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Based on this parameter and a simplified analytical model, the supersaturation and batch time can be determined for any supersaturation controller crystallisation process, using an optimization approach to obtain a target distribution with desired shape.
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As data cleaning and encoding are very time-consuming processes, the terms should be grouped in batches by frequency so more time can be spent on frequently occurring terms.
Moreover by adjusting the slope and intercept parameters, the closed-form solutions for settling height vs time can be matched to batch settling experimental data, and thereby the settling flux can be directly obtained.
Additional improvements in terms of both memory and running time can be made by using batch processing for queries.
By analyzing the contextual information of each time-slice, the batch process can be roughly divided using the time-varying characteristics.
It has been shown that filling a 10 m reactor with substrate before beginning the batch SSF can be a time-consuming process [ 18].
Computing can be done batch by batch or it can be done by individual transaction.
Batch updates can be performed in time that is constant per updated tuple.
It could be shown, that high and well-defined cooling rates (up to 104 times higher than standard batch processes) can be achieved by using a micro heat exchanger device for the continuous melt crystallization of lipid nanoparticles distributed in an emulsion, resulting in well-defined product qualities.
It is shown that stage and batch lengths can be estimated in real time with an average error that is not larger than 20% of the inherent batch-to-batch variability, whereas quality estimations can be provided within the accuracy of the hardware instrumentation, but 240 times faster.
Batch size can be flexible and is based on the throughput rate and run time.
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