Smart Diverter for Rainwater Harvesting Systems
The goal of this project was to create a smart diverter for Rainwater Harvesting system using Arduino technology to decrease material use and interm lowering the price for individuals in economically disadvantaged individuals.
Abstract
Many people around the world do not have access to clean and potable water to drink or use. This has led to many deaths arising from the consumption of unclean water. Governments have addressed this concern through the utilization of different strategies such as desalination, wastewater recycling and more simply Rainwater Harvesting (RWH). Uganda, a country located in East Africa, experiences a lack of access to clean water while exhibiting potential solutions through RWH, this is because it experiences bimodal rainfall patterns. The potential for a solution to the issue led to the proposal for the integration of a smart rainfall diverter into traditional RWH systems. for the purpose of collecting cleaner water from the rainfall collected while also being low cost for the faster adoption by the largely impoverished communities of Uganda. It does this through digitizing the first-flush process typically used in RWH systems. When rainfall was simulated on a model house and the turbidity of the water with the smart diverter was lower than that of the system without the smart diverter. This demonstrated the ability for the smart diverter to assist in the filtration. Given the results, the proposed smart rainfall diverter shows promising ability to obtain clean water from rainfall.
Keywords: Rainwater Harvesting (RWH), Smart Diverter, Water Access, Arduino-Technology, Turbidity, Uganda
Click to view my Project Proposal
Problem Statement:Most Ugandan RWH systems lack a first-flush mechanism for water cleanliness and face limited adoption due to cost barriers.
Engineering Goal: To create a Smart Diverter with a first-flush mechanism and turbidity monitoring to improve water cleanliness and address cost barrierss in Ugandan RWH systems.
Background
Water scarcity affects 1.2 billion people globally, worsened by climate change, population growth, and urbanization. Strategies like desalination, wastewater recycling, and rainwater harvesting (RWH) have been implemented to improve water access. RWH, the collection and storage of rainwater, has been practiced for centuries and is gaining renewed attention globally (Khanal et al., 2020). In Africa, it is viable in regions with seasonal rainfall patterns, and Uganda, with bimodal rainfall and 70% of houses having suitable roofs, has significant potential. However, adoption remains low due to a lack of information, knowledge, and cost concerns (Ministry of Water and Environment, 2022).
Procedure
The objective of this technique is to evaluate how effective the first-flush diverter is by diverting the first-flush away from the clean water tank. The equation: V=AxP (Sunar et al., 2018) Where V is the first-flush volume, A is the area of the roof, and P is the pollution factor which is determined based on environmental conditions: Environment Condition Pollution Factor (P) High tree cover & bird activity 2 L/m² Open area, low contamination 0.2 L/m² In the experiment the pollution factor of .2 was used for consistency with industry standards, and since less of the water would be diverted it allowed for the quicker obtainment of data. The V was calculated to be ≈500ml, equal to the volume of the turbid water. The system initializes with the solenoid to the dirty water tank open till the first flush volume has been collected. The turbid water was fed into the system, it was monitored visually while it was diverting the turbid water to the dirty water tank making sure no irregularities occurred. After all the turbid water was fed into the system, cleaner water was introduced mimicking complete cleansing of the roof. The turbidity values of both tanks were collected immediately using the Vernier turbidity sensor at conclusion of each run for 15 trials.
Figure 1: Turbidity values (NTU) of the clean and dirty water tanks across 15 trials. The dirty water tank consistently shows higher turbidity, indicating effective diversion of contaminants by the first-flush system.
Figure 2: This box plot compares the turbidity values (NTU) of the clean and dirty water tanks over 15 trials. The dirty water tank shows consistently higher turbidity with less variation, while the clean water tank exhibits greater variability but significantly lower turbidity levels.
Figure 3: This box plot compares the turbidity values (NTU) of the clean and dirty water tanks over 15 trials. The dirty water tank shows consistently higher turbidity with less variation, while the clean water tank exhibits greater variability but significantly lower turbidity levels.
Analysis
| Tank Type | Mean Turbidity (NTU) | Standard Deviation |
|---|---|---|
| Dirty Water Tank | 30.8 | 1.9 |
| Clean Water Tank | 16.1 | 3.6 |
The turbidity values of clean and dirty water tanks were recorded over 15 trials. The table above presents the turbidity values in Nephelometric Turbidity Units (NTU) for both tanks. While Figure 2 displays the turbidity levels for both tanks showing a consistently higher turbidity in the dirty water tank compared to the clean water tank The two-independent sample t-test was conducted, returning a t-statistic of 13.51 and a p-value of **** 7.62x10^(-12)≈0 . These values indicate a statistically significant difference in turbidity between the clean and dirty tanks.
Discussion and Conclusion
The flow sensor generally functioned well but showed reduced accuracy when flow was low or sporadic. The original plan to integrate a turbidity sensor into the system was not feasible due to technological constraints. The statistical analysis revealed a significant reduction in turbidity levels in the clean water tank compared to the dirty water tank, indicating the first-flush system's effectiveness. However, the clean water tank's turbidity remained above the WHO's recommended 5 NTU, likely due to residual contamination in the pipes. Additionally, the solenoid motors and controllers experienced power inefficiencies, generating excessive heat, which could discourage system adoption due to higher energy consumption. Conclusion This study demonstrated that the smart diverter system successfully reduced turbidity in the clean water tank, although it did not meet the WHO's 5 NTU threshold for safe drinking water. While the system showed promise, challenges such as the flow sensor's performance under low or sporadic flow and inefficiencies in power usage with the solenoid motors and controllers were identified. Future improvements should focus on integrating a turbidity sensor, addressing flow sensor accuracy, and optimizing power consumption to enhance the system's effectiveness and make it more practical for widespread use.