Real-time Online Monitoring Research Articles

Analysis of the pollution emission system of large-scale combustion of biomass briquette fuel in China

In order to realize the sustainable development strategy and reduce the air pollution caused by biomass boiler emissions, the current study added a Selective Catalyst Reduction (SCR) external denitration system and a wet electrostatic precipitator to the original “three-stage” dust removal system for biomass briquetting fuel boiler emissions and completed the upgrading of the “five-stage” dust removal system. Taking the soot concentration, Sulfur dioxide (SO2) concentration, and Nitrogen oxides (NOX) concentration in the flue gas as the evaluation indicators, real-time online monitoring of the operating conditions of the system is carried out. The results show that the average concentration of smoke and dust in the flue gas discharged from the “five-stage” dust removal system is 3.47 mg/m3, with 2.49 mg/m3 for the average concentration of SO2, and 25.48 mg/m3 for the average concentration of NOX. The removal rates of soot, SO2, and NOX have increased by 5.8%, 4%, and 48.5%, respectively, which are much lower than the emission standards of biomass boiler flue gas pollutants. The innovation of this system is unprecedented for the large-scale combustion of biomass briquettes in China, which greatly reduces the emission of harmful gases. This upgrading will not only make a certain contribution to environmental protection but also provide a theoretical basis to the biomass briquette fuel industry.

Vibration Analysis for Damage Detection in Composite Plate by using Piezoelectric Sensors

This study focuses on an active monitoring method for damage detection applied to composite structures. Different specimens made up of fiber epoxy resin in the form of composite structures are studied. Wavelet analysis methods are adopted to post process the raw monitored signal. A new damage signature is introduced to determine the presence and extent of damage in composites, while eliminating the influence of different distances between the active actuator and active monitoring elements. The proposed method is shown to be effective, reliable, and straightforward for the specimens considered in the present study, which are composed of different materials and suffer various levels of damage. An online real-time active monitoring system for damage detection is described that is based on this research.

All-fiber SPR microfluidic chip for GDF11 detection.

In order to perform microfluidic detection of cytokines with low concentration, such as growth differentiation factor 11 (GDF11), the most common method is to construct microfluidic channels and integrate them with SPR sensing units. In this paper, we proposed a novel all-fiber SPR microfluidic chip for GDF11 detection. The method was to construct the SPR sensing area on a designed D-shaped multimode fiber, which was nested inside a quartz tube to form a semi-cylindrical microfluidic channel. The surface of the SPR sensing area experienced sensitization and specifically modification to achieve the specific detection of GDF11. When the sensitivity of detection was 1.38 nm/lg(g/mL) and the limit of detection was 0.52 pg/mL, the sample consumption was only 0.4 µL for a single detection. The novel all-fiber SPR microfluidic detection chip has the advantages of flexible design, compact structure and low sample consumption, which is expected to be used in wearable biosensing devices for real-time online monitoring of trace cytokines in vivo.

Neurotoxicity mechanism of aconitine in HT22 cells studied by microfluidic chip-mass spectrometry

Aconitine, a common and main toxic component of Aconitum, is toxic to the central nervous system. However, the mechanism of aconitine neurotoxicity is not yet clear. In this work, we had the hypothesis that excitatory amino acids can trigger excitotoxicity as a pointcut to explore the mechanism of neurotoxicity induced by aconitine. HT22 cells were simulated by aconitine and the changes of target cell metabolites were real-time online investigated based on a microfluidic chip-mass spectrometry system. Meanwhile, to confirm the metabolic mechanism of aconitine toxicity on HT22 cells, the levels of lactate dehydrogenase, intracellular Ca2+, reactive oxygen species, glutathione and superoxide dismutase, and ratio of Bax/Bcl-2 protein were detected by molecular biotechnology. Integration of the detected results revealed that neurotoxicity induced by aconitine was associated with the process of excitotoxicity caused by glutamic acid and aspartic acid, which was followed by the accumulation of lactic acid and reduction of glucose. The surge of extracellular glutamic acid could further lead to a series of cascade reactions including intracellular Ca2+ overload and oxidative stress, and eventually result in cell apoptosis. In general, we illustrated a new mechanism of aconitine neurotoxicity and presented a novel analysis strategy that real-time online monitoring of cell metabolites can provide a new approach to mechanism analysis.

