Decision-making control method of saponification value of rare earth extraction based on BP neural network

Abstract:
Improving the control methods of rare earth production lines is an important measure to improve the automation level of our country's rare earth industry. In order to improve the automation of the calcium saponification process in the rare earth extraction process and solve the limitation of manual sampling and detection in the process, this paper uses the BP neural network's modeling and analysis capabilities for complex nonlinear systems to build a rare earth extraction saponification value decision-making control model. And use OPC technology to apply the decision-making algorithm to the PLC control system, which solves the problem of saponification value detection in the calcium saponification process. The results prove that the decision-making control method proposed in this paper can effectively solve the problem of insufficient control caused by the lack of material component sensors. By comparing with manual sampling and testing data, it is concluded that the decision-making deviation rate can reach within ±2%.