On-line Sensor Research Articles

Exploring the long-term performance of air purifiers in removing particulate matter and formaldehyde across different residential environments

Household air purifiers are widely used to enhance indoor air quality. However, limited information exists regarding the factors that influence their long-term performance. This study investigates the impact of various residential environments on the long-term efficacy of air purifiers. We deployed household air purifiers in three distinct environments: oily fumes (Group A), non-oily fumes (Group C), and a mixture of oily and non-oily fumes (Groups B-I and B-II). The selected air filter consisted of melt-blown polypropylene and activated carbon, materials commonly employed in commercial applications. The results demonstrated that the control efficiency of air purifiers in non-oily fume environments surpassed that in oily fume environments. After 12 months of operation, particulate matter (PM) concentrations rose by 92.7% and 76.5% in oily and non-oily fume environments, respectively. This increase was primarily attributed to the loss of electrostatic attraction in the polypropylene material due to the organic matter in oily particulate matter. After operating for 1000 hours, the clean air delivery rate (CADR) attenuation rates for particulate matter were 70.6%, 19.9%, 16.7%, and 12.5% in Groups A, B-I, B-II, and C, respectively. The CADR attenuation rates for formaldehyde were 80.6%, 48.4%, 38.9%, and 37.3% in the same groups. Additionally, we developed a real-time prediction model for the service life of air purifiers using data from online sensors. When operated for 12 hours daily at varying PM concentrations, the filters had an expected service life of 29 to 97 days in non-oily fume environments and 66 to 220 days in oily fume environments.

An intelligent agriculture monitoring framework for leaf disease detection using YOLOv7

Agriculture is one of the most important economic sectors on which societies have relied since ancient times. With the recent development of technology, agriculture has also been incorporating modern techniques such as the Internet of Things and Artificial Intelligence to improve productivity and monitor the farming process. One of agriculture’s most prominent issues is the spread of plant diseases and the lack of real-time monitoring. Various systems and operations have recently been developed to predict and diagnose plant diseases. However, current operations have been selective, focusing on a specific aspect without addressing other important aspects, resulting in either partial or compound application of results, rendering the desired outcomes ineffective. To deal with such challenges, we propose an intelligent framework for real-time agriculture monitoring and disease detection, namely a system for monitoring plant diseases using YOLOv7. In the proposed framework, a rule-based policy has been designed for detecting plant diseases using online plant leaf monitoring, sensors, and surveillance cameras. Images of plant leaves captured by different cameras are sent in real-time to central cloud servers for disease detection. The improved YOLOv7 technology is utilized for plant disease detection, and the proposed system has been evaluated using a dataset of diseased tomato leaves, comparing it with different models based on various performance metrics to demonstrate its effectiveness, achieving an accuracy of 96%.

A novel on-line dual sensing system for soil property measurement and mapping

Real-time assessment of within-field soil fertility variation is crucial for making informed and sustainable decisions, like crop nitrogen (N) fertilization. Two commonly used soil sensors i.e., visible-near-infrared (vis-NIR) spectroscopy and ion-selective electrodes (ISE) have been reported to successfully estimate various soil macronutrients and nitrate-N, respectively, however, their integrated use for mapping a full-scale soil fertility variation has not been taken into consideration to date. The aim of this study is to use an integrated on-line dual-sensor system of vis-NIRS and ISE to estimate and map within-field variation in multiple soil fertility parameters including pH, organic carbon (OC), extractable- phosphorus (P), potassium (K), calcium (Ca) and moisture content (MC) and nitrate-N. The sensing system was mounted to the three-point linkage of a tractor and surveyed two arable fields in Belgium. Partial least squares regression models for vis-NIRS sensor were calibrated and validated, while a linear regression model was established for validation of the ISE sensor. Geostatistical surface maps for on-line measured soil attributes were generated using the inverse distance weighting and ordinary kriging methods for ISE data and vis NIR, respectively. The linear correlation confirms a very high similarity between ISE-measured nitrate-N and laboratory-analyzed nitrate-N (coefficient of determination (R2) = 0.93). Besides, the vis-NIRS demonstrates very good prediction accuracies for all the fertility attributes with R2 = 0.70-0.78, root mean square error (RMSE) =0.52-2.46 %, residual of prediction deviation (RPD) = 1.89 – 2.13 and the ratio of the performance to interquartile distance (RPIQ) = 1.72 – 6.56. Validation between laboratory and on-line measured soil maps also shows quite comparable spatial distribution patterns. Therefore, the proposed dual on-line sensor system has a high potential to estimate and map within-field spatial fertility distributions including nitrate-N, offering a basis for sustainable management decisions for precision soil and crop production.

