This article presents data collected during a measurement campaign conducted on a synthetic gas test bench (SGB) at the Chair of Thermodynamics of Mobile Energy Conversion Systems (TME). The campaign includes 23 light-off experiments utilizing five state-of-the-art diesel oxidation catalysts (DOCs) with varying platinum and palladium formulations. The primary objective is to investigate the oxidation behavior of dimethyl ether (DME) and its impact on other reactions within exhaust gas aftertreatment. Five distinct gas compositions were employed to replicate reduced, model, and realistic conditions for simulating DME and DME/diesel fuel blend exhaust gases. The dataset comprises comprehensive test bench data, including temperature readings, mass flow controller metrics, and gas analytical values. These data are curated primarily for analyzing the light-off temperature ramp, while also encompassing the time frame from pre-conditioning to post-conditioning. This dataset offers valuable insights into DME's oxidation behavior, its co-oxidation effects, and the formation of secondary emissions. The dataset includes results from 20 experiments across DOCs 1-5 for four different gas matrices: 1. A reduced gas matrix for pure DME oxidation with only O2. 2. A realistic gas matrix for DME oxidation under actual exhaust gas conditions, incorporating CO, NO, and CO2 into the reduced mix to simulate DME combustion exhaust. 3. A reduced gas mix for pure DME and propene co-oxidation with only O2. 4. A realistic gas matrix for DME and propene co-oxidation under genuine exhaust conditions, again integrating CO, NO, and CO2 to simulate a DME/diesel exhaust scenario. Additionally, three experiments involving DOCs 1, 3, and 5 are included with gas mixture 5: 5. This realistic gas mix excludes any hydrocarbons (HC) to assess their influence on NO oxidation. The datasets follow this nomenclature: DOC_X_GM_Y, where: X indicates the respective DOC number (1-5), Y denotes the corresponding gas matrix (GM), ranging from 1 to 5. The datasets for exhaust gas analytics encompass key components such as DME, O2, CO, CO2, NO, along with additional HC species generated during the oxidation process of DME and propene. Notably prominent components include formaldehyde, formic acid, methanol, and nitric oxide; N2 is used as a carrier.
This article presents data collected during a measurement campaign conducted on a synthetic gas test bench (SGB) at the Chair of Thermodynamics of Mobile Energy Conversion Systems (TME). The campaign includes 23 light-off experiments utilizing five state-of-the-art diesel oxidation catalysts (DOCs) with varying platinum and palladium formulations. The primary objective is to investigate the oxidation behavior of dimethyl ether (DME) and its impact on other reactions within exhaust gas aftertreatment. Five distinct gas compositions were employed to replicate reduced, model, and realistic conditions for simulating DME and DME/diesel fuel blend exhaust gases. The dataset comprises comprehensive test bench data, including temperature readings, mass flow controller metrics, and gas analytical values. These data are curated primarily for analyzing the light-off temperature ramp, while also encompassing the time frame from pre-conditioning to post-conditioning. This dataset offers valuable insights into DME's oxidation behavior, its co-oxidation effects, and the formation of secondary emissions. The dataset includes results from 20 experiments across DOCs 1-5 for four different gas matrices: 1. A reduced gas matrix for pure DME oxidation with only O2. 2. A realistic gas matrix for DME oxidation under actual exhaust gas conditions, incorporating CO, NO, and CO2 into the reduced mix to simulate DME combustion exhaust. 3. A reduced gas mix for pure DME and propene co-oxidation with only O2. 4. A realistic gas matrix for DME and propene co-oxidation under genuine exhaust conditions, again integrating CO, NO, and CO2 to simulate a DME/diesel exhaust scenario. Additionally, three experiments involving DOCs 1, 3, and 5 are included with gas mixture 5: 5. This realistic gas mix excludes any hydrocarbons (HC) to assess their influence on NO oxidation. The datasets follow this nomenclature: DOC_X_GM_Y, where: X indicates the respective DOC number (1-5), Y denotes the corresponding gas matrix (GM), ranging from 1 to 5. The datasets for exhaust gas analytics encompass key components such as DME, O2, CO, CO2, NO, along with additional HC species generated during the oxidation process of DME and propene. Notably prominent components include formaldehyde, formic acid, methanol, and nitric oxide; N2 is used as a carrier.
Ein Chatbot ermöglicht es, über Textein- und -ausgabe mit einem IT-System zu kommunizieren. Diese technischen Dialogsysteme werden seit einigen Jahren unter anderem zur Unterstützung im Kundenservice eingesetzt. Hierdurch sollen die menschlichen Servicekräfte entlastet, Kosten gespart und die Kundenzufriedenheit erhöht werden. Die Leistungsfähigkeit der Chatbots stößt aber schnell an ihre Grenzen, weil die digitalen Assistenten in den meisten Fällen lediglich standardisierte Kundenanfragen beantworten können. Ganz neue Möglichkeiten im Kundenservice ergeben sich, wenn die Chatbots mit der Prozessautomatisierungstechnologie Robot Process Automation (RPA) gekoppelt werden. Dann kann digitale Servicekraft unternehmensinterne Prozesse starten und komplexere Serviceaufgaben übernehmen. In diesem Beitrag werden vier mögliche Einsatzszenarien vorgestellt, bei denen diese beiden Technologien kombiniert und gewinnbringend in Finanzinstituten eingesetzt werden.
The German food retail market is highly saturated and concentrated. Furthermore, the COVID-19 pandemic led to some changes in eating and grocery shopping behaviours. In such conditions, food retailers need to understand the customers’ expectations and how best to fulfil them as well as respective changes during the COVID-19 pandemic. To this end, we examined the aforementioned aspects in two studies, which represent the German population by gender and age, in 2019 and 2022. The results reveal that the customers’ evaluation of several performance elements of food retailers have significantly but not substantially changed during the COVID-19 pandemic. These performance elements should be analysed in more detail and monitored closely. Essentially, food retailers are advised to primarily focus on securing the quality of their core business, such as their location appeal and product range, whilst improving the quality of “secondary” performance categories, like services and communication, to provide their customers with added value.
