ParticleTensorAI® – System Configurations

ParticleTensorAI® is available in two current system configurations: PTA1 and PTA2. Each configuration uses the same AI-based signal-sequence analysis approach. The systems differ in their optical probe, control unit, and number of detector channels.

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ParticleTensorAI® – System Configurations

PTA1 and PTA2 at a Glance

FeaturePTA1PTA2
Optical probeLSS1LSS2
Control unitZEONIMEA
Detector channels12 synchronized channels
Hardware platformBased on the SQA1 platformBased on the SQA2 platform
Evaluation methodsAI-based (Particle Tensors)AI-based (Particle Tensors)
System detailsExplore PTA1Explore PTA2
PTA1

PTA1 — Single-Channel Configuration

PTA1 combines the LSS1 optical probe with the ZEON control unit and uses one detector channel. Its calculation electronics are configured for ParticleTensorAI® signal acquisition and AI-based tensor analysis.

PTA2

PTA2 — Two-Channel Configuration

PTA2 combines the LSS2 optical probe with the IMEA control unit and uses two detector channels. The recorded signals provide two channels of time-resolved light-scattering data for ParticleTensorAI® analysis.

Shared Evaluation Approach

Both configurations use AI-based evaluation. The recorded detector signals are retained over a defined measurement interval and arranged as Particle Tensors for analysis by an AI model developed for the measurement task.

The number of detector channels determines how many optical signal streams are available to the analysis. The most suitable configuration depends on the measurement task and the information required from the recorded signals. The value of each detector channel must be evaluated and validated for the intended application.

Selecting a Configuration

Configuration selection begins with the physical measurement question: the spray or particle system, the properties to be investigated, the available reference data, and the AI model to be developed and validated. A higher detector-channel count provides additional signal streams, while its benefit depends on the application and the information contained in those signals.

For the underlying analysis principle, see ParticleTensorAI® Overview and the ParticleTensorAI® measurement-principle pages.