Engineers, what cool things are you working on?
Engineers, what cool things are you working on?
Workshops is an open source, simple, dead-lightweight LMS (Learning Management System) application programmed in Python (version 3.8.x) with Django (version 2.2.x) web framework which main purpose ...
For the most part, making sure our products run safely and groaning at the previous philosophy of moving fast and using duct tape for everything.
Oh and creating standards and templates for documentation, because that doesn’t exist either. The downside of working for a newer green energy company is that the typically established processes and methods aren’t established yet. And you get the fun task of changing that.
and you get the fun task of changing that.
Honestly not sure if you’re being sarcastic or not
TL;DR, I throw a bunch of molecules at a pile of linear algebra, and hope predicted values line up with known experimental values; then I use the pile of linear algebra on novel molecules.
There’s a bit more to it than that, like how to represent molecules in a computer-readable format, generating additional input variables (molecular characteristics), input variable down-selection and/or dimensionality reduction, the specific ML models we use (feed-forward MLPs and graph convolution nets), and how to interpret results as they relate back to combustion.
From a broad perspective, our work is just a small part of a larger push from the Department of Energy to find economically-viable alternative liquid fuels. ML speeds up the process of screening candidate molecules, for example those found in bio-oil resulting from pyrolizing and catalytically-upgrading lignocellulosic biomass or other renewable sources. Our colleagues don’t have to synthesize large samples of many molecules just to test their properties and determine how they will behave in existing engines (a very costly and time-consuming process), instead we predict the properties and behaviors to highlight viable candidates so our colleagues can focus on analyzing those.
These papers (1, 2, 3) best outline the procedures and motivations for this work. PM me if you can’t get access and I’ll send you them!
Sort of - the models are able to predict numerical property values given a large amount of data to observe during training. In other words, given the scope of known data, we can extrapolate predictions for new data. The predictive capabilities of the model are only as reliable as the data used to train it, and unfortunately in our case we only have hundreds of samples per property, as opposed to other ML tasks with millions of samples. This highlights how much time it actually takes to find, synthesize, and experimentally test molecules!
Unfortunately neural networks, especially traditional multi-layered feed-forward networks, are often seen as a “black box” approach to regression and classification, where we don’t really understand how a network learns or why its weights are tuned the way they are. Analysis methods have come a long way, but ambiguity still exists.
What we have done, however, is find the statistical significance of specific molecular substructures as they relate to combustion properties. For example, when we trained our models to predict sooting propensity (amount of pollution formed during combustion), we noticed that various algorithms such as random forest regression were putting a heck of a lot more weight into a molecular variable measuring path length (length of carbon chains, number of higher order bonds); from this, we were able to conclude that long-chain hydrocarbons with a higher number of double or triple bonds form more soot, and an idea of what mechanistic pathways we should stay away from when producing bio-oil.
As for fuel-grade molecules, we’ve found that furanic compounds and compounds with cyclohexane substructures generally have equal operating efficiency (cetane number), equal energy density (lower heating value, MJ/kg), operate well in various environments (optimal flash, boiling, and cloud points, deg. C), all while producing much less soot compared to diesel fuel (yield sooting index). The next step is finding a cheap way to mass produce the stuff!
Recently we’ve started down the rabbit hole of fungus-derived bio-oils, terpenes (yes, those terpenes!) derived from fungus may be useful for use as soot-reducing fuel additives.
I’m working on open source session replay tool (skipping the name not to promote it explicitly, but its quite easy to guess since our niche has not too many fully OS companies) as R&D/js library maintainer; at the same time I’m making my own lemmy app :)
Very fun and quite the opposite experience (going in deep with browser specs and API vs thinking about mobile UI and features)
Been using some free time at work to make an inventory of pipeline stream crossings and plan on making it a GIS feature class for regular maintenance. This was inspired by an encased sanitary sewer essentially becoming a low head dam (just eroded, not discharging sewage) in a homeowners back yard and we were unaware until someone called.
It’s mostly just been tracing the features so far, but I’m thinking about where to take it next. Thinking a good direction to go next will be to use the elevation model to try and find manholes in high slope areas and ditches so they can be identified for monitoring for erosion or I&I.
I’m sure there are people who genuinely find that kind of work to be really cool, but I’m with you. It wasn’t enough for me.
