Porosity in ion-exchanged and acid activated clays evaluated using n-nonane pre-adsorption

The applicability of the n-nonane pre-adsorption method for characterising the porosity in clays is presented. Na-SD, a Na+-exchanged purified bentonite, and materials obtained by Al3+-exchange and acid treatments of Na-SD and SAz-1 were used. Nitrogen adsorption isotherms, at -196 ºC, were determined before and after n-nonane pre-adsorption on each of the samples. In all materials, n-nonane remained adsorbed in ultramicropores after outgassing at 25 ºC. Outgassing at higher temperatures (50, 75 and 200 ºC) removed nonane and ultramicropores became available for nitrogen adsorption. All treatments on Na-SD led to increase in micropore volume. Larger ultramicropore and supermicropore volumes were obtained for Na-SD acid activated with HCl at 95 ºC than for treatments at 25 ºC with HCl or following Al3+-exchange (Al-SD), and increased with increasing acid concentration to 3 M. Activation with 4 M HCl led to the largest pore volume with contribution from mesopores. However, the specific external surface area was the same as that obtained for Na-SD, Al-SD and for most of the other acid activated samples. Treatments at 95 ºC with 1 M and 6 M HCl promoted increase in specific external surface area. The micropore volumes and specific external surface area for SAz-1 treated with 1 M HCl at 95 ºC were larger than those of Al-SAz-1, but lower than those obtained for corresponding materials derived from Na-SD. The n-nonane pre-adsorption method enabled micropore volumes and specific external surface areas to be obtained for all samples.

Classi cation of new electricity customers based on surveys and smart metering data

This paper proposes a process for the classifi cation of new residential electricity customers. The current state of the art is extended by using a combination of smart metering and survey data and by using model-based feature selection for the classifi cation task. Firstly, the normalized representative consumption profi les of the population are derived through the clustering of data from households. Secondly, new customers are classifi ed using survey data and a limited amount of smart metering data. Thirdly, regression analysis and model-based feature selection results explain the importance of the variables and which are the drivers of diff erent consumption profi les, enabling the extraction of appropriate models. The results of a case study show that the use of survey data signi ficantly increases accuracy of the classifi cation task (up to 20%). Considering four consumption groups, more than half of the customers are correctly classifi ed with only one week of metering data, with more weeks the accuracy is signifi cantly improved. The use of model-based feature selection resulted in the use of a signifi cantly lower number of features allowing an easy interpretation of the derived models.