Milling Control System for Flour Yield Optimization
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Solution Overview
Problem
Current flour milling processes rely heavily on manual adjustments and are inefficient due to numerous influencing factors, leading to suboptimal moisture control, reduced flour output, increased energy consumption, and environmental impact, with existing automated solutions failing to capture non-linear relationships and seasonal variations effectively.
Innovation Solution
A machine-learning-based milling control system that uses historical data to generate digital models for predicting moisture content and optimizing the tempering process, adjusting moisture levels automatically to maximize flour yield while maintaining quality specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual adjustments are used for moisture control in tempering process, then the system is simple to operate, but the milling yield is reduced and energy consumption increases
Solution Approach 1:
The system automatically adjusts tempering parameters (moisture content, temperature, duration) based on real-time grain moisture predictions and historical data, optimizing milling yield without manual intervention. The machine learning model continuously learns from operational data to refine parameter settings for maximum efficiency.
Solution Approach 2:
The control system performs self-optimization by automatically adjusting tempering conditions based on predicted grain moisture content and learned patterns from historical operations. The system serves itself by making autonomous decisions about moisture addition and tempering duration without requiring expert manual adjustments.
2Productivity
If automated control systems are implemented, then productivity increases, but the system complexity and initial investment increase
Solution Approach 1:
The control system integrates multiple functions into a single platform: grain moisture prediction, tempering optimization, process monitoring, and automated control. This multi-functional approach increases productivity while avoiding the complexity of multiple separate systems.
Solution Approach 2:
The system incorporates feedback loops where actual milling results and grain moisture measurements are continuously fed back to the machine learning model, which adjusts future tempering predictions and control actions. This closed-loop feedback optimizes productivity while maintaining manageable system complexity through iterative learning.
3Manufacturing precision
If traditional tempering processes are used, then the process is simple to manage, but moisture control precision is insufficient leading to quality variations
Solution Approach 1:
The system performs preliminary prediction of grain moisture content before the tempering process begins, allowing proactive adjustment of tempering parameters to achieve target moisture precision. This advance prediction enables precise moisture control without complex real-time intervention during tempering.
Solution Approach 2:
The control system dynamically adapts tempering parameters based on real-time grain conditions and predicted moisture content. Rather than using fixed static settings, the system continuously adjusts moisture addition rates and tempering duration to maintain optimal precision, managing complexity through adaptive rather than rigid control.
4Measurement precision
If more sensors are added to monitor grain moisture, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The system uses machine learning models as intermediaries that process and integrate data from existing sensors (temperature, humidity, grain properties) to predict grain moisture content. This intermediary approach achieves high measurement precision without requiring direct moisture sensors, reducing system complexity.
Solution Approach 2:
Instead of physically measuring grain moisture with complex sensors, the system creates a virtual copy or digital twin of the grain moisture state through machine learning predictions based on other measurable parameters. This virtual measurement achieves precision without the complexity of direct physical sensing.
Data Source
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AI summary
A system, method and device for improving milling yield of a flour milling machine (102) is disclosed. The system includes a first processing unit (104) for receiving, from an operation data storage (106), a historical flour milling machine operations data. The system includes a modelling engine (1043) of the first processing unit (104) for generating at least one machine learning model structure to determine a target moisture based on historical flour milling machine operations data using machine learning. The system includes a water dosing controller (1024) of a flour milling machine (102) that receives the target moisture and adds moisture to the grains based on the target moisture.