Process Design Assistance for Scale-Up Quality Uniformity
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Solution Overview
Problem
Existing technologies face challenges in efficiently transitioning chemical and materials manufacturing processes from laboratory scale to full-scale production, leading to inconsistencies in quality and yield due to differences in reactor volume and operational conditions, which current methods like scale-up optimization and first-principle simulations are inadequate in addressing.
Innovation Solution
A design assistance system that utilizes a state distribution model and quality prediction model to predict and manage variations in internal states across different scales, enabling the optimization of process design parameters to maintain product quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reactor volume is increased during scale-up, then production capacity is improved, but heat transfer efficiency deteriorates due to decreased surface area per unit volume
Solution Approach 1:
The patent applies parameter changes by systematically modifying reactor specifications (volume, surface area, agitation speed, temperature profiles) during scale-up from laboratory to pilot to full-scale plants. The quality prediction model predicts how these parameter changes affect product quality, enabling optimization of scale-up parameters to maintain heat transfer efficiency while increasing production capacity.
2Productivity
If reactor volume is increased during scale-up, then production capacity is improved, but mixing efficiency deteriorates due to decreased surface area per unit volume in contact with agitator blade
Solution Approach 1:
The patent modifies agitation parameters (speed, blade design, impeller type) alongside reactor volume increases during scale-up. The quality prediction model incorporates these agitation parameter changes to predict their impact on mixing efficiency and product quality, enabling simultaneous optimization of both production capacity and mixing performance.
3Manufacturing precision
If scale-up optimization is performed using existing methods, then some quality parameters can be maintained, but the process remains time-consuming with frequent re-designs required
Solution Approach 1:
The patent performs preliminary quality predictions using the quality prediction model before actual scale-up experiments are conducted. This preliminary action identifies optimal process parameters and potential quality issues in advance, reducing the need for iterative re-designs and significantly shortening the overall scale-up timeline while maintaining quality consistency.
Solution Approach 2:
The patent implements a feedback mechanism where the quality prediction model continuously predicts product quality based on current process parameters, and these predictions feed back into the scale-up optimization process. This closed-loop feedback enables real-time parameter adjustments to maintain quality consistency throughout the scale-up process.
4Measurement precision
If first-principle simulations are used to predict quality, then theoretical accuracy can be improved, but computational load becomes enormous making repeated trials difficult
Solution Approach 1:
The patent replaces computationally expensive first-principle simulations with a pre-trained quality prediction model that provides accurate predictions at minimal computational cost. This 'cheap' model, trained once on comprehensive data, can be rapidly applied repeatedly during scale-up optimization without the enormous computational burden of first-principle simulations.
Data Source
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AI summary
A design assistance system 1 that calculates values representing the state ununiformity in a spatial area on the basis of information about a state at each position in a case where a process has been performed on raw materials distributed in the spatial area, generates a state distribution model 108 that outputs data representing the state ununiformity after the process on the basis of the values, acquires values of data representing the state ununiformity after the process by inputting set values of process design parameters to the state distribution model 108, acquires a quality parameter of a deliverable by inputting each piece of data identified from the values of the data, the identified data representing a possible state , to the quality prediction model 105, and changes the values of the process design parameters, on the basis of each quality parameter, such that the quality of a deliverable falls within a predetermined range.