ABSTRACT
Our aim was to investigate the feasibility of using limestone waste resulting from stone processing for the manufacturing of fired clay bricks. Waste materials were considered as a partial replacement for clays to reduce the exploitation of natural resources and as a response to the climate neutrality commitments. The samples were prepared to have a waste content of up to 15% and were fired at a temperature of 900 °C. The chemical and mineralogical composition and the physical analysis of raw materials were investigated by using SEM-EDS and XRD diffraction. The result showed an increase in CaO in the clay mixture due to the presence of limestone, which reduced the shrinkage of the products' compressive strength, up to 55% for samples with a higher content of limestone (15 wt.%), and influenced the samples' color by making them lighter than the reference sample.
ABSTRACT
The present work examines an innovative manufacturing technique for fired clay bricks, using tuff as a secondary raw material. Samples were made of clay and tuff (0-30 wt.%) fired at 900 to 1100 °C. The chemical and mineralogical compositions and physical and thermal analyses of raw materials were investigated by using SEM-EDS, RX and DTA-TG curves. The samples were analysed from the mineralogical, technological and mechanical points of view. The result show that the tuff's presence in the clay mixtures considerably reduced the shrinkage of the product during the firing process, and the manufactured samples were of excellent quality. The compressive strength of the bricks varied from 5-35.3MPa, being influenced by the tuff content, clay matrix properties and firing temperatures. Finally, the heat demand for increasing the temperature from room to the firing temperature of the sample with 10% tuff content was 22%.
ABSTRACT
The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO2 emissions reduction) or the resulted values were far much higher (>50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier's removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection.
