Haddouche, RezkiFredj, Mohamed2026-05-172025https://dspace.univ-boumerdes.dz/handle/123456789/1640530 p. : ill.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.enFilling system : PLCNeural network controlThesis