AI-Driven IoT Ecosystems for Real-Time Thermo-Chemical Decision Making

Year : 2026 | Volume : 04 | Issue : 02 | Page : 10 15
By

Kazi Kutubuddin Sayyad Liyakat,

  1. Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

The efficient management of thermo-chemical processes is a critical challenge in modern industry, where maintaining precision, operational safety, and energy efficiency directly influences productivity and sustainability. The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) has transformed conventional industrial systems into intelligent, data-driven environments capable of continuous monitoring, predictive analysis, and autonomous decision-making. By integrating interconnected sensors, edge computing, cloud platforms, and advanced machine learning algorithms, industries can obtain real-time insights into complex thermal and chemical processes while minimizing operational risks and reducing maintenance costs. This study explores the architecture of AI-driven IoT ecosystems designed to facilitate real-time decision-making in industrial environments characterized by rapid heat transfer, fluctuating process conditions, and non-linear chemical reaction kinetics. The proposed framework employs high-fidelity sensor networks to continuously monitor critical process parameters, including temperature, pressure, flow rate, and chemical composition. Edge computing is utilized to process streaming data with minimal latency, while deep learning models analyze historical and real-time information to predict process behavior, detect anomalies, and optimize system performance. The integration of predictive analytics into the IoT infrastructure enables industries to transition from reactive maintenance strategies to proactive and autonomous process optimization, thereby reducing the likelihood of thermal runaway, equipment failure, and reagent degradation. The proposed approach demonstrates the potential of intelligent industrial ecosystems to improve process reliability, enhance energy utilization, and increase production efficiency. Furthermore, AI-enabled IoT platforms support adaptive control, predictive maintenance, and sustainable resource management, providing a scalable foundation for next-generation smart manufacturing and industrial automation in increasingly complex thermo-chemical environments.

Keywords: Chemical, decision making, thero-chemical, AIIoT, IoT, sensors

[This article belongs to International Journal of Photochemistry and Photochemical Research ]

How to cite this article: Kazi Kutubuddin Sayyad Liyakat. AI-Driven IoT Ecosystems for Real-Time Thermo-Chemical Decision Making. International Journal of Photochemistry and Photochemical Research. 2026; 04(02):10-15.
How to cite this URL: Kazi Kutubuddin Sayyad Liyakat. AI-Driven IoT Ecosystems for Real-Time Thermo-Chemical Decision Making. International Journal of Photochemistry and Photochemical Research. 2026; 04(02):10-15. Available from: https://journals.stmjournals.com/ijppr/article=2026/view=256557

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Regular Issue Subscription Review Article
Volume 04
Issue 02
Received 25/06/2026
Accepted 30/06/2026
Published 20/07/2026
Publication Time 25 Days

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