High-sensitivity dynamic analysis of dissolved gas in oil based on differential photoacoustic cell

In order to realize real-time online monitoring of dissolved trace gas in transformer oil, a high-sensitivity dynamic analysis technology based on a low-noise differential photoacoustic (PA) cell (DPAC) is proposed. The characteristic gas dissolved in oil is separated by the means of headspace degassing and then pumped into the DPAC. The emitted laser light is amplified by an erbium-doped fiber amplifier (EDFA) and reflected in the DPAC to form a double-pass excitation enhancement. The DPAC with two expansion mufflers is equipped. The airflow noise in the two PA tubes with symmetrical design is basically the same. Differential processing is used to reduce the noise. In addition, the airflow noise with the same frequency as the PA signal is filtered by optimizing the band-stop filtering characteristic of mufflers. The experimental results show that both the differential detection method and the muffler reduce the noise during headspace degassing by more than 80%. The detection limit of the system for dissolved acetylene (C2H2) in oil reaches 0.1 μL/L. The technical solution has the advantages of high detection accuracy and fast response speed, which provides a technical solution for dynamic dissolved gas analysis.

External temperature field test and leakage fault diagnosis for SOFC stacks

The uniformity of Solid Oxide Fuel Cell (SOFC) stacks largely affects the stability and performance of the SOFC power generation system with multiple stacks. However, the health conditions of the stacks are usually assessed by the long-time electrochemical performance test, and some small faults are not able to be quickly identified when the I–V performances have small differences. In this paper, a non-contact temperature sensor array constructed by thermocouples is proposed for NDE (non-destructive evaluation) application in SOFC stacks. The temperature sensor array is fixed near the outer surfaces of the 1 kW-class stack to measure the external temperature field of the stack. Based on the experimental results, it is found that the proposed method is feasible for the examination of the external temperature field of stacks under different operation conditions, which helps to screening the stacks before assembling to the system. At the same time, a real-time online monitoring of fuel leakage is performed using the temperature sensor array, and stacks with unusual temperature distribution due to the leakages can be quickly recognized by comparing with the healthy stack.

Design and performance evaluation of electromechanical impedance instrumented quantitative corrosion measuring probe based on conical rods

Previous study has proved that using electromechanical impedance instrumented bar-type corrosion measuring probe can realize the quantitative assessment of the corrosion amount. To gain more insights into the working mechanism and design better probes, this work examined a new type of corrosion measuring probe based on the conical rod, and evaluated their performance. Theoretical model of this type of new probes was established based on one dimensional piezo-elasticity theory, and the electrical impedance was derived to obtain first resonant and anti-resonant frequencies in longitudinal vibration mode. Two experiments were performed to validate the feasibility of the probe for corrosion measurement, including the artificial uniform corrosion experiment and the accelerated corrosion test. Comparisons between the theoretical predictions and the experimental results from the artificial uniform corrosion experiment were made, and good agreement was found. Effects of piezoelectric patch thickness and cone angle on first resonant and anti-resonant frequencies were also analyzed. In addition, a wireless impedance measurement system was preliminarily realized, which is very promising in developing the low cost and high accuracy online real-time monitoring technology for the pipeline corrosion monitoring.

Characteristics of volatile organic compounds and secondary organic aerosol pollution in different functional areas of petrochemical industrial cities in Northwest China

