On-line Monitoring System Research Articles

Development of a simple biogas analyzer module (BAM) for real-time biogas production monitoring

ABSTRACT Anaerobic digestion (AD) relies on the cooperation of specific microbial communities, making it susceptible to process disruptions that could impact biogas production. In this regard, this study presents a technological solution based on the Arduino platform, in the form of a simple online monitoring system that can track the produced biogas profile, named as biogas analyzer module (BAM). The applicability of the BAM focused on monitoring the biogas produced from sugarcane vinasse inoculated with sewage sludge biodigestion processed in mesophilic conditions (38 oC), in a pH range of 6.5–7.5, and following a three-stage operational model: (i) an adaptation (168 h), (ii) complete mixing (168 h), and (iii) bio-stimulation with glycerol (192 h). Then, the lab-made BAM was used to trace the produced biogas profile, which registered a total biogas volume of 8,719.86 cm3 and biomethane concentration of 95.79% (vol.), removing 90.8% (vol) of carbon dioxide (CO2) and 65.2% (vol) of hydrogen sulfide (H2S). In conclusion, the results ensured good accuracy and efficiency to the device created by comparisons with established standards (chromatographic and colorimetric methods), as well as the cost reduction. The developed device would likely be six times cheaper than what is available in the market.

Experimentally derived models to detect onset of shunt resistance degradation in photovoltaic modules

It has been shown that a reduction in the shunt resistance can lead to solar module degradation over time, resulting ultimately in module failure. This paper reports how the effects of reduced shunt resistance on the current–voltage (I-V) characteristics of a PV cell can be used to identify degradation before it becomes critical. Five commercial polycrystalline solar cell samples had their shunt resistance artificially lowered before measuring their I-V characteristics. Analyses of the effect of lowered shunt resistance on maximum power output (Pmax), short-circuit current (ISC), open-circuit voltage (VOC) and fill factor were conducted. Reduction in shunt resistance was correlated with the cells’ electrical parameters to determine the critical shunt resistance where degradation becomes catastrophic. Linear models were developed relating reduction in shunt resistance to the solar cell’s Pmax and VOC. These relationships are proposed as strong predictors and observers of shunt resistance degradation and are suitable for implementation in online monitoring systems for operational PV modules.

Desalination performance of shale gas produced water by flow-electrode capacitive deionisation process

The cost-effective desalination of shale-gas produced water (PW) has long been an industrial research hotspot. Flow-electrode capacitive deionisation (FCDI), a recently developed electrochemical desalination technology, may be a novel option for PW disposal because of its attractive advantages. However, reports on the desalination test of actual high-salinity industrial wastewater by FCDI are lacking. In this study, a laboratory-scale FCDI device with an online monitoring system was used to desalinate shale-gas PW. FCDI achieved relatively stable desalination efficiency for pretreated PW with a salinity of 2.65 % and reached an energy-normalised salt removal of 0.479 mg/J. The flow electrode chemically activated by KOH increased the FCDI average desalination rate by 98.5 % for the pretreated saline PW. Electrochemical impedance spectroscopy analysis revealed that the majority of fouling accumulated on the anion-exchange membrane (AEM) side. Residual pollutants were adsorbed by the flow electrode in the AEM chamber, which destroyed the optimal ion-transport path and electric double-layer structure. Results showed that FCDI was more suitable for PW’s reverse-osmosis (RO) permeate desalination to form an RO–FCDI hybrid. This study provided a practical experimental case on the electrochemical desalination of saline PW by FCDI.

Hybridized Triboelectric-Electromagnetic Aeolian Vibration Generator as a Self-Powered System for Efficient Vibration Energy Harvesting and Vibration Online Monitoring of Transmission Lines.

