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Titlebook: Reactive Extraction; Hans-J?rg Bart Book 2001 Springer-Verlag Berlin Heidelberg 2001 bridge.design.development.extraction.ionic solutions.

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樓主: Thoracic
11#
發(fā)表于 2025-3-23 12:37:47 | 只看該作者
Reactive Extraction, in the chemical and bio-chemical industry and in environmental applications. An excellent survey of progress in these fields is given in the proceedings of the International Solvent Extraction Conferences [ISEC ′71 –′99].
12#
發(fā)表于 2025-3-23 17:20:33 | 只看該作者
Reactive Extraction,ch achieves the separation. According to this, solvent recovery in a continuous solvent circuit is necessary. Usually liquid-liquid extraction operates at ambient or slightly higher temperatures and reextraction may be accomplished by a back wash or thermal strip. The transfer of a dissolved compoun
13#
發(fā)表于 2025-3-23 20:37:42 | 只看該作者
Liquid-Liquid Phase Equilibria,e, the thermodynamic data for the distribution of the solute between the two phases is the most important. There are several available source books on liquid-liquid equilibria (LLE), e.g. for organic solutes [Wisniak & Tamir (1980), Sorensen & Ant (1979–1980)]. The most commonly used correlations fo
14#
發(fā)表于 2025-3-23 22:14:05 | 只看該作者
Current Developments and Apparatus Techniques, made for separating the phases after completed contacting. Four major types of extractors are distinguished: . and .. Mixer-settlers are rather easy to design, flexible in residence time (an important issue with reactions), pH control in each extraction stage is individually possible, and even whic
15#
發(fā)表于 2025-3-24 02:51:51 | 只看該作者
Liquid-Liquid Phase Equilibria,r LLE are also implemented in simulation tools like ChemSep., ASPEN., HYSIM., etc. Unfortunately, the available data may be incomplete or unreliable which then makes laborious experimental work necessary.
16#
發(fā)表于 2025-3-24 09:09:27 | 只看該作者
17#
發(fā)表于 2025-3-24 13:27:04 | 只看該作者
18#
發(fā)表于 2025-3-24 18:16:13 | 只看該作者
19#
發(fā)表于 2025-3-24 22:51:09 | 只看該作者
20#
發(fā)表于 2025-3-25 02:33:16 | 只看該作者
David Sapsford,Zafiris Tzannatosas two important branches of deep learning, Two-Stream relies on optical flow with complex computation and 3D convolution network is difficult for training. In this paper, we propose a novel Three-Stream Convolution networks after feature extraction for action recognition. For feature, we introduce
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