🎙️ Revolutionizing Gas Leak Detection with Visual AI & Machine Learning

In this episode of The Empirical Energy Podcast, host Mark Smith sits down with Jae Yoon Chung, Machine Learning Engineer at Clean Connect, to explore how visual AI is transforming gas leak detection in the energy industry.

Jae breaks down the real-world challenges of detecting methane and gas leaks using vision-based models — especially in harsh outdoor environments with wind, rain, snow, and limited edge-device compute. He introduces a breakthrough approach called channel stacking, a method that captures gas movement using just three consecutive frames to dramatically improve detection accuracy while reducing computational load and false alarms.

The conversation goes beyond theory, offering a behind-the-scenes look at how AI models are trained, optimized, and deployed at the edge — and where the technology is headed next. From edge computing to large language models (LLMs) and object-level incident classification, this episode highlights how AI, blockchain, and verification are reshaping the future of global energy markets.

⚡ If you work in energy, AI, emissions monitoring, or industrial technology, this episode is a must-listen.

⏱️ Episode Chapters

00:00 – Introduction to The Empirical Energy Podcast

00:57 – Meet the Guest: Jae Yoon Chung from Clean Connect

01:39 – Machine Learning Challenges in Energy Environments

03:44 – Innovations in Visual Gas Leak Detection

07:29 – Technical Deep Dive: Channel Stacking Explained

14:23 – The Future of Visual AI & LLM Integration

18:53 – Final Thoughts & Call to Action

🎧 Listen & Watch

▶️ YouTube: https://youtu.be/WOejlQSdr_g

🎙 Apple Podcasts: https://podcasts.apple.com/us/podcast/the-empirical-energy-podcast/id1822839881

💬 If this episode sparked new ideas or questions, join the conversation.

👉 Subscribe, rate the show, and share this episode with someone working in energy, AI, or climate tech.

👉 Drop a comment and tell us: Where do you see the biggest opportunity for AI in energy today?

#EmpiricalEnergyPodcast #VisualAI #MethaneDetection

#MachineLearning #EdgeAI #EnergyTech

#ClimateTech #IndustrialAI #ComputerVision

Uncovering the Future of Methane Emissions Management with Thomas Fox

In this episode of The Empirical Energy Podcast, host Mark Smith welcomes Thomas Fox, President of Highwood Emissions, to unpack the mechanisms reshaping global energy markets.

The conversation explores the industry’s shift from “alternative energy” toward measured, verified energy as the new gold standard—powered by empirical data, evolving regulations, and emerging verification frameworks. Thomas shares his journey from PhD research in methane emissions to building Highwood Emissions and launching the Emissions Intelligence Platform (EIP), a software solution designed to help operators create measurement-informed inventories at scale.

Together, they dive into the realities of methane reporting: reconciliation challenges, OGMP Level 5 requirements, EPA and Colorado regulatory approvals, and why harmonization across global reporting frameworks remains one of the industry’s biggest hurdles. The episode closes with a forward-looking discussion on how collaboration, technology, and credible data may finally unlock real market incentives for low-methane energy.

🎧 If you work in energy, ESG, emissions reporting, or climate compliance, this episode offers a clear look at where the industry is heading—and what it will take to get there.

⏱️ Episode Chapters

00:00 Introduction to the Empirical Energy Podcast

00:55 Meet Thomas Fox: Highwood Emissions

02:51 The Journey to Methane Mitigation

04:54 Building Measurement-Informed Inventories

08:27 Challenges and Innovations in Methane Reporting

18:34 The Future of Emissions Reporting

25:02 Closing Thoughts and Call to Action

✔️ Listen to the full episode to understand how methane measurement is evolving

✔️ Subscribe to the Empirical Energy Podcast for more conversations at the intersection of energy, AI, and climate policy

✔️ Share this episode with colleagues navigating methane reporting, OGMP, or ESG compliance

✔️ Join the discussion—what do you think will finally drive harmonized emissions reporting?

#EmpiricalEnergy #MethaneEmissions #EnergyTransition #OGMP #ClimateReporting #EnergyMarkets #OilAndGas #ESG #CarbonAccounting #Decarbonization #ClimateTech #EnergyPolicy #LowMethaneGas #Sustainability

In this episode of the Empirical Energy Podcast, host Mark Smith sits down with Zach Orlin of Clean Connect.ai to explore how digital twins, AI, blockchain, and verification technologies are redefining how energy is measured, monitored, and managed.

The conversation dives deep into Luminary, a digital twin platform that enables teams to virtually map energy sites, plan monitoring equipment placement, and integrate real-time data into operational models before anything is deployed in the field. Zach explains how Luminary connects with Promax process simulation models, remote monitoring systems, and verification frameworks to reduce costs, improve efficiency, and eliminate blind spots in energy operations.

This episode also explores what’s next—from augmented reality overlays to smarter, more empirical energy markets where measurement, transparency, and real-time intelligence become the new standard.

