Predictive Machine Learning System to Monitor and Regulate the Salt level in the Water Softener System

Water softeners are important because they make water safer and better for daily use by removing minerals that can cause issues. In water softener systems, two key components (resin and Sodium Ion Exchange Resin) are responsible for removing the hardness of the water. Monitoring and maintaining the salt level is crucial for individuals using water softeners at home. This study focuses on developing a detection method to assess the quantity of salt in a water softener system. To address these constraints, the development of a responsive system that continuously monitors and adjusts salt levels in real-time is emerging as a viable solution. This study examines the conception and implementation of a responsive system, utilizing machine learning methodologies, including random forest, decision tree, linear regression, and LSTM, to enhance salt management in water softeners. The system can promptly detect deviations from the optimal performance by integrating an Ultrasonic sensor, a Salt sensor, a Load cell, and a TDS sensor. Machine learning algorithms are employed to analyse the collected data, enabling the system to predict the salt quantity in the water softener system