I just could not get motivated over my projects being “Maybe we should store the pallets right here in packaging instead of 100ft away on the other side of the building” or “Let’s replace the screwdrivers in assembly with drills to increase productivity”. Who the fuck needs an engineering degree to tell them that drills are way faster than screwdrivers?
Let alone the bullshittery around monitoring every minute of each employee’s day and trying to squeeze every ounce of productivity out of it. One manager gave an entire presentation about how if every operator is 1 minute late coming back to their station from break it adds up to like 2 full weeks for 1 employee by the end of the year.
Like…I saw that manager gossiping with HR for 45 minutes every day. But here we’re trying to harass our employees for taking ONE EXTRA MINUTE of their 30 minute lunch break.
I just couldn’t. Continuous Improvement can be cool but not when it’s that kind of stuff, not to me anyway.
Just wanted to say that if you feel similarly and it’s making you miserable there are cool engineering jobs out there. Even Continuous Improvement if the manufacturing process is complex and requires actual engineering to improve.
Yeah I like a lot of what I do and it honestly gives me a lot of opportunities to express my class consciousness and stand up for reasonable expectations of workers. But yeah I do really miss research and want to be doing cooler stuff. I’m just still a junior engineer and can’t afford grad school yet.
I’m also absolutely looking for other work it just seems nobody is interested in junior engineers except the military.
Working on a camera system to automatically detect and notify operations of arc breakdowns in switchgear. Our facilities are big enough that when something goes bang, a lot of times we don’t know where it happened.
I do the hardware and software… Not the career I went to school for, but I’m having fun and it’s interesting.
Couple of reasons: this is a high energy pulsed-power environment, so aside from concerns on how to reliably power it (I’m designing PoE into this new version), we also need it to be reasonably small and really fast.
Are we saving money? Individual units are certainly cheaper than COTS solutions. Maybe no real savings, but it’s meeting our needs without any compromises.
Besides, this is one of my simpler projects off the top of my head that wouldn’t bust an NDA. 😆
New ways of cooling data servers and batteries for EVs. Rather than typical air or water/glycol cooling we’re immersing the components in a dielectric fluid. It’s an interesting space as both the hardware and fluids are being developed simultaneously. The company I work for is developing the fluid.
About 90% of the fluids out there are just oils taken directly from a refinery and repackaged under different names with a ton of marketing. Yet, end consumers don’t really understand the technical details of the the fluids so they tend to fall for whoever has nice marketing. We’re out to change that and show that the chemistry we add improves the performance and durability of the fluid. So half the job is engineering and the other half is educating customers.
I agree with the NDA comment, it’s difficult to give a lot of the cool details without breaking NDA.
I feel pretty safe in saying that I’m working to come up with new ways to melt glass for our process in order to be more flexible about what compositions we’re able to use. That’s a pretty fun one for me.
This engineer hasn’t worked on anything cool lately.
Hoping to find a new job later this year and move onto something more interesting as a byproduct of that. Assuming that doesn’t lead to me being drowned in meetings and emails…
Lots of different things. Lately I’ve been testing on whatever I can think of which has included having it order pizza for an office pizza party where it had to collect orders from both Slack and text message and then look up and call the pizza place. Finding and scheduling a house cleaner, tracking down events related to my interests happening this weekend and finding a place to eat after. I had it review my changes to its code, write a commit message, and commit it to git. It can write code for itself (it wrote an interface for it to be able to get the weather forecast for example).
Really I see it as eventually being able to do most tasks someone could do using a computer and cell phone. I’m just finishing up getting it connected to email, and it’s already able to manage your calendar, so it should be able to schedule a meeting with someone over email based on when you’re available.
It’s not open source. I haven’t really seen anything open source (or closed source minus HyperWrite ai assistant) that comes close. When I test tasks, I usually also try them on some of the web enabled things like ChatGPT browsing (before it got turned off), bing chat, etc. None of them are able to do that stuff though they’ll happily pretend they did and give you false info.
Anyway, yeah, I can definitely see so many areas where AI could make things better. I’m just going for giving people back some free time which isn’t quite as lofty a goal as distributing resources more efficiently, but there are definitely still many limits on the tech, and I’m not sure something like that is possible yet.