Bio


Currently I am PhD student in Prof. Adam Brandt's group at Stanford University, with a focus on natural gas leak. I am interested in applying computer science techniques, like computer vision, machine learning and deep learning, in solving problems in energy and environmental systems.
My linkedin profile is here: https://www.linkedin.com/in/jingfan-wang/
My Google Scholar profile is here: https://scholar.google.com/citations?user=MjOxM9EAAAAJ&hl=zh-CN

Honors & Awards


  • Stanford Interdisciplinary Graduate Fellowship, Stanford University

Stanford Advisors


Current Research and Scholarly Interests


Apply computer vision and deep learning to detect and quantify natural gas leak

All Publications


  • "Good versus Good Enough?" Empirical Tests of Methane Leak Detection Sensitivity of a Commercial Infrared Camera ENVIRONMENTAL SCIENCE & TECHNOLOGY Ravikumar, A. P., Wang, J., McGuire, M., Bell, C. S., Zimmerle, D., Brandt, A. R. 2018; 52 (4): 2368–74

    Abstract

    Methane, a key component of natural gas, is a potent greenhouse gas. A key feature of recent methane mitigation policies is the use of periodic leak detection surveys, typically done with optical gas imaging (OGI) technologies. The most common OGI technology is an infrared camera. In this work, we experimentally develop detection probability curves for OGI-based methane leak detection under different environmental and imaging conditions. Controlled single blind leak detection tests show that the median detection limit (50% detection likelihood) for FLIR-camera based OGI technology is about 20 g CH4/h at an imaging distance of 6 m, an order of magnitude higher than previously reported estimates of 1.4 g CH4/h. Furthermore, we show that median and 90% detection likelihood limit follows a power-law relationship with imaging distance. Finally, we demonstrate that real-world marginal effectiveness of methane mitigation through periodic surveys approaches zero as leak detection sensitivity improves. For example, a median detection limit of 100 g CH4/h is sufficient to detect the maximum amount of leakage that is possible through periodic surveys. Policy makers should take note of these limits while designing equivalence metrics for next-generation leak detection technologies that can trade sensitivity for cost without affecting mitigation priorities.

    View details for DOI 10.1021/acs.est.7b04945

    View details for Web of Science ID 000426143300076

    View details for PubMedID 29351718

  • 100% Clean and renewable wind, water, and sunlight all-sector energy roadmaps for 139 countries of the world JOULE Jacobson, M. Z., Delucchi, M. A., Bauer, Z. A., Goodman, S. C., Chapman, W. E., Cameron, M. A., et al 2017; 1 (1): 108-121
  • Potential solar energy use in the global petroleum sector Energy Wang, J., O'Donnell, J., Brandt, A. R. 2016
  • Are Optical Gas Imaging Technologies Effective For Methane Leak Detection Environmental Science & Technology Ravikumar, A. P., Wang, J., Brandt, A. R. 2016