Fluorescence sensor enabled control of contaminants of emerging concern in reclaimed wastewater using ozone-based treatment processes

Contaminants of emerging concern (CEC) pose significant challenges to environmental and human health. The development of the wastewater reuse sector, coupled with progressively stringent regulations, needs innovative systems that integrate advanced treatment processes with in-situ and real-time monitoring of CEC. This study investigates the use of a tryptophan-like fluorescence sensor for real-time and online monitoring of CEC within a pilot plant employing O3-based advanced oxidation processes (AOPs). Two tertiary wastewater effluents (WW-1 and WW-2) were tested, placing the pilot system downstream of two different wastewater treatment plants (WWTPs). Priority substances and micropollutants detected in the investigated water matrixes such as pharmaceuticals, per- and polyfluoroalkyl substances (PFAS) were selected as targeted compounds in this study. Fluorescence degradation was detected in real-time by the sensor, showing a high capability to detect fast changes in water quality induced by oxidation. Furthermore, the real-time fluorescence showed better sensitivity than lab-scale fluorescence in detecting the fast action of hydroxyl radicals (·OH) during the O3/H2O2 process, highlighting the importance of online monitoring. Selected CEC were degraded by AOPs with different percentages of removal efficiency (RE) (0%<RE<100% in WW-1 and 15%<RE<90% in WW-2) depending on oxidant doses and the reactivity of compounds with O3 and ·OH. Fluorescence data by online sensor enabled accurate prediction of the removal of a wide spectrum of CEC during O3 and O3/H2O2 processes (R2≥0.93). Furthermore, real-time fluorescence data were successfully used to predict observed pseudo-first-order rate constants of CEC with O3 or O3/H2O2. Obtained results suggest that real-time fluorescence monitoring is an excellent tool to control CEC removal during O3-based AOPs and monitor the transferred ozone in wastewater (R2≥0.94), contributing to the optimization of reagent dose, energy and costs.

Gaussian process-based online sensor selection for source localization

This paper addresses the sensor selection problem for source localization within cyber–physical systems (CPSs). While recent machine learning and reinforcement learning approaches aim to optimize sensor selection and placement within the Area of Interest (AoI), their need for intensive data collection and training precludes online operation. Furthermore, these methods often require prior knowledge of the unknown source’s characteristics and lack adaptability to the dynamic nature of CPSs, leading to inefficiencies in unseen environments. This paper addresses these shortcomings using Gaussian process Optimization coupled with an active sensor selection mechanism to locate the unknown source within the AoI. The proposed approach first builds a probabilistic model of the environment, which is discretized into a grid, without prior training using a Gaussian Process surrogate model. Next, the model iteratively and systematically learns the underlying spatial phenomenon using Gaussian Process optimization. Concurrently, the approach selects a subset of sensors by optimizing a fitness function that advocates selecting informative and energy-efficient sensors. Next, the probabilistic model, having accurately learned the environment, directs the algorithm to the unknown source by identifying the cell with the highest likelihood of containing it. Finally, a peak refinement step is performed, which computes the exact location of the source within the designated cell. The proposed method’s efficacy is validated through experiments in radioactive source localization, validation studies, and adaptability assessments across various environments. In terms of quality of localization (QoL), it outperforms recent localization benchmarks, such as a reinforcement learning-based approach and DANS, by around 18% and 100%, respectively.

