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

Supervisors


Honors & Awards


  • All-Round Student, Zhejiang University
  • First Prize in National Olympiad in Mathematics, Tianjin
  • First-Class Scholarship for Research and Innovation, Zhejiang University
  • Frank G. Miller Fellowship Award for High Academic Achievement, Stanford University
  • Master of Engineering Spring Financial Aid Award, UC Berkeley
  • Second-Class Award in National Students’ Energy Saving Technology Competition, National
  • Stanford Interdisciplinary Graduate Fellowship, Stanford University

Professional Affiliations and Activities


  • Founder of China Focus Community, Stanford Energy Club (2015 - 2018)
  • Cofounder, Silicon Valley China-US Energy Association (2015 - 2018)
  • Cofounder, Student Association of Energy Conservation And Emission Reduction, Zhejiang University (2011 - 2012)
  • Vice-President, Association of Chinese Entrepreneurs at Berkeley) )) (2012 - 2013)
  • Co-President, Chinese Entrepreneur Organization at Stanford University (2017 - 2018)
  • Advisory Board Member, Chinese Environmental Scholars Forum (2017 - Present)

Education & Certifications


  • Certificate, Associate Constructor (Municipal Public Works), China
  • Certificate, Optical Gas Imaging Thermographer
  • M.S., Stanford University (2016)
  • M.Eng, UC Berkeley (2013)
  • B.Eng, Zhejiang University (2012)

Service, Volunteer and Community Work


  • TA of New indicators of well-being and sustainability (CEE 171F/271F)

    Location

    Palo Alto

  • TA of Fundamentals of Energy Processes (ENERGY 293B)

    Location

    Palo Alto

  • Invited Guest at Young Scientist Seminar of 2018 Tianjin Summer Davos Forum

    Location

    Tianjin

  • Invited Guest at Zhejiang University - Stanford University Academic Exchange Week

    Location

    Hangzhou

  • Invited Guest at Global Energy Forum

    Location

    Palo Alto

Patents


  • Jingfan Wang. "China P.Rep.Terminal Intelligent Control System Plan for Air-Conditioning"
  • Anmin Wang, Jingfan Wang, Yingling Han. "China P.Rep.Dual-Source Heat Pump System"
  • Anmin Wang, Jingfan Wang, Yingling Han. "China P.Rep.Heat Pump Unit with Supplementary Heat Source"

Current Research and Scholarly Interests


Natural gas leaks waste money, reduce energy availability, induce sea-level rise, and result in both local air quality and global climate impacts. The climate impacts of leaked gas are particularly important due to the high global warming potential of methane (36 times more potent per kg than CO2 over 100 years). In addition to environmental concerns, the economic impacts of gas leakage are clear: lost natural gas costs nearly $2 billion per year at current prices.
Current EPA estimates suggest that about 1.5% of the natural gas produced in the U.S. is lost in leaks, while recent studies suggest that potential emissions from the gas system may be higher. Currently many natural gas leak detection and repair (LDAR) technologies exist. These methods include manually-operated flame ionization detectors and manually operated infrared (IR) video cameras for real-time optical gas imaging (OGI). EPA has recently released proposed regulations that codify the use of manually operated optical gas imaging as the standard leak detection technique.
Despite the current dominance of OGI, a number of fundamental problems with the technology exist: (1) labor costs for IR surveys are high, (2) continuous monitoring with IR is infeasible and (3) IR surveys cannot provide information about whether there is a leak happening in the real time.
To tackle the problems, we proposed an interdisciplinary project that harnesses the potential of computer science advances to allow for the rapid and automatic detection of methane leaks and estimation of their sizes. There are four main parts of my study: 1. We built a large video dataset of gas leaks for deep learning training purposes - GasVid, which includes a large number of labeled videos of methane leaks with representative leak sizes from different leak locations and imaging distances. 2. We developed a convolutional neural network (CNN) model to identify and detect leaks from imagery and systematically examined the efficiency of automatic system with data from the real world. 3. We are developing models based on both CNN and Recurrent Neural Network (RNN) to classify the leak size. 4. We will then perform economic and policy analysis to elaborate the benefits of automating pollution detection.

Projects


  • Deep Learning for Methane Emissions Detection and Quantification

    Location

    Palo Alto

  • Phase 2 of Oil-Climate Index

    Location

    Palo Alto

  • Methane Monitor Challenge

    Location

    Colorado

  • Optical Gas Imaging Test

    Location

    Colorado

  • The New Solar System

    Location

    Palo Alto

  • Prediction of Air Traffic Volumes with Time Series Data

    Location

    Palo Alto

  • Deep Learning-based Food Recognition

    Location

    Palo Alto

Work Experience


  • Research Intern, ExxonMobil Upstream Research Company (6/1/2018 - 8/1/2018)

    Location

    Houston

  • Investment Manager, Golden Earth New Energy (Tianjin) Group, Co., LTD.

    Location

    Tianjin

  • Energy Products Architecture and Modeling Intern, Tesla

    Location

    Palo Alto

  • Consultant, ENN Digital Energy Technology Co.LTD.

    Location

    Palo Alto

All Publications


  • Assessment of winter air pollution episodes using long-range transport modeling in Hangzhou, China, during World Internet Conference, 2015 ENVIRONMENTAL POLLUTION Ni, Z., Luo, K., Zhang, J., Feng, R., Zheng, H., Zhu, H., Wang, J., Fan, J., Gao, X., Cen, K. 2018; 236: 550–61

    Abstract

    A winter air pollution episode was observed in Hangzhou, South China, during the Second World Internet Conference, 2015. To study the pollution characteristics and underlying causes, the Weather Research and Forecasting with Chemistry model was used to simulate the spatial and temporal evolution of the pollution episode from December 8 to 19, 2015. In addition to scenario simulations, analysis of the atmospheric trajectory and synoptic weather conditions were also performed. The results demonstrated that control measures implemented during the week preceding the conference reduced the fine particulate matter (PM2.5) pollution level to some extent, with a decline in the total PM2.5 concentration in Hangzhou of 15% (7%-25% daily). Pollutant long-range transport, which occurred due to a southward intrusion of strong cold air driven by the Siberia High, led to severe pollution in Hangzhou on December 15, 2015, accounting for 85% of the PM2.5 concentration. This study provides new insights into the challenge of winter pollution prevention in Hangzhou. For adequate pollution prevention, more regional collaborations should be fostered when creating policies for northern China.

    View details for DOI 10.1016/j.envpol.2018.01.069

    View details for Web of Science ID 000429187500057

    View details for PubMedID 29428709

  • "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