Quantifying Particle Degradation Through Real-Time Imaging During Processing

Assessing particle breakage during handling is a critical concern in industries ranging from pharmaceuticals to food processing and mineral extraction.

When particles are subjected to mechanical stresses during conveying, mixing, screening, or packaging, they may fracture, deaggregate, or erode.

leading to changes in size distribution, flow properties, and product performance.

Methods including laser scattering and mechanical sieving deliver reliable averages yet miss the real-time evolution of particle failure.

This technique presents a breakthrough by visually tracking each particle’s journey with exceptional clarity and detail.

enabling accurate measurement of fragmentation events.

The core principle behind dynamic imaging lies in capturing high-speed images of particles in motion, typically using a high-frame-rate camera and controlled lighting conditions.

As particles pass through an imaging zone, their shapes, sizes, and surface features are recorded frame by frame.

Machine learning tools decode the imagery to compute critical shape indicators including area projection, equivalent sphere diameter, length-to-width ratio, and form factor.

Analyzing differences in particle morphology before and after processes like chute transfers, air transport, or impact events uncovers hidden degradation cues.

Unlike traditional methods, this approach reliably separates genuine breakage from temporary clumping or surface erosion.

Pharmaceutical granules can undergo unintended splitting or generate fines during blending operations.

This technology determines if size changes stem from designed granulation or accidental attrition.

supporting reproducibility and compliance with GMP and other regulatory frameworks.

Similarly, in mineral processing, understanding the extent of breakage during crushing and screening allows for optimization of equipment settings to minimize energy waste and maximize yield.

This method links particle failure directly to operational variables.

By synchronizing image data with process parameters such as conveyor speed, air velocity, or drop height, it becomes possible to map out the points in a system where particles are most vulnerable.

Engineers can implement specific modifications including altering descent angles, integrating padding, or fine-tuning material delivery to lessen mechanical shock.

Furthermore, 粒子形状測定 because dynamic imaging captures individual particle behavior, it can reveal heterogeneous breakage patterns that bulk methods might average out.

revealing previously undetected fracture pathways.

Independent validation is commonly achieved by comparing results with established techniques.

Imaging-based size profiles are often validated against laser diffraction outputs.

Fracture morphology can be further confirmed via scanning electron microscopy, adding texture and structural context to size measurements.

While powerful, this method presents several technical hurdles.

System accuracy demands rigorous tuning to compensate for optical errors, particle light absorption, and ambient lighting fluctuations.

Real-time analysis requires intensive processing capacity to handle massive image streams.

Each application requires customized hardware configurations based on particle dimensions and material behavior.

Dust-like materials necessitate finer resolution, whereas semi-opaque particles benefit from polarized or backlit illumination.

With evolving software and improved sensors, dynamic imaging is now viable for widespread industrial adoption.

Its capacity to transform qualitative observations into quantitative, actionable data makes it indispensable for modern process development and quality control.

Dynamic imaging provides the insight needed to engineer handling processes that minimize damage while maximizing throughput.

In summary, assessing particle breakage during handling using dynamic imaging provides a detailed, visual, and quantitative approach to understanding material degradation.

It moves beyond aggregate measurements to reveal the mechanics of individual particle failure.

delivering actionable intelligence for refining industrial workflows.

As manufacturers focus on quality control and efficiency gains, dynamic imaging becomes an indispensable asset in reducing particle loss and boosting end-product performance from start to finish