Reliable Real-time Monitoring Research Articles

Optimizing gula apong production with an IoT-based temperature monitoring system

<p>Determining the quality of <em>gula apong</em> is crucial to optimizing its production, with cooking temperature being a key factor affecting both taste and shelf life. The <em>gula apong</em> industry faced challenges due to the lack of reliable real-time temperature monitoring methods during the cooking process. Traditional approaches were inefficient and inaccurate, leading to difficulties in maintaining consistent product quality and meeting market demands. This highlights the necessity of monitoring the temperature throughout each cooking process. This research aims to develop an internet of things (IoT)- based cooking temperature monitoring system to enhance quality control measures in the production of <em>gula apong</em>. The IoT prototype collects temperature data from the thermocouple sensor, then transmits it to cloud storage through a Wi-Fi communication network, utilizing the Node-RED platform for data processing and analysis. Data obtained from the on-site measurement shows that the optimal temperature for producing standard-quality <em>gula apong</em> is approximately around 165 °C. The recommended boiling temperature for Nipah sap is 140 °C. This IoT system can reduce the cooking time of <em>gula apong</em> to 3 hours from the traditional 4 to 6 hours. Utilizing the data acquired from this study helps the producers not only maintaining the quality of <em>gula apong</em> but also reduce the cooking time and cost.</p>

Fully Integrated Multiplexed Wristwatch for Real-Time Monitoring of Electrolyte Ions in Sweat.

Considerable progress has already been made in sweat sensors based on electrochemical methods to realize real-time monitoring of biomarkers. However, realizing long-term monitoring of multiple targets at the atomic level remains extremely challenging, in terms of designing stable solid contact (SC) interfaces and fully integrating multiple modules for large-scale applications of sweat sensors. Herein, a fully integrated wristwatch was designed using mass-manufactured sensor arrays based on hierarchical multilayer-pore cross-linked N-doped porous carbon coated by reduced graphene oxide (NPCs@rGO-950) microspheres with high hydrophobicity as core SC, and highly selective monitoring simultaneously for K+, Na+, and Ca2+ ions in human sweat was achieved, exhibiting near-Nernst responses almost without forming an interfacial water layer. Combined with computed tomography, solid-solid interface potential diffusion simulation results reveal extremely low interface diffusion potential and high interface capacitance (598 μF), ensuring the excellent potential stability, reversibility, repeatability, and selectivity of sensor arrays. The developed highly integrated-multiplexed wristwatch with multiple modules, including SC, sensor array, microfluidic chip, signal transduction, signal processing, and data visualization, achieved reliable real-time monitoring for K+, Na+, and Ca2+ ion concentrations in sweat. Ingenious material design, scalable sensor fabrication, and electrical integration of multimodule wearables lay the foundation for developing reliable sweat-sensing systems for health monitoring.

Explainable deep learning enhances robust and reliable real-time monitoring of a chromatographic protein A capture step.

The application of model-based real-time monitoring in biopharmaceutical production is a major step toward quality-by-design and the fundament for model predictive control. Data-driven models have proven to be a viable option to model bioprocesses. In the high stakes setting of biopharmaceutical manufacturing it is essential to ensure high model accuracy, robustness, and reliability. That is only possible when (i) the data used for modeling is of high quality and sufficient size, (ii) state-of-the-art modeling algorithms are employed, and (iii) the input-output mapping of the model has been characterized. In this study, we evaluate the accuracy of multiple data-driven models in predicting the monoclonal antibody (mAb) concentration, double stranded DNA concentration, host cell protein concentration, and high molecular weight impurity content during elution from a protein A chromatography capture step. The models achieved high-quality predictions with a normalized root mean squared error of <4% for the mAb concentration and of ≈10% for the other process variables. Furthermore, we demonstrate how permutation/occlusion-based methods can be used to gain an understanding of dependencies learned by one of the most complex data-driven models, convolutional neural network ensembles. We observed that the models generally exhibited dependencies on correlations that agreed with first principles knowledge, thereby bolstering confidence in model reliability. Finally, we present a workflow to assess the model behavior in case of systematic measurement errors that may result from sensor fouling or failure. This study represents a major step toward improved viability of data-driven models in biopharmaceutical manufacturing.

