Accelerating particle size distribution estimation

The pharmaceutical manufacturing industry has long struggled with the difficulty of monitoring the characteristics of a drying mixture, a critical step in producing medication and chemical compounds. At present, there are two noninvasive characterization approaches which might be typically used: A sample is either imaged and individual particles are counted, or researchers use a scattered light to estimate the particle size distribution (PSD). The previous is time-intensive and results in increased waste, making the latter a more attractive option.

Lately, MIT engineers and researchers developed a physics and machine learning-based scattered light approach that has been shown to enhance manufacturing processes for pharmaceutical pills and powders, increasing efficiency and accuracy and leading to fewer failed batches of products. A brand new open-access paper, “Non-invasive estimation of the powder size distribution from a single speckle image,” available within the journal Light: Science & Application, expands on this work, introducing a fair faster approach. 

“Understanding the behavior of scattered light is some of the vital topics in optics,” says Qihang Zhang PhD ’23, an associate researcher at Tsinghua University. “By making progress in analyzing scattered light, we also invented a great tool for the pharmaceutical industry. Locating the pain point and solving it by investigating the elemental rule is essentially the most exciting thing to the research team.”

The paper proposes a brand new PSD estimation method, based on pupil engineering, that reduces the variety of frames needed for evaluation. “Our learning-based model can estimate the powder size distribution from a single snapshot speckle image, consequently reducing the reconstruction time from 15 seconds to a mere 0.25 seconds,” the researchers explain.

“Our predominant contribution on this work is accelerating a particle size detection method by 60 times, with a collective optimization of each algorithm and hardware,” says Zhang. “This high-speed probe is capable to detect the scale evolution in fast dynamical systems, providing a platform to check models of processes in pharmaceutical industry including drying, mixing and mixing.”

The technique offers a low-cost, noninvasive particle size probe by collecting back-scattered light from powder surfaces. The compact and portable prototype is compatible with most of drying systems available in the market, so long as there’s an remark window. This online measurement approach may help control manufacturing processes, improving efficiency and product quality. Further, the previous lack of online monitoring prevented systematical study of dynamical models in manufacturing processes. This probe could bring a brand new platform to perform series research and modeling for the particle size evolution.

This work, a successful collaboration between physicists and engineers, is generated from the MIT-Takeda program. Collaborators are affiliated with three MIT departments: Mechanical Engineering, Chemical Engineering, and Electrical Engineering and Computer Science. George Barbastathis, professor of mechanical engineering at MIT, is the article’s senior creator.