AI Driven Agile Enterprise Systems for Industrial Wastewater Management in Secure Software Defined Cloud Environments

Subramania Bharati

doi:10.15662/IJRAI.2021.0406014

Authors

Subramania Bharati SAM College of Engineering & Technology, Bhopal, Madhya Pradesh, India Author

DOI:

https://doi.org/10.15662/IJRAI.2021.0406014

Keywords:

Deep Learning, Artificial Neural Networks, Industrial Wastewater Management, Agile Enterprise Systems, Software-Defined Networking (SDN), Software-Defined Cloud, IoT Sensors, Predictive Analytics, Environmental Monitoring, Secure Cloud Computing

Abstract

Industrial wastewater management has become a critical environmental and regulatory priority due to increasing industrialization, stricter environmental compliance standards, and the need for sustainable resource utilization. This research proposes a Deep Learning (DL) and Artificial Neural Network (ANN)–driven agile enterprise system for intelligent industrial wastewater monitoring and control within secure Software-Defined Cloud (SDC) environments. The framework integrates IoT-enabled sensors, real-time data analytics, adaptive neural network models, and software-defined networking (SDN) for dynamic resource orchestration and secure data transmission. ANN and deep learning models are deployed to predict pollutant concentrations, detect anomalies, optimize chemical dosing, and enhance process efficiency. The software-defined cloud architecture enables scalable computing, flexible network control, and policy-driven security enforcement. Privacy-preserving mechanisms, encrypted communication channels, and role-based access control ensure regulatory compliance and data protection. Experimental simulations demonstrate improved pollutant prediction accuracy, reduced operational cost, optimized energy consumption, and enhanced regulatory reporting efficiency compared to conventional supervisory control systems. The proposed system contributes a secure, intelligent, and adaptive enterprise solution for sustainable industrial wastewater treatment and environmental risk mitigation

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