Sökning: "scale-up"
Visar resultat 16 - 20 av 142 avhandlingar innehållade ordet scale-up.
16. Experimental determination of pressure and solidosity profiles in cake filtration
Sammanfattning : .... LÄS MER
17. Bioprocess development for biochemical conversion of lignocellulose
Sammanfattning : Due to its low environmental impact and high maturity of the fuel ethanol market, lignocellulosic ethanol is a promising option for reducing the carbon footprint in the transport sector. The characteristics of lignocellulosic feedstocks, such as varied sugar composition, low sugar density, low solubility, recalcitrance to enzymatic degradation, and inhibitors formed during thermochemical pretreatment, have so far limited the production process, and costs for conversion of lignocellulosic materials to ethanol are still high. LÄS MER
18. Performance Characterization of In-Memory Data Analytics on a Scale-up Server
Sammanfattning : The sheer increase in volume of data over the last decade has triggered research in cluster computing frameworks that enable web enterprises to extract big insights from big data. While Apache Spark defines the state of the art in big data analytics platforms for (i) exploiting data-flow and in-memory computing and (ii) for exhibiting superior scale-out performance on the commodity machines, little effort has been devoted at understanding the performance of in-memory data analytics with Spark on modern scale-up servers. LÄS MER
19. Design of Cellulose-Based Electrically Conductive Composites: Fundamentals, Modifications, and Scale-up
Sammanfattning : Modern demand for consumer electronics is fueling the generation of 'E-waste.' Furthermore, theraw materials and manufacturing methods used in the fabrication of electronics are not sustainable. LÄS MER
20. Performance Characterization and Optimization of In-Memory Data Analytics on a Scale-up Server
Sammanfattning : The sheer increase in the volume of data over the last decade has triggered research in cluster computing frameworks that enable web enterprises to extract big insights from big data. While Apache Spark defines the state of the art in big data analytics platforms for (i) exploiting data-flow and in-memory computing and (ii) for exhibiting superior scale-out performance on the commodity machines, little effort has been devoted to understanding the performance of in-memory data analytics with Spark on modern scale-up servers. LÄS MER