FTIR Spectroscopy Machine Learning Mixture Composition Analysis
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Solution Overview
Problem
Current machine learning approaches for chemical process development face challenges in accurately determining the composition of multicomponent mixtures, particularly in chemical manufacturing, due to the complexity of interpreting Fourier Transform Infrared (FTIR) spectra and the need for multiple analytical techniques.
Innovation Solution
The development of machine learning models, specifically linear regression and artificial neural networks trained with Fourier Transform Infrared (FTIR) spectra, that can predict the composition of multicomponent mixtures with high accuracy, using techniques such as principal component analysis and hyperparameter optimization, enabling rapid and cost-effective characterization of chemical mixtures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple analytical techniques are used to determine mixture composition, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple analytical techniques into a single FTIR spectroscopy system with machine learning analysis. Instead of using separate NMR, chromatography, and spectroscopy instruments, the invention integrates their functional capabilities into one unified FTIR-based system that uses ML algorithms to achieve comprehensive mixture composition analysis.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between the FTIR spectral data and composition determination. The ML models act as a mediator that processes complex spectral information and translates it into accurate composition predictions, eliminating the need for multiple direct analytical techniques.
2Measurement precision
If traditional analytical methods are used for composition determination, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent replaces traditional mechanical and chemical analytical methods with an optical-based FTIR system combined with computational machine learning. This substitution enables rapid spectral acquisition and automated composition determination, dramatically reducing analysis time while maintaining or improving precision.
Solution Approach 2:
The patent performs preliminary action by pre-training machine learning models with extensive spectral data before actual composition analysis. This pre-processing step creates a ready-to-use predictive system that can rapidly determine compositions without requiring time-consuming traditional analysis procedures during actual use.
3Measurement precision
If complex machine learning models are used for spectrum interpretation, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent optimizes machine learning model parameters to achieve the best balance between accuracy and computational efficiency. By carefully selecting and tuning parameters such as model architecture, training data size, and processing algorithms, the system achieves high measurement precision while minimizing energy consumption during spectral analysis.
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
These models achieve regression scores of 0.98 to 0.99 for linear regression and 0.94 to 0.98 for artificial neural networks, demonstrating their ability to accurately determine chemical mixture compositions, enhancing analytical capabilities and supporting autonomous chemical process development and optimization.
Implementation Method 1
Fourier-transform infrared (FTIR) spectroscopy
Data Source
AI summary
A method of training a machine learning model for determining the composition of a mixture includes obtaining, using Fourier-transform infrared (FTIR) spectroscopy, a spectrum for each of a plurality of mixtures its constituent components. A concentration of each constituent component is known for each of the plurality of mixtures. A plurality of features is extracted from each of the obtained spectra. A machine learning model is trained using the plurality of features. An apparatus for determining formation of a product includes a reactor for containing a reaction mixture and an FTIR spectrometer for producing a spectrum of a sample of the reaction mixture. A processor extracts features from the spectrum; provides the features to an ML model trained using a plurality of mixtures of the constituent components to obtain a concentration of one or more of the constituent components; and determines the formation of the product based on the concentration.