Screw type multi-component material batching method

Abstract

The invention discloses a screw-type multi-component material batching method. The controller controls the operation of the screw conveyor and reads the sensing data of the weighing module in real time, respectively performs the calibration of the unloading of each component material, and the unloading of the unloading method. Iteratively predicting the amount in the air; adjusting the speed of the feed pump to control the material level of the lower silo within a small range; and dynamically adjusting the closing time of the screw conveyor based on the comparison between the accumulated feeding amount and the formula data . The materials of each component are discharged in turn, and when the material in the mixing hopper is detected to reach the set value, the material is mixed evenly and then the push plate is opened to discharge the mixture. The invention uses distance sensors, vibrating rods and distributors to reduce the change of material distribution and impact during the feeding process, speed up the iterative prediction convergence speed, obtain a higher feeding speed while ensuring accuracy, and is suitable for small Quick batching of batches.

Description

technical field [0001] The invention relates to the field of quantitative batching, in particular to a screw type multi-component material batching method. Background technique [0002] In industrial and agricultural manufacturing and commodity packaging, there are a large number of powder materials, such as polypropylene, polystyrene, polyvinyl chloride, light methyl cellulose, polypropylene nitrile, epoxy resin powder coating and other chemical raw materials, quartz sand, Raw materials for building materials such as cement, household chemical products such as washing powder, agricultural products such as millet and soybeans, grains and legumes, or processed food in powder, slag, and granular form, agricultural production materials such as feed, chemical fertilizers, and pesticides, as well as powdered and granular health care products, Chinese and Western medicines , condiments, etc. all need automatic quantitative packaging or ingredient manufacturing. [0003] At presen...

AI Technical Summary

Problems solved by technology

In order to improve the feeding accuracy, various adjustment methods have appeared, such as the Chinese patent application number 201320001933.3, which adopts frequency conversion speed regulation for the screw, and gradually slows down the feeding speed when it is close to the target value, reducing the drop in the air; the application number is 201310234280.8 Chinese patent, in the three-speed frequency conversion feeding process of soda ash packaging machine, large and small screws are used to feed in multiple stages; the Chinese patent with application number 200920248298.2 considers that it is difficult to control the quantitative during fast feeding, and it is reduced by the method of fast first and then slow The impact of feeding drop; the final value of the blanking of these non-weighing schemes can only be close to the expected value, and the accuracy is not high

[0005] Weighing type quantification is used to measure, fill or feed according to the set amount of material at one time. It needs to be weighed continuously during the feeding process, and the feeding amount is controlled according to the feedback of the weighing result. Because the weighing is affected by the impact of the feeding and the lagging material in the air. Large, component feeding speed and accuracy are facing many difficulties

In order to compensate for the interference of airborne materials on the measurement accuracy, many schemes adopt the technology of closing the valve in advance. For example, the Chinese patent application number 201410230888.8 divides the ingredient weighing process into three stages, and uses iterative learning control method to calculate in the last stage Turn off the advance control amount, but this scheme can only improve the blanking accuracy after the learning is completed, and the cumulative blanking accuracy during the learning process cannot be guaranteed

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