Intelligent Dosing Device with Machine Learning Control
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Solution Overview
Problem
Existing dosing devices for bulk solids in industrial processes face challenges in maintaining accurate and stable delivery curves due to variations in raw material properties, environmental influences, and system design, leading to inconsistencies in conveying speed and dosing accuracy.
Innovation Solution
A dosing device with an electronic system control that measures relative dosing errors and uses machine learning algorithms to adjust dosing parameters, detecting anomalies and optimizing dosing performance by analyzing historical data and real-time measurements to maintain consistent delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional dosing devices are used, then the structure is simple and easy to manufacture, but the dosing accuracy and delivery curve stability deteriorate due to material property variations and environmental influences
Solution Approach 1:
The patent implements a closed-loop feedback control system where weighing devices continuously measure the actual dosing quantity, compare it with the target value, and automatically adjust dosing parameters through control units. This feedback mechanism maintains high dosing accuracy despite variations in material properties and environmental conditions without requiring complex manual intervention.
Solution Approach 2:
The patent replaces traditional mechanical dosing mechanisms with an intelligent system combining weighing devices, electronic control units, and machine learning algorithms. This substitution enables adaptive optimization of dosing parameters based on real-time data analysis, achieving superior dosing precision while managing system complexity through software-based solutions.
2Stability of the object's composition
If dosing parameters are fixed, then the device operation is simple, but the delivery curve stability deteriorates when material properties or environmental conditions change
Solution Approach 1:
The patent implements dynamic dosing parameter adjustment where control units continuously modify operating parameters based on real-time feedback from weighing devices and predictions from machine learning models. This dynamic adaptation ensures stable delivery curves despite changes in material properties, humidity, temperature, or other environmental factors.
Solution Approach 2:
The patent systematically changes dosing parameters such as conveying speed, dosing rate, and timing based on analyzed data patterns and predicted material behavior. By adjusting these parameters dynamically, the system maintains consistent delivery accuracy across varying operating conditions while adapting to different material types and environmental circumstances.
3Manufacturing precision
If machine learning optimization is implemented, then dosing accuracy improves, but the difficulty of detecting and measuring system parameters increases
Solution Approach 1:
The patent integrates multiple functions into unified components: weighing devices serve both as measurement instruments and as data sources for machine learning analysis; control units perform both real-time dosing adjustment and historical data management. This multi-functionality reduces the need for separate specialized devices, making the system more manageable despite the complexity of machine learning operations.
Data Source
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AI summary
A system and method for dosing bulk material or other material into a mixture is proposed, comprising measuring data of one or more components of the dosing process and/or measured variables that capture physical mixing characteristics of the material being measured, and adjusting the dosing of the dosing device by means of a plant control system based on the measured data of one or more components of the dosing process.