Monitoring particulate composition changes during the flocculation process using polarized light scattering

Recognition of microplastics suspended in seawater via refractive index by Mueller matrix polarimetry

Microplastics have become the marine pollution posing a human health risk, but they are difficult to be detected and recognized for different materials, irregular shapes, and broad size distributions. Microplastics' refractive index (RI) is related to the materials and can be characterized by the Mueller matrix. In this work, the particles are suspended in water and their Mueller matrices are measured by a particulate Mueller matrix polarimetry setup. Four kinds of spherical particles including microplastics are effectively classified by their Mueller matrices. Moreover, two kinds of common microplastics with broad size distributions, irregular shapes, and random orientations are also well recognized by the Mueller matrix. These results imply that RI plays a vital role in the recognition of microplastics suspended in water. By using the Mie theory and discrete dipole approximation simulation, the discussions explain in physics origin how RI affects Mueller matrix coupling with size and structure, and give some decoupling methods. Results in this work help advance future tools to in situ recognize the microplastics in seawater.

PIV and CFD investigation of paddle flocculation hydrodynamics at low rotational speeds

In this study, flocculation hydrodynamics were evaluated by investigating the velocity field of turbulent flow experimentally and numerically, in a laboratory scale paddle flocculator. Turbulence that either promotes the particle aggregation or the breakage of flocs is complicated and has been considered and compared in this work using two turbulence models; namely, the SST k–ω and the IDDES. Results showed that IDDES provided very slight improvement as compared to SST k–ω, yielding the latter sufficient in accurately simulating the flow inside the paddle flocculator. A Goodness-of-Fit evaluation was adopted to study the convergence between PIV and CFD results, and to compare the results of the used CFD turbulence models. The study focused, as well, on the quantification of the slippage factor k, as 0.18 at low rotational speeds of 3 rpm and 4 rpm, and compared to conventional typical value of 0.25. This reduction of k from 0.25 to 0.18 yields around 27–30% increase in the power imparted to the fluid, and about 14% increase in velocity gradient (G). This implies that more mixing is provided than expected, and therefore, less energy is input and thus the electric consumption for the flocculation unit at a drinking water treatment plant could be potentially decreased.

Profile probing of suspended particles in water by Stokes vector polarimetry

Suspended particles are the important components of natural water. In this paper, a method based on polarized light scattering is proposed for profile probing of the particulate components in water. The profile probing is achieved by a polarized light sheet illuminating the suspension and the Stokes vector imaging system at a 120° backscattering angle, receiving the scattered light of the particles in the scattering volume. Each Stokes vector image (SVI) includes hundreds of star-studded particles whose Stokes vectors are used to retrieve the numbers of each particulate component in water. Experiments of typical particles are conducted. The classifications of these particles powered by the convolutional neural network (CNN) are demonstrated. The particulate components in mixed samples are successfully recognized and quantitatively compared. Considering at least 10 SVIs every second, the concentrations of each particulate component in water are effectively evaluated. The concept of profile probing the particulate components in water is proved to be powerful, by which we can measure up to almost 8000 particles per second. These results encourage the development of in-situ tools with this concept for particle profiling in future field surveying.

Machine Learning Powered Microalgae Classification by Use of Polarized Light Scattering Data

Microalgae are widely distributed in the ocean, which greatly affects the ocean environment. In this work, a dataset is presented, including the polarized light scattering data of 35 categories of marine microalgae. To analyze the dataset, several machine learning algorithms are applied and compared, such as linear discrimination analysis (LDA) and two types of support vector machine (SVM). Results show that non-linear SVM performs the best among these algorithms. Then, two data preparation approaches for non-linear SVM are compared. Subsequently, more than 10 categories of microalgae out of the dataset can be identified with an accuracy greater than 0.80. The basis of the dataset is shown by finding the categories independent to each other. The discussions about the performance of different incident polarization of light gives some clues to design the optimal incident polarization of light for future instrumentation. With this proposed technique and the dataset, these microalgae can be well differentiated by polarized light scattering data.