Pellet production method and device based on machine vision and data driving
A data-driven, machine vision technology, applied in the field of iron and steel metallurgy, can solve the problems of reduced production efficiency and quality stability, inability to control equipment parameters, lag and blindness of pellet production mode, etc., to reduce production and operation costs, improve The effect of quality and yield
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no. 1 example
[0046] First of all, it needs to be explained that the pellet production process is a large time-delay, multi-variable, strongly coupled nonlinear system, and the preheating temperature, roasting temperature and roasting machine belt speed are coupled to each other in the production process of a typical pellet production belt roaster , and its dynamic characteristics change with the change of operating conditions such as the particle size composition of green pellets, the amount of green pellets, moisture and mineral types. The particle size and distribution of its pellets are important indicators in quality testing. During the calcination process of the pellets, the growth of the mineral intercrystals generally causes the macroscopic volume to shrink. The volume shrinkage is affected by the type of raw materials and process parameters such as calcination temperature. It is the core feature of the quality of the pellets. performance) there is an obvious linear relationship. F...
no. 2 example
[0065] This embodiment provides a pellet production device based on machine vision and data drive, which includes the following modules:
[0066] A machine vision system, the machine vision system includes a green ball recognition module and a finished ball recognition module; wherein, the green ball recognition module is used to use an industrial camera to complete the collection of green ball images before roasting; the finished ball recognition module It is used to use industrial cameras to complete the image collection of the finished ball after roasting;
[0067] The control system is used to obtain the particle size of green pellets and the particle size of finished pellets based on the collected images of green pellets and finished pellets; determine the change in pellet size before and after roasting according to the particle size of green pellets and the pellets of finished pellets; based on the pre-determined The dynamic adjustment effect of the production process pa...
no. 3 example
[0098] This embodiment provides an electronic device, which includes a processor and a memory; at least one instruction is stored in the memory, and the instruction is loaded and executed by the processor, so as to implement the method of the first embodiment.
[0099] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) and one or more memories, wherein at least one instruction is stored in the memory, so The above instruction is loaded by the processor and executes the above method.
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Abstract
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