Monitoring Lubrication and Wear in-situ by triboelectrification Under Vacuum Conditions

Maintaining the condition of the friction pair is an important measure to reduce mechanical failure. This paper integrates a vacuum elastohydrodynamic oil film measurement device and a current amplifier to simultaneously observe the optical interference image, friction coefficient and triboelectric signal during the friction process. In a high vacuum environment, the discharge phenomenon of steel balls corresponds to the time when the friction pair wears. Changes in the friction state in high vacuum environments have a significant impact on triboelectricity. Based on the relationship between the two, a sensor for real-time monitoring of the friction and wear status of the friction pair is designed, which provides a reliable real-time monitoring method for friction pair monitoring in vacuum machinery.

Three-dimensional CNT-rGO/PDMS porous scaffold derived supercompressible lightweight body-mounted piezoresistive force sensor for human motion monitoring

Wearable electronics is an emerging field that demands composites with both reliable mechanical durability and suitable electrical sensitivity. Herein, we report a combination of supercompressible, ultralightweight, piezoresistive and mechanically robust sensor created by the infiltration of active fillers (CNTs/rGO) on the 3D porous scaffold of PDMS. Carbon nanotubes (CNTs) and reduced graphene oxide (rGO) hybrid synergistically improve the electrical and sensing properties by providing better interfacial contacts, high electron/phonon transfer; low inter tube slippage and reduction in phonon scattering. This study provides useful insights and experimental optimisation of parameters like dropping time and nanofiller concentrations to generate highly viable hybrid microstructure for sensor applications. The sensor shows excellent peizoresistive performance like wide linearity range, large-scale force sensing, supercompressibility, good durability, rapid response-recovery time and good sensitivity. The multifunctional sensor exhibits promising potential for the real-time monitoring of human flexion, like joint movements of finger, wrist, elbow and knee. It can directly be integrated on a shoe sole for reliable real-time gait monitoring. The smart insole could accurately monitor and distinguish gait phase while walking, jogging, squatting, sitting, standing. Based on the versatility of the sensor and the ability to accurately evaluate gait speed, stride length the sensor can be employed for bio-motion analysis, myoelectric prosthesis and other wearable devices.

A Cu/Ni alloy thin-film sensor integrated with current collector for in-situ monitoring of lithium-ion battery internal temperature by high-throughput selecting method

The high-energy-density lithium-ion batteries (LIBs) are susceptible to thermal runaway and lead to safety incidents. Monitoring the internal temperature of LIBs is a promising way to forewarn its thermal runaway. However, the current external temperature sensors show high delay and poor accuracy, and the built-in temperature sensors are incompatible with the assembly process and may damage the battery. Thus, a reliable real-time internal battery temperature monitoring sensor is desired. In this study, a high-throughput selected thin-film resistance temperature detector (TFRTD) is proposed. The TFRTD is integrated between the collectors of the pouch LIB, which is compatible with the battery assembly process. The built-in TFRTD responds 82% faster and measures 33% more accurately than an external RTD, allowing real-time monitoring of internal battery temperature at different current rates. With a discharge rate of 0.5 C, the internal temperature can be monitored to be 0.57 °C higher than the external temperature in a battery with a capacity of only 0.1 Ah. Cycling tests show that the battery with a built-in TFRTD has a capacity retention rate of 92.71% after 100 cycles, and the TFRTD has a limited impact on battery performance. Therefore, the developed novel TFRTD can be used to analyze the battery heating process under different conditions and has some potential for engineering applications.

Enabling glucose adaptive self-healing hydrogel based triboelectric biosensor for tracking a human perspiration

A breakthrough in intelligent healthcare enables the collection of real-time patient data and active diagnosis via the advancement of medical technology. Monitoring glucose levels steady is essential to minimize diabetes-related issues since glucose is used by cells as an essential source of energy. Despite the range of commercially available glucose monitoring devices, the pain of repetitive blood testing and complex power demands has addressed the shortcomings of painless compact glucose biosensors. To address these drawbacks, self-healing glucose adaptive hydrogel based triboelectric biosensors (GAH-TES) are proposed as a biocompatible noninvasive technique for simultaneous glucose monitoring. With the assistance of a -cyclodextrin inclusion complex, glucose-adaptive PVA hydrogels may be utilized as an immobilization matrix for the glucose oxidase enzyme. The modulation of dynamic hydrogel networks in the presence of diversified glucose environments leads in changes in conductivity that boost electrical performance. Introducing glucose-adaptive hydrogel into a triboelectric nanogenerator (TENG) allows for efficient energy conversion from motion-based glucose stimuli in human sweat to electrical output. Higher glucose concentrations were shown to boost TENG production due to the greater conductivity and polarization effect caused by the increased ionic strength carried by the enzymatic activity. Due to its ability to detect high glucose levels autonomously, GAH-TES has broadened the scope of diabetes management by highly selective, flexible, and reliable real-time monitoring of human perspiration.

