Big data analysis system for engine quality detection and prediction
US-2024362488-A1 · Oct 31, 2024 · US
US11073459B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-11073459-B2 |
| Application number | US-202017110440-A |
| Country | US |
| Kind code | B2 |
| Filing date | Dec 3, 2020 |
| Priority date | Dec 4, 2019 |
| Publication date | Jul 27, 2021 |
| Grant date | Jul 27, 2021 |
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Provided is a method for evaluating a pretreatment effect for organic solid waste based on fractal dimension, relating to biological conversion of organic solid waste. Samples of the organic solid waste are collected, and dried and broken up to obtain dried samples. The mixtures obtained are analyzed using a laser particle size analyzer, so as to measure a wave vector Q and a scattering intensity I of each of the mixtures. According to a fractal theory, the wave vector Q and the scattering intensity I obtained are analyzed using a data processing software to obtain a two-dimensional fractal dimension D f of each of the samples. Based on data from documents and experiments, a total organic carbon or an apparent activated energy is evaluated, so as to evaluate the pretreatment effects of samples of the organic solid waste.
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What is claimed is: 1. A method for evaluating a pretreatment effect for organic solid waste based on fractal dimension, comprising: 1) collecting samples of the organic solid waste, and drying and breaking up the samples to obtain dried samples; 2) adding water into the dried samples obtained in step (1) followed by stirring to obtain mixtures, respectively; 3) analyzing the mixtures obtained in step (2) using a laser particle size analyzer, so as to measure a wave vector Q and a scattering intensity I of each of the mixtures; 4) according to a fractal theory, analyzing the wave vector Q and the scattering intensity I obtained in step (3) using a data processing software; and obtaining a two-dimensional fractal dimension D f of each of the samples according to a formula I∝Q −D f ; 5) constructing a calculation model of the D f and a total organic carbon (TOC max ) or an apparent activated energy (AAE) using linear regression; and 6) plugging the D f of each of the samples into the calculation model obtained in step (5) to obtain the TOC max or the AAE of each of the samples, so as to evaluate the pretreatment effects for samples of the organic solid waste; wherein in step (4), the formula I∝Q −D f is an equation I=k·Q −D f , wherein k is a slope; both sides of the equation take natural logarithm to obtain: ln I=ln(k·Q −D f ); then the equation is simplified as: ln I=−D f ln Q+ln k; the I and the Q obtained in step (3) take the natural logarithm to obtain ln I and ln Q which are taken as a dependent variable and an independent variable of the equation, respectively, and the ln I and the ln Q are inputted into the data processing software to determine the D f of each of the samples using stochastic gradient descent; in step 5, the calculation model of the D f and the TOC max or AAE of each of the samples is: Y=A−BX; wherein X is the fractal dimension D f , Y is the TOC max or the AAE of each of the samples; A and B are coefficients of the calculation model of the TOC max or AAE; and in step 6, the TOC max is a maximum dissolution value of total organic carbon of each of the samples in an aqueous phase. 2. The method of claim 1 , wherein in step 1, the samples are samples which are pretreated. 3. The method of claim 2 , wherein in step 1, the samples are samples with different sizes or samples pretreated by different methods. 4. The method of claim 3 , wherein the samples are broken up as particle sizes ranging from 0.01 to 3500 microns. 5. The method of claim 1 , wherein in step 2, each of the dried samples is diluted with pure water in a container to obtain a mixture with a solid content of less than 5%; the mixture is stirred by an external magnetic stirrer having a magnetic stirring bar; and the container and the magnetic stirring bar are cleaned and dried before use. 6. The method of claim 1 , wherein in step (3), a light source of the laser particle size analyzer is a helium-neon laser emitting at 633 nm; a refractive index of a dispersant is 1.330; an absorption rate of particles in the dispersant is 0.100; a density of the dispersant is greater than 1; the dispersant is water; time for a background measurement is 10 s; time for measuring the mixture is 10-12 s; time for measuring the mixture with uneven dispersion is 10-20 s; the mixture is measured 3 times; during a measurement, a shading range of the mixture containing particles having a diameter of tens of microns is 10-20%; a shading range of the mixture containing particles having a diameter of a few microns is 6-10%; a shading range of the mixture containing particles have a diameter of hundreds of nanometers is 4-6%; a stirring speed is 2000-3000 r/min; and after the mixture is measured, a cleaning system of the laser particle size analyzer is operated. 7. The method of claim 6 , wherein the time for measuring the mixture with uneven disperse is set to 10-20 s, and the shading range thereof is 10-20%.
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