TD-NMR Spectroscopy for Real-Time Jet Fuel Cetane Prediction
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Solution Overview
Problem
Current methods for determining the Derived Cetane Number (DCN) of jet fuels are expensive, time-consuming, and impractical for real-time or on-site monitoring, as they require large sample sizes and complex equipment, limiting their applicability in portable scenarios.
Innovation Solution
A low-cost, compact time-domain Nuclear Magnetic Resonance (NMR) spectroscopy system combined with machine learning algorithms is developed for rapid and efficient prediction of DCN, enabling real-time monitoring and quality control of jet fuels using T2 relaxation times and functional chemical group analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional laboratory-based methods (combustion chambers, ASTM standards) are used to determine DCN, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent replaces complex mechanical combustion chamber systems with an NMR-based analytical system. Instead of using combustion chambers, ignition quality testers, and complex mechanical test equipment, the invention uses nuclear magnetic resonance spectroscopy to measure T2 relaxation times and predict DCN through machine learning models, thereby substituting a mechanical/thermal system with a field-based analytical system.
Solution Approach 2:
The patent changes the measurement parameter from direct combustion-based DCN measurement to T2 relaxation time measurement. By measuring the T2 relaxation times of fuel components via NMR and using machine learning to correlate these parameters with DCN, the system achieves accurate DCN prediction without requiring complex combustion testing equipment.
2Measurement precision
If traditional combustion chamber methods are used, then DCN measurement accuracy is improved, but measurement time increases
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models using T2 relaxation data from multiple fuel samples with known DCN values. Once trained, the model can rapidly predict DCN for new samples without requiring time-consuming combustion tests, enabling fast DCN determination while maintaining accuracy through the pre-established predictive relationship.
3Reliability
If laboratory-based DCN testing is performed, then measurement reliability is improved, but ease of operation deteriorates due to large sample size requirements
Solution Approach 1:
The patent extracts only the essential information needed for DCN determination—the T2 relaxation times of fuel components—using NMR spectroscopy. This extraction approach allows the system to obtain reliable DCN predictions from small sample volumes by focusing on the specific molecular relaxation properties that correlate with ignition quality, rather than requiring large samples for comprehensive combustion testing.
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
Enables rapid, efficient, and accurate prediction of DCN, reducing sample sizes and data collection time, allowing for real-time monitoring and improved engine performance with reduced emissions.
Implementation Method 1
A low-cost, compact time-domain Nuclear Magnetic Resonance (NMR) spectroscopy system combined with machine learning algorithms is developed for rapid and efficient prediction of DCN, enabling real-time monitoring and quality control of jet fuels using T2 relaxation times
Implementation Method 2
The T2 relaxation curve characterizes the decay of nuclear magnetization in a sample. By analyzing the T2 relaxation curve, valuable insights can be gained into the molecular composition and physical properties of the sample, including the DCN
Data Source
AI summary
The disclosure deals with a system and methodology for using a time-domain nuclear magnetic resonance (TD-NMR) system to measure the T2 relaxation curve of a sample, such as liquid hydrocarbon fuels. A machine-learned (ML) model is trained to predict a Derived Cetane Number (DCN) for the sample, based on the T2 relaxation curve data of the sample. The TD-NMR system is compact and can be placed in situ in a fuel system to predict the DCN of the stored fuel, to allow an operator to adapt operation of a corresponding engine accordingly, for maximized performance in real time. The ML Model can be trained using selected structural data features of the T2 relaxation curves, to bias the ML model for better working with either of hydrocarbon samples or jet fuel samples, or optimized to work with unknown samples.