In this work, based on the triboelectric-electromagnetic working principle, a comprehensive strategy appropriately hybridizes a multilayered elastic structure TENG (ME-TENG) and a double-electromagnetic generator (EMG) for efficient aeolian vibration energy harvesting and vibration state monitoring. The ME-TENG with the feature of elasticity is integrated with a movable plate embedded with a magnet as the counterweight, which acts as a spring-like mass system in response to external vibration excitation, making the inseparable integrity of the TENG and EMG. The basic hybridized triboelectric-electromagnetic aeolian vibration generator (HAVG) consisting of ME-TENG and double-EMGs in terms of structural parameters and response characteristics is first optimized and discussed, thereby the efficient vibration energy harvesting and effective vibration state response can be further improved through the mutual complementarity of TENG and EMG. Furthermore, the self-powered capacity of the HAVG in terms of LED arrays and a wireless ambient temperature and humidity monitoring system is verified through the hybrid charging strategy of TENG and EMG modules and the combination of HVAG and energy management circuits, benefiting from the sophisticated-designed structure and excellent output performance of the HAVG. Importantly, a self-powered aeolian vibration monitoring system is established and demonstrated for vibration-state sensing and abnormal vibration alarm. This work demonstrates a novel strategy for energy harvesting and state sensing of overhead transmission line aeolian vibration, which not only reveals TENG-EMG promising potential for energy harvesting for aeolian vibration, but also provides valuable guidance for the construction of a self-powered online-monitoring system for transmission lines.

A Comprehensive Review of Biosensor Integration in Microphysiological Systems for Online Monitoring: Current Challenges and Future Advancements

AbstractThe goal of microphysiological systems (MPS) is to replicate the relevant functionality of human organ tissues in in vitro. MPS technology so far has been used to simulate the various human organs and with the help of sensor integration in the MPS systems the biological activities of the organ to be modeled have been translated into data to be analyzed for further considerations. Most standard characterization approaches are intrusive and detrimental, and not feasible for online monitoring of cell cultures. Microfluidic biosensors, for instant, provide non‐invasive on‐line detection of biomarkers and molecules under targeted indicators with a high detection extent, successfully overcoming the limits of existing approaches. Microfluidic biosensors are rapidly being incorporated into MPS and employed for real‐time target identification as a result. In this review the focus is on emerging ways for miniaturizing and embedding biosensing systems in MPS also known as “organ‐on‐chip”. Cutting‐edge microfluidic biosensors are also covered with examples, showing their key benefits in monitoring MPS and highlighting current breakthroughs, before describing the remaining problems and anticipated future improvements in integrated microfluidic biosensors.

Design and study of a hydrogen concentration online monitoring system

This paper designs a hydrogen concentration online monitoring system, mainly completes the construction of the software and hardware platform, select the I.MX6ULL embedded processor as the core hardware platform and embedded Linux system transplantation, root file system construction, hardware peripherals driver design, complete the human-computer interface based on QT application framework and save the collected data to the memory of the embedded motherboard and send to the cloud in real time through the network port.

Prediction model of transformer top oil temperature based on data quality enhancement.

Top oil temperature (TOT) is an important parameter to evaluate the running state of a transformer. According to the variation trend of TOT, the internal thermal state of transformers can be predicted so as to arrange operation and maintenance reasonably and prevent the occurrence of accidents. However, due to the complex working environment in the field, there are often a large number of missing values in online monitoring data, which seriously affects the prediction of TOT. At the same time, it is affected by various factors such as load, ambient temperature, wind speed, and solar radiation, which cause the information of different time scales to be mixed in its monitoring data. Therefore, it is difficult to achieve the desired accuracy with a single model. In this article, a model for predicting TOT based on data quality enhancement is proposed. First, the Markov model is used to complete the online monitoring data containing missing values to obtain a complete and continuous time series. Then, using the ensemble empirical modal decomposition method, the time series of TOT is decomposed into multiple time series components to eliminate the interaction between different time scales of information, thus reducing the prediction difficulty. Finally, the sub-prediction model of the extreme learning machine is constructed, and the prediction results of all the sub-models are reconstructed to obtain the final prediction results of TOT. In order to verify the effectiveness of the model, the TOT of an operating transformer for the next two days is predicted in the article, and its mean absolute percentage error (MAPE) is 5.27% and its root mean square error (RMSE) is 2.46. Compared with the BP neural network model and the support vector machines (SVM) model, the MAPE is reduced by 69.56% and 61.92%, respectively, and the RMSE is reduced by 67.02% and 59.87%. The results of this study will provide important support for the fault diagnosis of the top oil temperature online monitoring system.

Low-Cost Online Monitoring System for the Etching Process in Fiber Optic Sensors by Computer Vision.

The present research exposes a novel methodology to manufacture fiber optic sensors following the etching process by Hydrofluoric Acid deposition through a real-time monitoring diameter measurement by computer vision. This is based on virtual instrumentation developed with the National Instruments® technology and a conventional digital microscope. Here, the system has been tested proving its feasibility by the SMS structure diameter reduction from its original diameter of 125 μ until approximately 42.5 μm. The results obtained have allowed us to demonstrate a stable state behavior of the developed system during the etching process through diameter measurement at three different structure sections. Therefore, this proposal will contribute to the etched fiber optic sensor development that requires reaching an enhanced sensitivity. Finally, to demonstrate the previously mentioned SMS without chemical corrosion, and the etched manufactured SMS, both have been applied as glucose concentration sensors.

A two-staged adsorption/thermal desorption GC/MS online system for monitoring volatile organic compounds.

Real-time online monitoring of volatile organic compounds (VOCs) in ambient air is crucial for timely and effective human health protection. Here, we developed an innovative, automated two-staged adsorption/thermal desorption gas chromatography/mass spectrometry (GC/MS) system for real-time online monitoring of 117 regulated volatile organic compounds (VOCs). This system comprised a sampling unit, water management trap, two-staged adsorption/thermal desorption unit, thermoelectric coolers (TECs), and a commercial GC/MS system. By implementing a micro-purge-and-trap (MP & T) step and a two-staged adsorption/thermal desorption unit, the presence of interfering substances was effectively minimized. The utilization of a heart-cutting GC, combined with a single MS detector, facilitated the precise separation and detection of 117 C2-C12 VOCs, while circumventing the identification and coelution challenges commonly associated with traditional GC-FID or GC-FID/MS methods. The performance of our newly developed online system was meticulously optimized and evaluated using standard gas mixtures. Under optimal conditions, we achieved impressive results, with R2 values ≥ 0.9946 for the standard linear curves of all 117 VOCs, demonstrating a precision (RSD) ranging from 0.2% to 6.4%. When applied in the field monitoring, the concentration drifts for 10 ppbv standard gas mixtures were 0.01-5.64% within 24h. Our study developed a system for online monitoring of 117 atmospheric VOCs with relatively high accuracy and robustness.

Integrated energy carbon emission monitoring and digital management system for smart cities

In recent years, although China’s economy has continued to grow, the environmental impact is greatly affected by the use of primary energy, such as global warming, which has become more and more serious. Under the background of energy conservation and emission reduction, China’s emission reduction pressure is very great. In this paper, an online monitoring system for carbon emissions is developed for real-time monitoring of carbon emissions, and the ant colony algorithm is used to perform multi-objective optimization based on “construction period-cost-carbon emissions.” Through the organic integration of wireless sensors, communication networks, cloud servers, and mobile devices, a real-time monitoring system for carbon emissions has been developed, which can monitor and visualize the carbon emissions generated by major machinery on site in real time. At the same time, the resource consumption of each process in different modes is sorted out, and the multi-objective optimization problem of “construction period-cost-carbon emission” is designed to seek the optimal solution by combining the multi-objective optimization theory. In this paper, the developed real-time monitoring system is applied in the actual field, the stability and practicability of the system are verified, and the process-related data is obtained by combining the monitoring system and field investigation. The experimental results show that the relative deviations of the two units are consistent, fluctuating between 0.54% and 6.14%, and the overall deviations are 3.61% and 3.63%, respectively. Therefore, the online carbon emission monitoring system has stable data and high accuracy. By comparing the data trends of the online monitoring method and the emission factor method, it is found that the two trends are consistent, which verifies the applicability of the online monitoring method in the field of carbon emission monitoring.

