Abstract

This study introduces the integration of an AI-driven control system into a self-cleaning cylinder pump designed for wastewater applications in remote and environmentally constrained settings. The system replaces static optimization methods by using artificial intelligence to detect early signs of clogging to adjust pulse flow parameters and autonomously select suitable water sources for reverse cleaning. 
The proposed AI model continuously adapts to changing conditions through real-time sensor feedback, edge learning, and predictive control. The simulation and training results were obtained under different flow conditions, including sediment buildup, organic blockage, and high-viscosity scenarios. It demonstrated effective recovery of flow purity, low energy consumption, and reliable autonomous operation.
The system positively responses to changes in viscosity, pressure, and proved autonomous operation. Simulation results suggest that the proposed design provides a robust and adaptable solution for sustaining flow in variable wastewater environments, especially in installations with limited access to maintenance.