1 of 1

0

Rainwater Collection Design

Team Members: Katie Hefty, Cole Collins, Harris Kopelman

Faculty Advisor: Dr. Stooksbury

Introduction

Solution

Sponsor/Client:

Objective

Impact

The rainwater harvesting market was projected to be worth $11.7 Billion USD in 2025. Their currently isn't a standardized way to size these systems because there are so many varying factors such as climate, collection area, water usage etc.​

Rainwater harvesting systems have many components including a collection tank, a filtration system based on demand and sometimes a secondary tank or a 'buffer tank' all of which need to be sized correctly.

​

​

​

​

​

�​�Problem Statement:​�Current systems are frequently sized incorrectly because designers rely on "worst-case" assumptions. This leads to oversized, expensive tanks that building owners eventually shut off, wasting the initial investment

Develop a reliable, easy-to-use Excel tool that helps engineers accurately size rainwater systems to improve cost-effectiveness and sustainability.​�​�Deliverables:​�A Mass Balance Daily Simulation calculator in Microsoft Excel that provides tank sizing, performance metrics, and financial analysis.​

The team has created an Excel based simulation that uses 30+ years of weather data and a water balance equation to compare and determine the best tank size. This model  has the capabilities to calculate direct and indirect system as well as run a cost analysis while considering spatial constraints and calculation a return on investment. ​

Key Features

• Missing Data: The tool places zeros in for the days with missing weather data.
• Size Constraints: Users can input specific roof areas and tank capacity constraints to account for architectural limitations.
• Sizing Logic: The program accurately sizes both storage and buffer tanks while providing flow rates and filtration requirements.

​

Implementation:​

Application to Driftmier:

​

​

​

​

​

​

​

​

​

​

​

​

​

​

Verification:​

To account for weather sets with missing data, the team compared efficiency of a full weather data set versus one with various missing data solutions. This included interpolation, different averages and zeroing the missing days. The team determined the solution that had the least variability to the full data set was filling in missing days with zeros.

​

Less mis sized tanks, more efficient process, increased return on investment, higher interest in rainwater systems​

Cole Collins

Team lead

Katie Hefty

Research Lead

Harris Kopelman

Communication Lead