Abstract: Aspects of the invention are directed towards a system and a method for generating service actionable for a plurality of equipment. Embodiments of the invention describe the method comprises steps of behaviorally classifying an equipment into normalizing classification and behavior classification. The method further comprises steps of processing the normalizing and behavior classifications to generate one or more profiles corresponding to the equipment. The one or more profiles represent time-granular behavior patterns of the equipment. The method comprises steps of generating time-granular normalized characteristics for the equipment and normalizing variances of the time-granular normalized characteristics and the time-granular behavior patterns to generate possible service actionable (SACT) recommendations that are integrated into workflows to drive action and receive prediction confirmation.

Abstract: Aspects of the invention are directed towards a system and a method for generating service actionable for a plurality of equipment. Embodiments of the invention describe the method comprises steps of behaviorally classifying an equipment into normalizing classification and behavior classification. The method further comprises steps of processing the normalizing and behavior classifications to generate one or more profiles corresponding to the equipment. The one or more profiles represent time-granular behavior patterns of the equipment. The method comprises steps of generating time-granular normalized characteristics for the equipment and normalizing variances of the time-granular normalized characteristics and the time-granular behavior patterns to generate possible service actionable (SACT) recommendations that are integrated into workflows to drive action and receive prediction confirmation.

Abstract: The present disclosure relates to method and system for dynamically managing waste water treatment process in a water treatment plant. Operational data related to water treatment process are collected from various data sources and operational parameters are identified at various levels using the operational data. Historical and real-time threshold values of operational parameters are identified based on historic and real-time operational data and real-time operational data respectively. Degrees of significance of operational parameters on the water treatment processes are calculated at each level. Further, plurality of inflection points, indicating optimal range of operational data, are identified based on degrees of significance, historical and real-time thresholds. Finally, water treatment processes are optimized based on inflection points, thereby optimizing power consumption for the water treatment plant.