952 resultados para Full-scale Physical Modelling


Relevância:

100.00% 100.00%

Publicador:

Resumo:

"Progress report of the work done on Research Project Sr-96 under Contract NObs-31217 between the Bureau of Ships, Navy Department and the Pennsylvania State College."

Relevância:

100.00% 100.00%

Publicador:

Resumo:

Enhanced biological phosphorus removal (EBPR) has been used at many wastewater treatment plants all over the world for many years. In this study a full-scale sludge with good EBPR was tested with P-release batch tests and combined FISH/MAR (fluorescence in situ hybridisation and microautoradiography). Proposed models of PAOs and GAOs (polyphosphate- and glycogen-accumulating organisms) and microbial methods suggested from studies of laboratory reactors were found to be applicable also on sludge from full-scale plants. Dependency of pH and the uptake of both acetate and propionate were studied and used for calculations for verifying the models and results from microbial methods. All rates found from the batch tests with acetate were higher than in the batch tests with propionate, which was explained by the finding that only those parts of the bacterial community that were able to take up acetate anaerobically were able to take up propionate anaerobically.

Relevância:

100.00% 100.00%

Publicador:

Resumo:

This article investigates the performance of a model called Full-Scale Optimisation, which was presented recently and is used for financial investment advice. The investor’s preferences of expected risk and return are entered into the model, and a recommended portfolio is produced. This model is theoretically more accurate than the mainstream investment advice model, called Mean-Variance Optimization, as there are fewer assumptions made. Our investigation of the model’s performance is broader when it comes to investor preferences, and more general when it comes to investment type, as compared to previous studies. Our investigation shows that Full-Scale Optimisation is more widely applicable than earlier known.

Relevância:

100.00% 100.00%

Publicador:

Resumo:

When composing stock portfolios, managers frequently choose among hundreds of stocks. The stocks' risk properties are analyzed with statistical tools, and managers try to combine these to meet the investors' risk profiles. A recently developed tool for performing such optimization is called full-scale optimization (FSO). This methodology is very flexible for investor preferences, but because of computational limitations it has until now been infeasible to use when many stocks are considered. We apply the artificial intelligence technique of differential evolution to solve FSO-type stock selection problems of 97 assets. Differential evolution finds the optimal solutions by self-learning from randomly drawn candidate solutions. We show that this search technique makes large scale problem computationally feasible and that the solutions retrieved are stable. The study also gives further merit to the FSO technique, as it shows that the solutions suit investor risk profiles better than portfolios retrieved from traditional methods.