The aim of this study was to better understand the characteristics of volatile organic compounds (VOCs) and secondary organic aerosol (SOA) pollution in different functional areas of petrochemical industrial cities. In Lanzhou, a typical petrochemical industrial city in Northwest China, with the use of an Integrated Atmospheric Mobile Monitoring Vehicle (IAMMV), various real-time online monitoring instruments, including a VOC monitoring instrument (TH-300B) and single-particle aerosol mass spectrometer (SPAMS), were used in combination. These instruments were employed to determine PM2.5, VOCs and other factors at monitoring sites in Xigu (XG) and Chengguan (CG) districts in September 2020 and 2021, respectively. The results revealed that during the monitoring period, the average VOC concentrations at the XG and CG monitoring sites were 102.3 and 35.8 ppb, respectively. Benzene (45.58 %) and toluene (24.47 %) significantly contributed to the SOA formation potential at the XG site. M/P-xylene (27.88 %) and toluene (23.64 %) more notably contributed to the SOA formation potential at the CG site. The PM2.5 mass concentration at the XG site (24.1 μg·m−3) was similar to that at the CG site (21.2 μg·m−3), but the proportion of particulate matter components greatly differed. The proportion of organic carbon (OC) at the XG site (19.00 %) was higher than that at the CG site (9.97 %). The number of particles containing C2H3O+ (m/z = 43) accounted for 36.96 % and 15.41 % of the total particles at the XG and CG sites, respectively. The mixing ratios of OC and hybrid carbon (OCEC) with C2H3O+ (m/z = 43) were 0.81 and 0.53, respectively, at the XG site and reached only 0.48 and 0.25, respectively, at the CG site. The secondary ageing degree of particles in XG district was high. These results could provide a reference for ambient air quality improvement and the formulation of governance measures in different functional areas of petrochemical industrial cities.

Design, optimization and application of a highly sensitive microbial electrolytic cell-based BOD biosensor

Biochemical oxygen demand (BOD) is an important biochemical indicator for determining the degree of water pollution and guiding the design of wastewater treatment processes. BOD sensors based on microbial electrochemical technology can conduct real-time online monitoring of organic matter and have attracted extensive attention. However, research on microbial electrolytic cell (MEC)-type BOD sensors is at the stage of theoretical exploration. Here, we designed and optimized a highly sensitive MEC-type BOD sensor by screening inoculants, comparing electrode materials, and optimizing the reactor configuration. The results showed that effective means to optimize a BOD sensor for fast activation and sensitive testing included the inoculation of the MEC reactor effluent with large amounts of biomass and highly active bacteria, selection of carbon felt electrodes with excellent adsorption and permeability, miniaturization of the reactor, regulation of suitable electrode spacing, and design of the penetrating fluid structure. Then, the optimized sensing system was applied to determine the BOD concentration in model solutions of sodium acetate in a laboratory environment, where it accurately measured BOD concentrations in the range of 10–500 mg/L and maintained good parallelism during long-term operation. Next, the MEC-type BOD sensors were put into practice in the field as an alarm for accidents at an actual sewage plant. The whole BOD sensing system was quickly assembled on site and started up, and it gave an early warning shortly after the concentration of organic matter in the water suddenly increased, thus showing a high potential for engineering applications. This study broadened the domains of application of MEC-type BOD sensors in environmental monitoring, and promoted the development of technological innovation in water ecology and environmental monitoring.

Real-time monitoring for manual operations with machine vision in smart manufacturing

Online real-time production process monitoring is the basis for intelligent manufacturing refinement management. This paper proposes a contactless monitoring framework with machine vision to realize real-time production/assembly process monitoring and repetition counting. In addition, a two-stage approach is presented to solve the issue of manual operation recognition and counting by pieces. In the first stage, a high-efficient method for object detection and classification, YOLOv4-tiny, is used to recognize manual operations/sub-operations and classify the sub-operations of the operations using monitoring video. And a Sliding Window Counter algorithm is utilized to find appropriate boundary points of different categories of the sub-operations for counting in the second stage. A dataset of repetitive manual operations is constructed for training this approach. The experiment results on the production/assembly line operations indicate that the approach can identify and count worker operations in real time.

Real-time monitoring control of sequencing batch anammox process.

