PLC based Bottle filling system using neural network control

Abstract

This thesis presents an intelligent control strategy for an automated liquid bottle filling system, where the operation time of an electric pump is determined using a neural network-based identification approach. The objective is to improve filling precision and efficiency by predicting the optimal pump control based on the weight measurement. A Nneural network is trained on system data to model the dynamic behavior of the fillin gprocess, serving as a digital twin that captures the system’s dynamics. This model is then used to train a second neural network that functions as a controller, predicting the optimal pump runtime needed to achieve precise filling. The resulting control logic is implemented on a Siemens S7-1200 Programmable Logic Controller (PLC), ensuring reliable and consistent operation in an industrial environment. Experi-mental results illustrate the potential of data-driven system identification in enhancing automated filling processes, offering a foundation for further refinement and optimization.