Research Progress on Pulverized Coal Concentration Detection Device

Background: Pulverized coal detection is an indispensable detection measure in the coal industry. The current detection devices can be divided into two types: invasive and non-invasive. The coal dust detection methods and devices based on acoustics, optics, and electricity have been extensively studied. In order to achieve a high-efficiency online detection scheme, improving the accuracy and stability of the detection means is the primary goal of the research. Objective: The general problems and characteristics of coal dust detection device design are summarized, as well as recent technological developments and the needs for online testing to predict future research trends. Methods: The current typical detection devices are classified according to the detection principle and whether they invade the target, analyzing its advantages and disadvantages according to the device performance and application scenarios. Results: It has a beneficial effect on the design of the pulverized coal concentration detection device. Conclusion: The paper summarizes and analyzes several representative coal concentration detection device patents in recent years. Then it points out advantages and main problems. On this basis, the main development direction of the coal dust concentration detection device in the future is discussed.

A 77-dB Dynamic-Range Analog Front-End for Fine-Dust Detection Systems with Dual-Mode Ultra-Low Noise TIA

This paper presents an analog front-end for fine-dust detection systems with a 77-dB-wide dynamic range and a dual-mode ultra-low noise TIA with 142-dBΩ towards the maximum gain. The required high sensitivity of the analog signal conditioning path dictates having a high sensitivity at the front-end while the Input-Referred Noise (IRN) is kept low. Therefore, a TIA with a high sensitivity to detected current bio-signals is provided by a photodiode module. The analog front end is formed by the TIA, a DC-Offset Cancellation (DCOC) circuit, a Single-to-Differential Amplifier (SDA), and two Programmable Gain Amplifiers (PGAs). Gain adjustment is implemented by a coarse-gain-step using selective loads with four different gain values and fine-gain steps by 42 dB dynamic range during 16 fine steps. The settling time of the TIA is compensated using a capacitive compensation which is applied for the last stage. An off-state circuitry is proposed to avoid any off-current leakage. This TIA is designed in a 0.18 µm standard CMOS technology. Post-layout simulations show a high gain operation with a 67 dB dynamic range, input-referred noise, less than 600 fA/√Hz in low frequencies, and less than 27 fA/√Hz at 20 kHz, a minimum detectable current signal of 4 pA, and a 2.71 mW power consumption. After measuring the full path of the analog signal conditioning path, the experimental results of the fabricated chip show a maximum gain of 142 dB for the TIA. The Single-to-Differential Amplifier delivers a differential waveform with a unity gain. The PGA1 and PGA2 show a maximum gain of 6.7 dB and 6.3 dB, respectively. The full-path analog front-end shows a wide dynamic range of up to 77 dB in the measurement results.

Charged dust in the D-region incoherent scatter spectrum

We investigate the influence of charged dust on the incoherent scatter from the D-region ionosphere. Incoherent scatter is observed with high-power, large aperture radars and results from electromagnetic waves scattering at electrons that are coupled to other charged components through plasma oscillations. The influence of charged dust can hence be considered an effect of dusty plasma. The D-region contains meteoric smoke particles that are of nanometre size and form from incoming ablating meteors. Detection of such charged dust in the incoherent scatter spectrum from the D-region has previously been proposed and studied to some degree. We here present model calculations to investigate the influence of the charged dust component with a size distribution, instead of the one size dust components assumed in other works. The developed code to calculate the incoherent scatter spectrum from the D-region including dust particles with different sizes and different positive and negative charge states is made available (https://doi.org/10.18710/GHZIIY). We investigate how sizes, number density and charge state of the dust influence the spectrum during different ionospheric conditions. We consider the ionospheric parameters for the location of the EISCAT VHF radar during a year and find that conditions are most suitable for dust detection in winter below 80 km at times with increased electron densities. The prospects to derive dust parameters increase, when the incoherent scatter observations are combined with those of other instruments to provide independent information on electron density, neutral density and temperature.

