| Literature DB >> 35956706 |
Abstract
Approximately 45% of global greenhouse gas emissions are caused by the construction and use of buildings. Thermal insulation of buildings in the current context of climate change is a well-known strategy to improve the energy efficiency of buildings. The development of renewable insulation material can overcome the drawbacks of widely used insulation systems based on polystyrene or mineral wool. This study analyzes the sustainability and thermal conductivity of new insulation materials made of Miscanthus x giganteus fibers, foaming agents, and alkali-activated fly ash binder. Life cycle assessments (LCA) are necessary to perform benchmarking of environmental impacts of new formulations of geopolymer-based insulation materials. The global warming potential (GWP) of the product is primarily determined by the main binder component sodium silicate. Sodium silicate's CO2 emissions depend on local production, transportation, and energy consumption. The results, which have been published during recent years, vary in a wide range from 0.3 kg to 3.3 kg CO2-eq. kg-1. The overall GWP of the insulation system based on Miscanthus fibers, with properties according to current thermal insulation regulations, reaches up to 95% savings of CO2 emissions compared to conventional systems. Carbon neutrality can be achieved through formulations containing raw materials with carbon dioxide emissions and renewable materials with negative GWP, thus balancing CO2 emissions.Entities:
Keywords: Miscanthus; fiber composites; geopolymer; thermal insulation materials
Year: 2022 PMID: 35956706 PMCID: PMC9371078 DOI: 10.3390/polym14153191
Source DB: PubMed Journal: Polymers (Basel) ISSN: 2073-4360 Impact factor: 4.967
GWP Data of Sodium Silicate—Literature Values and Calculated Values (*) for Comparison of Different Weight Concentrations (36% and 100%) Sorted by Country of Origin.
| Country of Origin | Conc. | GWP | GWP | GWP | SiO2 [%] | Na2O [%] | Ref. |
|---|---|---|---|---|---|---|---|
| Turner and Collins (2013) | 44.1 | 1.514 | 1.236 * | 3.433 * | 29.4 | 14.7 | [ |
| Teh et al. (2017) | 36 | 0.33 * | 0.91 | 29.4 | 14.7 | [ | |
| Sandanayake et al. (2018) | 44.1 | 0.78 | 0.63 * | 1.77 * | 29.4 | 14.7 | [ |
| Rivera et al. (2020) | 44.75 | 0.823 | 0.662 * | 1.84 * | 30.18 | 14.57 | [ |
| Robayo-Salazar et al. (2018) | 44.01 | 0.7925 | 0.6483 * | 1.801 * | 32.09 | 11.92 | [ |
| Robayo-Salazar et al. (2017) | 44.01 | 0.926 | 0.758 * | 2.10 * | 32.09 | 11.92 | [ |
| Mastali et al. (2020) | 36 | 0.59 | 0.590 * | 1.640 | 71.5 * | 28.5 * | [ |
| ProBas (2005) | 100 | 0.737 | 0.737 | n.a. | n.a. | [ | |
| Coppola et al. (2018) | 36 | 0.45 | 0.45 * | 1.24 | 75 * | 25 * | [ |
| Coffetti et al. (2018) | 36 | 1.24 | 1.24 | 1.92 * | 4 | [ | |
| Cristelo et al. (2015) | 39 | 1.096 | 1.012 * | 2.810 * | 26 | 13 | [ |
| Abdollahnejad et al. (2017) | 100 | 1.76 | 1.76 | n.a. | n.a. | [ | |
| Mellado et al. (2014) | 36 | 1.2 | 1.20 * | 3.3 * | 28 | 8 | [ |
| Font et al. (2020) | 36 | 1.213 | 1.213 * | 3.369 * | 28 | 8 | [ |
| Naqi and Jang (2019) | 100 | 3.61 | 3.61 | n.a. | n.a. | [ | |
| Ouellet-Plamondon et al. (2015) | 36 | 1.14 | 3.08 * | 28.4 * | 8.6 * | [ | |
| Ouellet-Plamondon et al. (2015) | 36 | 0.63 * | 1.76 | 66.7 * | 33.3 * | [ | |
| Habert and Ouellet-Plamondon (2016) | 36 | 1.08 (Europe) | 2.92 * (Europe) | n.a. | n.a. | [ | |
| Scrivener et al. (2018) | 55 | 1.1 | 0.72 * | 2 * | n.a. | n.a. | [ |
| Nguyen et al. (2018) | 36 | 0.241 * | 0.671 | 66.7 * | 33.3 * | [ | |
| Alghamdi et al. (2018) | 36 | 0.55 | 0.55 * | 1.53 * | 27.7 * | 8.3 * | [ |
| Heath et al. (2014) | 0.445 | 1.203 * | 28.4 * | 8.6 * | [ | ||
| Fawer et al. (1999) | 48 | 288 | 1.066 * | 32 * | 16 * | [ |
* calculated based on original concentration to compare the different concentrations.
GWP Data of Fly Ash Sorted by Country of Origin.
| Country of Origin | GWP | Allocation of | Ref. |
|---|---|---|---|
| Flower and Sanjayan (2007) | 27 | processing, transport | [ |
| Gunasekara et al. (2021) | 0.0032 | material extraction to production, | [ |
| Arrigoni et al. (2020) | 0.024 (cut-off approach) | transport; | [ |
| Balaguera et al. (2019) | 2.89 × 10−1 | transport | [ |
| Naroznova et al. (2016) | 0.0132 | production and combustion, | [ |
| Ohenoja et al. (2020) | −0.15 * | sequestration | [ |
| Habert et al. (2011) | 5.26 × 10−3 | processing | [ |
| Hossain et al. (2016) | 0.006 | collection, processing, transport | [ |
| Kurda et al. (2018) | 0.004 | economic allocation (combustion, extraction, transport) | [ |
| Teixeira et al. (2016) | 1.01 × 10−2 | classification, combustion, extraction, transport | [ |
| Lee at al. (2021) | 1.73 × 10−3 | economic allocation (combustion, extraction, transport) | [ |
| Yang et al. (2015) | 0.0196 | processing, storage | [ |
| Chen et al. (2019) | 15.894 | processing, transport | [ |
| Nguyen et al. (2018) | 0.006 | collection, processing | [ |
* calculated based on values in the literature.
Specification of Thermal Conductivity of Popular Insulation Materials and Calculation of GWP for 1 m² Wall Insulation of U = 0.24 W m−2 K−1.
| Properties | Density | Thermal Conductivity | GWP for 1 m³ Insulation Material | Thickness of Insulation | GWP for 1 m² Wall Insulation | Ref. |
|---|---|---|---|---|---|---|
| [kg m−3] | [W m−1 K−1] | [kg CO2 m−³] | [cm] | [kg CO2-eq.] | ||
|
| 30 | 0.035 | 75 | 15 | 389 | [ |
|
| 50 | 0.04 | −96 | 17 | −16 | [ |
|
| 40 | 0.035 | −82 | 15 | −12 | [ |
|
| 80 | 0.035 | 37 | 15 | 432 | [ |
|
| 250 | 0.07 | 167 | 29 | 12,177 | [ |
|
| 400 | 0.1 | 167 | 42 | 27,833 | [ |
|
| 650 | 0.13 | −500 | 54 | −35 | [ |
Figure 1Heat flow through a brick wall combined with an insulation layer (left) and possible external and internal positions of insulation material in house construction alongside walls, roof, and flooring (right).