2. Overview of Each Project’s:
1) Prototype
2) Control System
3) Testing and Results
4) Impact
Discussion and Conclusion
Questions and Comments

3. Inline Fan - Air Redistribution:
Primary Aims:
- Reduce energy and cost needed to regulate temperature
- Use a design that can be retrofit into existing homes
- Implement an elegant and simple solution
Secondary Aims:
- Offset Alberta’s Greenhouse Gas Emissions

4. - Scaled Model (1:20)
- House made entirely of Plexiglas
- 2 fans to simulate reversible flow
- PVC piping and adapters
Prototype

5. Control System
- Arduino Uno Microcontroller
- Adafruit Motorshield (MOS-FET, etc)
- Thermocouple Inputs (x3, +/- 0.5 C)
- Controller programmed in MATLAB
1) GUI-based control system
2) Stand-alone control system

6. Testing & Results
1) Thermocouple performance
2) Fan performance (8.5 cfm nominal,
<8 cfm actual).
3) Flow Analysis
-Dry Ice (CO2(s)) used to track flow
patterns
-Flow patterns replicated in
Solidworks based on forced and
convective flow studies.

7. Developed a Graphical User Interface (GUI)
- Stable and fully functional
- Permits performance testing due to temperature perturbations
- Measurements “rolling-time averaged” on the second scale

8. Testing & Results Cont’d
Fan system can control
heating in a home at all
flow rates.
Further testing
required at still lower
flow rates
- requires a larger scale
physical prototype.
Figure: AB – testing scenario trace
Figure: BA – testing scenario trace
Figure: Response times;
cuts response time in half.

9. Testing & Results Cont’d
‘Scaling Up’ with Simulink Model:
1) Control System, 2) Energy Use, 3) Cost and
4)Human Factors Performance
Fig. BAU model – Standard Home Fig. Alternative model – RetrofitHome

10. Impact: Market Deployment &
Economics Assumptions:
- 5% annual growth, 10
year ramp up
- Retrofit into AB SD-
homes (10-30 y.o.)
- Offset high efficiency
(95%) natural gas
furnaces and AC units
Retrofit payback time of 3-
5 yrs assuming:
CAPEX: $3000.00-
$5000.00
OPEX: $100.00 annually
Figure: Deployment of inline fan
system to AB residences
Figure: AB Greenhouse gas emissions
avoided due to inline fan systems

11.  Scale up production to full scale in order to test:
 Lower flow rates
 PID Control Parameters?
 Real World Energy Savings and Direct GHG reductions
 Human Factors
 Costs: Capital Costs - Installation? (CF=3), Control Tuning
 Marketing: Selling as AC may offer the greatest GHG
reduction potential in the near term.
Gap Analysis: How to move forward

12. Rainwater Management Specific
Aims
Primary Aims:
- Reduce home water usage using a simple retrofit
design
- Reduce net cost for outdoor water use
Secondary Aim:
- Reduce municipally treated water usage

13. Background Research
- In 2003, the City of Calgary found, on average, lawns
received 11.25 cm (4.5 inches) of water a week
- On average this is an excess of 236,000 L or $686.20
annually
- Rainwater harvesting alone will account for 95% of the
yards water demands
- In combination with the control system this value
should increase
- System efficiency should save hundreds of thousands
of liters of water and hundreds of dollars.

14. Overview – Rainwater Collection System
Figure: Rainwater Collection
and Management System –
Prototype Conceptual Design

15. Prototypes
- Two drip irrigation lines feeding water
to two soil cups
- One dry soil cup, one moist soil cup
- Swapping cups allows for repeated
demonstrations for viewing at design
fair
- Irrigation lines actuated through valves
- Stand constructed out of PVC tubing
- Water pumped out from small
reservoir (Bucket)

16. Control System
- Coded in Python
- Comprised of an infinite
loop taking in voltages
- Simple logical flow
comprised of if and else
statements
- Loops will run accordingly
to researched horticultural
constants

17. Control System
- Bread-boarded Analog to Digital
Converter running i2C digital protocol
- 2 inputs per converter
- Control system coded in Python
actuating pumps and valves through
general purpose input/outputs on
Wandboard
- Ability to run autonomously as an
embedded program
- Ability to wirelessly log in and tune
system through SSH

18. Testing and Results
- This test displays the use of
different calibration
formulae.
- Based on testing, we found
that the sensors must be:
- Away from container edges
and other sensors
- Soil must be packed
around/over sensors for
more accurate results

19. Testing and Results
- Tests were conducted in
different ranges of soil
moisture
- The graph displays means
from both sensors in the
same dry soil – consistency
was validated
- Similarly, tests were
conducted for humid soil
to compare values

20. Testing and Results
- This test demonstrates
what happens when you
add water to dry soil
- Clearly, there is a much
lower average once water is
added
- Numerous similar tests
helped us determine a
constant to be used in the
control logic

21. Testing and Results
- Based on tests performed, we have created a
control logic that determines when the system
should water
- This is based on
- The calibrated values of the sensors in dry soil
- Continuous while loop that considers average
moisture content
- The system will only activate if the average moisture
content in a zone is greater than the calibrated
constant

22. Data Collection and Analysis
Engineering Analysis based on housebrand test house:
- Collectable rainwater based on 1425 ft2 roof size is 11,210 Gallons
- From the Alberta Guidelines for Residential Rainwater Harvesting Systems
Handbook the required tank size is 4000 L or 1000 Gallons
- This value is calculated for a yard that is 70% lawn, which has the
highest water consumption rate.

23. Future Model Implementation
USER INPUT:
- Roof area
- Number of zones
- Plant type per zone
- Soil type
- Zone size
RESULTS:
- Tank size
- # of sensors
- Watering threshold
values
- Plant WUE
- Sensor positioning
- Ease of use: Excel sheet to properly size system based on
home/yard
- Useful for system calibration and user interface

24. Environmental Analysis – Water Use:
Deployment Model: Water Usage Avoided – Residential:
At 10 Years:
• 9.3 million m3 / year
• 36.4 million m3 / year
At 20 Years:
• 25.9 million m3 / year
• 10.9 billion m3 / year

25. Conclusion:
Each project has the potential to
1) Decrease material and energy use
2) Lower GHG emissions while cutting costs for homeowners
When considering the problems of global climate change and
clean water supply, the usefulness of these projects becomes clear.
Remaining Objectives:
1) Minor prototype adjustments
2) Unify models into a single sustainable home prototype

26. Thank You!