Disclosed is a screening method for adsorbent in environment-friendly gas-insulating equipment, the method steps include: establishing screening sets, pre-experiment screening, standard gas adsorption experiments screening, mixed gas adsorption experiments screening, establishing mapping relationship between the decomposed gas type set and the third adsorbent type set under different working conditions, and selecting adsorbent combination mode suitable for the working condition type and the mixed gas composition mode based on the mapping relationship. Through adsorption experiments of a single standard gas and a mixed gas under different working conditions, an adsorbent combination mode suitable for adsorbing mixed decomposed gas under different working conditions is obtained; at the same time, in view of the situation that the suitable adsorbent has not been screened through the adsorption experiment, the suitable adsorbent is further screened with the molecular dynamics theory, so that all the adsorbent combinations suitable for different working conditions can be obtained.

The present invention provides a composite oxide that can achieve a high low-temperature output characteristic, a method for manufacturing the same, and a positive electrode active material in which the generation of soluble lithium is suppressed and a problem of gelation is not caused during the paste preparation. A positive electrode active material for non-aqueous electrolyte secondary batteries, including a lithium-metal composite oxide powder including a secondary particle configured by aggregating primary particles containing lithium, nickel, manganese, and cobalt, or a lithium-metal composite oxide powder including both the primary particles and the secondary particle. The secondary particle has a porous structure inside as a main inside structure, the slurry pH is 11.5 or less, the soluble lithium content rate is 0.5[% by mass] or less, the specific surface area is 3.0 to 4.0 [m.sup.2/g], and the porosity is more than 50 to 80[%].

An ore volume-based zonal injection method for ionic rare earth includes six steps of ore body data acquisition; ore volume calculation by units; calculation of leaching agent consumption γ per unit ore volume; calculation of unit ore volume-based zoning range difference; merging of the units into injection zones; and injection.

A SAP evaluation apparatus includes: a main body installed with a lifting bar that is raised or lowered; a container portion installed under the lifting bar in the main body and having an internal containing space for containing an absorber; an operating portion connected to the lifting bar and having a lifting plate that is raised or lowered within the containing space and applies pressure to the absorber and an injection portion for injecting an ink in the direction of the absorber; a dispersion measurement portion for measuring the dispersion of the ink through the absorber; and a controller installed at the main body to measure absorption of the ink into the absorber and measure swelling capacity of the absorber while the ink is injected into the absorber.

A method of analyzing a composite plug includes creating a composite plug, where the composite plug includes a formation layer, a cement layer, and an interface region between them, and the cement extends into the formation sample in the interface region. The method further includes imaging the composite plug to gather a series of discrete images, where each discrete image in the series depicts a cross section of the composite plug and the discrete images are taken at set increments throughout a depth of the composite plug. The method further includes analyzing each discrete image in the series of discrete images to determine a porosity measurement of each discrete image, determine a first and second boundary of the interface region from the porosity measurement of each discrete image, and determine a depth of the interface region by a number of discrete images between the first boundary and the second boundary.

A method and apparatus for identifying porosity in a structure made by an additive manufacturing process in which a laser is scanned across layers of material to form the structure. Pyrometry data comprising images of the layers acquired during additive manufacturing of the structure is received. The pyrometry data is used to generate temperature data comprising estimated temperatures of points in the layers in the images of the layers. The temperature data is used to identify shapes fit to high temperature areas in the images of the layers. Conditions of the shapes fit to the high temperature areas in the images of the layers are identified. Outlier shapes are identified in the shapes fit to the high temperature areas in the images of the layers using the conditions of the shapes.

The present disclosure provides a high-pressure helium shale porosity testing device and method. The device comprises a reference cylinder, a pressure cylinder, a sample cylinder, a differential pressure sensor, a pressure gauge, an venting and vacuumizing system, a temperature control system and a tubing and valve system, wherein the reference cylinder is respectively connected with a helium source, the pressure cylinder and the sample cylinder through the tubing and valve system, the differential pressure sensor is configured to measure changes of pressure difference between the sample cylinder and the pressure cylinder, the pressure gauge is configured to measure pressure at the pressure cylinder, the sample cylinder is further connected with the venting and vacuumizing system through the tubing and valve system, and the temperature control system is used for controlling the temperature of the whole device.

A method for estimating in-situ porosity based on cutting images employs a neural network trained with labeled images, the labels indicating wireline porosity values. The method may be used to obtain porosity values along a vertical, deviated or horizontal well, where wireline logging data is not available or unreliable. The method uses machine learning. Training and validating the neural network may be ongoing processes in the sense that any new labeled image that becomes available can be added to the training set and the neural network being retrained to enhance its predictive performance.

Example implementations relate to substrate porosity determination. Some examples refer to a substrate porosity determination comprising an image capture device a controller to obtain a reference image by the image capture device; obtain a measurement image after a printing operation has been performed on a substrate located over the image capture device; compare the reference image and the measurement image—determine whether the substrate is porous based on the comparison; and determine a print parameter based on the determination of whether the substrate is porous.

A method and device for obtaining microscopic occurrence characteristics of oil stored in a shale, where the microscopic occurrence characteristics include the adsorbed oil film thicknesses in the shale and the oil distribution in the shale. The method includes four steps. The first step is an experiment step in which a N-Hexane vapor adsorption experiment is performed on a sample made from a shale. The second step is a first obtaining step for calculating and obtaining the adsorbed oil film thicknesses in the shale. The third step is a first calculating step and the fourth step is a second obtaining step. They aim to obtain the oil distribution in the shale.