Bio-material Matrix Control via Near-Infrared Spectral Prediction Models
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Solution Overview
Problem
Existing methods for controlling morphological modification of bio-material matrices in production processes require complex and costly experimental designs to develop and update prediction models, especially when dealing with multiple bio-material ingredients and varying compositions, leading to significant production downtime and economic challenges.
Innovation Solution
A method that uses digital input data from actual production runs, including probe radiation interaction information and process control parameters, to generate prediction models that link interacted probe radiation with control parameters, allowing for real-time process control without the need for special experimental arrangements or constructed reference samples, and enables easy updating of models with new production data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex experimental designs with constructed reference samples are used to develop prediction models for morphological modification control, then measurement precision and manufacturing precision are improved, but device complexity and loss of time increase significantly
Solution Approach 1:
The patent uses near-infrared spectral data as a copy or proxy representation of the actual bio-material matrix properties. Instead of physically constructing reference samples with exact compositions, the system creates virtual spectral copies that capture the essential information needed for prediction model development, dramatically simplifying the experimental design while maintaining control precision
Solution Approach 2:
The patent transforms the control approach by changing from direct physical measurement and control to indirect spectral parameter-based control. Near-infrared spectral parameters serve as surrogate variables that correlate with the actual material properties, allowing prediction models to control morphological modification through spectral data rather than complex physical experiments
2Manufacturing precision
If constructed reference samples are used for developing prediction models, then manufacturing precision is improved, but loss of time and productivity decrease due to production downtime
Solution Approach 1:
The system uses near-infrared spectral copies of the bio-material matrix to develop prediction models without requiring actual production samples. This virtual copying approach allows model development and updates to occur independently of production operations, eliminating the need for production downtime while maintaining model accuracy
Solution Approach 2:
The patent enables prediction models to be developed and updated in advance using spectral data from routine production runs, rather than requiring interruptive experiments. The spectral information is continuously available during normal operation, allowing model preparation to be performed preliminarily without affecting production schedules
3Manufacturing precision
If experiments are repeated for each re-formulation to maintain prediction models, then manufacturing precision is preserved, but loss of time and device complexity increase
Solution Approach 1:
The system implements continuous feedback by monitoring near-infrared spectral data during production and automatically updating prediction models based on this ongoing information flow. When re-formulations occur, the spectral feedback from actual production runs provides the data needed for model adaptation without requiring separate experimental validation cycles
Solution Approach 2:
The patent maintains continuous collection and utilization of near-infrared spectral data throughout production operations. This continuous data stream serves dual purposes: real-time process monitoring and ongoing prediction model refinement. The useful action of spectral measurement continues uninterrupted, providing both control information and model training data without requiring separate experimental interruptions
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach optimizes process control by using prediction models based on actual production data, reducing the complexity and cost of maintaining accurate control over morphological modification processes, ensuring reliable performance and minimizing production downtime.
Implementation Method 1
The use of infra-red, particularly near infra-red, probe radiation has found wide-spread practical application in this respect. It is now well established that absorption spectral patterns of near infra-red radiation very often contain information regarding the bio-material matrix with which the radiation has interacted.
Implementation Method 2
Probe radiation from other portions of the electromagnetic spectrum, for example X-ray, microwave or visible portions, or from ultrasound may also be interacted with and thereby modified by the bio-material matrix. Consequently, such interacted radiation will likely also contain useful information related to the bio-material matrix.
Data Source
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AI summary
A method of controlling a production process including a process step for the morphological modification of a bio-material matrix comprises obtaining digital input data acquired during each of a plurality of production runs of the process, which input data includes information from radiation within a portion of the electromagnetic or acoustic spectrum having interacted with the matrix at one or more locations within the process together with a process control parameter and production event data for the associated production run; generating in a computer a prediction model from a multivariate analysis of the digital input data, which model links the information directly with one or more of process control parameters, production run events and process control settings; and applying in the computer the prediction model to interacted information obtained from a new production run to generate as an output one or more of a process control parameter a process control event and a predicted production run event for the new production run for use in controlling the production process.