矿质肥料粒度组成的工业自动化光电控制方法

D. Yunovidov, V. Shabalov, V. Sokolov
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引用次数: 0

摘要

本文介绍了用光电方法解决工业生产的矿物肥料粒度组成的操作控制问题。提出了工业条件下自动在线控制的装置和算法。该方案由三个独立的部分组成:传送带取样系统、分析现场取样系统(给料器)和颗粒光电检测模块。该方案的特点包括在线控制颗粒的比例(颗粒组成,通过质量或光学分布校准)和几种能力(分析颗粒的形状和颜色;在工业条件下工作,并将数据传输到工厂信息系统(“pissystem”)。从传送带上取样由机器人旋转系统进行。样品传递到分析区域是通过样品馈送系统的线性振动来实现的。集成的计算机控制整个采样和馈送方案。在此基础上,提出了颗粒参数的计算算法。它包括获取光电图像(像素强度的三维红绿蓝矩阵)、图像预处理(平滑、二值化和形态学)、闭合轮廓的计算、发现的轮廓的椭圆逼近和椭圆参数(长、短轴、轴比、平均颜色)的计算。接收和处理信息的所有阶段都是自动化的,并在编程语言Python 3.7上实现。计算了该方法的统计指标,并与实验室颗粒成分分析技术进行了比较。给出了不同工艺过程参数(计算粒度组成、原料消耗、滚筒干燥造粒机、振动筛和破碎机的工作)的线性相关图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Method of Industrial Automated Optical-Electronic Control of Granulometric Composition of Mineral Fertilizers
The article describes the solution of the operational control problem of particle size composition of industrially produced mineral fertilizers by the optical-electronic method. The device and algorithm for automated online control in industrial conditions is proposed. This scheme consists of three independent parts: the system of sampling from the conveyor belt, the system of sampling in the field of analysis (feeder) and the block of optical-electronic detection of granules. The peculiarities of the proposed scheme include online control of the fraction of granules (granulometric composition, which was calibrated by mass or optical distribution) and several abilities (to analyze the shape and color of granules; to work in industrial conditions and to transfer data into the factory information system - "PISystem"). Sampling from the conveyor belt is carried out by a robotic rotary system. The transfer of the sample to the analysis area was performed by the linear vibrations of the sample feed system. The integrated computer controls the entire sampling and feeding scheme. Furthermore, the algorithm for calculating parameters of granules was developed. It consists of obtaining an optical-electronic image (three-dimensional Red-Green-Blue matrix of pixel intensities), image pre-processing (smoothing, binaryization and morphology), calculation of closed contours, approximation of the found contours by ellipses and calculation of ellipse parameters (long and short axis, axis ratios, average color). All stages of receiving and processing of the information are automated and implemented on programming language Python 3.7. Statistical indicators of the proposed method are calculated and the results are compared with the laboratory techniques of granulometric composition analyses. The linear correlation map of different parameters of technological process (calculated granulometric composition, raw material consumption, work of drum dryer-granulator, vibrating screen and crushers) is given.
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