Decoupling thermal effects in GaN photodetectors for accurate measurement of ultraviolet intensity using deep neural network

Precise and reliable monitoring systems under various harsh conditions are important to realize an overall future automation system for diverse industries. In this study, a facile fabrication method for a gallium nitride (GaN)-based ultraviolet (UV) photodetector was developed as a system-on-chip decoupling system, and a novel decoupling system was proposed to separate the thermal effects from the GaN-based sensor using a deep neural network (DNN)-based regression model. To predict the true values of UV intensity, the currents of both UV- and thermal-responsive GaN film and thermal-responsive copper film were used as input features without additional measurement devices. In addition, the performance of the DNN regression model was evaluated using the mean absolute percentage error. Consequently, the proposed model showed a prediction accuracy of ∼97.86% with two input features. Furthermore, the thermal decoupling method using the proposed sensor was successfully demonstrated, even when the thermal conditions changed significantly. These results support a novel decoupling method for reliable real-time monitoring systems for various harsh environmental industries, such as aerospace, ocean, subterranean, power plants, and combustion engines.

Experimental study on uplift mechanism of pipeline buried in sand using high-resolution fiber optic strain sensing nerves

Reliable assessment of uplift capacity of buried pipelines against upheaval buckling requires a valid failure mechanism and a reliable real-time monitoring technique. This paper presents a sensing solution for evaluating uplift capacity of pipelines buried in sand using fiber optic strain sensing (FOSS) nerves. Upward pipe-soil interaction (PSI) was investigated through a series of scaled tests, in which the FOSS and image analysis techniques were used to capture the failure patterns. The published prediction models were evaluated and modified according to observations in the present study as well as a database of 41 pipe loading tests assembled from the literature. Axial strain measurements of FOSS nerves horizontally installed above the pipeline were correlated with the failure behavior of the overlying soil. The test results indicate that the previous analytical models could be further improved regarding their estimations in the failure geometry and mobilization distance at the peak uplift resistance. For typical slip plane failure forms, inclined shear bands star from the pipe shoulder, instead of the springline, and have not yet reached the ground surface at the peak resistance. The vertical inclination of curved shear bands decreases with increasing uplift displacements at the post-peak periods. At large displacements, the upward movement is confined to the deeper ground, and the slip plane failure progressively changes to the flow-around. The feasibility of FOSS in pipe uplift resistance prediction was validated through the comparison with image analyses. In addition, the shear band locations can be identified using fiber optic strain measurements. Finally, the advantages and limits of the FOSS system are discussed in terms of different levels in upward PSI assessment, including failure identification, location, and quantification.

Real-time monitoring system for rotor temperature of a large turbogenerator based on SmartMesh IP wireless network communication technology

In this study, a real-time rotor temperature monitoring system for large turbogenerators using SmartMesh IP wireless network communication technology was designed and tested. The system is capable of providing comprehensive, accurate, continuous, and reliable real-time temperature monitoring for turbogenerators. Additionally, it has demonstrated satisfactory results in a real-time monitoring test of the rotor temperature of various famous large-scale turbogenerators and giant nuclear power half-speed turbogenerators designed and manufactured in China. The development and application of this wireless temperature measurement system would aid in improving the intelligent operation quality, safety, and stability of China's large turbine generators and even the entire power system.

Scalable Functionalization of Polyaniline-Grafted rGO Field-Effect Transistors for a Highly Sensitive Enzymatic Acetylcholine Biosensor.

For decades, acetylcholine (Ach) has been considered a critical biomarker for several degenerative brain diseases, including Alzheimer’s, Parkinson’s disease, Huntington’s disease, and schizophrenia. Here, we propose a wafer-scale fabrication of polyaniline (PAni)-grafted graphene-based field-effect transistors (PGFET) and their biosensing applications for highly sensitive and reliable real-time monitoring of Ach in flow configuration. The grafted PAni provides suitable electrostatic binding sites for enzyme immobilization and enhances the pH sensitivity (2.68%/pH), compared to that of bare graphene-FET (1.81%/pH) for a pH range of 3–9 without any pH-hysteresis. We further evaluated the PGFET’s sensing performance for Ach detection with a limit of detection at the nanomolar level and significantly improved sensitivity (~103%) in the concentration range of 108 nM to 2 mM. Moreover, the PGFET exhibits excellent selectivity against various interferences, including glucose, ascorbic acid, and neurotransmitters dopamine and serotonin. Finally, we investigated the effects of an inhibitor (rivastigmine) on the AchE activity of the PGFET. From the results, we demonstrated that the PGFET has great potential as a real-time drug-screening platform by monitoring the inhibitory effects on enzymatic activity.