Machine learning using synthetic images for detecting dust emissions on construction sites

PurposeAutomated dust monitoring in workplaces helps provide timely alerts to over-exposed workers and effective mitigation measures for proactive dust control. However, the cluttered nature of construction sites poses a practical challenge to obtain enough high-quality images in the real world. The study aims to establish a framework that overcomes the challenges of lacking sufficient imagery data (“data-hungry problem”) for training computer vision algorithms to monitor construction dust.Design/methodology/approachThis study develops a synthetic image generation method that incorporates virtual environments of construction dust for producing training samples. Three state-of-the-art object detection algorithms, including Faster-RCNN, you only look once (YOLO) and single shot detection (SSD), are trained using solely synthetic images. Finally, this research provides a comparative analysis of object detection algorithms for real-world dust monitoring regarding the accuracy and computational efficiency.FindingsThis study creates a construction dust emission (CDE) dataset consisting of 3,860 synthetic dust images as the training dataset and 1,015 real-world images as the testing dataset. The YOLO-v3 model achieves the best performance with a 0.93 F1 score and 31.44 fps among all three object detection models. The experimental results indicate that training dust detection algorithms with only synthetic images can achieve acceptable performance on real-world images.Originality/valueThis study provides insights into two questions: (1) how synthetic images could help train dust detection models to overcome data-hungry problems and (2) how well state-of-the-art deep learning algorithms can detect nonrigid construction dust.

A comparison of contact charging and impact ionization in low-velocity impacts: implications for dust detection in space

We investigate the generation of charge due to collision between projectiles with sizes below ∼1 µm and metal surfaces at speeds ∼0.1 to 10 km s−1. This corresponds to speeds above the elastic limit and well below speeds where volume ionization can occur. Impact charge production at these low to intermediate speeds has traditionally been described by invoking the theory of shock wave ionization. By looking at the thermodynamics of the low-velocity solution of shock wave ionization, we find that such a mechanism alone is not sufficient to account for the recorded charge production in a number of scenarios in the laboratory and in space. We propose a model of capacitive contact charging that involves no direct ionization, in which we allow for projectile fragmentation upon impact. Furthermore, we show that this model describes measurements of metal–metal impacts in the laboratory well. We also address contact charging in the context of ice-on-metal collisions and apply our results to rocket observations of mesospheric dust. In general, we find that contact charging dominates at speeds of up to a few kilometres per second and complements shock wave ionization up to speeds where direct ionization can take place. The conditions that we consider can be applied to dust particles naturally occurring in space and in Earth's upper atmosphere and their direct impacts on rockets, spacecraft, and impacts of secondary ejecta.

Application of Global Dust Detection Index (GDDI) for Sand and Dust Storm Monitoring Over Kingdom of Saudi Arabia

Sand and dust storm events are frequent natural hazards in the Kingdom of Saudi Arabia. Sand and dust storm monitoring is therefore essential to mitigate their environmental-related issues. Satellite remote sensing has been successfully used for sand and dust storm monitoring in various parts of the world. In the current endeavor, we are applying the Global Dust Detection Index (GDDI) on Moderate Resolution Imaging Spectroradiometer (MODIS) data onboard Terra satellite to monitor sand and dust storm activities over the Kingdom of Saudi Arabia. In the current study, fourteen sand and dust storm events are analyzed between the years 2000 to 2017. The GDDI based results are validated by using MODIS combined Dark Target (DT) and Deep Blue (DB) Aerosol Optical Depth (AOD) product, Meteosat satellite images, ground-based meteorological stations data, and AOD data from AERONET (Aerosol Robotic Network) stations in the study area. Also, GDDI based results are analyzed by determining algorithm accuracy, Probability Of Correct positive Detection (POCD), and Probability Of False positive Detection (POFD). Results of the study show that GDDI can successfully identify sand and dust storm events with various threshold values over the Kingdom of Saudi Arabia. It is envisaged that the outcome of this study would be very beneficial to understand sand and dust storm characteristics in the study region.