Model Predictive Control for Bulk Material Blending
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Solution Overview
Problem
Bulk material blending processes face challenges in maintaining stable raw material chemistry and meeting quality specifications due to variations in material delivery and cost considerations, leading to unstable kiln operations and non-conformance with industry standards.
Innovation Solution
A model predictive control system that adjusts the proportioning of raw material feeds based on chemical and quality composition analysis, using inferential models and dynamic predictive models to optimize feed rates and minimize costs while maintaining quality targets, and includes features for monitoring and compensating for deviations and tolerating temporary quality deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual control of proportioning set points is used to meet quality parameters, then quality specifications can be satisfied, but the system cannot optimize costs and requires trial and error adjustments
Solution Approach 1:
The patent replaces manual mechanical adjustment of proportioning set points with an automated computer-based control system. The controller automatically calculates and adjusts feed rates based on real-time chemistry data and cost parameters, eliminating the need for manual trial and error while simultaneously optimizing both quality and cost.
Solution Approach 2:
The control system performs self-optimization by automatically adjusting proportioning set points based on real-time feedback from chemistry analyzers and cost data. The system autonomously determines the optimal blend without requiring manual intervention, achieving both quality conformance and cost optimization simultaneously.
2Ease of manufacture
If material chemistry is allowed to vary to reduce costs, then cost optimization improves, but kiln operation stability deteriorates
Solution Approach 1:
The system continuously monitors raw material chemistry through online analyzers and uses this feedback to dynamically adjust proportioning set points. This closed-loop control ensures that chemistry variations are compensated in real-time, maintaining kiln stability while allowing flexibility in material selection for cost optimization.
Solution Approach 2:
The control system dynamically adjusts proportioning set points in response to changing material chemistry and cost conditions. Rather than using fixed ratios, the system continuously adapts the blend composition to maintain quality and stability while optimizing costs based on current material availability and pricing.
3Stability of the object's composition
If strict control of material chemistry is maintained, then quality stability improves, but production flexibility and cost optimization are reduced
Solution Approach 1:
The system uses dynamic control to adjust proportioning set points in real-time based on material variations. This allows the system to maintain chemistry stability through active compensation rather than rigid fixed ratios, providing both stability and adaptability to changing material conditions and cost parameters.
Solution Approach 2:
The control system changes the proportioning parameters dynamically based on real-time chemistry data and cost information. By adjusting the blend ratios in response to material variations, the system maintains product stability while adapting to different material sources and cost conditions, achieving both consistency and flexibility.
Data Source
AI summary
A technique is disclosed for controlling a material blending process for blending a plurality of raw material feeds using model predictive control in order to produce a blended product conforming to one or more quality standards. The quality standards may be based upon control variables derived from relationships and/or ratios of certain materials in the composition of the raw material feeds (e.g., moduli). The determined control variables are compared to desired set points and, if a deviation from the desired set points is detected, the proportions of the raw material feeds are adjusted to align the control variables with the desired set points. The technique may further take material cost factors into account, wherein the prediction model determines a proper proportioning of the raw material feeds such that the blended product conforms to the desired specifications and is produced for the lowest cost.