green hacks

757 Articles

Building A Full-Fat Air Quality Monitor

September 3, 2019 by 13 Comments

Over the years many people have made an air quality monitor station, usually of some configuration which measures particulates (PM2.5 & PM10). Some will also measure ozone (O3), but very few will meet the requirements that will allow one to calculate the Air Quality Index (AQI) as used by the EPA and other organizations. [Ryan Kinnett]’s project is one of those AQI-capable stations.

The AQI requires the measurement of the aforementioned PM2.5 (µg/m3), PM10 (µg/m3) and O3 (ppb), but also CO (ppm), SO2 (ppb) and NO2 (ppb), all of which has to be done with specific sensitivities and tolerances. This means getting sensitive enough sensors that are also calibrated. [Ryan] found a company called Spec Sensors who sell sensors which are pretty much perfect for this goal.

Using Spec Sensor’s Ultra-Low Power Sensor Modules (ULPSM) for ozone, nitrogen-dioxide, carbon monoxide and sulfur dioxide, a BME280 for air temperature, pressure and relative humidity, as well as a Plantower PMS5003 laser particle counter and an ADS1115 ADC, a package was created that fit nicely alongside an ESP8266-based NodeMCU board, making for a convenient way to read out these sensors. The total one-off BOM cost is about $250.

The resulting data can be read out and the AQI calculated from them, giving the desired results. Originally [Ryan] had planned to take this sensor package along for a ride around Los Angeles, to get more AQI data than the EPA currently provides, but with the time it takes for the sensors to stabilize and average readings (1 hour) it would take a very long time to get the readings across a large area.

Ideally many of such nodes should be installed in the area, but this would be fairly costly, which raises for [Ryan] the question of how one could take this to the level of the Air Quality Citizen Science project in the LA area. Please leave your thoughts and any tips in the comments.

Automate Sorting Your Trash With Some Healthy Machine Learning

September 1, 2019 by 10 Comments

Sorting trash into the right categories is pretty much a daily bother. Who hasn’t stood there in front of the two, three, five or more bins (depending on your area and country), pondering which bin it should go into? [Alvaro Ferrán Cifuentes]’s SeparAItor project is a proof of concept robot that uses a robotic sorting tray and a camera setup that aims to identify and sort trash that is put into the sorting tray.

The hardware consists of a sorting tray mounted to the top of a Bluetooth-connected pan and tilt platform. The platform communicates with the rest of the system, which uses a camera and OpenCV to obtain the image data, and a Keras-based back-end which implements a deep learning neural network in Python.

Training of the system was performed by using self-made photos of the items that would need to be sorted as these would most closely match real-life conditions. After getting good enough recognition results, the system was put together, with a motion detection feature added to respond when a new item was tossed into the tray. The system will then attempt to identify the item, categorize it, and instruct the platform to rotate to the correct orientation before tilting and dropping it into the appropriate bin. See the embedded video after the break for the system in action.

Believe it or not, this isn’t the first trash-sorting robot to grace the pages of Hackaday. Potentially concepts like these, that rely on automation and machine vision, could one day be deployed on a large scale to help reduce how much recyclable material end up in landfills. Continue reading “Automate Sorting Your Trash With Some Healthy Machine Learning” →

Human-Powered Henhouse Keeps Chickens On The Job

September 1, 2019 by 37 Comments

While it’s not exactly in the same vein as other projects around here, like restoring vintage video game systems or tricking an ESP32 to output VGA, keeping chickens can also be a rewarding hobby. They make decent pets and can also provide you with eggs. You can also keep them on a surprisingly small amount of land, but if you have a larger farm you can use them to help condition the soil all over your property. For that you’ll need a mobile henhouse, and as [AtomicZombie] shows, they don’t all have to be towed by a tractor.

This henhouse is human-powered, meaning any regular human can lift it up and scoot it around to different areas without help from heavy equipment. It uses a set of bicycle wheels which rotate around to lift up the frame of the house. A steering wheel in the back allows it to be guided anywhere and then set down. It also has anti-digging protection, which is a must-have for any henhouse to keep the foxes out.

We like this one for its simplicity and ease-of-use. Not needing a tractor on a small farm can be a major cost savings, but if you really need one, [AtomicZombie] also designed a robust all-electric tractor-like device that we featured a little while back.

Continue reading “Human-Powered Henhouse Keeps Chickens On The Job” →

Exploring The Science Behind Dirty Air Filters

August 18, 2019 by 34 Comments

Obviously, if the air filters in your home HVAC system are dirty, you should change them. But exactly how dirty is dirty? [Tim Rightnour] had heard it said that if you didn’t change your filter every month or so, it could have a detrimental effect on the system’s energy consumption. Thinking that sounded suspiciously like a rumor Big Filter™ would spread to bump up their sales, he decided to collect his own data and see if there was any truth to it.

There’s a number of ways you could tackle a project like this, but [Tim] wanted to keep it relatively simple. A pressure sensor on either side of the filter should tell him how much it’s restricting the airflow, and recording the wattage of the ventilation fan would give him an idea on roughly how hard the system was working.

Now [Tim] could have got this all set up and ran it for a couple months to see the values gradually change…but who’s got time for all that? Instead, he recorded data while he switched between a clean filter, a mildly dirty one, and one that should have been taken out back and shot. Each one got 10 minutes in the system to make its impression on the sensors, including a run with no filter at all to serve as a baseline.

