Client Introduction

Our client is a $20 billion conglomerate that is ranked among the world’s leading businesses. Through their innovative and sustainable presence in a wide range of sectors including Steel, Energy, Infrastructure, Cement, Paints, Venture Capital and Sports, they have been playing a vital role in driving India’s economic growth.

Problem Statement & Business Requirements

  • The plant seeks a solution to centralize real-time data in the cloud, with the aim of attaining industrial excellence and enabling online analytics. The current challenges and requirements include:
  • Asset Tracking: To track the positions of various critical components through digital twin  for Charging cars, pushing cars, Hot-coke cars and to enhance operational visibility and efficiency.
  • Cloud-Based Data Platform: Establish a secure and scalable cloud-based platform for centralizing and storing real-time data, enabling seamless access and analysis from anywhere .
  • Predictive Analytics: Requirement for analytics models to monitor the health of assets and proactively prevent unplanned breakdowns by providing early warnings and actionable insights to prevent breakdowns.
  • Oven Health Analysis: Prediction of the health and performance of ovens based on user-entered data and sensor inputs and generating reports that highlight trends, potential issues and recommendations for maintenance.
  • Automated Scheduling and Sequencing: The requirement for an automated scheduling and sequencing system via Digital Twin to understand oven readiness and enhance resource allocation.
  • Alerting System: To set up an alerting system to notify personnel in case of asset or oven health degradation or when predefined thresholds are breached.
  • Data Integration:  Integrating the machine data with manual entry human data such as oven temperature, coal cake parameters for performing advance analytics.

Machine Data Connectivity Architecture


Machine Data Connectivity solution Architecture

A wireless IoT edge gateway collects data from moving assets like pushing cars, charging cars, etc for real-time data acquisition. Here’s how the process works:

  • IoT edge gateways are Deployed with the moving assets PLCs (e.g., pushing cars, charging cars).
  • The IoT edge gateways continuously collect data from the moving assets in real-time which includes GPS coordinates, sensor readings, equipment status, and any other relevant information.
  • A data diode solution to ensure secure and unidirectional transmission of data which allows data to flow in one direction, preventing any external interference or cyberattacks from reaching the industrial network.
  • Once data the data is moved to the cloud, the data can be processed, analyzed, and visualized using cloud-based analytics tools and platforms.
  • Dashboards and reports are then generated to provide real-time insights into asset performance, location, health, and other relevant metrics.

The Knowledge Lens Solution

The implemented solution proposes the following solution that addresses the above challenges.

1.Digital Twin (To monitor Oven readiness, Asset health and Oven health)

  • Visualization: A comprehensive Digital Twin of the entire coke oven process which included visual representations of components like pushing cars, charging cars, and the coke ovens themselves.
  • Color Changing Notifications: Implemented a visual system where color changes indicate the readiness status of ovens, enabling operators to quickly identify their operational status.
  • Time Remaining to Cook: Real-time estimates of the time remaining for each oven’s cooking cycle to enhance production planning and scheduling.
  • Oven Health: Displaying the health status of each oven including any potential issues or maintenance requirements.

2.Dashboard (Visualization and Real-Time Insights)

  • Temperature Profile: A dashboard that visualizes the temperature profiles of individual ovens thereby allowing operators to monitor and understand temperatures needed for optimal coke production.
  • Production Analysis: Generating daily, monthly, and yearly production reports that compare actual coke production against planned targets. This helps in evaluating and optimizing production efficiency.

3.Rule Engine (Rules for Calculating Oven readiness)

  • Streamlined¬†Business¬†Logic: Implemented a complex business rule for calculating the oven readiness in real time basis without any human intervention.
  • KPI Calculation: Calculating key performance indicators (KPIs) such as oven health, status, and other relevant metrics based on user-defined rules

4.AI Engine

  • Scheduling and Sequencing: Implemented an AI-based scheduling and sequencing system that considers oven temperature and coal quantity to optimize oven operations. This ensures efficient production and reduced losses.
  • Oven Health Analysis: Utilizing AI to analyze oven health based on user-entered data and sensor inputs. Generate reports that highlight trends, potential issues, and recommendations for maintenance.
Digital Twin of Coke Oven

Key Features

1. User-Friendly Interface: A user-friendly digital logbook interface that allows operators to input manual data easily. Which is accessible on a computer or mobile device.

2. Data Input and Validation: Validation checks to ensure data accuracy and consistency, reducing the likelihood of errors.

3. Real-Time Database Integration: Integrating the manual entry logbook with instrumental data in a single time series historian database.

4. Data Analysis and Visualization: analytics tools and visualization of oven performance via dashboards and reports that monitor oven temperatures and other relevant parameters.

5. Real-Time Insights: With manual data available in real-time on the platform, decision-makers can access up-to-the-minute information for process monitoring and control.

6. Immediate Issue Resolution: Rapid access to manual data allows for prompt identification and resolution of issues related to oven temperatures or other factors.

7. Custom Alerts and Notifications: Alerts and notifications are generated based on specific data thresholds or conditions. For instance, if oven temperatures exceed predefined limits, relevant personnel can be notified immediately.

Coke Day Wise Production Analysis

Key Benefits

  • 100% Reduction in Overcooking losses.
  • 100% Data transparency for Comprehensive production and quality reporting.
  • $1000 per day of Significant cost savings.

Conclusion

At Knowledge Lens, we are on a mission to help our product technologies serve you better. Visit us here to learn more about our products & solution offerings. Drop us a message or email us at sales@knowledgelens.com, if you would like to schedule an exploratory call with us

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