Continuous Process Verification 4.0 application in upstream: adaptiveness implementation managed by AI in the hypoxic bioprocess of the Pichia pastoris cell factory.

The experimental approach developed in this research demonstrated how the cloud, the Internet of Things (IoT), edge computing, and Artificial Intelligence (AI), considered key technologies in Industry 4.0, provide the expected horizon for adaptive vision in Continued Process Verification (CPV), the final stage of Process Validation (PV). Pichia pastoris producing Candida rugosa lipase 1 under the regulation of the constitutive GAP promoter was selected as an experimental bioprocess. The bioprocess worked under hypoxic conditions in carbon-limited fed-batch cultures through a physiological control based on the respiratory quotient (RQ). In this novel bioprocess, a digital twin (DT) was built and successfully tested. The implementation of online sensors worked as a bridge between the microorganism and AI models, to provide predictions from the edge and the cloud. AI models emulated the metabolism of Pichia based on critical process parameters and actionable factors to achieve the expected quality attributes. This innovative AI-aided Adaptive-Proportional Control strategy (AI-APC) improved the reproducibility comparing to a Manual-Heuristic Control strategy (MHC), showing better performance than the Boolean-Logic-Controller (BLC) tested. The accuracy, indicated by the Mean Relative Error (MRE), was for the AI-APC lower than 4%, better than the obtained for MHC (10%) and BLC (5%). Moreover, in terms of precision, the same trend was observed when comparing the Root Mean Square Deviation (RMSD) values, becoming lower as the complexity of the controller increases. The successful automatic real time control of the bioprocess orchestrated by AI models proved the 4.0 capabilities brought by the adaptive concept and its validity in biopharmaceutical upstream operations.

Online functional connectivity analysis of large all-to-all networks in MNE Scan

Abstract The analysis of electroencephalography (EEG)/magnetoencephalography (MEG) functional connectivity has become an important tool in neuroscience. Especially the high time resolution of EEG/MEG enables important insight into the functioning of the human brain. To date, functional connectivity is commonly estimated offline, that is, after the conclusion of the experiment. However, online computation of functional connectivity has the potential to enable unique experimental paradigms. For example, changes of functional connectivity due to learning processes could be tracked in real time and the experiment be adjusted based on these observations. Furthermore, the connectivity estimates can be used for neurofeedback applications or the instantaneous inspection of measurement results. In this study, we present the implementation and evaluation of online sensor and source space functional connectivity estimation in the open-source software MNE Scan. Online capable implementations of several functional connectivity metrics were established in the Connectivity library within MNE-CPP and made available as a plugin in MNE Scan. Online capability was achieved by enforcing multithreading and high efficiency for all computations, so that repeated computations were avoided wherever possible, which allows for a major speed-up in the case of overlapping intervals. We present comprehensive performance evaluations of these implementations proving the online capability for the computation of large all-to-all functional connectivity networks. As a proof of principle, we demonstrate the feasibility of online functional connectivity estimation in the evaluation of somatosensory evoked brain activity

Image based Modeling and Control for Batch Processes

This manuscript addresses the problem of leveraging thermal images for modeling and feedback control, specifically tailored for terminal quality control of batch processes. The primary objective, common in many batch processes, is to produce products with quality variables aligning with user specifications, available for measurement only at batch termination, precluding the direct use of classical control strategies. Furthermore, in many instances, traditional online sensors such as thermocouples may not be available, but instead spectral inputs like thermal images or acoustic data may be more readily available for feedback control. The challenge is to not only use the non-traditional sensor data for building a dynamic model but also to use that model for terminal quality control. The proposed approach involves a multi-layered modeling strategy. Initially, a dimensionality reduction technique is employed to condense the high-dimensional image into a set of representative outputs. Subsequently, subspace identification (SSID) is applied to develop a Linear Time-Invariant (LTI) State Space (SS) model between the inputs and the reduced outputs. Finally, a Partial Least Squares (PLS) model is constructed linking the terminal states of a batch (identified using SSID) with the product qualities obtained for that specific batch. This model is then incorporated into a Model Predictive Control (MPC) formulation. The effectiveness of the MPC is illustrated by showcasing its capability to generate products of high quality by deploying the MPC on a bi-axial lab-scale rotational molding setup.