Rapid recognition and timely management of the emergent situation in wastewater treatment are crucial to maintaining the stable operation of anammox process. In this study, the feasibility of pH, conductivity(Cond) and oxidation reduction potential (ORP) profiles for monitoring and controlling anammox process for synthetic wastewater treatment was evaluated, and the practicability of the method was further verified by using real wastewater. The results showed that the characteristic values of these parameter profiles exhibited high accuracy and reproducibility in indicating the endpoint of the anammox reaction. Moreover, the positive correlations between TN removal and ΔpH, ΔCond and ΔORP were found. Nevertheless, only the slope of the Cond curve was found to be significantly linearly correlated with the specific anammox activity, which was further validated by the Haldane inhibition kinetic model, suggesting that the Cond curve can be used as an immediate feedback signal on whether anammox activity was inhibited. Overall, this study presents a fast, convenient and accurate strategy based on online real-time monitoring of instrument parameters, which was conducive to tracking the nitrogen removal process dynamics and performing the necessary operations in a timely manner, and to improving the stability of anammox process in wastewater treatment.

A deep-learning reduced-order model for thermal hydraulic characteristics rapid estimation of steam generators

Model reduction is a method that maps full-order conservation equations into lower-order subspaces or establish a data-driven surrogate model to reduce the complexity of the entire physical system, which has been widely applied in various fields in recent years. Compared with computational fluid dynamics (CFD) simulations, reduced-order model (ROM) can quickly and instantly obtain simulation results at low cost, which provides an economical alternative approach for the research and design process which need large number of repetitive simulations. In this paper, a deep-learning ROM was developed based on the proper orthogonal decomposition (POD) and machine learning (ML) method. The rapid estimation of two significant thermal hydraulic parameters in steam generator (SG), including the void fraction and temperature, was carried out by ROM. By POD mode analysis, the order for void fraction and temperature field was reduced by 88.3% and 96.7%, respectively. An artificial neural network was trained to reflect the implicit nonlinear mapping relationship between the CFD inputs and feature coefficients. The ROM was validated by comparing the predicted results with refined CFD results. The maximum absolute errors of void fraction and temperature are 0.1 and 0.03 K with speedup on the order of 104, indicating that the developed ROM can quickly and accurately estimate the thermal hydraulic characteristics of SG under different operating conditions. This work may provide a novel approach for the parameter sensitivity analysis and optimization design of SG and give valuable reference for the digital twin and the real-time online monitoring of the SG.

Design and Test of the Seedling Cavitation and Lodging Monitoring System for the Rape Blanket Seedling Transplanter

To realize the real-time monitoring of the lodging and cavitation state of the rape blanket seedling transplanter, a real-time online monitoring system set for cavitation and lodging based on machine vision was designed. The system was mainly composed of an image acquisition module, a height sensor, and an IPC (Industrial Personal Computer). When the system starts to work, the height of the lens from the ground and the collected images are saved in real time, and the operating status of the equipment is judged according to the height of the lens from the ground, and the actual corresponding size of the image pixels is corrected. We proposed a calculation method for judging the cavitation and lodging state of the seedlings based on the comparison between the actual planting position and the theoretical planting position of the seedlings. The processing and analysis software for the cavitation and lodging monitoring system of the rape blanket transplanter was compiled, and the monitoring system was developed. The dynamic test showed that the maximum absolute error of the cavitation recognition rate was 1.23%, and the maximum absolute error of the lodging recognition rate was 3.80%. The total time consumed of a 1 m image collected in real time from the collection, image processing and stitching, to seedling status recognition of the rape blanket seedling field was about 1 s. The accuracy and system processing time of the seedling cavitation and lodging state judgment can meet the accuracy and real-time requirements of cavitation and lodging state recognition for field transfer of rape blanket seedlings.

Gas-Sensitive Cellulosic Triboelectric Materials for Self-Powered Ammonia Sensing.

