Smart Classification of Submersible Pumps

Abstract

Submersible pumps play a vital role in diverse industrial applications, ranging from water supply to the transfer of hazardous chemicals. Their classification, traditionally based on operational and design characteristics, often faces limitations in precision and adaptability. This study explores the integration of artificial intelligence (AI) and machine learning (ML) to enhance the classification and optimization of submersible pumps. Utilizing datasets of pump parameters, advanced ML algorithms like Random

Forest and Support Vector Machines were applied, achieving significant improvements in prediction accuracy. Experimental results highlight the inverse relationship between flow rate and head, as well as the impact of pump diameter on performance. The research underscores the potential of smart systems in creating adaptive, data-driven classification models that surpass traditional methods. Future work involves refining algorithms, expanding datasets, and incorporating real-time decision-making systems to address dynamic operational challenges. This approach offers a promising direction for improving the efficiency and reliability of submersible pump systems in modern engineering.