Canadian Wildfires clouded Northeastern air. I turned to Google Bard and ChatGPT for some answers.
During the entirety of last week, a sizeable chunk of the New England and Mid-Atlantic regions found itself blanketed in sooty, orange-sky-coloring ashen smog thick with particulate matter from Canadian wildfires. The rampaging blazes north of the border sending never-ending plumes of smoke southward causing air quality measures in New York City to briefly exceed the worst recorded indications globally in present times.
The air quality degradation brought climate change and environmental emergency into sharp focus for the entire region. As the great northern forests had been incomparably dry due to heat and lack of moisture, touched off by lightning strikes, a desiccated tinder box the width of the entire continent. The fires produced PM2.5 contaminants — in NYC rising to above 350ppm, reminiscent of COVID times, — requiring N95 masks for even being able to ambulate outside.
Stuck inside with a vast expanse of time to dedicate towards incomplete projects, I called up an offhanded search I’d completed after a recent Project Management Institute conference where the confluence of AI + PM became major topics. The presenter even described techniques of how to leverage AI in PM tasks to improve efficiency and find solutions. Since then, I’ve used similar techniques already in other PM situations to solve questions that needed answers.
The question related to both Canada and wildfires is the following: receding Arctic Sea Ice due to global climate change — as well as permafrost — releases trapped methane below the sea surface, as well as the permafrost (not to mention Nitrogen, but that’s the subject of a future post.) How can we convert escaping methane into utilizable products, and prevent the even more potent greenhouse gas than CO2 from getting into the atmosphere?
According to Google Bard — one of the main processes for doing just that is something already being worked on in both research and commercial environments. A chemical reaction known as catalytic pyrolysis. “Why select this specific one?” you may ask when steam methane reforming is available and photocatalytic methane splitting are other options? Easy. The steam methane reforming requires high heat and high pressure, and it’s already hot enough and we haven’t even felt the effects of El Niño yet this year. Photocatalytic splitting seems promising, but it’s still being worked on (see articles in Nature, ScienceDirect, and Wiley, especially the “challenges“ part.)
“That’s great”, you say, “but who do I call about all this?” Funny you should ask. I said the same thing to the AI and got the following names:
One other company made it onto the list: Catalytica Energy, however, in researching the company, they seem to have been acquired and folded into their purchaser’s structure.
Naturally, the next logical thing to ask is: what are factors affecting catalytic pyrolysis pricing? Bard had this to say:
There are a number of factors that affect the cost basis of catalytic pyrolysis, including:
- The cost of the catalyst: The catalyst is a key component of the catalytic pyrolysis process, and its cost can have a significant impact on the overall cost of the process.
2) The cost of the feedstock: The feedstock is the material that is converted into products through catalytic pyrolysis. The cost of the feedstock can vary depending on the type of feedstock and the location of the facility.
3) The cost of energy: Energy is required to heat the feedstock and to operate the catalytic pyrolysis unit. The cost of energy can vary depending on the type of energy source and the location of the facility.
4) The cost of labor: Labor is required to operate and maintain the catalytic pyrolysis unit. The cost of labor can vary depending on the location of the facility.
5) The cost of capital: Capital is required to build and operate the catalytic pyrolysis unit. The cost of capital can vary depending on the size of the facility and the location of the facility.
The overall cost of catalytic pyrolysis is expected to come down as the technology matures and as the cost of the catalyst, feedstock, energy, labor, and capital decreases.
What is the feasibility of (catalytic pyrolysis) placement near methane producing environments? Again, the Bard (edited for length):
The feasibility of placing catalytic pyrolysis facilities near methane producing environments is high. By capturing and converting methane into other products, catalytic pyrolysis can help to reduce greenhouse gas emissions. There are a number of methane producing environments that are ideal for the placement of catalytic pyrolysis facilities. These include:
- Landfills
- Sewage treatment plants
- Animal waste
Catalytic pyrolysis could help to break down methane and prevent it from being released into the atmosphere. The technology could be used to capture methane from permafrost and seabed sediments, and then convert it into other products, such as hydrogen or carbon black. This would help to reduce the amount of methane that is released into the atmosphere and slow the pace of climate change.
However, there are some challenges that need to be addressed before catalytic pyrolysis can be used on a large scale in the Arctic. One challenge is the cost of the technology. Another challenge is the harsh environment of the Arctic, which can damage equipment and make it difficult to operate.
So, to sum up: did AI solve the entire problem? No. Defitinitively not. However, what AI did do in this situation is efficiently aggregate the collected information and directly indicate the inflection points — the areas where human ingenuity and dedicated hard work — is required in order to solve the problem. That, specificially, is what tools like the generative AIs are supposed to do. They are information aggregators and natural language processors, not the actual problem solvers. Both political and actual capital would need to be spent in order to complete these projects. Sounds like it may also involve some analytics. Seems like an ideal job for a #PMPatWork. Stay tuned next time for the Microsoft side of the equation.
Feel free to contact me about projects, data visualization, or PM training. I’m available via LinkedIn, my website, or Instagram.