Design and characterization of a novel turbidity sensor based on quadrature demodulation

Abstract Turbidity is regarded as a comprehensive indicator in water quality monitoring, and the turbidity sensor deployed in the water supply network can record the dynamic changes of water quality in time. However, the weak photoelectric signal from the photodetector contains a quantity of noise. In order to improve signal-to-noise ratio (SNR), a novel on-line turbidity sensor based on quadrature demodulation principle has been proposed in this paper. A near-infrared light-emitting diode (LED) with a wavelength of 860 nm was selected as a stable monochromatic light source, and a photodiode (PD) with an angle of 90° to the incident light from the LED was selected as the photodetector. Using signal modulation and demodulation technology, the weak photoelectric signal extraction, conversion, amplification and output of the turbidity sensor were realized through the effective integration. A corresponding test apparatus of the turbidity sensor was established and experimental results showed that within a 0~5 NTU measurement range, the turbidity sensor had good linearity and stability, the relative measurement error was within ±1% and the limit of detection could reach as low as 0.0049 NTU. The developed turbidity sensor has good detection performance and can meet the needs of low turbidity detection of drinking water.&amp;#xD;

Hybrid Twins Modeling of a High-Level Radioactive Waste Cell Demonstrator for Long-Term Temperature Monitoring and Forecasting.

Monitoring a deep geological repository for radioactive waste during the operational phases relies on a combination of fit-for-purpose numerical simulations and online sensor measurements, both producing complementary massive data, which can then be compared to predict reliable and integrated information (e.g., in a digital twin) reflecting the actual physical evolution of the installation over the long term (i.e., a century), the ultimate objective being to assess that the repository components/processes are effectively following the expected trajectory towards the closure phase. Data prediction involves using historical data and statistical methods to forecast future outcomes, but it faces challenges such as data quality issues, the complexity of real-world data, and the difficulty in balancing model complexity. Feature selection, overfitting, and the interpretability of complex models further contribute to the complexity. Data reconciliation involves aligning model with in situ data, but a major challenge is to create models capturing all the complexity of the real world, encompassing dynamic variables, as well as the residual and complex near-field effects on measurements (e.g., sensors coupling). This difficulty can result in residual discrepancies between simulated and real data, highlighting the challenge of accurately estimating real-world intricacies within predictive models during the reconciliation process. The paper delves into these challenges for complex and instrumented systems (multi-scale, multi-physics, and multi-media), discussing practical applications of machine and deep learning methods in the case study of thermal loading monitoring of a high-level waste (HLW) cell demonstrator (called ALC1605) implemented at Andra's underground research laboratory.

Soft sensor for the dry solid content in thickened primary sludge

ABSTRACT Software sensors, or soft sensors, can be a feasible option to monitor parameters that are difficult (or impossible) to measure with hardware sensors. At Henriksdal water resource recovery facility (WRRF), the operators have long experienced issues with a clogging sensor for the dry solid (DS) content in thickened primary sludge. A soft sensor was developed, and in the process, two methods were compared: long short-term memory (LSTM) network and linear regression. The first is a recurrent neural network that can capture non-linear dynamics, whereas the latter is a linear static model. The LSTM network was the best at predicting the DS content, with a mean squared error (MSE) of 0.341 with respect to laboratory data. The linear regression model performed worse than estimating a long-time average of daily manual samples but outperformed the online sensor. Replacing the existing sensor with the developed soft sensor can open up possibilities for more efficient control and operation of the thickener unit.

