial measurement across column cross-sections detects the precursor pattern of flooding — rising liquid holdup, changing phase distribution — before the column loses separation performance. Control action becomes proactive rather than corrective.
Operate closer to true capacity limits. Prevent unplanned shutdowns. Reduce reboiler energy by correlating internal column state with separation efficiency.
Reactive mixing in multiphase systems is difficult to characterize and nearly impossible to monitor inline. Concentration gradients, phase distribution, and selectivity losses are inferred from conversion rates — long after they occur.
Inline spatial measurement quantifies mixing quality and phase distribution continuously across the reactor cross-section — enabling real-time detection of concentration gradients and early identification of conditions that reduce selectivity or yield.
Scale-up with a measured fingerprint of mixing behavior. Reduce selectivity losses. Detect early deviations before they propagate through the batch.
Multiphase flows — gas-liquid, liquid-liquid, solid-liquid — exhibit complex regime transitions that conventional sensors cannot characterize. Slug flow, flooding, and phase separation are detected indirectly or not at all.
Real-time cross-sectional imaging identifies flow regimes, phase fractions, and spatial distributions across the pipe. Transitions between flow regimes — the critical moments for process stability — are visible before they become operational problems.
Characterize flow regimes for the first time. Detect instabilities before they propagate. Build process models grounded in measured physical state.
Spatial deviations are detected in real time — before they propagate to final product quality. Intervention happens while correction is still possible.
Reboiler duty, aeration rates, and agitation intensity calibrated to actual process state — not conservative set-points based on worst-case assumptions.
End conditions based on measured process completion rather than fixed time windows. Cycles end when the process says so — not when the clock does.
Transfer a quantified mixing and flow fingerprint from lab to pilot to production. Spatial data replaces empirical guesswork in scale-up decisions.
Spatial data locates where in the vessel a deviation originates — not just that a deviation occurred. Process investigation time collapses.
Dense, physically structured data trains process models that generalize — enabling closed-loop optimization that improves as deployments accumulate.
Reactive mixing control, concentration uniformity, selectivity optimization, and scale-up fingerprinting for complex synthesis routes.
Spatial aeration monitoring, foam detection, mixing uniformity, and fermentation endpoint determination across large-scale bioreactors.
Inline process understanding for regulated manufacturing — batch consistency, contamination precursor detection, and PAT integration.
Mixing homogeneity, phase transition monitoring, emulsion stability, and batch-to-batch quality consistency.
Distillation column optimization, separator control, two-phase flow monitoring, and energy efficiency improvement.
Crystallization monitoring, phase transition detection, and flow regime identification in polymerization reactors.
A structured pilot delivers working results in your plant — configured for your geometry, your process, your team.