An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring

An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear data-driven process monitoring in this paper. To realize this purpose, the technique of a mixture of probabilistic principal component analyzers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is proposed based on the integration of two monitoring statistics in modified PPCA-based fault detection approach. Besides, the weighted mean of the monitoring statistics aforementioned is utilized as a metrics to detect potential abnormalities. The virtues of the proposed algorithm are discussed in comparison with several unsupervised algorithms. Finally, Tennessee Eastman process and an autosuspension model are employed to demonstrate the effectiveness of the proposed scheme further

Software-defined Architecture for Urban Regional Traffic Signal Control

Regional Traffic Signal Control (RTSC) is believed to be a promising approach to alleviate urban traffic congestion. However, the current ecology of RTSC platforms is too closed to meet the needs of urban development, which has also seriously affected their own development. Therefore, the paper proposes virtualizing the traffic signal control devices to create software-defined RTSC systems, which can provide a better innovation platform for coordinated control of urban transportation. The novel architecture for RTSC is presented in detail, and microscopic traffic simulation experiments are designed and conducted to verify the feasibility.</p

Scintillation Crystal Growth Quality Evaluation Based on Machine Learning

The scintillator crystal is a crystalline material that can light under the influence of high-energy rays and be widely used for detecting high-energy particles. With the development of industrial CT and other fields, the demand for inorganic scintillator crystals with high melting points and high performance is increasing yearly, which results in the abandonment of the traditional laboratory mode of manufacturing inorganic scintillator crystals. For achieving the needs of high-standard inorganic scintillator crystals, the market attaches great importance to the intelligent manufacturing of scintillator crystals. One of the critical technical difficulties in intelligent crystal manufacturing is the numerical assessment of crystal quality. The traditional laboratory research and development mode relies on manually measuring crystal quality, which has many defects, such as the inability to be numerical, and the quality evaluation varies from person to person. Therefore, this paper proposes a methodology for assessing crystal quality based on the residual depth network. At the same time, in order to make crystal quality evaluation more refined, this paper uses a target detection method based on depth learning to determine the crystal quality assessment area. The target detection model in this article is developed based on the yolov5 deep learning framework, which is suitable for crystal target detection, and its detection accuracy is 98&#x0025;. The residual depth network proposed in this paper is based on the network developed by ResNet-18, which is more suitable for crystal grading, and its accuracy of crystal quality evaluation reaches 84.8&#x0025;. The quality evaluation of inorganic scintillation crystals is a solution to the problem of numerical representation of the quality of inorganic scintillation crystals, which opens up a technical channel for closed-loop control of intelligent crystal manufacturing processes

Modes of Occurrence and Abundance of Trace Elements in Pennsylvanian Coals from the Pingshuo Mine, Ningwu Coalfield, Shanxi Province, China

The Pingshuo Mine is an important coal mine of the Ningwu coalfield in northern Shanxi Province, China. To investigate the mineralogy and geochemistry of Pingshuo coals, core samples from the mineable No. 4 coals were collected. The minerals, major element oxides, and trace elements were analyzed by scanning electron microscopy (SEM), LTA-XRD in combination with Siroquant software, X-ray fluorescence (XRF), inductively coupled plasma mass spectrometry (ICP-MS) and ICP-CCT-MS (As and Se). The minerals in the Pennsylvanian coals from the Pingshuo Mine dominantly consist of kaolinite and boehmite, with minor amounts of siderite, anatase, goyazite, calcite, apatite and florencite. Major-element oxides including SiO2 (9.54 wt %), Al2O3 (9.68 wt %), and TiO2 (0.63 wt %), as well as trace elements including Hg (449.63 ng/g), Zr (285.95 μg/g), Cu (36.72 μg/g), Ga (18.47 μg/g), Se (5.99 μg/g), Cd (0.43 μg/g), Hf (7.14 μg/g), and Pb (40.63 μg/g) are enriched in the coal. Lithium and Hg present strong positive correlations with ash yield and SiO2, indicating an inorganic affinity. Elements Sr, Ba, Be, As and Ga have strong positive correlations with CaO and P2O5, indicating that most of these elements may be either associated with phosphates and carbonates or have an inorganic–organic affinity. Some of the Zr and Hf may occur in anatase due to their strong positive correlations with TiO2

Mineralogical and Geochemical Compositions of the No. 5 Coal in Chuancaogedan Mine, Junger Coalfield, China

