Recycling Textile Waste to Enhance Building Thermal Insulation and Reduce Carbon Emissions: Experimentation and Model-Based Dynamic Assessment
Abstract
:1. Introduction
2. Textile Reinforced Mortar: Preparing and Testing Methods
2.1. Samples Preparation
2.2. Workability and Density Measurement
2.3. Mechanical Characterization
2.4. Thermal Characterization
2.4.1. Analytical Prediction of Composite Thermal Conductivity
- Series and Parallel models: Figure 5 shows the two plain theoretical methodologies that have been followed to predict the effect of adding a reinforcing material on the thermal conductivity of a matrix. The first approach focuses on individually considering the contribution of each component to model the thermal conductivity of the composite through the application of the percolation theory [54]. Based on the electrical analogy, this model is called a series model. In this case, the effective thermal conductivity of the composite material is given by [55,56,57]:
- Maxwell model: This model was developed to define the electrical conductivity of a heterogeneous medium composed of dispersed spheres. The development of this model provides an accurate solution for the effective thermal conductivity of arbitrarily distributed homogeneous spherical particles without interaction in a homogeneous matrix [56]:
- Rayleigh model: This model was adapted to predict the effect of cylindrical reinforcement materials on the thermal conductivity of a matrix. The equation below allows for the effective thermal conductivity to be calculated [58]:
- Hashin and Shtrikman model: Following the approach of Maxwell and using the perturbation hypothesis, Hashin and Shtrikman developed a model to predict the thermal conductivity of randomly scattered units in a continuous matrix. This model provides upper and lower limits of the effective thermal conductivity rather than deriving an equation for it. Moreover, it is shown that, in the case of composite material, these limits are the most restrictive that one can obtain in terms of volume fractions of charge and conductivity [59]. Equations (5) and (6) indicate the lower and upper limits of the conductivity [55]:
- Hatta and Taya model: Hatta and Taya developed a model to predict the thermal conductivity of a composite consisting of short fibers with different orientations [60,61] based on the analogy of Eshelby [62]. This approach is based on predicting the steady-state equivalent thermal conductivity of the composite by considering the shape and interactions between the additions with different orientations. The equation they arrived at is [55]:
- Nielsen and Lewis model: Nielsen and Lewis derived a semi-theoretical model for predicting thermal conductivity [63,64], based on the Halpin-Tsai equation [65]. They adjusted the model to handle non-spherical additives using a coefficient, which depends on the shape and orientation of the particles. Moreover, Nielsen and Lewis’s model considers the effect of the maximum fraction of the additive, φmax. The semi-empirical model developed is as follows [66]:
2.4.2. Experimental Characterization
2.4.3. Numerical Characterization
3. Evaluation of Characterization Results
3.1. Workability Testing
3.2. Bulk Density Testing
3.3. Mechanical Characterization
3.4. Thermal Characterization
4. Case Study: Numerical Investigation of a Hollow Brick Wall Coated with Textile-Reinforced Mortar
4.1. Numerical Model and Validation
4.2. Computational Assessment of a Textile Reinforced Wall
5. Conclusions and Perspective
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Material | Thermal Conductivity [W/m∙K] | Thermal Diffusivity [mm2/s] | Volumetric Heat Capacity [MJ/m3 K] |
---|---|---|---|
Cement | 0.140 | 0.201 | 0.694 |
Sand | 0.335 | 0.278 | 0.278 |
Textile fibers | 0.082 | 0.418 | 0.196 |
Materials | PM | M10 | M20 | M30 | M40 |
---|---|---|---|---|---|
Cement | 215 | 215 | 215 | 215 | 215 |
Sand | 1540 | 1386 | 1232 | 1078 | 924 |
Textile | 0 | 5.26 | 10.52 | 15.78 | 21.04 |
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Ayed, R.; Bouadila, S.; Skouri, S.; Boquera, L.; Cabeza, L.F.; Lazaar, M. Recycling Textile Waste to Enhance Building Thermal Insulation and Reduce Carbon Emissions: Experimentation and Model-Based Dynamic Assessment. Buildings 2023, 13, 535. https://doi.org/10.3390/buildings13020535
Ayed R, Bouadila S, Skouri S, Boquera L, Cabeza LF, Lazaar M. Recycling Textile Waste to Enhance Building Thermal Insulation and Reduce Carbon Emissions: Experimentation and Model-Based Dynamic Assessment. Buildings. 2023; 13(2):535. https://doi.org/10.3390/buildings13020535
Chicago/Turabian StyleAyed, Rabeb, Salwa Bouadila, Safa Skouri, Laura Boquera, Luisa F. Cabeza, and Mariem Lazaar. 2023. "Recycling Textile Waste to Enhance Building Thermal Insulation and Reduce Carbon Emissions: Experimentation and Model-Based Dynamic Assessment" Buildings 13, no. 2: 535. https://doi.org/10.3390/buildings13020535