Digital Image Processing Technology in Design and Development of Automatic Sorting System for Energy Meter Recovery

In order to solve the problems of low sorting efficiency and poor quality caused by manual sorting in traditional electricity meter recovery, this study adopts digital image processing technology to construct an automatic sorting system for electricity meter recovery based on artificial neural network. Firstly, the basic requirements of system construction are analyzed in detail, and then the principle and method of image recognition of artificial neural network are introduced in detail. On this basis, an overall framework of automatic sorting of electricity meter recovery is constructed. Finally, the functional modules are designed and applied, and Azure database is built through SQL Server platform, so as to realize the system application of this research. The final application shows that the automatic sorting system constructed by this study has simple interface and easy operation, which can greatly improve the efficiency and quality of the electricity meter recycling and sorting, and has certain practical significance for the development of the state grid industry.

Review Paper on Microcontroller Based Object Sorting

Sorting of products is a very difficult industrial process. Continuous manual sorting creates consistency issues. This paper describes a working prototype designed for automatic sorting of objects based on the metal detector KY-036 sensor was used to detect the colour of the product and the PIC16F628A microcontroller was used to control the overall process. The identification of the colour is based on the frequency analysis of the output of TCS230 sensor. One conveyor belts were used, it controlled by separate DC motors. The belt is for placing the product to be analysed by the colour sensor, having separated compartments, in order to separate the products. The experimental results promise that the prototype will fulfil the needs for higher production and precise quality in the field of automation.

Pine Logs Sorting as a Function of Bark Thickness

The process of sorting softwood raw materials is aimed at full automation. Techniques such as laser and optical scanning, used in measuring and sorting wood raw material with the layer of bark, are based on an analysis of the external shape of the log. The consequence of this is the use of constant ranges of bark deductions, which are often affected by errors resulting from averaging the values. The thickness of the bark is influenced by many factors, such as the tree species and the quality of habitat in which the trees have grown. In the case of pine wood, the range of adopted diametral intervals for the processed raw material plays a significant role. The analysis of the automatic sorting results showed numerous cases of a log-size mismatch. In methods that assume the measurement of wood with the bark, deductions for bark should be made based on experiments that take into account the raw resources base. Despite the high correlation between the size of the deduction and the average thickness of the bark (r = 0.85), the mean value of an error of the adjustment to the maximum thickness of the bark in the automatic sorting was 45%. The maximum bark thickness for the analyzed sorting intervals was correlated. The level of the correlation coefficient value was r = 0.72. In order to increase the accuracy of the sorting process, the value of the deduction for bark should be adjusted to the maximum values in each sorting group.

Combination of Dual-Energy X-ray Transmission and Variable Gas-Ejection for the In-Line Automatic Sorting of Many Types of Scrap in One Measurement

As an important part of pretreatment before recycling, sorting has a great impact on the quality, efficiency, cost and difficulty of recycling. In this paper, dual-energy X-ray transmission (DE-XRT) combined with variable gas-ejection is used to improve the quality and efficiency of in-line automatic sorting of waste non-ferrous metals. A method was proposed to judge the sorting ability, identify the types, and calculate the mass and center-of-gravity coordinates according to the shading of low-energy, the line scan direction coordinate and transparency natural logarithm ratio of low energy to high energy (R_value). The material identification was satisfied by the nearest neighbor algorithm of effective points in the material range to the R_value calibration surface. The flow-process of identification was also presented. Based on the thickness of the calibration surface, the material mass and center-of-gravity coordinates were calculated. The feasibility of controlling material falling points by variable gas-ejection was analyzed. The experimental verification of self-made materials showed that identification accuracy by count basis was 85%, mass and center-of-gravity coordinates calculation errors were both below 5%. The method proposed features high accuracy, high efficiency, and low operation cost and is of great application value even to other solid waste sorting, such as plastics, glass and ceramics.