What does this research mean for the field?

An Artificial Intelligence-Enabled Control System (AI-ECS) integrating IoT with hybrid Random Forest and LSTM algorithms reduces control error by 73.3%, decreases energy consumption by 9.1%, and detects faults up to 36 hours in advance compared to traditional control systems. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Develop an Artificial Intelligence-Enabled Control System (AI-ECS) integrating IoT sensing and hybrid machine learning to optimize real-time control, energy efficiency, and predictive maintenance in dynamic industrial environments.

August 22, 2026Scientific ReportsOpen Access

An intelligent internet of things-based artificial intelligence framework for adaptive mechanical process control and real-time performance optimization

View Full Paper

Ask AI

Bookmark

Authors

VSVijaya Bhaskar Sadu GVGopala Rao L. V. V.GAGopala krishna A

Discussion

Member takes

Overview

Experimental study demonstrates reduced error and energy consumption in industrial process control, indicating a viable model for smart manufacturing.

Key Points

Develop an Artificial Intelligence-Enabled Control System (AI-ECS) integrating IoT sensing and hybrid machine learning to optimize real-time control, energy efficiency, and predictive maintenance in dynamic industrial environments.
Integrated IoT sensor networks with hybrid Random Forest and Long Short-Term Memory algorithms alongside dynamically tuned PID and MPC controllers on an edge-computing platform.
Evaluated the framework on a continuous-flow industrial process testbed using multi-rate sensor sampling and MQTT communication over 24-hour and 30-day operational windows.
AI-ECS achieved a 73.3% relative reduction in Mean Absolute Percentage Error (MAPE) and decreased energy consumption by 9.1% compared to traditional PID, APC, and IoT-only setups.
Impending mechanical faults were detected up to 36 hours before failure across a 48-hour pre-failure monitoring window alongside faster disturbance rejection.

Cite This Study

Sadu et al. (2026) studied this question.

synapsesocial.com/papers/6a89600eca7ade938187f09a https://doi.org/10.1038/s41598-026-67298-z

View Full Paper

Ask AI

Bookmark

August 22, 2026Scientific ReportsOpen Access

An intelligent internet of things-based artificial intelligence framework for adaptive mechanical process control and real-time performance optimization

View Full Paper

Ask AI

Bookmark

Authors

VSVijaya Bhaskar Sadu GVGopala Rao L. V. V.GAGopala krishna A

Discussion

Member takes

Overview

Experimental study demonstrates reduced error and energy consumption in industrial process control, indicating a viable model for smart manufacturing.

Key Points

Develop an Artificial Intelligence-Enabled Control System (AI-ECS) integrating IoT sensing and hybrid machine learning to optimize real-time control, energy efficiency, and predictive maintenance in dynamic industrial environments.
Integrated IoT sensor networks with hybrid Random Forest and Long Short-Term Memory algorithms alongside dynamically tuned PID and MPC controllers on an edge-computing platform.
Evaluated the framework on a continuous-flow industrial process testbed using multi-rate sensor sampling and MQTT communication over 24-hour and 30-day operational windows.
AI-ECS achieved a 73.3% relative reduction in Mean Absolute Percentage Error (MAPE) and decreased energy consumption by 9.1% compared to traditional PID, APC, and IoT-only setups.
Impending mechanical faults were detected up to 36 hours before failure across a 48-hour pre-failure monitoring window alongside faster disturbance rejection.

Cite This Study

Sadu et al. (2026) studied this question.

synapsesocial.com/papers/6a89600eca7ade938187f09a https://doi.org/10.1038/s41598-026-67298-z

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark