Continuous On-line Monitoring Research Articles

Influence of constructed rapid infiltration system on groundwater recharge and quality

Infiltration of stormwater through green surfaces is an important means of groundwater recharge. However, the increase in constructed impervious area caused by intensive urbanization has led to a reduction in rainwater infiltration over the past decade. The constructed rapid infiltration system (CRIS) with an ample open space plays an important role in groundwater recharge. This study aims to explore the influence of stormwater (roof runoff) concentration infiltration on the groundwater table and quality in the CRIS. Groundwater table monitoring is conducted for more than 2 years (October 2017–December 2019) by continuous online monitoring combined with manual sampling. Results show that the addition of zeolite to the CRIS has a good removal effect on rainfall runoff pollutants, and the influence of stormwater concentration infiltration on groundwater quality is small when the CRIS enters the stable running stage. The increased proportion of chemical oxygen demand, N, and P in J1 are all less than 10% from 2018 to 2019, and they are less than 20% for heavy metals. The stormwater concentration infiltration can recharge groundwater and increase the groundwater depth, and the groundwater depth varies from 0.5 m to 1.5 m during the monitoring period. The influence scope of the concentrated infiltration on the groundwater table and quality is between 25 and 45 m. The response of the groundwater table and quality to the stormwater concentration infiltration of J1, 25 m away from the CRIS, exhibits hysteresis, and the lag time is about 3–4 months. Conclusion from relevant research can provide important theoretical support for the further study of groundwater recharge by the CRIS.

Ultrasensitive evanescent wave optical fiber aptasensor for online, continuous, type-specific detection of sulfonamides in environmental water

Sensors capable for online continuous monitoring of total sulfonamides in environmental waters are highly desired due to their adverse effects on ecosystem, unexpected concentration fluctuation, and diversity. At present, no sensor with this capability has been reported. In this study, we evaluated the cross reactivity (CR) of the previously reported sulfadimethoxine-binding aptamer using DNase I assay and found that the aptamer was type-specific to sulfonamides. We then fabricated the first type-specific sulfonamide sensor, where the aptamer was immobilized on the optical fiber of the evanescent wave sensor, followed by the surface coating with Tween 80. The competitive binding of sulfonamides and Cy5.5 labeled complementary DNA enabled the low femtomolar to picomolar sensitivity and the detection of total 14 sulfonamides spiked in the lake water. The sensor also exhibited high selectivity, regeneration capability (40 cycles), stability (65 days), and short detection time (5 min). In addition, we found that the CRs were greatly dependent on the buffer composition. By performing the parallel detections in two buffers, the sensors detected 18 out of the 24 sulfonamides with the diversity coverage higher than commercial ELISA kits. Our aptasensor fills the technical gap for continuous monitoring of total sulfonamides in environmental waters.

The patient-centered diabetes management during the COVID-19 pandemic.

Since December 2019, in the fight against the coronavirus disease 2019 (COVID-19) pandemic, we observed that glycemic control in people with diabetes is easily affected by lifestyle changes. To maintain a good health condition, a patient-centered approach with mental support and close monitoring is required. For these, telemedicine and online continuous glucose monitoring (CGM), are effective systems. Therefore, based on our experience during the two-year period, we reviewed the literature for appropriate actions required for the management of diabetes to prevent COVID-19 infection and avoid unfavorable outcomes in COVID-19 cases. Once infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is a high risk of a poor prognosis in patients with diabetes. Glucocorticoid therapy in severe COVID-19 cases leads to further hyperglycemia. Since good glycemic control has been shown to improve outcomes, strict glycemic control using CGM is recommended. Using CGM data, insulin can be adequately titrated without causing hypoglycemia, and remote data monitoring can reduce the risk of infection for health care professionals, by reducing the frequency of patient contact. Among patients with COVID-19, some are found to have newly-diagnosed diabetes at admission. Those newly diagnosed patients present with a higher risk of poor prognosis compared to those with pre-existing diabetes. Therefore, glycemic status should be evaluated in all patients with COVID-19 admitted to hospitals.

Detection of Particle Concentration and Particle Size Based on Aerodynamic Particle Size Spectrometer

In order to solve the problem of the continuous online monitoring of the fixed pollution source emission particles, on the basis of the advantages and disadvantages of the comprehensive analysis of the extinction method, the β-ray absorption method, the micro-oscillating balance method, the charge transfer method, and the light scattering method, the particle size distribution and concentration optical detection system based on aerodynamic particle size spectrometer is designed according to the Mie light scattering principle. A high-performance aerodynamic particle size spectrometer is designed using a single-wavelength method to measure the concentration of particulate matter, and the equipment and the detection system are used in the continuous monitoring of particulate matter emitted by thermal power plants and compared with the ES632 equipment. Through the system test, the results show that compared with the ES632 equipment performance, the lowest concentration measured by the design of aerodynamic particle size spectrometer equipment can reach 0.1 mg/m3 with a measurement accuracy of 0.01 mg/m3. Compared with the ES632 equipment, the measurement concentration value is 8% higher, which meets the test requirements.

Continuous Monitoring and Characterization of Copolymerization Reactions of Acrylate Monomers with Indistinguishable Ultraviolet Spectra Using Infrared Spectroscopy

AbstractApplications of Automatic Continuous Online Monitoring of Polymerization reactions (ACOMP) have steadily expanded over the past 20 years. An ultraviolet (UV) detector, often combined with a differential refractive index detector (RID), is the usual method to track monomer conversion, but the method can fail when comonomers have similar UV spectra. Here, Fourier‐transform infrared spectroscopy (FTIR) is coupled to ACOMP to separate comonomer conversion during solution copolymerization of tert‐butyl acrylate (tBA) and n‐butyl acrylate (nBA). The UV spectra of these comonomers are too similar for separation. The FTIR region between 1100 and 1410 cm−1, however, shows a strong difference between tBA and nBA peaks—1403 and 1192 cm−1, respectively. Reaction rates determined from FTIR data show that tBA's is higher than nBA's. The terpolymerization of tBA, nBA, and methyl methacrylate (MMA) is also monitored, using MMA's 1325 cm−1 peak. Reaction rates are significantly lower than in the nBA/tBA copolymerization. The incorporation of FTIR into ACOMP opens the path for further expansion to a wide variety of copolymerization reactions, including systems of three or more comonomers. It can be a key new detector in the next steps for development of fully automatic feedback control of composition and molecular weight.

A new-designed system for continuous measurement of radon in water

On-line continuous monitoring of radon concentration in water is of great significance for its environmental application as a radioactive tracer, for example, as a potential precursor for earthquake forecast and volcanic eruption. To realize on-line continuous measurement on radon in complex water body, a compact measurement system mainly consisted of a simple degassing device and an electrostatic radon monitor is newly developed. The sensitivity of the measurement system is 73 ± 5 cph/(Bq/L), and the detection limit is 0.04 Bq/L with a 60-min cycle at 25 °C water temperature. Intercomparison measurements with RAD H2O were performed both in laboratory condition and in field, and consistent results within the error range were achieved. To test the developed measurement system, a continuous monitoring of radon concentration in water in the drainage tunnel of Mount Jinping was performed for 3 months. The arithmetic mean of radon concentration in water is 0.34 ± 0.09 Bq/L, varying in the range of 0.04–0.60 Bq/L during the period. Several rapid decreases of radon concentration in water were observed, which might be attributed to the increase of rainwater mixing in the drainage tunnel caused by heavy rainfall. The stability of long-term operation of the system enables it to be widely used in the field of radon in water as a tracer.

