Our project will be based around wastewater detection in rivers and lakes. Specifically we will be testing in the Pittsburgh rivers region because in times of heavy rainfall, waste tends to end up in the rivers due to infrastructure problems. Our device will be able to tell the concentration of wastewater in the location where the sensor is placed.
1. High-level project introduction and performance expectation
Figure 1: Map of Sanitary Sewer and Combined Sewer Outfalls. Source: Alcosan
Our native city, Pittsburgh, as well as many other cities have trouble with wastewater finding its way into the local rivers or bodies of water. This is an unacceptable problem with water treatment. A constant stream of data to monitor the contents of the city’s freshwater would be tremendously helpful in determining where the pollutants are coming from, and if they are exceeding dangerous levels for the animals that may inhabit these waters.
We will be using sensors that detect E. Coli in the water to determine that level at which the water is contaminated. This can be done by using either a fluorescence sensor that can detect BOD, COD, TOC and Total coliforms or via petri dishes and computer vision. E. Coli is a strong indicator of wastewater contamination which is why we are focusing primarily on this strain of bacteria. Ultimately, the goal of this project is to leverage FPGAs’ low power, multiple-sensor computing capabilities to provide constant data on the water contents found in water treatment plants. The FPGA will be able to take information from attached sensors and upload that information to a cloud-based data center, such that all information on the water’s quality is remotely and autonomously collected.
These sensors will be placed throughout the river to see what locations are generating the most amount of wastewater. They are connected through transmitters that are floating at the top of the water that are connected to a self-contained solar panel to make sure that no toxins get in the water. This way the transmitter can be kept in the water for long periods of time while still being able to transmit data. The sensor will also be located near the surface of the water, just deep enough to be able to collect the proper data. In the middle of all of the sensors will be the FPGA that will gather data and send it to the cloud.
Once in the cloud, we will gather all of the data and create a simple interface that can be used as an executable on a local machine that will pull data from the cloud and generate points on a map that show where wastewater is present in large amounts and where it is present in lesser amounts.
Through this, treatment at these locations would be easier to manage from a city perspective by knowing where the wastewater is coming from. The city could then choose to change the infrastructure because the knowledge of where the wastewater is coming from would be provided, or to build a treatment plant that treats a sufficient amount of water which would also be detected by the sensor via sensing the concentration of the E. Coli in the water and then applying this knowledge to the river as a whole.
2. Block Diagram
3. Expected sustainability results, projected resource savings
Cities will have access to precise information on where wastewater is coming into bodies of water.
In addition to raw data concerning the water quality in a city, a map of all the sensors’ territory will be made to represent areas with poor quality water and better quality water.
Infrastructure can be built with this data in mind, where poorer waters would get more attention than cleaner waters.
Knowledge of where to look for water contamination will make changes or repairs to pipelines faster, more efficient, and therefore cheaper.
Sensors should be easy to add/remove, for switching out devices if necessary. Network may be easily expanded and placed in any location with water.
4. Design Introduction
5. Functional description and implementation
6. Performance metrics, performance to expectation
7. Sustainability results, resource savings achieved