Research on a liquid sample injection system based on BP neural network PID

In traditional laboratory equipment, liquid sample injection devices typically exhibit slow injection speeds and low precision. Reliance on methods based on individual experience not only results in reagent waste but also further restricts the performance of devices in high-pressure application scenarios, such as high-performance liquid chromatography and capillary electrophoresis. Whether the precision and speed of the injection device meet application requirements directly affects the instrument detection results. To address these issues, a control system combining a back propagation neural network-based PID control algorithm was designed and compared with a conventional PID controller. According to the experiments, the results demonstrate that such system can effectively improve the performance and stability of liquid sample injection, reduce injection errors, and provide a new solution for laboratory automation and precise control.