Fault classification using convolutional neural networks and color channels for time-frequency analysis of acoustic emissions

We present a novel method for real-time fault classification using the time history of acoustic emissions (AEs) recorded from a lab-scale gas turbine operating under normal and faulty conditions across multiple turbine speeds. Time-frequency features are extracted using the continuous wavelet transform, and for each signal, the root mean square (RMS) and kurtosis are calculated. We employ a color mapping technique to combine the time-frequency and statistical features into a single red–green–blue (RGB) image. The red channel is mapped to the time-frequency data, whereas the green and blue channels are mapped to the RMS and kurtosis, respectively. Subsequently, a deep convolutional neural network is trained on the generated images to classify the gas turbine condition. We show that the proposed model can form an online monitoring system using AEs to classify multiple running conditions at various turbine speeds. The methodology not only achieves real-time classification of faults but also minimizes the human intervention in identifying these faults. The datasets and codes used in this paper will be openly available.

Cathodic Protection Online Monitoring Using a Low Power Wide Area Network Communication System

Cathodic protection is a corrosion control and prevention system that is applied to underground pipe structures by providing an electric charge to the structure to be protected. An online monitoring system for cathodic protection is urgently needed to save costs and ensure operator safety in the field in the care of the protected structure. This study aimed to evaluate cathodic protection online monitoring using a low power wide area network (LPWAN) communication system. Cathodic protection online monitoring in this study was applied using LPWAN with a radio frequency of 433 MHz. In this work, the type of communication with wireless technology has been chosen with low power consumption and can accommodate the distance between test stations in the distribution pipeline. From the results of testing the cathodic protection online monitoring system, it can be seen that measurement data that has been done manually can be replaced in real-time and monitored remotely. This system is highly recommended for areas that have so far not been covered by manual measurements, for example, remote areas prone to flooding, swamps, and interference areas that require denser data information to make handling easier to do early.

Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data

Implementing the concept of a “smart furnace transformer” should stipulate its information support throughout its life cycle. This requires improving techniques for estimating the transformer’s health and forecasting its remaining useful life (RUL). A brief review of the problem being solved has shown that the known RUL estimation techniques include processing the results of measuring the facility state parameters using various mathematical methods. Data processing techniques (deep learning, SOLA, etc.) are used, but there is no information on their application in online monitoring systems. Herewith, fast (shock) changes in the resource caused by the failures and subsequent recoveries of the facility’s health have not been considered. This reduces the RUL forecasting accuracy for the repairable equipment, including transformers. It is especially relevant to consider the impact of sudden state changes when it comes to furnace transformers due to a cumulative wear effect determined by their frequent connections to the grid (up to 100 times a day). The proposed approach is based on calculating the RUL by analytical dependencies, considering the failures and recoveries of the facility state. For the first time, an engineering RUL forecasting technique has been developed, based on the online diagnostic monitoring data results provided in the form of time series. The equipment’s relative failure tolerance index, calculated with analytical dependencies, has first been used in RUL forecasting. As a generalized indicator, a relative failure tolerance index considering the facility’s state change dynamics has been proposed. The application of the RUL forecasting technique based on the results of dissolved gas analysis of a ladle furnace unit’s transformer is demonstrated. The changes in the transformer state during the operation period from 2014 to 2022 have been studied. The RUL was calculated in the intensive aging interval; the winding dismantling results were demonstrated, which confirmed developing destructive processes in the insulation. The key practical result of the study is reducing accidents and increasing the service life of the arc and ladle furnace transformers. The techno-economic effect aims to ensure process continuity and increase the metallurgical enterprise’s output (we cannot quantify this effect since it depends on the performance of a particular enterprise). It is recommended to use the technique to forecast the RUL of repairable facilities equipped with online monitoring systems.