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power.
 This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

고장수목분석(FTA) 기법을 이용한 무인잠수정 건전성 진단

As an autonomous underwater vehicle (AUV) operates over a long period of time in the sea, continuous online monitoring of the vehicle is crucial. By monitoring the health status of the AUV, it is possible not only to avoid a serious damage or even loss of the vehicle, but also to effectively manage the missions being carried out. This paper presents an online health monitoring technique for AUVs using Fault Tree Analysis (FTA). The use of both information about the reliability and performance of subsystems can be highlighted as the main contribution from this work. The whole system is divided into several subsystems for which a fault tree is designed. Then, the system health is evaluated using the given fault tree by considering not only the performance of each component, but also the weighting factors, reliability, and fault status of various parts in each subsystem. In order to determine the health status of the AUV in real-time, the fault tree is structurally analyzed using the information mentioned above. The effectiveness of the proposed method is demonstrated using a set of simulations.

Machine Diagnosis based Multi-Agent Technology by Autonomous Sensor with Energy Harvesting

A machine diagnosis system based on multi-agent technology is proposed. We mainly focus on developing a multi-agent system for rotating machinery fault diagnosis by vibration sensor with energy harvesting. To estimate the inner rotation of machines in plant frequency analysis is frequently used. Our approach for diagnosis is agent-based, where vibration data is analyzed by a set of software agents coming from distributed servers to the user side. Another feature of this study is the development of autonomous vibration sensors. It earns electric power from vibration so that we are free from battery maintenance, and continuous online monitoring is enabled. Based on the implementation results of the existing multi-agent system design prototype, the harvesting sensor working process can produce total energy of 205µW with a working cycle of about 6.5 minutes, the energy harvester works, and the power accumulates continuously.

Monitoring Neurochemistry in Traumatic Brain Injury Patients Using Microdialysis Integrated with Biosensors: A Review.

In a traumatically injured brain, the cerebral microdialysis technique allows continuous sampling of fluid from the brain’s extracellular space. The retrieved brain fluid contains useful metabolites that indicate the brain’s energy state. Assessment of these metabolites along with other parameters, such as intracranial pressure, brain tissue oxygenation, and cerebral perfusion pressure, may help inform clinical decision making, guide medical treatments, and aid in the prognostication of patient outcomes. Currently, brain metabolites are assayed on bedside analysers and results can only be achieved hourly. This is a major drawback because critical information within each hour is lost. To address this, recent advances have focussed on developing biosensing techniques for integration with microdialysis to achieve continuous online monitoring. In this review, we discuss progress in this field, focusing on various types of sensing devices and their ability to quantify specific cerebral metabolites at clinically relevant concentrations. Important points that require further investigation are highlighted, and comments on future perspectives are provided.

Experimental Regulating Parameters of Bladder-Type Hydraulic Accumulator

Experimental regulating parameters of the non-stationary expansion of air inside a bladder-type hydraulic accumulator, working with the simple short pipeline, are presented in the paper. The technique of continuous online monitoring of changes in time of volume and absolute air pressure inside the hydraulic accumulator during the discharge process has been improved. Three series of experimental studies of transient gas processes inside the accumulator at different values of the average volume flow rate are made. Tendencies of change of the integral parameters of the hydraulic accumulator are obtained and analyzed depending on the serial number of the discharge cycle. A general dependence of dimensionless storage volume <i>Kreg</i> on the polytropic index <i>n</i> in series # 1–3, approximated by single power-law dependence, is obtained. The systematic changes of integral parameters in each subsequent discharge cycle can be explained by the non-stationary transient thermodynamic processes in air inside the accumulator until the thermodynamic equilibrium with the ambient air parameters is reached.