A Multi-functional NO2 gas monitor and Self-Alarm based on Laser-Induced graphene

The increasingly serious air pollution problem makes the development of high-performance gas monitoring system extremely urgent. Especially for nitrogen dioxide (NO2), which is harmful to both people and the environment. The combination of reliable real-time monitoring and early warning is of great significance yet less study. Here, we designed a laser-induced graphene (LIG)-based multi-functional NO2 monitor and self-alarm (MMSA), in which a rectangle area is used as a dual-functional module to realize high-performance temperature condition and acoustic alarm by applying direct/alternating current voltage excitation source. The molybdenum disulfide (MoS2) sensitive material works in concert with the LIG interdigital electrodes (IDEs), and the gas monitor achieves high sensitivity (86.81% for 5 ppm, and 22.90% for 250 ppb) and excellent recoverability, aided by the good electrothermal property of LIG, overcoming the dilemma of poor sensing performance of MoS2 at room temperature (RT). To top it off, the thermoacoustic effect of the LIG could bring about an intuitional sound signal, and a proof-of-concept demonstrates the timely acoustic warning response of the MMSA to exceeded NO2 concentrations.

Real-time fire detection system based on dynamic time warping of multichannel sensor networks

Fires are considered one of the most devastating accidents that could occur within a building. However, existing fire detection systems result in late fire detection or produce a high false alarm rate due to the complex patterns and temporal dynamics found in sensor signals that come from the various types of fires. This paper develops a novel fire detection algorithm that is based on multiple sensors in different locations to provide reliable real-time fire monitoring. The proposed fire detection algorithm takes into account both current and past sensor measurements and evaluates the similarity of sensor signals based on a dynamic time warping distance measure. Moreover, the proposed algorithm adaptively selects the sensors that are critical in detecting a fire in an early stage based on the developed k-out-of-P fire voting rule, which effectively combines decision rules from P multichannel sensor signals. A case study was performed using a real-life fire dataset obtained from the National Institute of Standards and Technology to demonstrate the effectiveness of the proposed approach.

Clustering Algorithm-Based Network Planning for Advanced Metering Infrastructure in Smart Grid

Nowadays, legacy electrical distribution systems are migrating to a new modern electric grid with the capability of supporting different applications such as advanced metering infrastructure (AMI), distributed energy resources, and electric vehicles. Among these applications, AMI is playing an important role in delivering data from customers to power utilities, supporting reliable real-time monitoring, and remote operation of power quality data and voltage profile. The AMI consists of smart meters, data aggregation points (DAPs), the utility control center, and communication networks. Appropriate network planning plays an essential role in facilitating the exchange of data between consumers and power utilities as well as accommodating new smart grid applications and future growth. This work proposes an optimal placement of DAPs for AMI based on machine learning clustering techniques in residential grids. Network partitioning is introduced to create sub-networks and graph algorithms generate a deployment topology given optimization constraints. A new measurement metric called coverage density is considered to indicate neighborhood area networks (NAN) zones with the appropriate coverage. Three real scenarios of NAN are considered: urban, suburban, and rural. The proposed algorithm is evaluated and compared with conventional heuristic optimization methods with respect to average and maximum distance between smart meters and DAPs, coverage density, and execution time.

Application Value of Vital Signs Telemetry System for 2019 Novel Coronavirus Disease Suspected Cases in Isolation Wards.