Achieving a real-time online monitoring system for conservation culturomics.

Environmental monitoring is increasingly shifting toward a set of systems that describe changes in real time. In ecology specifically, a series of challenges have prevented the rollout of real-time monitoring for features such as biodiversity change or ecosystem service provision. Conservation culturomics, a field concerned with interactions between people and nature, is well placed to demonstrate how monitoring might move toward a network of real-time platforms, given its existence exclusively in the digital realm. We examined a set of considerations associated with the development of real-time monitoring platforms for conservation culturomics and introduce a near real-time platform for the Species Awareness Index, a global index of changing biodiversity awareness derived from the rate of change in page views for species on Wikipedia. This platform will update automatically each month, operating in near real time (https://joemillard.shinyapps.io/Real_time_SAI/). There are plans to make the underlying data queryable via an application programing interface independent of the platform. The real-time Species Awareness Index will represent the first real-time and entirely automated conservation culturomic platform and one of the first real-time platforms in the discipline of ecology. Real-time monitoring for culturomics can provide insight into human-nature interactions as they play out in the physical realm and provide a framework for the development of real-time monitoring in ecology. Real-time monitoring metrics can be processed on private virtual machines and hosted on publicly available cloud services. Conservation now needs an online, real-time observatory that can evolve with the structure of the web.

Strengthening oil pollution monitoring system in aquatic environment through development of IoT-based Oil-Water Separator Device

Oil spills are a serious pollution problem in water ecosystems, causing significant damage to living things and the environment. The demand for devices to handle oil remediation is increasing due to the frequent occurrence of oil spills. To date, numerous works have been reported to address this pollution problem, but most of them do not utilize Internet of Things (IoT) technology. Having an IoT system of this kind would improve operational efficiency by providing real-time data, alerts, and notifications of oil pollution through monitoring systems. This study presents the development of an IoT-based automatic oil-water separator device to support an oil pollution monitoring system in water ecosystems. The built device consists of sensors, a microcontroller, and an actuator, with the capability to automatically detect oil pollution and perform water-oil separation. The device incorporates a monitoring system. The water-oil separation was carried out by applying semi-permeable membrane nanotechnology. Moreover, the device has undergone a series of oily-water separation experiments to simulate oil pollution, achieving an oil separation efficiency of 62.8% (on average) with good repeatability. Additionally, the automatic device is developed with IoT pairing with a website application for an online monitoring system. Users can monitor the process using a personal computer or smartphone via the web/mobile application at any time and from any place. It is of interest to explore this further since it offers an effective and implementable method to solve oil pollution problems in real-world applications.

Development of a Monitoring System for Huangjiu Storage based on Electrical Conductivity

Abstract In this study, a fast rancidification detection method was developed for Huangjiu in pottery jars based on the principle of electrical conductivity changes caused by microbial contamination. The change of total acid in Huangjiu was positively correlated with the increase of electrical conductivity. This method was applied to an online monitoring system for Huangjiu storage in stainless steel tanks. When the electrical conductivity exceeds the normal fluctuation range (Mean + 3 standard deviations) of previous data, the monitoring system recognizes the microbial contamination. By optimizing the conductivity-temperature compensation coefficient and conductivity statistical method, the standard deviation of the method was reduced and the sensitivity of microbial pollution monitoring was improved. The ranges of conductivity and compensation coefficient of common types of Huangjiu were estimated. Interference in conductivity measurements due to environmental factors was minimised through the synchronous comparison of conductivity data for multiple tanks of Huangjiu. The standard deviation (SD), which indicates the fluctuation range of the system, decreased from 143 μS/cm to 2 μS/cm. The monitoring system was then applied in Huangjiu storage tanks with capacities of 60 t and 300 t. Through the comparison of conductivity data change, the abnormal signals caused by microbial contamination during the storage of Huangjiu were found in time. Meanwhile, through off-line detection of total acid of Huangjiu, the effectiveness of microbial contamination on-line detection was verified.