Study on denoising of continuous spectrum on-line monitoring signal of water quality with micro-reagents based on HHT

Study on denoising of continuous spectrum on-line monitoring signal of water quality with micro-reagents based on HHT

Online monitoring of 3D printing of steel via optical emission spectroscopy

Abstract Additive manufacturing by laser metal deposition (LMD) requires continuous online monitoring to ensure quality of printed parts. Optical emission spectroscopy (OES) is proposed for the online detection of printing defects by monitoring minute variations in the temperature of a printed spot during laser scan. A two-lens optical system is attached to a moving laser head and focused on a molten pool created on a substrate during LMD. The light emitted by the pool is collected by an ultraviolet–visible (UV–vis) spectrometer and processed. Two metrics are used to monitor variations in the surface temperature: the spectrally integrated emission intensity and correlation coefficient. The variations in the temperature are introduced by artificial defects, shallow grooves, and holes of various widths and diameters carved on a substrate surface. The metrics show sufficient sensitivity for revealing the surface defects, except for the smallest holes with an under-millimeter diameter. Additionally, numeric simulations are carried out for the detection of emission in the UV–vis and near-infrared (NIR) spectral ranges at various surface temperatures. It is concluded that both the metrics perform better in the NIR range. In general, this work demonstrates that spectrally resolved OES suits well for monitoring surface defects during 3D metal printing.

Echem methods and electrode types of the current in vivo electrochemical sensing.

For a long time, people have been eager to realize continuous real-time online monitoring of biological compounds. Fortunately, in vivo electrochemical biosensor technology has greatly promoted the development of biological compound detection. This article summarizes the existing in vivo electrochemical detection technologies into two categories: microdialysis (MD) and microelectrode (ME). Then we summarized and discussed the electrode surface time, pollution resistance, linearity and the number of instances of simultaneous detection and analysis, the composition and characteristics of the sensor, and finally, we also predicted and prospected the development of electrochemical technology and sensors in vivo.

Detection of free chlorine in water using graphene-like carbon based chemiresistive sensors.

Free chlorine is the most commonly used water disinfectant. Measuring its concentration during and after water treatment is crucial to ensure its effectiveness. However, many of the existing methods do not allow for continuous on-line monitoring. Here we demonstrate a solid state chemiresistive sensor using graphene-like carbon (GLC) that overcomes that issue. GLC films that were either bare or non-covalently functionalized with the redox-active phenyl-capped aniline tetramer (PCAT) were successfully employed to quantify aqueous free chlorine, although functionalized devices showed better performance. The response of the sensors to increasing concentrations of free chlorine followed a Langmuir adsorption isotherm in the two tested ranges: 0.01–0.2 ppm and 0.2–1.4 ppm. The limit of detection was estimated to be 1 ppb, permitting the detection of breaches in chlorine filters. The devices respond to decreasing levels of free chlorine without the need for a reset, allowing for the continuous monitoring of fluctuations in the concentration. The maximum sensor response and saturation concentration were found to depend on the thickness of the GLC film. Hence, the sensitivity and dynamic range of the sensors can be tailored to different applications by adjusting the thickness of the films. Tap water samples from a residential area were tested using these sensors, which showed good agreement with standard colorimetric measurement methods. The devices did not suffer from interferences in the presence of ions commonly found in drinking water. Overall, these sensors are a cost-effective option for the continuous automated monitoring of free chlorine in drinking water.

ELECTRICAL POTENTIAL DROP FOR MONITORING CREEP DAMAGE IN HIGH TEMPERATURE PLANT

ABSTRACT Electrical potential drop (EPD) is a powerful technique to gauge crack depth in many contexts, including fracture, creep and fatigue testing, and in-field NDE, however it has only seen limited use for monitoring pre-crack creep cavitation damage. The authors have previously reported promising results using a combination of AC and DC EPD on large pressure vessel creep tests, even detecting incipient damage. However, that study lacked linkage to the underlying microstructural mechanisms. Here we present the results of a more fundamental creep study using EPD on P91 pressure vessel steel specimens taken from weldments, suffering HAZ-related Type IV cracking. This work confirmed that EPD could detect incipient damage, with ACPD being particularly sensitive to life fraction, but allowed linkage to changes in material properties, such as permeability and resistivity. Discussion of the implications for future off-line (in-field) NDE methods and on-line continuous monitoring of high-temperature plant components is also made.

