Emissions Monitoring Data Fusion for High-Resolution Measurement Control
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Solution Overview
Problem
Current scientific measurement systems face challenges in achieving accurate and precise measurements of physical phenomena due to high costs, complexity, and limitations in space and time resolution, often requiring expensive instrumentation and struggling with systematic and random errors.
Innovation Solution
The system combines information from multiple instruments using space-time deconvolution to enhance measurement accuracy, precision, and resolution by aligning environmental response and driver data, reducing the need for extensive input data and computational resources, and incorporating categorical data for improved cost-efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive instrumentation is used to measure momentum, energy and mass fluxes, then measurement accuracy is improved, but device cost and complexity increase
Solution Approach 1:
The patent introduces an intermediary computational system that processes data from multiple simpler instruments (anemometers, thermocouples, hygrometers) to derive flux measurements. This mediator system uses coordinate transformations and data fusion algorithms to produce accuracy comparable to expensive direct measurement instruments while avoiding their complexity and cost.
Solution Approach 2:
The system employs a multi-functional measurement platform that uses a single instrument array to measure multiple physical quantities (wind speed, temperature, humidity, fluxes) simultaneously. This universal system replaces the need for separate specialized instruments for each measurement type, reducing overall device complexity while maintaining measurement accuracy.
2Measurement precision
If multiple instruments are deployed to improve space and time resolution, then measurement coverage is improved, but device complexity and cost increase
Solution Approach 1:
The patent merges data from multiple instruments (anemometers, thermocouples, hygrometers) deployed at different locations into a unified measurement system. By combining these instruments and using coordinate transformations, the system achieves high space-time resolution equivalent to having instruments at every location, while avoiding the complexity and cost of deploying instruments everywhere.
Solution Approach 2:
The system transforms spatially distributed measurements into a unified coordinate system, effectively adding a mathematical dimension to the physical measurement space. This allows data from instruments at discrete locations to represent conditions across continuous space and time, achieving high resolution without proportionally increasing instrument density.
3Measurement precision
If instruments are deployed at all required physical locations, then measurement coverage is improved, but deployment difficulty and cost increase
Solution Approach 1:
The system uses coordinate transformations and mathematical models to create virtual copies of measurement data across space and time. Instead of physically deploying instruments at every location, the system computationally generates equivalent measurements at uninstrumented locations by transforming data from instrumented locations, significantly reducing deployment complexity.
4Measurement precision
If high-frequency measurement is performed to capture fast-changing phenomena, then time resolution is improved, but data processing requirements and computational resources increase
Solution Approach 1:
The system extracts only the essential information needed for flux calculation from high-frequency raw measurements. By filtering and transforming the high-frequency data to extract relevant statistical moments and coordinate-transformed values, the system maintains high time resolution for capturing fast-changing phenomena while reducing computational processing requirements.
Data Source
AI summary
A method and system for controlling emissions by combining environmental response information, first environmental driver information and/or second environmental driver information to produce a space and time aligned data set that in turn can be used to produce a driver-response relationship model. Then using the driver-response relationship model to generate enhanced environmental response output information, which can be used to control emissions.