Online meta-learning approach for sensor fault diagnosis using limited data

The accurate and timely diagnosis of sensor faults plays a critical role in ensuring the reliability and performance of structural health monitoring (SHM) systems. However, the challenge is detecting, locating, and estimating sensor faults in an online manner using limited training data. To resolve this problem, a novel approach for online sensor fault diagnosis is proposed for SHM. The proposed approach is based on meta-learning, which enables superior model generalization capabilities using limited data. The detection, localization, and estimation of typical sensor faults in an online manner can be achieved efficiently by the proposed approach. First, a one-dimensional convolutional neural network (1D CNN) is designed to detect and locate faulty sensors. The initial model parameters of the 1D CNN are optimized using a model-agnostic meta-learning training strategy. This strategy allows the acquisition of transferable prior knowledge, which can speed up the learning process on new sensor fault detection and localization tasks. The meta-learning strategy also enables efficient and accurate detection and localization of potential faulty sensors with limited data. After detecting and locating the faulty sensors, an online updating algorithm based on a dual Kalman filter is used to estimate the severity of sensor faults and structural states simultaneously. The proposed approach is demonstrated with simulated sensor faults that cover a numerical example and field measurements from the Canton Tower. The results show that the proposed approach is applicable for online sensor fault diagnosis in SHM.

Prediction maintenance based on vibration analysis and deep learning — A case study of a drying press supported on a Hidden Markov Model

The main objective of this paper is to describe a methodology that was developed to support maintenance decision-making methods based on equipment condition. Condition-Based Maintenance allows to increase equipment availability and maximize investments. This is mainly due to the prevention of unexpected equipment downtime. By avoiding turning on/off industrial equipment, production flows are more efficient, allowing manufacturers to improve the quality of the end-product. The industry aims more and more to correspond satisfying customer expectations. We argue that the methodology developed in this paper adds value to the existing literature, namely because the fact that it is possible to anticipate the state of an equipment without a large amount of support data. In other words, although one could find information gaps regarding the occurrence of failures, it was possible to accurately assess the state of the equipment. This approach is robust, as it can be used in distinct equipment with different sensors, making this methodology generalizable for Condition-Based Maintenance. The paper presents the validation of the preceding through a case study on drying presses in the paper industry. To do so, three states were adopted, namely: “Proper function”; “Alert state”; and “Equipment failure”. The methodology follows a series of steps, going through the collection of values from vibration sensors, imputation of values using Deep Artificial Neural Networks through on-line sensors, until reaching the last stage of classification carried out by the Hidden Markov Model. Through optimized observations from the previous steps, it was possible to define the hidden states through the Viterbi algorithm, which corresponds to the health states of the equipment. Additionally, it was possible to demonstrate that the proposed methodology can accurately characterize the condition states of the equipment based on the data obtained and can be generalized to other types of equipment.

A cloud-based infrastructure to deploy supervisory forecast models for predictive coagulant dosing control

Abstract Advanced optimal dosing control based on multiple online sensor data is operational in several treatment facilities in Norway. The benefits of the dosing control system in maintaining stable phosphate/solids removal and saving coagulant usage are documented in the literature. The dosing algorithm is currently implemented in a programmable logic controller (PLC) connected to the treatment plant's Supervisory Control and Data Acquisition System (SCADA) system. The PLC receives online sensor data from the plant's SCADA, calculates the optimal dosing values, and transmits optimal dosage values back to the SCADA system. The dosing algorithm is frequently updated to keep in sync with the process and equipment upgrades of the treatment plant and advances in control algorithm schemes. The upgrades include new regulatory feedback loops structural changes to the dose equation, and the addition of conditional setpoints. Each maintenance and upgrade routine entails operational downtime where the dosing algorithm is set to a sub-optimal flow-proportional dose. This paper presents a non-intrusive Internet of Things (IoT) infrastructure to implement a predictive/forecast component to an existing dosing control algorithm. The benefits of the new cloud-based system in improving nutrient removal, increasing operational flexibility, and reducing maintenance downtime are presented in this work.