A real-time continuous monitoring system for long-term voltage stability with sliding 3D convolutional neural network

A real-time continuous monitoring system (CMS) for long-term voltage stability assessment with sliding three-dimensional convolutional neural network (3D-CNN) is proposed in this paper. Given a power system, its dynamic responses and topological information are strategically fused and converted into sequential state images, which constitute the learning input of the sliding 3D-CNN. In this way, both spatial and temporal correlation are considered in the CMS. The bus indices are reordered to strengthen the topology information in these images. With the localized weight-shared convolution operations, 3D-CNN can be conducted more efficiently with fewer parameters, and extract features independently of spatial locations. The translation invariance characteristic makes 3D-CNN highly generic to unknown scenarios, which is further enhanced after bus indices reordering. The sliding window of the 3D-CNN enables the CMS to handle varying degrees of disturbance and performs continuous online monitoring. Numerical tests are carried out on the New England 10-generator-39-bus system to demonstrate the effectiveness. Test results also show the potential of the CMS in robustness to PMU measurement errors and information losses.

Safety Science and Engineering Technology of Dust Control and Air Quality Improvement in Large Coal Mines

The paper introduces a new type of large-scale coal mine dust monitoring and efficient treatment technology, which realizes online continuous monitoring and control of mine dust concentration, and can transmit the dust concentration at the detection point to the underground control system in real time, realizing intelligent coal mine dust management, When the dust concentration exceeds the set standard, the coal shearer and road header can automatically carry out high-pressure spraying to reduce dust. Compared with the traditional technology, the dust removal efficiency is increased by more than 80%.

Continuous online monitoring in pressurized water reactors during flexible operation using PLSR-based technique – Case study: Load following test

This paper proposes the application of a multivariant-based technique, namely, Partial Least Square Regression (PLSR) to enhance the process of continuous online monitoring and control in pressurized water reactors (PWR) during flexible operation. The results of the investigations of this paper have shown that the proposed technique provided effective performance during flexible PWR operation even with a small size of available history operating data samples. More specific, the proposed model provides the reactor operator with a comprehensive and simple presentation of the reactor status via a single dynamic trajectory during flexible operation which helps detecting anomalies at an early stage. In addition, results also have shown that the proposed PLSR-based model minimized the limitation on the ex-core accuracy resulting from the operation in the flexible mode, provided reliable, fast with excellent monitoring efficiency. Moreover, the results have also shown that the proposed model provides a graphical control guide which is achieved via a prioritized, reduced, comprehensive and manageable set of the PWR physical parameters. The analysis of this graphical guide has shown that it provides the fine control tuning, which in many cases is left for the operator, thus it minimizes the human error by relieving operators from having to pay attention to additional tasks during transient contingencies. In the context of this paper, a load-following test is conducted to ascertain the efficiency of the proposed technique. The data of the test was observed at Unit-1, Tihange nuclear power station, Belgium (three-loop PWR, 2873 MWth, 962 MWe net). The PLSR-based model for continuous model for continuous monitoring as well as the graphical control guide are developed and validated using SIMCA software package.

A Robust Multi-Layer Framework for Online Condition Assessment of Power Transformers

Online continuous monitoring of dissolved gasses in transformer oil has become an emerging trend in the recent years. This way, in addition to sending the warning signal in the case of gas concentration, a fault diagnosis procedure can be conducted using standard Dissolved Gas Analysis (DGA) methods. However, since the fault diagnosis outcome and result of these methods are sometimes different, an appropriate solution should be suggested to accurately describe the transformer internal condition. To do so, a robust multi-layer framework has been proposed in this paper. Handling the measurement uncertainties and fusing the results of independent DGA methods without losing their diagnosis resolutions are the interesting features of this framework. To improve the overall accuracy of fused diagnosis result, an artificial neural network-based approach is proposed. It intelligently assigns the weight of each independent method in the fusion procedure according to its fault type detection accuracy for the range of input gasses concentrations. To evaluate performance of the proposed framework, comprehensive test studies have been conducted and the promising obtained results prove its superior performance for use in online applications.