Raw Mix Preparation Optimization – Cement Industry
Delivered a standalone, enterprise-grade AI software for Raw Mix Preparation that integrates Model Predictive Control (MPC) with Time-Series Analytics, designed to work independently with existing plant automation.
Client: Leading Cement Manufacturer
40%
Quality Improvement
Reduced deviations
15%
Energy Savings
Operational efficiency
60%
Rework Reduction
Process optimization
Enterprise
Deployment
Production-ready
The Challenge
What problems did they face?
The cement manufacturing plant faced significant challenges in maintaining consistent raw meal quality. High variability in raw material properties (Limestone, Clay, Additives) led to frequent deviations in key quality parameters (LSF, SM, AM). The manual and rule-based control approach resulted in increased rework, higher energy consumption, inconsistent kiln feed quality, and limited predictive visibility for operators.
Our Solution
How we addressed it
We delivered a standalone, enterprise-grade AI software for Raw Mix Preparation that integrates Model Predictive Control (MPC) with Time-Series Analytics. The solution was designed to work independently with existing plant automation (DCS/PLC). Key technologies included: MPC for multivariable control of raw material proportioning with constraint handling, Time-Series Analysis for trend detection and early identification of drifts, and AI/ML models for process modeling using historical and live plant data with continuous model adaptation.
Technical Implementation
How We Built It
Key Technologies Used
Model Predictive Control (MPC) – Multivariable control for raw material proportioning with constraint handling for quality and operational limits
Time-Series Analysis – Trend detection, process behavior learning, and early identification of drifts and disturbances
AI & ML Models – Process modeling using historical and live plant data with continuous model adaptation based on operating conditions
System Architecture
Standalone enterprise application (not embedded in PLC)
Secure integration with Plant DCS/PLC
Integration with Laboratory data systems
Connection to Historical data historians
Modular and scalable design for future expansion
Functional Capabilities
Real-time prediction of raw mix quality parameters
Optimal setpoint recommendations for feeders and weigh belts
Closed-loop / advisory mode MPC control
Operator dashboards with explainable insights
Alarm and deviation forecasting
Key Differentiators
Combination of MPC + AI/ML + Time-Series Analytics
Standalone yet fully integrated solution
Industrial-grade, scalable, and secure
Designed specifically for cement process complexity
The Results
Measurable Impact
Improved raw mix consistency with reduced quality deviations
Reduced material wastage through optimal setpoint recommendations
Lower energy and operational costs
Enhanced kiln stability downstream
Faster decision-making for operators with predictive visibility
Reduced dependency on manual operator intervention
Self-learning models improving accuracy over time
"The combination of MPC, AI/ML, and Time-Series Analytics has transformed our raw mix preparation process. The predictive visibility and self-learning capabilities have significantly improved our operational efficiency."
Plant Operations Head
Leading Cement Manufacturer
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