Open Access

Der Kurzbericht gibt Einblicke in die wichtigsten Ergebnisse aus einer Umfrage die im Oktober 2022 durchgeführt wurde.

AbstractThe construction and building sector is responsible for a large part of the world’s resource and energy consumption and is considered the largest global emitter of greenhouse gas (GHG) emissions. Hazardous and toxic substances in building materials affect indoor air quality as well as the environment and thus have a high impact on human health, as we spend around 90 percent of our lives in buildings. Life cycle assessment (LCA) and hazardous building material requirements of green building certification systems allow to reduce the environmental and health impacts of building products and materials. However, they are usually very complex and time-consuming to perform and require expert knowledge to use the results for decision support. Digital approaches to support the simplified application of these methods and intuitive visualization of results are becoming increasingly important. Especially Building Information Modeling (BIM) offers a high potential for this purpose, as the integration and linking of geometric and semantic information in 3D-models for LCA and hazardous building material assessment can be done much more efficiently and intuitively. Within the scope of this work, the following three objectives were pursued (1) development of a method for combining LCA and hazardous building material assessment, (2) simplification of the results by converting them into comprehensible indicators for decision support, and (3) implementation of the method in a BIM-based digital assistant for intuitive visualization and communication. The preliminary results show a concept for combined use of LCA and hazardous building material assessment in Germany with differentiation in six use cases. A prototypical implementation as BIM-integrated digital assistant was developed for one of these use cases. For the first time, this prototype provides understandable feedback in real time of LCA and hazardous building material requirements. This research project contributes to the awareness in the context of embodied impacts and low emitting materials in buildings and advances the current digitalization potentials.

Potential analyses identify possible locations for renewable energy installations, such as wind turbines and photovoltaic arrays. The results of previous potential studies for Germany, however, are not consistent due to different assumptions, methods, and datasets being used. For example, different land-use datasets are applied in the literature to identify suitable areas for technologies requiring open land. For the first time, commonly used datasets are compared regarding the area and position of identified features to analyze their impact on potential analyses. It is shown that the use of Corine Land Cover is not recommended as it leads to potential area overestimation in a typical wind potential analyses by a factor of 4.7 and 5.2 in comparison to Basis-DLM and Open Street Map, respectively. Furthermore, we develop scenarios for onshore wind, offshore wind, and open-field photovoltaic potential estimations based on land-eligibility analyses using the land-use datasets that were proven to be best by our pre-analysis. Moreover, we calculate the rooftop photovoltaic potential using 3D building data nationwide for the first time. The potentials have a high sensitivity towards exclusion conditions, which are also currently discussed in public. For example, if restrictive exclusions are chosen for the onshore wind analysis the necessary potential for climate neutrality cannot be met. The potential capacities and possible locations are published for all administrative levels in Germany in the freely accessible database (Tool for Renewable Energy Potentials—Database), for example, to be incorporated into energy system models.

Remaining-useful-life (RUL) prediction of Li-ion batteries is used to provide an early indication of the expected lifetime of the battery, thereby reducing the risk of failure and increasing safety. In this paper, a detailed method is presented to make long-term predictions for the RUL based on a combination of gated recurrent unit neural network (GRU NN) and soft-sensing method. Firstly, an indirect health indicator (HI) was extracted from the charging processes using a soft-sensing method that can accurately describe power degradation instead of capacity. Then, a GRU NN with a sliding window was applied to learn the long-term performance development. The method also uses a dropout and early stopping method to prevent overfitting. To build the models and validate the effectiveness of the proposed method, a real-world NASA battery data set with various battery measurements was used. The results show that the method can produce a long-term and accurate RUL prediction at each position of the degradation progression based on several historical battery data sets.

Wheat straw could be used for pellet production and therefore as solid fuel. However, it presents challenges due to its inferior combustion properties such as high ash content, low gross calorific value (GCV), and low ash melting temperature. To evaluate its combustion properties and based on recent work that improved methane production, wheat straw was subjected to thermobiological pretreatments. Nine pretreated samples based on wheat straw and nine pretreated samples based on compost-wheat straw mixture were produced. In addition, due to the ability to remove minerals and decrease the ash content, a washing process with water as a solvent was used. Ash content, net calorific value (NCV) and ash melting temperatures were evaluated. For the pretreated wheat straw (SW) samples, a 5,8% reduction in ash content was obtained due to the pretreatments when compared to untreated wheat straw. A 55% decrease in ash content was obtained when comparing the same materials before and after the washing process. No statistically significant changes in GCV were found. As for the ash melting temperatures, due to the incubation pretreatment, an average increase in the shrinkage starting temperature (SST) of 4,4% was obtained for anaerobic conditions and a decrease of 2,5% for aerobic conditions, compared to the same material without heat treatment. In addition, an increase in all ash melting temperatures was observed because of the washing process. It was possible to obtain a pellet complying with standard ISO 17225-6 that can be used in medium or large burners and significantly reduces the effort during combustion. For samples pretreated with a homogeneous compost-wheat straw (SKW) mixture, an average ash content decrease of 27% was obtained after using autoclave pretreatment at 140°C, compared to the same material without thermal pretreatment. The biggest decrease was due to the washing process, reducing the ash content on average by 43% when comparing the same materials before and after washing. GCV were 13% lower than samples pretreated with wheat straw, due to the low calorific value and high ash content of the compost. During ash melting temperature tests, an average 60% increase in SST was observed compared to pretreated SW ashes due to the high melting temperature of compost. Results are considered satisfactory since pellets based on this mixture would not cause ash sintering or slagging. However, counter effects were observed as the addition of compost increased the ash content and decreased the GCV, not complying with ISO 17225-6 for non-woody pellets. To achieve a pellet based on a compost-wheat straw mixture that complies with the standards, it is recommended for future research to control the percentage of compost added to the mixture.