Publications & Presentations

Versatile Advanced Mobile Natural Gas Leak Detection System

Shin-Juh Chen, Nicholas F. Aubut, Michael B. Frish, Kevin Bendele, Paul D. Wehnert, Vineet Aggarwal
SPIE Defense + Commercial Sensing
30 April - 4 May 2023 Orlando, FL
SPIE Paper No. 12516-28

The need to rapidly detect, locate and repair natural gas leaks from natural gas infrastructure has become ever more urgent since methane is recognized as a potent greenhouse gas and is contributing to the global climate change.

An optical gas sensor based on mid-infrared lasers and the method of backscattering tunable diode laser absorption spectroscopy is developed and provides gas concentration measurements with parts per billion by volume sensitivity. Ethane is a secondary component of natural gas. Concurrent methane and ethane measurement discriminates natural gas from biogas. The ability to approximate emission rate and leak location helps to prioritize repairs. This dual-gas optical gas sensor weighs about 2 kg, is battery-powered and is designed to be easily installed and removed from survey vehicles. A scattering target placed 1-m away from the gas sensor unit provides an open-path configuration for the laser beams to analyze ambient air. While the vehicle is traveling at 10 m/s, this gas sensor package is sensitive to cm-scale gas plumes due to a sample frequency of 100 Hz with data output rate at 10 Hz. Gas concentrations, GPS, and wind information along the survey route are collected wirelessly and processed with a computing tablet. Cloud-based data analytics further process the survey data. Early blind survey testing covered 54 natural gas leaks and 7 sewer emissions. Nearly 100% find rate for all true natural gas leaks was achieved with very low false positives and negatives. Leak indications were verified with follow-up survey with boots on the ground.

Long-open-path fixed continuous methane emission monitor

Shin-Juh Chen*, Michael B. Frish, and Nicholas F. Aubut
SPIE Defense + Commercial Sensing
30 April - 4 May 2023 Orlando, FL
SPIE Paper No. 12516-30

There are 100,000’s of oil and gas storage tanks and tank batteries at upstream production sites. These sites have shown to be inadvertent, intermittent, generally unmonitored, high flow rate (flux) methane emitters; their emission rates are poorly quantified.

Flux measurements are inhibited by the difficulty to directly access emission sources, instrument limitations and high-cost, and inability to distinguish between unintentional fugitive emission events (leaks) versus routine venting from pneumatic valves and compressors. Novel cost-effective and reliable continuous quantitative methane flux measurement technologies are needed to address these challenges. Methane is a potent greenhouse gas, and emissions from these sites need to be detected and prioritized for repairs based on emission rates. This paper describes a continuous methane emission monitor that combines our easily-installed high-speed laser-based long-open-path sensor, the Remote Emissions Monitor (REM), with a unique and novel fast laser beam scanning mechanism to create “flux planes” along site perimeters. This Enhanced REM (eREM) directly measures and reports emission rates (e.g. scfh) of methane plumes transported through the flux plane at about 1Hz without the need for plume modeling. The inherent temporal resolution enables novel statistical data processing that identifies routine vents and distinguishes them from unintended emissions. The simplicity of design, ease of installation, and minimal maintenance enable economically attractive fast and accurate detection and quantification of methane leakage.

One Dimensional Convolutional Neural Networks for Raman Spectral Analysis

Michael S. Primrose, Gabriel H. Weedon, Jay Giblin
SPIE Defense and Commercial Sensing – Orlando, FL Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXIV. May 2023
Paper 12541-33

A machine learning based approach has been developed to classify Raman spectroscopic data. The algorithm is based on a one dimensional neural network (1D-CNN) architecture which is trained with synthetic data that can incorporate sensor specific characteristics such as spectral range, spectral resolution and noise.

The synthetic spectra are based on high SNR measurements which are then augmented by mixing target and background signatures. The CNN is trained to consider target representations in the presence of certain background materials including glass and HDPE. These additional target representations allow the CNN to make detections for materials taken through a container. Within this paper the performance of CNNs trained for Raman sensor systems has been evaluated using real data collected using the ThermoFisher FirstDefender. The evaluation data consists of various target chemicals (including explosives) and interferents (including household materials) collected through glass and plastic vials. The data was acquired with a controlled range of collection settings, including integration time and laser power, available on the unit. The performance of the 1D-CNN approach has demonstrated high classification accuracies, high probability of detection and low false alarm rates. Specifically, these metrics have been calculated as a function of signal to noise ratio. Additionally, a sensitivity analysis was conducted using an acetonitrile standard diluted in water which demonstrates the CNN’s capability of detecting all dilutions of acetonitrile down to weight concentrations of <1%. This sensitivity analysis was mirrored using a mixture of potassium chlorate and Vaseline. The CNN demonstrated detections down to 10% by weight of potassium chlorate.

Portable LIBS for Field Analysis of Trace Metals in Fuels

Richard Wainner, Nicholas Aubut, Kristin Galbally-Kinney, Matan Aviram, David Gamliel, John Grimble, Mickey Frish, and Shin-Juh Chen
Presented at SPIE Conference 12516-29
Next-Generation Spectroscopic Technologies XV, May 3, 2023.

Physical Sciences Inc. (PSI) is developing a sensitive, rugged, person-portable, and safe instrument for the quick analysis of metals in jet fuels in fuel depots and transfer stations. The instrument fills a needed role for easy and affordable analysis of catalyzing metals content in fuel batches before they are used in, or shipped to, critical engines such as military aviation platforms.

The instrument targets a panel of most likely and problematic metals that are often found in kerosene-based fuels, both refined and synthetic. The cause for concern lies in the potential for many metals, even at part per billion (ppb) concentrations, to catalyze rapid degradation of fuel performance, especially at elevated storage temperatures. The laser-induced breakdown spectroscopy (LIBS) technology development reported here has demonstrated a robust and viable measurement system for multiple contaminants of importance to military (and commercial) fuel distribution. Estimated detection limits for all elements of interest, save phosphorus, are at sub-ppm levels. Signal normalization with an added internal reference has demonstrated an adjusted concentration measurement accuracy <95% and useful operation of the method near the noise floor of the instrument. The accomplishments are strong indicators for commercial potential for the technology as a useful tool in the intended fuel monitoring application, as well as other industrial sample analysis needs.

Motorized Template for MRI-Guided Focal Cryoablation of Prostate Cancer

Pedro Moreira, John Grimble, Mariana C. Bernardes, Nicusor Iftimia, Vincent M. Levesque, Lori Foley, Kemal Tuncali,
Junichi Tokuda, and Jesung Park
DOI: 10.1109/TMRB.2023.3272025 Electronic ISSN: 2576-3202

MR-guided focal cryoablation of prostate cancer has often been selected as a minimally-invasive treatment option. Placing multiple cryo-needles accurately to form an ablation volume
that adequately covers the target volume is crucial for better oncological/functional outcomes. This paper presents an MRIcompatible system combining a motorized tilting grid template

with insertion depth sensing capabilities, enabling the physician to precisely place the cryo-needles into the desired location. In vivo animal study in a swine model (3 animals) was performed to test the device performance including targeting accuracy and the procedure workflow. The study showed that the insertion depth feedback improved the 3D targeting accuracy when compared to the conventional insertion technique (7.4 mm vs. 11.2 mm, p=0.04). All three cases achieved full iceball coverage without repositioning the cryo-needles. The results demonstrate the advantages of the motorized tilting mechanism and real-time insertion depth feedback, as well as the feasibility of the proposed workflow for MRI-guided focal cryoablation of prostate cancer.