Sediment microbial fuel cell monitoring repeated Cr(VI) shocks in different wetlands for a year: Performance and mechanism

Cr(VI) is a major heavy metal contaminant that poses great threats to aquatic ecosystems and human health. To guarantee the safety of water quality, the development of in-situ and online sensors for monitoring Cr(VI) contamination events is crucial. In this work, sediment microbial fuel cell (SMFC) based sensors using floating cathode as sensing element were operated in outdoor environment for one year to monitor repeated Cr(VI) shocks in wetlands constructed with coastal alkaline soil (YC), paddy of acidic soil (YT), and paddy of neutral soil (NJ). K2CrO4 solutions were added to the cathode four times in January, March, June, and September, respectively. Results showed that each Cr(VI) addition triggered a voltage peak and the voltage increments were linearly correlated with Cr(VI) concentrations within the range of 5–120 mg L−1 Cr(VI). The highest sensitivity was observed for YC (1.2 mV L mg−1) and YT (1.4 mV L mg−1) in June and for NJ (1.2 mV L mg−1) in September. XPS analysis demonstrated the reduction reaction of Cr(VI) to Cr(III) on the cathode might contribute to the generation of voltage peaks. Despite repeated shocks, Cr(VI) was immobilized in the soil and completely reduced to low-toxic Cr(III) with the consumption of soil organic matter, and the diversity and abundance of exoelectrogenic bacteria-associated genera were not inhibited. As a result, stable operation of the SMFC sensors was ensured. The SMFC sensors exhibited great potential for in-situ and online monitoring of Cr(VI) contamination events across various wetlands.

Fuzzy-Based Control System of Drilling Fluids Density and Apparent Viscosity Simultaneously: An Alternative Strategy to Support Autonomous Drilling Operations

Summary Among all the systems that make up a drilling operation, the production and correction of drilling fluid can be considered the heart of the process. Among the main objectives of the drilling fluid are to cool the drill bit and maintain the pressure gradient inside the drilling well, which is done by controlling its density. Another important function is transporting the cuttings from the bottom to the surface and keeping them in suspension in case of stoppage, which directly depends on the viscosity of the drilling fluid. Density and viscosity must be constantly maintained within an operational window, and failures can lead to serious accidents, even the loss of the well. Currently, this control is done manually: An operator collects samples of the fluid and takes them for analysis in the laboratory and subsequently makes the necessary corrections by manually adding products to the fluid. To reduce process dead time, keep personnel on board, and increase operation safety, a control and monitoring system is necessary. Fuzzy logic was chosen because it can be combined with classical methods, is cheap to develop and implement, and can be customized in terms of natural language, capturing the knowledge acquired by operators from equipment operation, bench tests, etc. This work aimed to develop a novel real-time monitoring and fuzzy-based system for simultaneous control of the apparent viscosity and density of non-Newtonian fluids, dealing with the inevitable interactions between them in a pilot experimental unit. A pilot plant was built to evaluate the fuzzy system approach for modeling and controlling of density and apparent viscosity of drilling fluids. The pilot flow loop comprises a mixing tank, solids vibrating feeders, and a water-dosing pump. The unit was instrumented with online sensors to measure fluid density, temperature, flow rate, differential pressure, and viscosity. The apparent viscosity and density of the non-Newtonian fluid were controlled by manipulating the dosage of carboxymethylcellulose (CMC), barite, and water. The proposed methodology was compared to a classical proportional-integral-derivative (PID) controller in servo and regulatory scenarios for apparent viscosity and density. The results showed that the fuzzy controller dealt adequately with the effect of variable interactions, keeping both variables within their setpoint ranges, demonstrating the ability to control them individually despite their interactions. These results also showed that the fuzzy-based controller could easily be integrated into a diagnostic-predictive monitoring system to control fluid properties, accomplishing setpoint changes and rejecting undesirable disturbances presenting a maximum overshoot of 7.5% for apparent viscosity and 0.3% for density.