Gas‐sensitive materials are capable of dynamic identification and content monitoring of specific gases in the environment, and their applications in the field of gas sensing are promising. However, weak adsorption properties are the main challenge limiting the application of gas‐sensitive materials. A highly adsorbent gas‐sensitive cellulose nanofibril (CNF)‐based triboelectric material with a layered structure is prepared here and it is applied to self‐powered gas sensing. The layered structure of the triethoxy‐1H,1H,2H,2H‐tridecafluoro‐n‐octylsilane cellulose nanofiber (PFOTES‐CNF)‐based gas‐sensitive material further enhances the adsorption of the material due to electrostatic adsorption in the electrostatic field induced by triboelectricity. It is found that the ammonia‐sensitive material obtained by loading Ti3C2T x in PFOTES‐CNF has a fast response/recovery (12/14 s), high sensitivity response (V air/V gas = 2.1), high selectivity response (37.6%), and low detection limit (10 ppm) for 100 ppm of ammonia gas. In addition, the ammonia‐sensitive CNF‐based triboelectric material can accurately identify NH3 concentration changes in the range of 10–120 ppm and transmit the signal wirelessly to the user interface, facilitating real‐time online monitoring of NH3 in the environment. A novel strategy is provided here for designing and preparing high‐performance gas‐sensitive composites and the analysis of self‐powered gas sensing is guided.

Pd, Rh-decorated Se-vacancy MoSe2 monolayer: A promising candidate for sensing and detecting SO2F2, SOF2, H2S and SO2

Owing to the decomposition gases created by the breakdown of insulation gas induced by partial discharge poses a major threat to high-voltage insulation equipment and human health, real-time and accurate online monitoring of SF6 decomposition species is critical and indispensable. In this paper, utilizing density functional theory a gas-sensitive material based on transition metal (Pd, Rh) doped with S-vacancy MoSe2 monolayer is proposed to accomplish accurate detection of the target gases (SO2F2, SOF2, SO2 and H2S). The modification process implies that the transition metal (Pd, Rh) utilized as dopants act as electron acceptors (-0.068 e and -0.086 e) as well as retaining an effective theoretical recovery time under specific high temperature. Meanwhile, the decoration of Pd and Rh can greatly affect the electron transmission function and electron occupancy, which reduces the band gap and enhances the adsorption properties of Se-vacancy MoSe2 monolayer. Calculated results illustrate that the S-vacancy MoSe2 monolayer modified with Pd and Rh exhibits excellent adsorption properties for SF6 decomposition species, especially for SO2 (adsorption energy of -1.348 eV and -1.665 eV). This research can aid in the development of innovative surface-modified sensing materials for the detection of SF6-based electrical equipment in high-voltage insulated equipment and power systems.

A Real-Time Monitoring and Warning System for Power Grids Based on Edge Computing

It is very important for the safe operation of the power grid to be able to achieve real-time online monitoring, but along with the expansion of the smart grid, powerful computing power is required to achieve online monitoring, which cannot be loaded by ordinary servers. Due to the limited computing and storage resources of the server, it is difficult to fully meet the real-time requirements of online power line monitoring. Therefore, this research proposes a smart grid real-time monitoring and early warning system based on edge computing, which can reasonably allocate computing tasks according to the effective use of edge node resources. It can also collect and process information in real time, and can monitor the power grid online, which improves the fault identification efficiency of the power grid, effectively reduces the economic cost, and relieves the computing pressure of the online monitoring equipment on the cloud computing server. Meanwhile, considering the burst problem in allocation queue optimization, a multipriority allocation queue algorithm is proposed, and an improved greedy algorithm allocation model is used to solve the optimal scheduling problem between bursts. Simulation analysis shows that the scheduling scheme proposed in this paper can effectively reduce the monitoring delay of the power line monitoring system, improve the overall adaptability and compatibility of the system, meet the market demand, and facilitate promotion.

One-step high-throughput detection of low-abundance biomarker BDNF using a biolayer interferometry-based 3D aptasensor

Although biosensors for signal monitoring have been extensively developed, their application in one-step high-throughput detection of low-abundance disease biomarkers remains challenging. This study presents a 3D aptasensor based on a biolayer interferometry (BLI) technique, followed by the sensitive and rapid detection of the specific biomarker brain-derived neurotrophic factor (BDNF) for early screening of glaucoma, an irreversible disease that causes blindness. The developed 3D aptasensor enabled one-step batch conversion of the low-abundance biomarker BDNF binding into optical interference signal, which was mainly attributed to the following factors: (1) A dimeric aptamer with extremely high targeting affinity was constructed as a biorecognition molecule, (2) highly sensitive 3D matrix sensors were integrated as signal transduction elements, and (3) the BLI Octet system with automated, high-throughput, and real-time online monitoring capabilities was used for reporting. The 3D aptasensor exhibited a broad detection window from 0.41 to 250 ng/mL BDNF, with a limit of detection of 0.2 ng/mL. Furthermore, detection of BDNF in glaucoma patient serum using the aptasensor showed good agreement with ELISA findings as well as the clinical diagnosis of the patient, demonstrating the feasibility of the system as a screening tool for glaucoma. This one-step high-throughput screening approach provides a valuable solution for the early diagnosis of glaucoma and may reduce the risk of blindness in visually impaired people.