If you’re working in energy, infrastructure, emissions monitoring, or digital transformation, this episode offers a clear look at how legacy systems are being replaced by data-driven, verifiable solutions.

⏱ Episode Chapters

00:00 Introduction to Empirical Energy

00:44 Meet Zach Orlin (Clean Connect.ai)

02:23 Digital Twins & the Luminary Platform

05:05 Virtual Site Mapping & Monitoring Design

11:11 Advanced Capabilities & Future Applications

18:13 Empirical Energy’s Broader Impact & Wrap-Up

🎧 Subscribe for conversations on energy verification, AI, blockchain, and the future of global energy markets.

#EmpiricalEnergy #DigitalTwins #EnergyTechnology #EnergyInnovation #AIinEnergy #BlockchainEnergy #EnergyMarkets #RealTimeMonitoring #EnergyManagement #CleanTech #IndustrialAI #EnergyTransition #OperationalIntelligence

In this episode of the Empirical Energy Podcast, host Mark Smith is joined by Deb Ryan from Geo Financial to discuss the seismic shift in global energy markets. They dive deep into how verification via blockchain, coupled with AI, is reshaping energy systems. Deb shares insights on her background, the methane performance certificates, and the integration of satellite data with real-time monitoring to achieve market solutions. This rich conversation touches on the evolving role of methane detection, the synergy between different technologies, and the economic drivers for emission reduction, while also offering a preview of how data fidelity is revolutionizing the industry. Tune in to find out how companies can leverage these innovative solutions for a sustainable future.

00:00 Introduction to the Empirical Energy Podcast

00:45 Meet Deb Ryan from Geo Financial

01:05 Navigating Carbon: Deb's Podcast and Background

01:40 Methane Performance Certificates and S&P Global

04:32 Challenges in Methane Detection and Solutions

05:16 Geo Financial's Satellite Data and Methane Detection

07:37 Combining Satellite and Ground Data for Better Accuracy

19:28 Market Solutions and Certified Natural Gas

21:31 The Future of Energy Trading and EACs

24:40 Conclusion and Call to Action

Don’t forget to:

👍 Like this if energy markets matter to your world

🔔 Subscribe to follow the people shaping the future of energy

💬 Share your biggest insight—traders and operators learn from each other

🔗 Pass this to someone who works behind the scenes of our energy system

#EnergyMarkets

#MethaneMonitoring

#ClimateTech

#CarbonMarkets

#EnergyTransition

#BlockchainEnergy

#AIinEnergy

#VerifiedEnergy

#CertifiedGas

#MethaneReduction

Gas scheduling, pipeline data, arbitrage, LNG globalization, AI-powered tariffs, and the rapid shift toward reliable low-carbon fuel — this conversation with Jay Bhatty shines a light on how the natural gas market is being rewritten from the inside out.

Jay shares his path from Enron to building NatGasHub into a core scheduling platform for North America, why data is the real commodity traders fight over, and how AI data centers are quietly reshaping the future of energy reliability.

Key Discussion Points

• 00:00 – Opening: the silent revolution in verified energy
• 00:44 – Jay’s journey: India → Ohio → Enron
• 02:25 – Early lessons from Enron alumni & top-tier traders
• 04:10 – Electricity shortages and why energy became Jay’s obsession
• 05:18 – The pain point that sparked NatGasHub
• 06:52 – Turning 50 pipeline websites into one streamlined platform
• 08:40 – Why empirical pipeline data gives traders an edge
• 10:22 – Automation: from whiteboards to real-time digital scheduling
• 12:15 – Arbitrage opportunities and information asymmetry
• 14:10 – The motivation behind Nat Gas Millions
• 15:55 – What new traders misunderstand about the physical market
• 17:48 – “All the money is made in the margins” — real trading war stories
• 19:35 – How geopolitical shocks hit energy markets
• 20:48 – LNG and the rise of natural gas as a global commodity
• 22:36 – AI data centers and the real priorities behind their power demand
• 24:18 – Why big tech is turning toward natural gas infrastructure
• 25:52 – The challenge of tracking low-carbon gas molecules
• 27:05 – Advice for new traders on mastering risk
• 28:20 – Using AI to decode 500-page pipeline tariffs
• 29:10 – Where to find Jay’s book & how to work with NatGasHub
• 
  

If you're in trading, infrastructure, energy technology, or you're trying to understand how physical markets actually work, this episode gives you the “behind the curtain” view.

Don’t forget to:

👍 Like this if energy markets matter to your world

🔔 Subscribe to follow the people shaping the future of energy

💬 Share your biggest insight—traders and operators learn from each other

🔗 Pass this to someone who works behind the scenes of our energy system

#EnergyTrading #NaturalGas #AIandEnergy #EnergyTech #LNG #CommodityTrading #NatGasHub #PipelineData #DataCenters #EmpiricalEnergy #LowCarbonEnergy #EnergyInfrastructure

The energy market is entering a new era where data is currency, transparency is leverage, and trust is tokenized.

