Bulk Material Tracking System Using RFID and Discrete Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for characterizing and controlling bulk material flows in storage containers, such as silos, are inadequate for accurately predicting and optimizing the flow of complex materials in real-time, leading to inefficiencies and suboptimal operational and economic outcomes in industries like coal-fired power plants, ethanol production, and recycling facilities.
Innovation Solution
Implementing a real-time tracking system that uses RFID tags to record and monitor the physical properties of bulk materials, coupled with a fast discrete simulation algorithm to predict flow and mixing, and feedback mechanisms to adjust filling protocols for optimal output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional characterization methods based on past experience and supplier information are used, then the system is simple to operate, but the measurement precision and reliability of material property identification deteriorate
Solution Approach 1:
The system performs preliminary characterization of bulk material properties (density, flow rate, moisture content, etc.) at the point of entry into storage containers. This advance measurement and recording of material properties enables accurate tracking and prediction without requiring complex real-time analysis during storage or discharge operations.
Solution Approach 2:
The system creates digital copies or models of the bulk material properties through RFID tags and database records. These digital representations capture essential material characteristics and are used for tracking, prediction, and optimization throughout the storage and discharge process, eliminating the need for continuous physical measurement.
2Productivity
If real-time tracking and control of bulk material flows is implemented, then the productivity and operational efficiency improve, but the device complexity and measurement requirements increase
Solution Approach 1:
The system replaces complex mechanical flow measurement devices with RFID-based tracking and computational modeling. Instead of using elaborate sensors and measurement equipment to continuously monitor bulk material flow, the system uses digital tags and software algorithms to predict flow behavior based on previously measured material properties.
Solution Approach 2:
The system focuses on measuring and tracking key parameters (density, moisture content, particle size distribution, flow rate) that significantly influence bulk material behavior. By identifying and monitoring these critical parameters rather than all possible properties, the system achieves accurate prediction with reduced measurement complexity.
3Manufacturing precision
If accurate prediction of bulk material flow and mixing is achieved, then the manufacturing precision of fuel blend control improves, but the computational requirements and device complexity increase
Solution Approach 1:
The system performs computational simulations and flow predictions in advance, before actual discharge operations. By pre-calculating expected flow patterns and mixing behavior based on stored material properties and container geometry, the system enables precise control decisions without requiring complex real-time computation during critical operations.
Solution Approach 2:
The system uses feedback from actual discharge measurements to validate and refine computational models. By comparing predicted flow behavior with actual observations and adjusting model parameters accordingly, the system improves prediction accuracy while maintaining manageable computational complexity through iterative optimization.
Data Source
AI summary
The invention provides a system of devices and a set of methods for accurately characterizing, tracking and controlling bulk material flows. Measurements of input material properties, dynamical measurements involving identifiable tags, and fast, discrete simulations are combined to predict and control the composition of bulk material exiting a container, such as a silo, bunker or hopper. In one embodiment, a system is provided for a coal-fired power plant where the properties of coal are assessed, then the coal is marked and tracked to provide the plant's operators improved control over the coal mixture and target bulk properties as it makes its way from a coal receiving station to delivery for combustion in a furnace.