A methane monitoring station siting method based on WRF-STILT and genetic algorithm

Reducing methane emissions in the oil and gas industry is a top priority for the current international community in addressing climate change. Methane emissions from the energy sector exhibit strong temporal variability and ground monitoring networks can provide time-continuous measurements of methane concentrations, enabling the rapid detection of sudden methane leaks in the oil and gas industry. Therefore, identifying specific locations within oil fields to establish a cost-effective and reliable methane monitoring ground network is an urgent and significant task. In response to this challenge, this study proposes a technical workflow that, utilizing emission inventories, atmospheric transport models, and intelligent computing techniques, automatically determines the optimal locations for monitoring stations based on the input quantity of monitoring sites. This methodology can automatically and quantitatively assess the observational effectiveness of the monitoring network. The effectiveness of the proposed technical workflow is demonstrated using the Shengli Oilfield, the second-largest oil and gas extraction base in China, as a case study. We found that the Genetic Algorithm can help find the optimum locations effectively. Besides, the overall observation effectiveness grew from 1.7 to 5.6 when the number of site increased from 1 to 9. However, the growth decreased with the increasing site number. Such a technology can assist the oil and gas industry in better monitoring methane emissions resulting from oil and gas extraction

Application of Electrochemical Sensors Based on Carbon Nanomaterials for Detection of Flavonoids

Flavonoids have a variety of physiological activities such as anti-free radicals, regulating hormone levels, antibacterial factors, and anti-cancer factors, which are widely present in edible and medicinal plants. Real-time detection of flavonoids is a key step in the quality control of diverse matrices closely related to social, economic, and health issues. Traditional detection methods are time-consuming and require expensive equipment and complicated working conditions. Therefore, electrochemical sensors with high sensitivity and fast detection speed have aroused extensive research interest. Carbon nanomaterials are preferred material in improving the performance of electrochemical sensing. In this paper, we review the progress of electrochemical sensors based on carbon nanomaterials including carbon nanotubes, graphene, carbon and graphene quantum dots, mesoporous carbon, and carbon black for detecting flavonoids in food and drug homologous substances in the last four years. In addition, we look forward to the prospects and challenges of this research field

An Electrochemical Sensor Based on Chalcogenide Molybdenum Disulfide-Gold-Silver Nanocomposite for Detection of Hydrogen Peroxide Released by Cancer Cells

Hydrogen peroxide (H2O2) as a crucial signal molecule plays a vital part in the growth and development of various cells under normal physiological conditions. The development of H2O2 sensors has received great research interest because of the importance of H2O2 in biological systems and its practical applications in other fields. In this study, a H2O2 electrochemical sensor was constructed based on chalcogenide molybdenum disulfide–gold–silver nanocomposite (MoS2-Au-Ag). Transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and energy dispersive spectroscopy (EDS) were utilized to characterize the nanocomposites, and the electrochemical performances of the obtained sensor were assessed by two electrochemical detection methods: cyclic voltammetry and chronoamperometry. The results showed that the MoS2-Au-Ag-modified glassy carbon electrode (GCE) has higher sensitivity (405.24 µA mM−1 cm−2), wider linear detection range (0.05–20 mM) and satisfactory repeatability and stability. Moreover, the prepared sensor was able to detect the H2O2 discharge from living tumor cells. Therefore, this study offers a platform for the early diagnosis of cancer and other applications in the fields of biology and biomedicine

Evaluation of Infrared Detector Response Characteristics Drift Based on Time Sequence

The response characteristic drift of infrared detector seriously degrades the imaging quality and system performance. Aiming at the lack of effective evaluation index and difficulty in modeling and evaluating the response characteristic drift of infrared detectors, the sequence image output by infrared detector is regarded as time sequence data, drift degree and drift entropy are defined based on time difference image to evaluate the drift degree of infrared detector response characteristics, and a method for evaluating the drift of infrared detector response characteristics based on time sequence prediction is proposed. Simulation data and real data are used for experiments. The results show that drift degree and drift entropy can effectively measure the drift degree of infrared detector response characteristics, and the evaluation method can establish the response characteristic drift model of infrared detector and realize prediction and evaluation. The research work can be used to help select appropriate infrared detectors and it’s nonuniformity correction algorithm to improve the combat performance of infrared imaging systems

System parameter identification: information criteria and algorithms

Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors' research p

Ultraprecision Real-Time Displacements Calculation Algorithm for the Grating Interferometer System

Grating interferometry is an environmentally stable displacement measurement technique that has significant potential for identifying the position of the wafer stage. A fast and precise algorithm is required for real-time calculation of six degrees-of-freedom (DOF) displacement using phase shifts of interference signals. Based on affine transformation, we analyze diffraction spot displacement and changes in the internal and external effective optical paths of the grating interferometer caused by the displacement of the wafer stage (DOWS); then, we establish a phase shift-DOWS model. To solve the DOWS in real time, we present a polynomial approximation algorithm that uses the frequency domain characteristics of nonlinearities to achieve model reduction. The presented algorithm is verified by experiment and ZEMAX simulation