Steel Rolling Mill Monitoring and Data Analytics

Case Study

Case Study

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A major steel company faced significant challenges in acquiring reliable data due to a legacy data acquisition system. This led to ambiguous insights and poor decision-making. SpringCT partnered with the client to enhance their industrial IoT (IIoT) capabilities and deliver a robust, scalable analytics and monitoring platform.

Problem Statement and Proposed Solution

Problem Statement

The client's existing data acquisition system was outdated and unreliable, leading to frequent inaccuracies in the collected data. As a result, the insights generated were often ambiguous or misleading, making it difficult for decision-makers to trust or act on the information. The lack of modern infrastructure also posed challenges in scaling the system and integrating data from various sources across multiple steel mills.

Solution

SpringCT helped the client enhance their existing IIoT application built using the open-source ThingsBoard platform. The solution enabled seamless data acquisition from various sensors and PLCs using OPC DA/UA and Modbus protocols, and real-time transmission to the cloud using MQTT. The enhanced platform included advanced data processing capabilities for better monitoring, analytics, and actionable insights.

Product Features

Enhanced IIoT Platform

Enhanced IIoT platform developed on top of ThingsBoard with multi-tenancy and custom asset libraries.

Multi-Protocol Data Acquisition

Data acquisition from multiple steel mills via OPC DA/UA and Modbus protocols.

Real-time Cloud Integration

Real-time data ingestion to cloud using MQTT protocol.

Advanced Data Storage

Data storage and time-series analytics using TimescaleDB.

Rule Engine & Analytics

Rule Engine for real-time computation of derived metrics and trends.

Anomaly Detection

Anomaly detection and alerting based on business-defined rules with customizable alarm and notification configuration.

Key Technical Achievements

Heterogeneous Data Integration

Integrated heterogeneous data sources across multiple steel mill equipment.

Scalable Real-time Monitoring

Enabled scalable, real-time monitoring for around 10k process parameters.

Advanced Anomaly Detection

Developed rule-based anomaly detection and automated alerting mechanisms.

Data Pipeline Development

Built data pipelines for derived metric computation and trend visualization.

Data Quality Improvement

Improved data reliability and accuracy for analytics and reporting.

Technologies Used

ThingsBoard IIoT Platform: Open source platform for industrial IoT applications.
MQTT Protocol: For cloud data transfer and real-time communication.
OPC DA/UA & Modbus: For PLC and sensor communication across steel mills.
TimescaleDB: For time-series data storage and analytics.
Real-time Data Processing Engine: For analytics, alerts, and derived metrics computation.

Results

Improved data accuracy and integrity across steel mills
Enabled real-time operational monitoring and visibility
Reduced downtime through timely alerts and anomaly detection
Provided actionable insights, resulting in better decision-making
Enhanced scalability for monitoring additional units with ease

Conclusion

By modernizing the IIoT infrastructure and implementing a scalable data acquisition and analytics solution, SpringCT helped the steel rolling mill gain accurate, real-time visibility into their day-to-day operations. This led to improved efficiency, reduced downtime, and smarter, data-driven decisions.