ObjectiveA large number of isolation wards were built to screen suspected patients because of the outbreak of coronavirus disease 2019 (COVID-19). The particularity of the isolation wards would lead to more medical resource consumption and heavier hospital control tasks. Therefore, we adopted a vital signs telemetry system in the isolation wards to improve this situation.Materials and TechnologiesTwenty sets of vital signs telemetry system were installed in the east district of the isolation area and the wards were used as the telemetry system wards (TSWs). The wards in the west district were used as the routine wards (RW). The daily telephone questionnaire was used to collect the frequency and time of ward rounds by medical staff and lasted for one week.ResultsWithin one-week survey, the average frequency of RW rounds was 3.00 ± 1.00 times per day, and the average time was 93.57 ± 66.25 min. The daily frequency of RW rounds was 0.428 ± 0.394 times per capita, and the time was 7.88 ± 2.36 min. There was a statistically significant difference in the time of ward rounds per capita, which presented that the daily time of TSW rounds per capita was shorter than that of RW rounds. No security events related to telemetry equipment were found throughout the study.ConclusionThe application of vital signs telemetry system as an alternative to traditional ward monitoring is considered feasible. The use of telemetry system can significantly reduce the consumption of medical resources, the workload of medical staff along with the administration and labor cost for isolation wards. The telemetry system provides sensitive and reliable real-time monitoring for the key indicators used for disease judgment and can make an accurate warning of the patients with disease aggravation in time. Thus, it is worthy of promotion and wide application.

Satellite-Based Operational Real-Time Drought Monitoring in the Transboundary Lancang–Mekong River Basin

Existing gauging networks are sparse and not readily available in real-time over the transboundary Lancang–Mekong River (LMR) basin, making it difficult to accurately identify drought. In this study, we aimed to build an operational real-time Lancang–Mekong drought monitor (LMDM), through combining satellite real-time data and the Variable Infiltration Capacity (VIC) hydrological model at a 0.25° spatial resolution. Toward this, three VIC runs were conducted: (1) a 60-year (1951–2010) historical simulation driven by Princeton’s global meteorological forcing (PGF) for yielding ‘normal’ conditions (PGF-VIC), wherein the VIC was calibrated with 20-year observed streamflow at six hydrological stations; (2) a short-period (2011–2014) simulation to bridge the gap between the historical and the real-time modeling; (3) the real-time (2015–present) simulation driven by bias-corrected satellite data, wherein the real-time soil moisture (SM) estimate was expressed as percentile (relative to the ‘normal’) for drought monitoring. Results show that VIC can successfully reproduce the observed hydrographs, with the Nash–Sutcliffe efficiency exceeding 0.70 and the relative bias mostly within 15%. Assessment on the performance of LMDM shows that the real-time SM estimates bear good spatial similarity to the reference, with the correlation coefficient beyond 0.80 across &gt;70% of the domain. In terms of drought monitoring, the LMDM can reasonably reproduce the two recorded droughts, implying extreme droughts covering the Lower LMR during 2004/05 and widespread severe 2009/10 drought across the upper domain. The percentage drought area implied by the LMDM and the reference is close, corresponding to 66% and 60%, 43% and 40%, and 44% and 36% for each typical drought month. Since January 2015, the LMDM was running in an operational mode, from which the 2016 unprecedented drought was successfully identified in Mekong Delta. This study highlights the LMDM’s capability for reliable real-time drought monitoring, which can serve as a valuable drought early warning prototype for other data-poor regions.

Development and validation of a real-time microelectrochemical sensor for clinical monitoring of tissue oxygenation/perfusion.

Oxygen is of critical importance to tissue viability and there is increasing demand for its reliable real-time clinical monitoring in order to prevent, diagnose, and treat several pathological disorders, including hypoxia, stroke and reperfusion injury. Herein we report the development and characterisation of a prototype clinical O2 sensor, and its validation in vivo, including proof-of-concept monitoring in patients undergoing surgery for carpal tunnel release. An integrated platinum-based microelectrochemical device was custom designed and controlled using a miniaturised telemetry-operated single channel clinical potentiostat. The in vitro performance of different sensor configurations is presented, with the best sensor design (S2) displaying appropriate linearity (R2 = 0.994) and sensitivity (0.569 ± 0.022 nA μM-1). Pre-clinical validation of S2 was performed in the hind limb muscle of anaesthetised rats; tourniquet application resulted in a significant rapid decrease in signal (90 ± 27%, [ΔO2] ca. 140 ± 18 μM), with a return to baseline within a period of ca. 3 min following tourniquet release. Similar trends were observed in the clinical study; an immediate decrease in signal (39 ± 3%, [ΔO2] ca. 30 ± 20 μM), with basal levels re-established within 2 min of tourniquet release. These results confirm that continuous real-time monitoring of dynamic changes in tissue O2 can serve as an indicator of reperfusion status in patients undergoing carpal tunnel surgery, and suggests the potential usefulness of the developed microelectrochemical sensor for other medical conditions where clinical monitoring of O2 and perfusion is important.

