Blend Composition Adjustment Using ML Flow Control
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Solution Overview
Problem
Adjusting non-compliant blend compositions to meet desired blend specifications is a complex process due to multiple variables and requires efficient optimization methods to determine the necessary adjustments, considering cost, performance, and volume.
Innovation Solution
A method using a processor to receive target blend composition data, control blend component flow streams, determine properties, compare with target properties, generate control flow instructions based on machine learning analysis of historical data, and adjust flow streams to form a modified blend composition that matches the target specifications, potentially updating target properties for cost and volume optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual adjustment methods are used to correct non-compliant blend compositions, then flexibility in handling different scenarios is maintained, but the process becomes complex and time-consuming with multiple variables to consider
Solution Approach 1:
The system enables self-service by using machine learning models to automatically analyze historical blend data and generate optimal adjustment instructions without requiring manual intervention. The processor autonomously determines the necessary adjustments to meet blend specifications, eliminating the need for operators to manually evaluate multiple variables and scenarios.
Solution Approach 2:
The system implements feedback by continuously monitoring blend composition properties and comparing them against target specifications. The machine learning model uses this feedback along with historical data to dynamically generate adjustment instructions, creating a closed-loop control system that automatically corrects non-compliant blends.
2Manufacturing precision
If multiple adjustment variables are considered to optimize blend composition, then the quality of the adjusted blend improves, but the time required to determine adjustments increases
Solution Approach 1:
The system applies preliminary action by pre-processing and storing historical blend data in structured formats before it is needed for adjustment decisions. The machine learning models are trained in advance on this historical data, so when adjustments are needed, the system can quickly retrieve relevant patterns and generate optimal adjustment instructions without performing complex real-time analysis.
3Productivity
If historical blend data is analyzed to determine optimal adjustments, then cost and volume optimization is achieved, but the computational requirements and processing complexity increase
Solution Approach 1:
The system segments the historical blend data into distinct categories and features (e.g., blend components, concentrations, properties, costs, volumes) that can be independently analyzed by specialized machine learning models. This segmentation allows the computational task to be divided into manageable parts, each handling specific aspects of the data, thereby reducing overall processing complexity while maintaining optimization capabilities.
Data Source
AI summary
A method for adjusting a blend composition includes: receiving target blend composition data associated with a target blend composition having a target blend property; controlling blend component flow streams to cause blend components to be added to a blend tank to form a blend composition; determining a property of the blend composition; comparing the property of the blend composition with the target blend property; generating control flow instructions based on the property of the blend composition and a machine learning analysis of historical blend data; and adjusting a blend component flow stream of the blend component flow streams to form a modified blend composition in the blend tank.