The findings were somewhat surprising. While there was a sizable drop in airflow when the dirty filter was installed, [Tim] found the difference between the clean filter and mildly soiled filter was almost negligible. This would seem to indicate that there’s little value in preemptively changing your filter. Counter-intuitively, he also found that the energy consumption of the ventilation fan actually dropped by nearly 50 watts when the dirty filter was installed. So much for a clean filter keeping your energy bill lower.

With today’s cheap sensors and virtually infinite storage space to hold the data from them, we’re seeing hackers find all kinds of interesting trends in everyday life. While we don’t think your air filters are spying on you, we can’t say the same for those fancy new water meters.

Data Mining Home Water Usage; Your Water Meter Knows You A Bit Too Well

August 14, 2019 by 70 Comments

The average person has become depressingly comfortable with the surveillance dystopia we live in. For better or for worse, they’ve come to accept the fact that data about their lives is constantly being collected and analyzed. We’re at the point where a sizable chunk of people believe their smartphone is listening in on their personal conversations and tailoring advertisements to overheard keywords, yet it’s unlikely they’re troubled enough by the idea that they’d actually turn off the phone.

But even the most privacy-conscious among us probably wouldn’t consider our water usage to be any great secret. After all, what could anyone possibly learn from studying how much water you use? Well, as [Jason Bowling] has proven with his fascinating water-meter data research, it turns out you can learn a whole hell of a lot by watching water use patterns. By polling a whole-house water flow meter every second and running the resulting data through various machine learning algorithms, [Jason] found there is a lot of personal information hidden in this seemingly innocuous data stream.

The key is that every water-consuming device in your home has a discernible “fingerprint” that, with enough time, can be identified and tracked. Appliances that always use the same amount of water, like an ice maker or dishwasher, are obvious spikes among the noise. But [Jason] was able to pick up even more subtle differences, such as which individual toilet in the home had been flushed and when.

Further, if you watch the data long enough, you can even start to identify information about individuals within the home. Want to know how many kids are in the family? Monitoring for frequent baths that don’t fill the tub all the way would be a good start. Want to know how restful somebody’s sleep was? A count of how many times the toilet was flushed overnight could give you an idea.

In terms of the privacy implications of what [Jason] has discovered, we’re mildly horrified. Especially since we’ve already seen how utility meters can be sniffed with nothing more exotic than an RTL-SDR. But on the other hand, his write-up is a fantastic look at how you can put machine learning to work in even the most unlikely of applications. The information he’s collected on using Python to classify time series data and create visualizations will undoubtedly be of interest to anyone who’s got a big data problem they’re looking to solve.

A Post-Mortem For An Electric Car Charger

August 7, 2019 by 46 Comments

[Mastro Gippo] recently purchased a wall mounted charger for his electric car that looked great and had all the bells and whistles he wanted. There was only one problem: the thing burned up on him. Looking to find out how this seemingly high-end piece of hardware gave up the ghost so easily, he took it apart and tried to figure out where things went wrong. While he’s not looking to sling any mud and actually name the company who produced the charger, he certainly has some choice words for whoever green-lit this particular design.

With the charger open, there’s little doubt that something became very toasty inside. A large swath of the PCB has a black char mark on it, and in some places it looks like the board burned right through. After a close examination, [Mastro] is of the opinion that the board heated up to the point that the solder actually liquified on some connections. This conductive flow then shorted out components below it, and things went from bad to worse.

But where did all the heat come from? [Mastro] was stunned to see that a number of the components inside the charger were only rated for 30 amps, despite the label for the product clearly stating it’s good for up to 32A. With components pushed past their limits, something had to give. He wonders how such a device could have made it through the certification process; an excellent question we’d love to know the answer to.

The worst part is, it looks like the designers might have even known there was an overheating issue. [Mastro] notes that there are heatsinks bolted not to a component as you might assume, but directly to the PCB itself. We’ve seen what happens when designers take a cavalier attitude towards overheating components, and the fact that something like an electric vehicle charger was designed so poorly is quite concerning.

A Trillion Trees – How Hard Can It Be?

August 5, 2019 by 167 Comments

Data from 2016 pegs it as the hottest year since recording began way back in 1880. Carbon dioxide levels continue to sit at historical highs, and last year the UN Intergovernmental Panel on Climate Change warned that humanity has just 12 years to limit warming to 1.5 C.

Reducing emissions is the gold standard, but it’s not the only way to go about solving the problem. There has been much research into the field of carbon sequestration — the practice of capturing atmospheric carbon and locking it away. Often times, this consists of grand plans of pumping old oil wells and aquifers full of captured CO2, but there’s another method of carbon capture that’s as old as nature itself.

As is taught in most primary school science courses, the trees around us are responsible for capturing carbon dioxide, in the process releasing breathable oxygen. The carbon becomes part of the biomass of the tree, no longer out in the atmosphere trapping heat on our precious Earth. It follows that planting more trees could help manage carbon levels and stave off global temperature rises. But just how many trees are we talking? The figure recently floated was 1,000,000,000,000 trees, which boggles the mind and has us wondering what it would take to succeed in such an ambitious program.

Continue reading “A Trillion Trees – How Hard Can It Be?” →