Classification-regression concatenated approach for simultaneous optical measurement of combustion smoke aerosol...

The particle size distribution (PSD) of combustion smoke aerosols is a crucial microphysical parameter for combustion diagnostics, energy efficiency evaluations, and environmental monitoring. Existing methods for online aerosol PSD sensing typically assumes spherical particles and employs optical scattering methods. However, in practical scenarios, both the size and shape of particles jointly influence the optical scattering characteristics, and neglecting either factor results in inaccurate measurements. To address this issue, we propose a multidimensional scattering angle spectrum (MDSAS) sensing technique, integrated with a classification-regression concatenated (CRC) framework, for the simultaneous evaluation of aerosol shape and PSD. The MDSAS technique constructs a two-dimensional scattering intensity matrix by synthesizing data from three distinct incident wavelengths and 17 scattering angles. Thus, it effectively captures the coupled effects of particle shape, defined by ovality, and size. The CRC framework comprises two sequential sensing tasks. Initially, a classification model utilizing a convolutional neural network categorizes particle shapes into 10 distinct types. Subsequently, a PSD regression model is individually trained for each shape using the classification results. Furthermore, a 51-channel optical sensor is validated on an aerosol integration experimental platform using four typical combustion smokes as specified in the Chinese National Standard GB 4715-2025. The experimental results indicate that the average mean absolute percentage error of PSD measurements is only 10.76%, which is noticeably lower than that of a standard scanning mobility particle sizer. The proposed method achieves noncontact, online sensing with robust noise immunity, providing a reliable technical solution for real-time combustion monitoring and aerosol characterization.