Using acoustic emission signal as the detection medium for particle characteristic parameters has the advantages of real-time, non-destructive, safe, and non-invasive flow field. In order to extract the rich information contained in the acoustic emission signal and establish the quantitative relationship between the acoustic emission signal and the particle characteristic parameters, it is necessary to carry out a series of mathematical processes on the acoustic emission signal in order to extract valuable features from it, and then take these features as the model variables and, through modern modeling methods, establish the quantitative relationship between the acoustic emission signal mode characteristics and the particle characteristic parameters. This chapter first introduces the application status and research progress of acoustic emission technology in chemical processes, then introduces the processing methods of acoustic emission signals, and finally focuses on the basic principles of wavelet (packet) analysis, the types of wavelet (packet) functions, the Mallat algorithm, signal wavelet (packet) noise reduction, and other basic theories, as well as the research progress of particle detection based on modern modeling technology of acoustic emission signals.

Carbide slag is a bulk industrial solid waste, and its open‑air stockpiling causes severe environmental pollution. In this work, nano‑calcium hydroxide with high dispersity, high activity and large specific surface area was prepared from carbide slag via a coupled process consisting of high‑temperature calcination for impurity removal, controllable crystallization and organic surface composite modification. Key parameters including crystallization temperature, modifier dosage, modifier type and drying temperature were regulated by orthogonal tests to precisely control the nano‑crystal size of calcium hydroxide and effectively mitigate nanoparticle agglomeration. The effects of the four above‑mentioned parameters on the specific surface area of products were investigated using an L₉(3⁴) orthogonal array, and the optimal preparation conditions were determined. Combined with multiple micro‑characterization techniques including TG‑DTA, XRD, SEM and BET, the optimal process for synthesizing nano‑calcium hydroxide was established. Under the optimal conditions, the as‑prepared calcium hydroxide exhibited a grain size of 50–150 nm, a mass fraction of 95.1 %, a specific surface area of 48.97 m²/g, a bulk density of 0.65 g/mL and a pore volume of 0.24 cm³/g. This proposed process enables high‑value resource utilization of carbide slag and provides reliable technical support for preparing desulfurization and denitrification materials from industrial solid waste.