A computer technique for predicting smoke movement in tall buildings

An approach to the analysis of stack effect has been developed which allows the prediction of internal air flow quantities and associated pressure differentials for specific buildings prior to construction. An example of the application of this analysis is presented for a hypothetical 75-story office building. Note: The authors presented this paper at a symposium on “The Control of Smoke Movement on Escape Routes in Buildings,” sponsored by the Joint Fire Research Organization and held on April 9 and 10, 1969 in Hertfordshire, England.     

Automated self seeding of batch crystallizations via plug flow seed generation

In this study, seed slurry from a single addition anti-solvent plug flow crystallization of benzoic acid was used to seed the equivalent batch cooling crystallization. The experimental conditions were carried out to simulate automated self-seeding. This involves withdrawal of solution from a batch crystallizer, which is then mixed with anti-solvent within a plug flow crystallizer, in order to generate a seed slurry which is fed directly back to the batch crystallizer. This seeding strategy allowed the final CSD of the batch crystallization to be controlled by variation of the crystal size from the plug flow seeding device at a constant seed loading. The ability to use unequal feed/anti-solvent inlet flowrates (in the Roughton vortex mixer) proved effective in controlling the batch CSD at 2% seed loading and constant feed composition.     

A multitime-steps-ahead prediction approach for scheduling live migration in cloud data centers

One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime-ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.