Ore Agglomeration Plant Optimization With Virtual Process Sensing
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Solution Overview
Problem
Agglomeration processes in ore processing are sensitive to input feed material characteristics, leading to variability in yield and productivity, and lack of real-time measurement of key process parameters hinders optimization, making it difficult to maintain optimal plant operation.
Innovation Solution
A system and method for optimizing agglomeration processes using physics-based and data-driven models for real-time monitoring and optimization, integrating data from various sources to determine optimal settings for manipulated variables, thereby enhancing productivity and product quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time measurement of key process parameters is implemented, then process optimization capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces physical measurement instruments with virtual measurement techniques using soft sensors and data analytics. Instead of installing complex hardware sensors for parameters like particle size distribution and green bed permeability, the system uses mathematical models that process data from existing sensors to calculate these parameters virtually, thereby reducing device complexity while maintaining optimization capability
Solution Approach 2:
The patent introduces soft sensors as intermediary computational models that bridge the gap between available sensor data and required process parameters. These soft sensors act as virtual measurement devices that translate readily available process data into meaningful metrics for optimization without requiring direct physical measurement of each parameter
2Manufacturing precision
If comprehensive process monitoring is implemented, then product quality is improved, but loss of time for data collection and processing increases
Solution Approach 1:
The patent implements continuous real-time monitoring and optimization where the system continuously collects, processes, and acts on process data without interruption. The optimization algorithm runs continuously to adjust process parameters, ensuring product quality is maintained throughout production rather than through periodic checks, thereby eliminating time losses associated with batch-wise monitoring
Solution Approach 2:
The patent establishes a closed-loop feedback system where process parameters are continuously measured, compared against target values, and automatically adjusted through the optimization algorithm. This real-time feedback mechanism ensures rapid detection and correction of quality deviations, maintaining high manufacturing precision without time delays
3Measurement precision
If multiple physics-based and data-driven models are integrated, then optimization accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple physics-based models and data-driven models into a unified optimization framework. By combining first-principles models (mass balance, energy balance, particle size distribution) with data-driven models (machine learning algorithms), the system achieves high optimization accuracy while managing complexity through integrated software architecture that coordinates all models systematically
Data Source
AI summary
Agglomeration process in agglomeration plants is quite sensitive to changes in input feed material characteristics. End-to-end optimization of the agglomerate process by combining all the units is difficult due to unique complexities and challenges associated with combining the individual process outputs. A method and system for optimizing the operation of an agglomeration plant has been provided. The system performs real time optimization on integrated wet agglomeration and thermal agglomeration process which subsequently increases the plant productivity and agglomerate quality and minimizes the operating cost and emissions from the plant. The optimization process involves various steps such as receiving data, pre-processing of data, prediction using physics-based and data-driven models of agglomeration plant, and optimization execution and configuration. The process also involves continuous monitoring of model performance and self-learning of the models in case of a performance drift. The system is also configured to estimate the key performance parameters of agglomeration plant.