Intelligent Automation for Greener Processes: An AI Framework for Environmental Monitoring and Efficiency

Abstract:

This paper presents a conceptual AI framework for real-time environmental monitoring and efficiency in industrial processes. Most industrial operations run on fixed schedules, leading to energy waste, excess emissions, and reactive rather than preventive problem detection [1]. We propose a framework combining sensor data acquisition, machine learning-based pattern recognition, and automated control triggers to continuously monitor energy use, emissions, waste, and resource consumption. The framework is designed to detect inefficiencies and recommend real-time adjustments before waste accumulates or regulatory limits are breached [2,3]. It targets adaptability across manufacturing, chemical processing, and food production — sectors with high environmental impact and persistent monitoring gaps [4]. As this is a conceptual framework, empirical validation is proposed as future work through simulation and pilot deployment in partner facilities. This paper details the framework's architecture, data flow, and decision logic, and discusses the technical and organizational challenges of deployment — including investment justification, transition management, and adoption — drawing on technology management principles [5]. This presentation introduces the framework and its intended validation pathway toward greener industrial processes.

References

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