Flexible and Thermally Induced Switchable Fire Alarm Fabric Based On Layer-by-Layer Self-Assembled Silver Sheet/Fe3O4 Nanowire Composite.

Textiles with fire detection will appeal for the interior decoration of houses and play a critical role in public security. Herein, we fabricated a sandwichlike fire alarm fabric (Ag@Fe3O4-MS) based on Fe3O4 nanowire (NW) arrays and fish-scale-like silver sheets, designed by in situ layer-by-layer assembly on the surface of polypropylene (PP) nonwoven fabric. The Ag@Fe3O4-MS sensor has fish-scale-like silver sheets as self-assembling electrode layers on the upper and lower sides of fabric, which can be tailored into various shapes and integrated into other flexible electronics. The sensor provides a real-time monitoring strategy for early warning fire detection (below 100 °C). At room temperature, the fabricated Ag@Fe3O4-MS sensor is electrically insulating, while it switches to an electrical conductor when exposed to flame. In view of its fast response time (2 s) and sustained working time (at least 15 min), the sensor with a connected alarm light can immediately alert people of house fires. More importantly, this sensor can provide additional real-time information on the fire location and reliable real-time monitoring of fire rekindling. The sensor was exposed to fire for successive cycles with an average response ofI = 43 mA, confirming the reliable repeatability to detect fires. This ultralight, flexible Ag@Fe3O4-MS sensor could have broad applications in home safety. Moreover, the sandwichlike design provides a reliable strategy to modify household fabric items to provide a fire warning function.

Core-sheath nanofiber yarn for textile pressure sensor with high pressure sensitivity and spatial tactile acuity

Highly sensitive wearable textile pressure sensors represent the key components of smart textiles and personalized electronics, with potential applications in biomedical monitoring, electronic skin, and human-machine interfacing. Here, we present a simple and low-cost strategy to fabricate highly sensitive wearable textile pressure sensors for non-invasive human motion and physiological signal monitoring and the detection of dynamic tactile stimuli. The wearable textile sensor was woven using a one-dimensional (1D) weavable core-sheath nanofiber yarn, which was obtained by coating a Ni-coated cotton yarn electrode with carbon nanotube (CNT)-embedded polyurethane (PU) nanofibers using a simple electrospinning technique. In our design, the three-dimensional elastic porous nanofiber structure of the force-sensing layer and hierarchical fiber-bundled structure of the conductive Ni-coated electrode provide the sensor with a relatively large surface area, and a sufficient surface roughness and elasticity. This leads to rapid and sharp increases in the contact area under stimuli with low external pressure. As a result, the textile pressure sensor exhibits the advantages of a high sensitivity (16.52 N−1), wide sensing range (0.003–5 N), and short response time (~0.03 s). Owing to these merits, our textile-based sensor can be directly attached to the skin as usual and conformally fit the shape deformations of the body’s complex flexible curved surfaces. This contributes to the reliable real-time monitoring of human movements, ranging from subtle physiological signals to vigorous movements. Moreover, a large-area textile sensing matrix is successfully fabricated for tactile mapping of spatial pressure by being worn on the surface of wrist, highlighting the tremendous potential for applications in smart textiles and wearable electronics.

Online Conductimetric Flow-Through Analyzer Based on Membrane Diffusion for Ammonia Control in Wastewater Treatment Process.

Ammonia is a necessary monitoring parameter that should be controlled within an optimum range in the whole process of wastewater treatment and recycling, but few reliable real-time monitoring technologies are available currently under such harsh water conditions. This study proposes a continuous conductometric flow-through analyzer for ammonia monitoring (CFAA) in the wastewater treatment process. It is developed based on the gas diffusion mechanisms, and the proposed analytical principle has been validated in which the real-time conductivity increment rate is linearly proportional to the real-time ammonia concentration in the sample. This method could be generally applicable in monitoring a wide ammonia concentration range (10.2 μg L-1 to 500 mg L-1), and it is capable of achieving long-term ammonia monitoring by periodic renewal of the receiving solution. The potential impact factors and corresponding calibration principles are also developed to avoid tedious ongoing calibration. The field application results demonstrate that CFAA can effectively and directly achieve real-time and average ammoniacal nitrogen monitoring at different treatment stages regardless of the complexity of wastewater, not requiring any sample pretreatment. Compared with other ammonia online monitoring technologies, the proposed CFAA shows remarkable advantages in high reliability, durability, and accuracy, especially under severe monitoring condition. It can be a useful monitoring tool for continuous ammonia control in the wastewater treatment process.