Coal and other materialsGranularity detection system

Coal particle size refers to the size and distribution pattern of material particles. In industrial production, the fineness of pulverized coal is an important parameter to evaluate the quality of fuel processing. Its value directly affects the combustion characteristics, transportation efficiency and pollution control effect. In general, the higher the degree of material crushing, the lower the ignition point and the higher the burnout rate, which can effectively reduce residual carbon loss. Ultra fine particles can not only improve the uniformity of gas-solid mixing, but also stabilize the thermal field distribution inside the furnace, reducing the thermal efficiency loss caused by local high or low temperatures. However, excessive crushing will lead to an increase in energy consumption costs of grinding equipment, accelerate mechanical wear of core components such as grinding rollers and steel balls, significantly increase equipment maintenance frequency, and cause excessive dust escape, resulting in resource waste and environmental risks. Given the differentiated requirements for fuel specifications in different industrial scenarios, establishing a precise particle size monitoring system has dual significance in achieving energy conservation, consumption reduction, and process optimization.

The material particle size intelligent analysis system launched by Gewu Youxin adopts a fusion scheme of optical imaging and intelligent algorithms, replacing the traditional manual sampling mode through high-frequency dynamic monitoring. The system is deployed next to the transmission lines of metallurgical sintering, raw material transportation and other processes. It relies on high-precision image sensors to capture the real-time movement status of materials, and combines deep learning frameworks to perform multidimensional analysis of mineral morphology features. It can complete particle size distribution calculation and mineral composition classification in milliseconds, and synchronously generate visual quality reports. Its core architecture consists of three parts: the front-end high-definition camera device ensures stable image acquisition under complex working conditions, the middle platform big data analysis platform realizes intelligent extraction and pattern recognition of massive image features, and the terminal interaction interface supports granularity threshold setting and process parameter linkage adjustment, providing accurate data support for optimizing crushing efficiency and raw material ratio.

Common methods for measuring material particle size include:

Screening analysis method:

Implement graded screening of bulk materials through multi-layer standard sieves, and calculate the proportion of each particle size range based on the mass of the remaining material. This traditional method has shortcomings such as low data accuracy and inability to present continuous granularity spectra, and has gradually withdrawn from the field of fine detection.

Optical diffraction technology:

Based on the Mie scattering theory, the particle size parameters are inverted by analyzing the diffraction pattern generated by the laser passing through the particle group. Although it has the advantage of easy operation, it is limited by the fixed layout of optical sensors and the lack of system calibration function, making it difficult to correct detection deviations through software and requiring manual secondary correction.

Intelligent imaging technology:

By optimizing the light source layout and industrial camera shooting parameters, high-definition material images are obtained, and digital image processing algorithms are used to perform morphological analysis on scattered or stacked particles. For example, a certain brand's intelligent detection device adopts a 24/7 automated working mode, which not only realizes real-time collection and cloud storage of particle size data, but also automatically generates dynamic trend curves and visual reports. This type of system can significantly improve the quality grading and value conversion ability of coal resources by precisely regulating fuel processing parameters.

Functions and characteristics of particle size detection system for coal and other materials:

1) Real time online detection

Real time online detection of material operation status and real-time display in the system interface.

2) Material particle size detection

The material particle size detection module calculates particle size by detecting and measuring material particles. The various intervals identified by particle size analysis can be divided into multiple stages for statistical analysis based on particle size requirements. Output trend data on the proportion of each stage.

3) Granularity distribution calculation and occlusion compensation

Automatically detect the particle size distribution of materials in real-time, and set the particle size ratio according to the detection range. For thick material conditions, the shading coefficient adjustment function can be used to correct the actual particle size ratio.

4) The system for automatically identifying the types of coke and ore materials can recognize the types of ore materials based on real-time images collected through image features.

5) Historical trend chart display

The trend chart of average particle size of materials displays the trend changes of materials intuitively.

6) Historical query function

Ability to query the particle size and particle size distribution ratio of coke and ore at any time period, export all data or export a single data separately.

7) Detection alarm prompt

Custom alarm thresholds can be set for the detection quantity of particle size and mineral type ratio. When the detection data reaches the threshold, the system automatically alerts the staff to handle it in a timely manner. After the material particle size intelligent detection system is put into use, it can work continuously for 24 hours, which is convenient and efficient. Real time automatic detection of material particle size can be achieved, and the measurement data can be stored. The results of each detection point can be displayed in a curve chart and stored, which is convenient for on-site personnel to conduct follow-up inspections and detect material quality. Accurate and timely detection can significantly shorten the reaction time for particle size control and improve the stability of material particle size control, which is of great significance for analyzing and guiding production of materials entering the furnace.