A Weather-Independent and Renewable Power Supply With Wireless Power Transfer Feature for Powering Online Monitoring Systems in Smart Grid

The merge of the power and information infrastructures in smart grid requires increasing number of sensors and online monitoring systems on the high-voltage (HV) transmission towers. These systems are traditionally powered by small solar panels or wind turbines that are weather-dependent. This new contribution involves a full analysis of a complete weather-independent power supply to replace existing approaches. For the first time, the complete system comprising the energy harvesting, wireless power transfer (WPT) and output power stages is practically evaluated using printed circuit board (PCB) resonators embedded in a 35-kV composite insulator. We leverage the nearly constant current, transresistance, and voltage gains characteristics of the PCB domino-resonator structure under different self-oscillating frequencies and propose a coordinated control scheme of transmitter-side and receiver-side converters to regulate the constant current (CC) or constant voltage (CV) through entire charging process. Any current surge caused by the burst operation of the active rectifier on the receiver side triggers a transition between different self-oscillating operations on the transmitter side, leading to operating region extension of the active rectifier. Thus, the proposed system can easily adjust the output to follow different battery charging profiles without wireless communication between the transmitter and receiver.

Design of Reactive Power Online Monitoring System of Intelligent Distribution Transformer Based on Edge Computing

Due to the unbalanced development of various regions, in most regions, the distribution transformer of medium voltage line has not realized automatic monitoring, so the staff cannot monitor it in real-time. Therefore, an online monitoring system for the reactive power status of intelligent distribution transformers based on edge computing is designed. The system hardware structure configuration consisting of the master station layer, the edge layer and the acquisition layer is constructed, and the system software functional structure consisting of data processing, online monitoring, and system management is designed. On this basis, the continuous sampling data of the reactive power of the intelligent distribution transformer is collected, and based on edge computing, the reactive power operation characteristics of the intelligent distribution transformer are obtained and matched to judge the reactive state of the intelligent distribution transformer operation, so as to realize online monitoring of the reactive state of the intelligent distribution transformer. The experimental results show that the online monitoring accuracy and efficiency of the intelligent distribution transformer reactive state are high.

Establishment and Application of Management Evaluation System for Transmission Line Icing Monitoring Terminal

The transmission line icing on-line monitoring device, which integrates many functions, such as line icing thickness monitoring, wire tension, micro weather, and image/video monitoring, can realize real-time icing condition monitoring of transmission lines and significantly improve the early warning ability of transmission lines. It is the foundation of a smart grid on-line monitoring system. Therefore, how to effectively evaluate the operational effectiveness of the icing monitoring terminal has become particularly important. Aiming at the automatic identification of icing monitoring terminal defects, this paper establishes a comprehensive evaluation system of icing monitoring terminal management and makes a comprehensive evaluation from the two dimensions of the communication index and efficiency index. Combined with the operation data of more than 290 sets of terminals in the power grid, the existing problems of the terminal are analyzed and some suggestions for further improvement are put forward.

Development and Field Testing of an Online Monitoring System for Atmospheric Particle-Bound Reactive Oxygen Species (ROS)

Excessive accumulation of reactive oxygen species (ROS) in the body can lead to a redox imbalance and result in cellular and tissue damage. Since ROS are highly reactive, traditional offline methods may underestimate their true concentration. In this study, we developed an online monitoring system for particle-bound ROS based on the fluorescent probe 2′,7′-dichlorofluorescin (DCFH), which consists of an Aerosol Collector and a Fluorescence Detector. The performance of the system was evaluated in terms of collection efficiency, instrument calibration, and comparison with offline methods. The results demonstrate that the collection efficiency of the system is over 93%, the calibration correlation coefficient (R2) is 99.75%, and the online system reduces ROS loss due to offline methods by more than 60%. The system has a temporal resolution of 20 min and the limit of detection of the system was 1.9 nmol H2O2/m3. Field observations revealed that particle-bound ROS exhibited similar diurnal variations to O3, and photochemical reactions were the main factors affecting its diurnal variation.