Concentrated solar-thermal methane pyrolysis in a porous substrate: Yield analysis via infrared laser absorption

In situ compositional analysis via laser absorption spectroscopy is used to characterize the output of a concentrated solar-thermal methane pyrolysis process over a range of test conditions. The novel thermo-chemical process involves directed radiation of a simulated solar source, focused onto a porous carbon felt substrate through which methane flows and decomposes to produce primarily hydrogen gas and graphitic carbon, along with residual hydrocarbons. A mid-infrared laser diagnostic setup has been developed to measure the residual concentrations of light hydrocarbons in the pyrolysis product gas downstream of the reaction zone, with real-time on-line monitoring capability. The infrared absorption sensor utilizes two interband cascade lasers (ICLs) to probe the fundamental C-H stretch vibrational bands, targeting (1) the R(15) manifold of the ν3 band of CH4 as well as one R(14) transition of the ν9 band of C2H4 near 3.16 μm and (2) the RQ3 line cluster of C2H6 near 3.34 μm, respectively. A compositional analysis based on carbon and hydrogen balances is developed to characterize the hydrogen and carbon yields of the pyrolysis process using laser absorption measurements and deposited carbon weight. A range of test conditions are examined with variations of flow rate and solar flux, demonstrating real-time monitoring of product gas composition and sensitivity to operating conditions in variable and transient conditions.

Turning Knife Health Monitoring Based on Vibration Signal with Machine Learning Algorithms

The wear of the turning tool has a great impact on product quality. With the development of sensor technology and increasing computing power, more and more research focuses on online monitoring and prediction of tool condition using machine learning methods. Nevertheless, most research has been conducted in laboratories, making it difficult to apply the results to the shop floor under complex production conditions. The research is based on vibration data measured in a real production environment of a turning machine. Machine learning models such as Random Forest, XGBoost, etc. were trained to predict the wear condition of the turning tool after features were extracted using digital signal processing techniques such as wavelet decomposition and empirical mode decomposition. A moving average-based method was proposed to convert the binary classification results into the probability of tool failure. On a real manufacturing data set, the online real-time monitoring is realized with an accuracy of 93.8%. It proves to be a good approach for managing spare parts in a real-time industrial environment with limited and isolated data sources.

A Real-Time Measurement-Modeling System for Ship Air Pollution Emission Factors

The lack of techniques for monitoring ship emissions all day and in all weather conditions to obtain real-time emission factor values is the main problem in understanding the characteristics of ship emissions, and there is still no perfect solution. In this study, a real-time measurement-modeling system was designed and implemented. The system was divided into three parts: (1) a portable exhaust monitoring device, which could be mounted on a drone, aircraft, patrol boat, dock, and bridge crane, as well as on the shore, to conduct all-weather and real-time online monitoring of ship emissions; (2) a monitoring information platform for ship emissions, based on a Spring + Spring MVC + MyBatis (SSM) framework and Vue front-end technology; and (3) a cloud server that received real-time ship emission measurement data and stored it after verification and analysis to calculate the pollutant gas and particulate matter emission factors. Following development, this system was used to monitor the emissions of ocean-going and inland river ships. Analysis of the acquired data showed that the system could effectively measure the emission factors of ship exhausts full-time in a variety of weather scenarios. This system can improve the efficiency of maritime law enforcement and provide technical support for promoting the construction of ship emission control areas. It can also help researchers obtain ship emission data, as well as an improved understanding of the emission characteristics of ships.