This paper reports the mineralogy and geochemistry of the Early Permian No. 5 coal from the Chuancaogedan Mine, Junger Coalfield, China, using optical microscopy, scanning electron microscopy (SEM), Low-temperature ashing X-ray diffraction (LTA-XRD) in combination with Siroquant software, X-ray fluorescence (XRF), and inductively coupled plasma mass spectrometry (ICP-MS). The minerals in the No. 5 coal from the Chuancaogedan Mine dominantly consist of kaolinite, with minor amounts of quartz, pyrite, magnetite, gypsum, calcite, jarosite and mixed-layer illite/smectite (I/S). The most abundant species within high-temperature plasma-derived coals were SiO2 (averaging 16.90%), Al2O3 (13.87%), TiO2 (0.55%) and P2O5 (0.05%). Notable minor and trace elements of the coal include Zr (245.89 mg/kg), Li (78.54 mg/kg), Hg (65.42 mg/kg), Pb (38.95 mg/kg), U (7.85 mg/kg) and Se (6.69 mg/kg). The coal has an ultra-low sulfur content (0.40%). Lithium, Ga, Se, Zr and Hf present strongly positive correlation with ash yield, Si and Al, suggesting they are associated with aluminosilicate minerals in the No. 5 coal. Arsenic is only weakly associated with mineral matter and Ge in the No. 5 coals might be of organic and/or sulfide affinity

Fluorine in Chinese Coal: A Review of Distribution, Abundance, Modes of Occurrence, Genetic Factors and Environmental Effects

Fluorine, a hazard that is associated with coal, has resulted in serious environmental issues during the production and utilization of coal. In this paper, we provide a detailed review of fluorine in Chinese coal, including the distribution, concentration, modes of occurrence, genetic factors, and environmental effects. The average concentration of fluorine in Chinese coal is 130.0 mg/kg, which is slightly higher than coal worldwide (88.0 mg/kg). The enrichment of fluorine in Chinese coal varies across different coal deposit regions, and it is especially high in Inner Mongolia (Junger coalfield, Daqingshan coalfield) and southwest China (coal mining regions in Yunnan, Guizhou province). The fluorine distribution is uneven, with a relatively high content in southwest coal (including Yunnan, Guizhou, Chongqing, and Sichuan provinces), very high content in the coal of North China (Inner Mongolia) and South China (Guangxi), and is occasionally found in the northwest (Qinghai). Fluorine occurs in various forms in coal, such as independent minerals (fluorine exists as fluorapatite or fluorite in coal from Muli of Qinghai, Taoshuping of Yunnan, Guiding of Guizhou, and Daqingshan of Inner Mongolia), adsorption on minerals (fluorine in coal from Nantong, Songzao of Chongqing, Guxu of Sichuan, and Shengli, Daqingshan, and Junger from Inner Mongolia), substitution in minerals (Wuda coal, Inner Mongolia), and a water-soluble form (Haerwusu coal, Inner Mongolia). The enrichment of fluorine is mainly attributed to the weathering of source rock and hydrothermal fluids; in addition to that, volcanic ash, marine water influence, and groundwater affect the fluorine enrichment in some cases. Some environmental and human health problems are related to fluorine in coal, such as damage to the surrounding environment and husbandry (poisoning of livestock) during the coal combustion process, and many people have suffered from fluorosis due to the burning of coal (endemic fluorosis in southwest China)

Laboratory study of gas permeability and cleat compressibility for CBM/ECBM in Chinese coals

Coal permeability is regarded as one of the most critical parameters for the success of coalbed methane recovery. It is also a key parameter for enhanced coalbed methane recovery via CO and/or N injection. Coal permeability is sensitive to stress and cleat compressibility is often used to describe how sensitive the permeability change to stress change for coal reservoirs. Coalbed methane exploration and production activities and interest of enhanced coalbed methane recovery increased dramatically in China in recent years, however, how permeability and cleat compressibility change with respect to gas species, effective stress and pore pressure have not been well understood for Chinese coals, despite that they are the key parameters for primary and enhanced coalbed methane production. In this work, two dry Chinese bituminous coal samples from Qinshui Basin and Junggar Basin are studied. Four gases, including H , N , CH and CO are used to study permeability behaviour with respect to different effective stresses, pore pressures, and temperatures. The effective stress is up to 5 MPa and pore pressure is up to 7 MPa. Permeability measurements are also carried out at highest pore pressures for each adsorbing gas, at three temperatures, 35, 40 and 45°C. The experimental results show that gas species, effective stress and pore pressure all have significant impact on permeability change for both coal samples. Moreover, the results demonstrate that cleat compressibility is strongly dependent on effective stress. More importantly, the results show that cleat compressibility is also strongly dependent on pore pressure. Cleat compressibility initially decreases with pore pressure increase then it increases slightly at higher pore pressures. However, temperature only has marginal impact on permeability and cleat compressibility change