Multi-Objective Optimization of Building Design Parameters for Cost Reduction and CO2 Emission Control Using Four Different Algorithms
Abstract
:1. Introduction
2. Mathematical Model
2.1. Degree-Day Method Incorporating Solar Radiation Effect
2.2. Determination of Annual Heat Loss and Gain through Building Walls
2.3. Economic Assessment and CO2 Emission Calculations
Heating Source | Emission Factor, fh | Lower Heating Value, Hu | Efficiency, η | Price, Cf |
---|---|---|---|---|
(kgCO2/kWh) | (%) | |||
Natural Gas | 0.194 | 34.526 × 106 J/m3 | 93 | 0.327 USD/m3 |
Electricity | 0.588 | 3.599 × 106 J/kWh | 99 | 0.1059 USD/kWh |
Fuel Oil | 0.268 | 40.594 × 106 J/kg | 80 | 0.734 USD/kg |
2.4. Multi-Objective Optimization and Knee Point
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Wall Components | Emission Factor, f | Density, ρ | Conductivity, k | Price, C |
---|---|---|---|---|
(kgCO2/kg) | (kg/m3) | (W/mK) | (USD/m3) | |
Expanded Polystyrene (EPS) | 3.51 | 20 | 0.036 | 100 |
Extruded Polystyrene (XPS) | 3.83 | 30 | 0.037 | 150 |
Mineral Wool (MW) | 1.16 | 55 | 0.04 | 130 |
Polyurethane Foam (PUR) | 4.47 | 40 | 0.036 | 200 |
Plaster | 0.36 | 1800 | 0.87 | 90 |
Light Concrete | 0.09 | 1700 | 0.71 | 85 |
Reinforced Concrete | 0.12 | 2400 | 2.5 | 120 |
Brick | 0.31 | 1200 | 0.45 | 45 |
Insulation Thickness (m) | Insulation Material | Wall Material | Heating Source | |
---|---|---|---|---|
Base Case Scenario | 0.02 | XPS | Brick | Fuel Oil |
Optimum Cost | 0.0470 | EPS | Brick | Natural Gas |
Optimum CO2 | 0.1961 | EPS | Light concrete | Natural Gas |
Cost-Oriented Knee Point | 0.1312 | EPS | Brick | Natural Gas |
CO2-Oriented Knee Point | 0.0790 | EPS | Light concrete | Natural Gas |
Method | Scenario | CT (USD/m2) | MCO2 (kg/m2) | % CT | % MCO2 |
---|---|---|---|---|---|
Base Case Scenario | 42.60 | 20.14 | - | - | |
CCGDE3 | Optimum Cost | 34.27 | 15.56 | 20% | 23% |
Optimum CO2 | 52.44 | 9.18 | −23% | 54% | |
MOEA/D-UR | Optimum Cost | 34.27 | 15.55 | 20% | 23% |
Optimum CO2 | 52.41 | 9.18 | −23% | 54% | |
DEA-GNG | Optimum Cost | 34.27 | 15.55 | 20% | 23% |
Optimum CO2 | 52.45 | 9.18 | −23% | 54% | |
NSGA-II | Optimum Cost | 34.27 | 15.50 | 20% | 23% |
Optimum CO2 | 52.45 | 9.18 | −23% | 54% | |
Cost-Oriented Knee Point | 38.84 | 13.68 | 9% | 32% | |
CO2-Oriented Knee Point | 43.56 | 10.17 | −2% | 50% |
Insulation Thickness (m) | Insulation Material | Wall Material | Heating Source | |
---|---|---|---|---|
Base Case Scenario | 0.02 | XPS | Brick | Fuel Oil |
Optimum Cost | 0.0499 | EPS | Brick | Natural Gas |
Optimum CO2 | 0.2055 | EPS | Light concrete | Natural Gas |
Cost-Oriented Knee Point | 0.1368 | EPS | Brick | Natural Gas |
CO2-Oriented Knee Point | 0.0829 | EPS | Light concrete | Natural Gas |
Method | Scenario | CT (USD/m2) | MCO2 (kg/m2) | % CT | % MCO2 |
---|---|---|---|---|---|
Base Case Scenario | 48.45 | 22.40 | - | - | |
CCGDE3 | Optimum Cost | 34.84 | 15.79 | 28% | 30% |
Optimum CO2 | 53.44 | 9.31 | 28% | 30% | |
MOEA/D-UR | Optimum Cost | 34.84 | 15.79 | 28% | 30% |
Optimum CO2 | 53.44 | 9.31 | 28% | 30% | |
DEA-GNG | Optimum Cost | 34.84 | 15.80 | 28% | 30% |