In this episode of The Empirical Energy Podcast, host Mark Smith speaks with Brad Handler, Program Director for the Energy Finance Lab at the Payne Institute for Public Policy, Colorado School of Mines.

They explore how tokenizing environmental attributes in LNG (liquefied natural gas) is creating a measurable, verifiable, and tradeable standard for global energy accountability.

Episode Breakdown

00:00 – Introduction

03:45 – The Payne Institute and the Energy Finance Lab

09:10 – From Research to Real-World Application

14:50 – The Rise of Tokenized LNG

22:15 – Carbon-Neutral LNG vs. Tokenized LNG

28:40 – EACs and the Blockchain Advantage

35:05 – Standards That Matter: ISO 14067, ISCC & OGMP 2.0

41:30 – Market, Regulation & Technology: The Balancing Act

48:20 – Building Trust in Energy Data

55:10 – The Future: EACs as the Standard

1:02:00 – Closing Thoughts

Energy’s next competitive edge isn’t scale—it’s proof.

Tune in and discover how verified data, blockchain, and policy are converging to reshape the future of energy markets.

#EnergyTransition #Blockchain #Tokenization #CleanEnergy #LNG #Sustainability #ClimateTech #Decarbonization #EmpiricalEnergyPodcast #CarbonMarkets

The silent revolution in energy has already begun — and it’s measured, not modeled.

In this powerful episode of The Empirical Energy Podcast, host Mark Smith sits down with Robert Kleinberg, physicist, researcher, and former Schlumberger scientist, to explore the truth behind global methane reporting — and how measurement-based verification is reshaping the world’s energy future.

Kleinberg exposes the startling differences between U.S. and Russian methane emission methodologies, showing how emission factors — outdated, estimated reporting tools — distort climate data, global trade, and even geopolitics.

As the world pushes toward verified, empirical energy, this conversation reveals the urgent need for accuracy, transparency, and data integrity across every link in the global energy chain.

This is more than policy — it’s a race for truth in the age of misinformation and market disruption.

💡 Key Takeaways

Emission factors are outdated — the future of energy is empirical measurement.

Russia’s “cleaner” data reveals flaws in global reporting, not environmental success.

Verified data = verified trade. Energy credibility depends on transparency.

Methane accuracy affects not just policy — but global economics and geopolitics.

Collaboration between science, industry, and policy is key to verified progress.

🔔 Join the Empirical Energy Movement

If this episode opened your eyes to the data behind the energy revolution:

✅ Subscribe to The Empirical Energy Podcast for more verified conversations

💬 Comment what you think: Should all energy reporting be measurement-based?

📢 Share this episode — truth in data starts with awareness

Empirical Energy — measured, verified, unstoppable. ⚡🌍

Welcome to the Empirical Energy Podcast, where host Mark Smith sits down with Mehdi, author of Enterprise Gen AI for Energy Executives, to uncover how Generative AI, blockchain, and verified data are transforming the global energy industry.

This episode dives deep into real-world AI adoption — from C-suite strategy and data management to cultural transformation and workforce adaptation. Learn how energy leaders can identify the right AI use cases, navigate the “build vs. buy” debate, and prepare for the era of Artificial General Intelligence (AGI).

If you’re an energy executive, innovator, or tech strategist, this episode offers an unfiltered roadmap to the future of verified, intelligent energy.

Timestamps

00:00 – Welcome to the Empirical Energy Podcast: The verified energy revolution

00:33 – Meet your host, Mark Smith

00:45 – Introducing Mehdi and his book Enterprise Gen AI for Energy Executives

01:08 – The inspiration behind writing the book

01:41 – Bridging the gap between technical experts and executives

02:12 – The need for continuous learning in AI and energy

02:33 – Mehdi’s background: From robotics to oil to data-driven strategy

03:18 – How Generative AI is transforming business operations

03:39 – Why AI is no longer a luxury — it’s essential

04:27 – What executives will gain from Mehdi’s book

05:11 – Identifying the most valuable AI use cases

06:12 – How to prioritize, budget, and assess AI initiatives

07:11 – The “Build vs. Buy” dilemma for modern organizations

08:15 – Vendor partnerships, data security, and compliance challenges

09:40 – The reality of implementing AI in the energy sector

10:01 – Lessons from real projects and the “dumb tax” of innovation

10:38 – Prompt engineering, fine-tuning, and agent-based AI

11:30 – Managing fear and resistance to AI adoption

12:17 – Building a culture of learning and adaptation

13:04 – Behind the scenes: Mark and Mehdi’s “book club” collaboration

13:35 – Upcoming Energy AI Conference — join the movement

14:17 – The future: From Generative AI to AGI

15:31 – New jobs, new leadership, and managing intelligent systems

16:13 – How AI will reshape energy companies in the next 3 years

17:21 – Why every organization must embrace AI now

17:44 – Closing thoughts and gratitude

18:09 – How you can support and stay connected

🔔 Join the Movement

If this conversation sparked new ideas for you:

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💬 Comment your biggest takeaway from this episode

📢 Share this video with your team or peers in the energy space