Phase II SBIR - An Artificially Intelligent Raman Spectrometer for Aviation Jet Fuel Quality Testing in a Field Setting

January 11, 2024

Today, aviation jet fuel inspection, quality and specification testing are provided through global networks of certified and accredited laboratories. Aviation fuel laboratories provide expert quality analysis services using expensive laboratory equipment to meet the American Society for Testing and Materials (ASTM), US Military (MIL), United Kingdom Defense, Aviation Fuel Quality Requirements for Jointly Operated System, Energy Institute, and EI/Joint Inspection Group 1530 standards.

Testing aviation jet fuels for thermal stability is more challenging and expensive because according to the governing specifications (e.g., ASTM, DEFSTAN, MIL-STD, etc.) a fuel sample is required to be drawn and shipped to a laboratory in order to perform the Thermal Oxidation Stability Test (D3241). Thermal Oxidation Stability Testing requires an expensive instrument, is time-consuming and involves a long supply chain, and customs inspections, resulting in increased costs and reduced operational readiness.

The scope of this Direct to Phase II SBIR project is to develop an artificially intelligent instrument capable of detection and quantification of chemical contaminates in jet fuel in remote, limited environments. Forward Edge-AI also is developing a low cost Raman spectrometer based on a $150 cellphone-based device. If we are successful, tests can be performed in the field in minutes and at one (1) cent per sample versus $300 - $2,300 per sample, even when logistics are contested.

$150 Cellphone Raman Spectrometer

Forward Edge-AI is also developing a Deep Learning (DL) algorithm to detect and quantify aviation grade Polyalphaolefin (PAO), hydraulic fluid, and microbial contamination in jet fuel samples. Potentially, the algorithm may be able to provide a jet fuel thermal stability indication.

The project also includes development of a Federated Learning (FL) Marketplace. We are training an FL model to detect new contaminants from Raman spectra. As a result, multiple users (e.g. Commercial Airline, US Air Force, Navy) in different locations can collaborate on training the DL model to detect contaminants in aviation fuel. Each organization can have their own data, and can train a model on all data without directly sharing data with each other, or with a central entity. The DL model will then be trained in a federated manner, where each entity will train a model on its own data, and the models will be aggregated to produce a final model.

The potential commercial payoff of an affordable, intelligent, and networked micro Raman system capable of determining aviation fuel quality is profound in that it can ultimately prevent aircraft catastrophes. The proposed solution extends to a wide range of related use cases including: 1) Security - identifying/analyzing trace explosives, narcotics, chemical warfare agents, and other toxic chemicals, 2) Consumer – low cost capability to scan common goods for dangerous pathogens, pesticides and other chemicals, 3) Water – similar to fuel, testing water quality in expeditionary environments can be expensive requiring lab-based testing supply chains that include refrigeration, 4) Life Sciences - single cells drug interactions, disease diagnosis, and 5) Pharmaceutics - uniformity and component distribution.

Related Research:

Phase I - Artificially Intelligent RAMAN Spectrometer to Detect Chemical and Biological Pathogens in a Field Setting

Stakeholders

Award a Phase III Sole Source Contract

A Federal Agency may enter into a Phase III SBIR/STTR agreement at any time with a Phase I OR II Awardee. A subcontract to a Federally funded prime contract may be a Phase III award.

Follow our 8 step process:

Step 1 Requirements Document: Prepare a Statement of Work (SOW), Statement of Objectives (SOO), or Performance Work Statement (PWS), or use our automated tool to generate a document
Step 2 Market Research: Use this page as your market research, or view a list of other eligible projects, then request a Rough Order Magnitude (ROM) from Forward Edge-AI
Step 3 Funding: Performed by the government
Step 4 Sole Source Justification: A Memorandum for the Record is required in lieu of a J&A or SSJ
Step 5 Provide Requirements Package to Contracting Officer: Performed by the government
Step 6 Solicitation: Performed by the government
Step 7 Pre-Negotiation Memorandum: Use GSA CALC as a benchmark to determine fair and reasonableness of our ROM
Step 8: Contract Award: Performed by the government

Language for Step 4 (Determination and Finding):

Artificial Intelligence (CNN/RNN), Android ATAK, Federated Learning Marketplace, Machine Learning, Microsoft Azure, signal processing (Wavelet and Fourier transformation), Raman Spectroscopy
Contested logistics (fuel and water quality testing), Chemical, Biological, Radiological, and Nuclear (CBRN) detection, explosives detection, Expeditionary Environments

List of Phase III contracts awarded so far

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Restrictions on use of Forward Edge-AI Information on Bid and Proposals

SBIR DATA RIGHTS:

Awarding Agency: US Air Force

Contract Number: FA810024C0003

Contractor Name: Forward Edge-AI, Inc.

Contractor Address: 10108 Carter Canyon, San Antonio, TX 78255

Expiration of SBIR Data: March 08, 2044

Protection Period: 20 years from award of contract on March 08, 2024

The Government's rights to use, modify, reproduce, release, perform, display, or disclose technical data or computer software marked with this legend are restricted during the period shown as provided in paragraph (b)(5) of the Rights In Other Than Commercial Technical Data and Computer Software–Small Business Innovation Research (SBIR) Program clause contained in the above identified contract. After the expiration date shown above, the Government has perpetual government purpose rights as provided in paragraph (b)(5) of that clause. Any reproduction of technical data, computer software, or portions thereof marked with this legend must also reproduce the markings.