Fluidization Process Control for Target Particle Property Accuracy
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Solution Overview
Problem
Existing methods for controlling particle-forming fluidization processes in fluidization apparatuses often result in deviations from target product properties, negatively impacting product quality.
Innovation Solution
A method that determines multiple process parameters at a first time, calculates process model product property values using a stored process model, generates optimization parameter sets from provided optimization values, calculates optimization forecast values, and adjusts these values with correction factors to achieve target product properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If process parameter sets (recipes) are used to control the fluidization process, then the process follows a predetermined sequence, but deviations from target product properties occur, negatively affecting product quality
Solution Approach 1:
The patent implements feedback control by continuously measuring actual product properties and using this information to adjust process parameters in real-time. The control device receives actual product property values, compares them with target values, and automatically modifies process parameters to eliminate deviations, thereby maintaining manufacturing precision while preserving operational simplicity.
Solution Approach 2:
The patent transitions from static predetermined recipes to dynamic adaptive control. The control device continuously adjusts process parameters based on real-time process state and product property measurements, enabling the system to adapt to changing conditions and maintain optimal product quality throughout the fluidization process.
2Manufacturing precision
If process parameters are adjusted to approach desired target values, then product quality improves, but small deviations remain that negatively affect product quality
Solution Approach 1:
The control device continuously monitors actual product properties and uses this feedback to make real-time adjustments to process parameters. This closed-loop control eliminates residual deviations by constantly correcting the process state, ensuring both high manufacturing precision and reliable consistent product quality across multiple batches.
Solution Approach 2:
The control device automatically adjusts process parameters without requiring manual intervention. The system self-corrects deviations by comparing actual product properties with target values and autonomously modifying process parameters, thereby achieving consistent high-quality results reliably.
3Manufacturing precision
If multiple optimization parameter sets are generated and evaluated, then product quality is optimized, but control device complexity increases
Solution Approach 1:
The control device uses a process model that creates a virtual copy of the physical fluidization process. This digital model allows the system to evaluate multiple optimization parameter sets and predict their effects on product properties without requiring physical experimentation, thereby achieving optimization while managing control complexity through simulation rather than extensive hardware.
Data Source
AI summary
A method for controlling a particle-forming fluidization process taking place in a fluidization apparatus with regard to at least one product property of a process material. In a process cycle, a plurality of process parameters of the fluidization process are determined at a first time, which are forwarded as process parameter actual values to a control device having a control functionality. A process model product property value for a second time subsequent to the first time is calculated in the control device using the process parameter actual values on the basis of a process model stored for the at least one product property. A plurality of optimization parameter sets is generated in the control device from a plurality of process parameter optimization values provided, using which a plurality of optimization forecast values is calculated at a third time by means of an optimization model.