Normalized Hydrogen Correction Factor for Market Pricing
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Solution Overview
Problem
The hydrogen market lacks a standardized and transparent method for pricing and trading, with existing qualitative descriptors of carbon intensity being inaccurate and imprecise, hindering market growth and development.
Innovation Solution
A system utilizing sensors, a database, and a processor with a machine learning model to calculate a normalized hydrogen correction factor based on telemetry data, supplier data, and customer data, providing a common, universal, and standardized measurement for hydrogen exchange.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If qualitative descriptors (colors) are used to label hydrogen carbon intensity, then the labeling process is simple, but the measurement precision and accuracy deteriorate
Solution Approach 1:
The patent transforms the qualitative color-labeling system into a quantitative measurement system by introducing specific parameters: carbon intensity (CI) expressed in kg CO2/kg H2, molecular weight (MW) in g/mol, and density (D) in kg/m3. These numerical parameters replace the imprecise color descriptors and enable accurate comparison and normalization of hydrogen from different sources.
Solution Approach 2:
The patent replaces the subjective, qualitative assessment mechanism (color labeling) with an objective, data-driven computational mechanism. A machine learning model processes telemetry data, supplier data, and customer data to automatically calculate normalized hydrogen correction factors, eliminating human subjectivity and improving measurement consistency.
2Reliability
If a standardized measurement system is implemented, then market transparency and trading efficiency improve, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent creates a universal normalization system that handles multiple hydrogen sources (electrolysis, steam methane reforming, biomass gasification) and multiple applications (transportation, industrial, power generation) through a single standardized framework. The correction factor system serves multiple functions: quality assessment, pricing normalization, carbon intensity comparison, and market transparency.
Solution Approach 2:
The patent introduces normalized hydrogen correction factors as an intermediary metric that bridges different hydrogen production methods and applications. This correction factor acts as a mediator that translates diverse hydrogen characteristics into a common standardized measure, enabling fair comparison and transparent trading without requiring complex direct evaluation of each hydrogen source.
3Measurement precision
If comprehensive data collection from multiple sources is performed, then the accuracy of hydrogen normalization improves, but the data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary data collection and processing by gathering telemetry data from sensors, supplier data from databases, and customer data in advance of the normalization calculation. This pre-prepared data foundation enables faster real-time correction factor calculation when hydrogen transactions occur, reducing processing delays.
Solution Approach 2:
The patent uses machine learning models that have been trained on historical data to create computational copies of complex normalization relationships. Once trained, these model copies can rapidly calculate correction factors for new data without requiring full reprocessing of all underlying parameters, significantly reducing computation time while maintaining accuracy.
Data Source
AI summary
An apparatus for providing normalized hydrogen correction factor comprises: a set of sensors configured to measure telemetry data of a hydrogen production plant; a memory; a database; and a processor operatively coupled to the memory, the database, and the set of sensors. The processor is configured to: receive the telemetry data from the set of sensors; receive supplier data from the database; receive customer data from a customer; determine, using a machine learning model stored in the memory, and based on the telemetry data, the supplier data, and the customer data, a normalized hydrogen correction factor; and provide the normalized hydrogen correction factor to the customer.