Research On Quantitative Batching-Weighing System

In the process of industry production, several kinds of materials always need to be mixed in according to some proportion, this is quantitative mixing. In the process of produce concrete, quantitative mixing is also needed, the precision and speed can directly influence the quality and production efficiency. Based on the practical condition of batching-weighing system of concrete batching plant, this paper has studied on two key problems, which are dynamic weighing and quantitative baiting.This paper has designed a kind of quantitative batching-weighing system. The baiting of materials is realized through pneumatic valve's open and shut, which is controlled by PLC through electromagnetic valve. This system is suited for the situation when the flow rate of materials can not be adjusted continuous. The mechanical structure is simple and with strong reliability. The feed opening can be open and shut rapidly. The upper computer monitor software can saved the weight data. It is convenient for later data analysis.In the process of dynamic weighing and batching, lots of factors may do harm to the result. In this paper, some factors are analyzed, and some methods were adopted to avoid the harmful factors. The factors that effect batching also be analyzed.On the part of weighing, there are two states, one is static state, the other is dynamic state. The IIR filter is with good static effect but it's speed of dynamic response is slow, Kalman filter has good performance in dynamic response. Considering both the static performance and dynamic performance, proposed the algorithm of combine Kalman filter and IIR filter, when system is in static state, IIR filter was adopted, in dynamic state, Kalman filter was adopted. This algorithm has improved performance.On the part of quantitative mixing, the process of batching is with strong repeatability, so Iterative Learning Control was adopted. Because the learning gain is constant in traditional Iterative Learning Control, it has some shortage in performance, if the gain increase, the convergence speed will increase but the stability will decrease. proposed the improved learning gain, it can be adaptive adjusted according to the error, then the system's convergence speed and stability has improved. Dead area for the control is also proposed, it can improved the stability.According to the filtering algorithm and control method, experiment has done on the batching-weighing system platform. Good effect has achieved, it proved the algorithm's effectiveness.