Casestudy Detail

We developed a data-driven coal blending model designed to optimize the mixing of different coal grades based on quality, cost, and operational requirements. The system supports real-time analysis and predictive outcomes, enabling industries to achieve efficient, cost-effective blends.

• Multi-Parameter Input System for Coal Grades, Properties & Constraints

• Automated Blend Optimization Based on Quality & Cost Goals

• Real-Time Quality Prediction of Final Blended Output

• Cost Analysis & Side-by-Side Comparison of Blend Scenarios

• Custom Blend Configuration with Option to Save & Reuse

• Report Generation with Exportable Summaries & Charts

• Interactive Dashboard for Data Visualization and Analysis

• User Role Management for Secure Access

• Scalable Design for Industrial Use Cases

• Fast Computation Engine for Instant Feedback

• Improved operational efficiency through accurate blend recommendations

• Reduced production costs with optimized blend combinations

• Enabled real-time decision-making with predictive analysis

• Enhanced planning with configurable and reusable blend setups

• Streamlined reporting for audits and performance reviews

• Delivered a user-friendly yet powerful industrial-grade tool

• Data Accuracy & Migration:
  Challenge: Converting old paper-based student records into the digital format.
  Solution: Designed a custom data import tool with validation and cleanup processes.
• Multi-Level Access Control:
  Challenge: Avoid data overlap or unauthorized edits by users.
  Solution: Built a robust role-permission matrix system to segregate user capabilities.
• Result Processing Delays:
  Challenge: Manual calculation and error-prone result entries.
  Solution: Developed an automated grade calculation engine with real-time preview and bulk upload.
• Remote Coordination with Staff:
  Challenge: Training university staff remotely during initial rollout.
  Solution: Provided video tutorials, PDF user guides, and live remote sessions to ease the transition.