![]() ![]() Specific to membrane-based gas separation, machine learning has been applied to the performance prediction and structural optimization of polymer membranes, zeolite membranes, metal-organic framework membranes, and composite membranes. In the fields of biomedicine, chemistry, materials, and the environment, the machine learning technique has been successfully applied to the analysis of the relationship between the characteristics and the performances of materials. Machine learning is a computational technology to quantitatively predict the relationship between the multiple conditions and the target based on given samples. The machine learning technique, which is the core of artificial intelligence, makes it possible to realize the correlation. However, gas permeability of CMS membrane is hard to correlate functionally with the plural factors which are not directly interrelated. Exploring the influence of factors, such as the microstructural characteristics and gas properties, on the gas separation performance, or the so-called permselectivity, would be helpful to the adjustment of the microstructure and the optimization to the membrane preparation. The microstructure of CMS membranes varies largely with the choice of precursors and preparation processes, affecting the separation performance. As a novel membrane for gas separation with a broad development prospect, CMS membrane has the advantages of excellent gas permeability and selectivity, high thermal and chemical stability, and anti-plasticization. Carbon molecular sieve (CMS) membrane is a carbon-based membrane fabricated from the pyrolysis of polymeric precursor film. In order to broaden the application of the technology, it is important to develop novel membrane materials with excellent gas separation performance. Membrane-based gas separation technology has been widely concerned because of its high separation efficiency, environmental friendship, and easy operation. ![]() The results would be helpful to the structural optimization and the separation performance improvement of CMS membrane. Moreover, the most influential factors for the gas separation performance were supposed to be the two structural factors of precursor influencing the porosity of CMS membrane, the carbon residue and the FFV, and the ratio of the gas kinetic diameters. Based on the calculation results, the inferred key factors affecting the gas permeability of CMS membrane were the fractional free volume (FFV) of the precursor, the average interlayer spacing of graphite-like carbon sheet, and the final carbonization temperature. A simple quantitative index based on the Robeson’s upper bound line, which indicated the gas permeability and selectivity simultaneously, was proposed to measure the gas separation performance of CMS membrane. In this work, the support vector regression (SVR) method as a machine learning technique was applied to the correlation between the gas separation performance, the multiple membrane structure, and gas characteristic factors of the self-manufactured CMS membrane. ASME torispherical heads generally have a knuckle radius parameter of 0.Gas separation performance of the carbon molecular sieve (CMS) membrane is influenced by multiple factors including the microstructural characteristics of carbon and gas properties.Enter the knuckle radius parameter (torispherical heads only).Select the vessel orientation: horizontal or vertical.For vertical vessels the maximum liquid height is the depth of the bottom head plus the cylinder length.For horizontal vessels the maximum liquid height is the vessel diameter.Most elliptical heads are 2:1 elliptical heads with a head depth equal to 1/4 of the vessel diameter.Note that the vessel head depth is not required for torispherical heads.Enter the vessel head depth for the required vessel head.A liquid height - volume chart is also generated for the vessel. The partially filled liquid volume is determined automatically. This method calculates the volume rigourously it does not use correlated estimates. ![]() The calculation is based on the method described in "Computing Tank Volumes" Chemical Processing, November 17, 2002, written by Dan Jones. Horizontal vessels can be specified with: The calculator determines the volume occupied by a liquid in partially filled horizontal and vertical vessels. Vessel Volume Calculator Guide Show Instructions The volume of partially filled vessel heads is included in the calculation. This calculator determines the liquid volume in partially filled horizontal and vertical vessels.
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