Identification of Sludge in Water Pumping System Using Support Vector Machine

Pumps play a pivotal role in both energy and water conservation. They account for the 20% of the world's total energy consumption and thus monitoring it becomes more relevant to decrease an energy wastage. The performance of the pump deteriorates for various reasons, such as cavitation, sedimentation of silt and water hammering, electrical and mechanical faults. Performance of the pump under silt-laden and identification of silt is seldom studied. It causes severe damage in the pumping system. Identification of sludge particles can productively result in energy savings, water conservation, and energy efficiency. With the advent of machine learning and artificial intelligence, pumps can incorporate these methods to become self-reliant in the identification of type and concentration of silt while pumping the fluid. Machine learning is a modern advanced technology, which leads to predict the anomalies of the machine in ground level. This paper presents the how to identify and predict sludge problem in water pumping system using machine learning algorithm.