Optimum CO2 | 53.47 | 9.31 | 28% | 30% | |
NSGA-II | Optimum Cost | 34.84 | 15.79 | 28% | 30% |
Optimum CO2 | 53.47 | 9.31 | −10% | 58% | |
Cost-Oriented Knee Point | 39.51 | 13.83 | 18% | 38% | |
CO2-Oriented Knee Point | 44.16 | 10.36 | 9% | 54% |
Insulation Thickness (m) | Insulation Material | Wall Material | Heating Source | |
---|---|---|---|---|
Base Case Scenario | 0.02 | XPS | Brick | Fuel Oil |
Optimum Cost | 0.056 | EPS | Brick | Natural Gas |
Optimum CO2 | 0.2245 | EPS | Light concrete | Natural Gas |
Cost-Oriented Knee Point | 0.1493 | EPS | Brick | Natural Gas |
CO2-Oriented Knee Point | 0.0906 | EPS | Light concrete | Natural Gas |
Method | Scenario | CT (USD/m2) | MCO2 (kg/m2) | % CT | % MCO2 |
---|---|---|---|---|---|
Base Case Scenario | 54.52 | 25.10 | - | - | |
CCGDE3 | Optimum Cost | 36.07 | 16.26 | 34% | 35% |
Optimum CO2 | 55.62 | 9.58 | −2% | 62% | |
MOEA/D-UR | Optimum Cost | 36.07 | 16.24 | 34% | 35% |
Optimum CO2 | 55.50 | 9.58 | −2% | 62% | |
DEA-GNG | Optimum Cost | 36.07 | 16.24 | 34% | 35% |
Optimum CO2 | 55.57 | 9.58 | −2% | 62% | |
NSGA-II | Optimum Cost | 36.07 | 16.25 | 34% | 35% |
Optimum CO2 | 55.57 | 9.58 | −2% | 62% | |
Cost-Oriented Knee Point | 41.10 | 14.11 | 25% | 44% | |
CO2-Oriented Knee Point | 45.42 | 10.74 | 17% | 57% |
Insulation Thickness (m) | Insulation Material | Wall Material | Heating Source | |
---|---|---|---|---|
Base Case Scenario | 0.02 | XPS | Brick | Fuel Oil |
Optimum Cost | 0.0797 | EPS | Brick | Natural Gas |
Optimum CO2 | 0.2971 | EPS | Light concrete | Natural Gas |
Cost-Oriented Knee Point | 0.1992 | EPS | Brick | Natural Gas |
CO2-Oriented Knee Point | 0.1298 | EPS | Light concrete | Natural Gas |
Method | Scenario | CT (USD/m2) | MCO2 (kg/m2) | % CT | % MCO2 |
---|---|---|---|---|---|
Base Case Scenario | 79.66 | 36.58 | - | - | |
CCGDE3 | Optimum Cost | 40.80 | 17.97 | 49% | 51% |
Optimum CO2 | 63.64 | 10.60 | 20% | 71% | |
MOEA/D-UR | Optimum Cost | 40.80 | 17.97 | 49% | 51% |
Optimum CO2 | 63.52 | 10.60 | 20% | 71% | |
DEA-GNG | Optimum Cost | 40.80 | 17.97 | 49% | 51% |
Optimum CO2 | 63.84 | 10.60 | 20% | 71% | |
NSGA-II | Optimum Cost | 40.80 | 17.97 | 49% | 51% |
Optimum CO2 | 63.60 | 10.60 | 20% | 71% | |
Cost-Oriented Knee Point | 47.21 | 15.20 | 41% | 58% | |
CO2-Oriented Knee Point | 50.71 | 11.93 | 36% | 67% |
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Canbolat, A.S.; Albak, E.İ. Multi-Objective Optimization of Building Design Parameters for Cost Reduction and CO2 Emission Control Using Four Different Algorithms. Appl. Sci. 2024, 14, 7668. https://doi.org/10.3390/app14177668
Canbolat AS, Albak Eİ. Multi-Objective Optimization of Building Design Parameters for Cost Reduction and CO2 Emission Control Using Four Different Algorithms. Applied Sciences. 2024; 14(17):7668. https://doi.org/10.3390/app14177668
Chicago/Turabian StyleCanbolat, Ahmet Serhan, and Emre İsa Albak. 2024. "Multi-Objective Optimization of Building Design Parameters for Cost Reduction and CO2 Emission Control Using Four Different Algorithms" Applied Sciences 14, no. 17: 7668. https://doi.org/10.3390/app14177668