The Role of PHREEQC Model and Sensor Analysis in Chemical Coagulation Processes Supported by Online Sensors

Population growth and industrial development have led to an increasing demand for water and wastewater treatment in Turkey and around the world. To ensure sustainable treatment, it is necessary to have real-time control and monitor the system. Therefore, this study aims to reveal the removal mechanism and control of the coagulation process using the PHREEQC modeling software, which has a promising potential for simulating the chemical equilibrium and reactions of water. The sensor effectiveness determined by the model was confirmed by experimental tests in the laboratory. This was done to identify the shortcomings and differences of the model, to understand and develop mechanistic structure. To observe the effects of temperature changes in the treatment, PHREEQC software was run for each of the temperatures (T) 1, 9, and 25.3oC, with the addition of FeCl3. The data obtained from pH, conductivity, temperature, and Eh sensors were evaluated. As a result of the study, it was found that different temperatures affect the solubility of the ions, with higher temperatures leading to increased solubility and conductivity. With increasing temperature, the solubility of oxygen in water decreases, while pH, Cl-, and the precipitate Fe(OH)3 are not affected by the temperature change. In general, the modeling results are in line with the analytical results of the samples taken in the laboratory. This highlights the attractiveness of using online sensors for sustainable wastewater treatment. PHREEQC has produced more reliable results by using actual chemical equilibrium constants as it considers equilibrium conditions and includes the effects of ionic bonds and ion pairs.

Can Short-Term Online-Monitoring Improve the Current WFD Water Quality Assessment Regime? Systematic Resampling of High-Resolution Data from Four Saxon Catchments

The European Union Water Framework Directive (2000/60/EC; WFD) aims to achieve a good ecological and chemical status of all bodies of surface water by 2027. The development of integrated guidance on surface water chemical monitoring (e.g., WFD Guidance Document No. 7/19) has been transferred into national German law (Ordinance for the Protection of Surface Waters, OGewV). For the majority of compounds, this act requires monthly sampling to assess the chemical quality status of a body of surface water. To evaluate the representativeness of the sampling strategy under the OGewV, high-frequency online monitoring data are investigated under different sampling scenarios and compared with current, monthly grab sampling data. About 23 million data points were analyzed for this study. Three chemical parameters (dissolved oxygen, nitrate-nitrogen, and chloride concentration) and discharge data were selected from four catchments of different sizes, ranging from 51,391 km2 to 84 km2 (Elbe, Vereinigte Mulde, Neiße and two stations at Lockwitzbach). In this paper, we propose short-term online-monitoring (STOM) as a sampling alternative. STOM considers the placement of online sensors over a limited duration and return interval. In general, we: (I) compare the results of conventional grab sampling with STOM, (II) investigate the different performance of STOM and grab sampling using discharge data as a proxy for analyzing event-mobilized pollutants, and (III) investigate the related uncertainties and costs of both sampling methods. Results show that STOM outperforms grab sampling for parameters where minimum/maximum concentrations are required by law, as the probability of catching a single extreme value is higher with STOM. Furthermore, parameters showing a pronounced diurnal pattern, such as dissolved oxygen, are also captured considerably better. The performance of STOM showed no substantial improvements for parameters with small concentration variability, such as nitrogen-nitrate or chloride. The analysis of discharge events as a proxy parameter for event-mobilized pollutants proves that the probability of capturing samples during events is significantly increased by STOM.

An on-line imaging sensor based on magnetic deposition and flowing dispersion for wear debris feature monitoring

Sensors that can real-time monitor the wear particles suspended in lubricating oil are of great value in evaluating the health state of various machines (e.g., engines, transmissions). Here we report an on-line imaging sensor with a new structure, operating with magnetic deposition and flowing dispersion methods. A highly divergent magnetic field is generated by the cage electromagnet to collect ferromagnetic particles which will then disperse via the flow field in the disc-like channel. Wear particles in the chain- and separated-form are imaged respectively by the imaging module to acquire four debris features (concentration, size distribution, shape, and colour). We demonstrated that the developed sensor offers a low size detection limit (1–10 μm), a broad concentration-sensing range (2–200 ppm), and a high throughput (∼100 ml/min). Beyond that, the type and material of wear debris can also be identified in the test, utilising the shape and colour features getting by the sensor. This sensor shows promise for use in monitoring the machine health condition and studying the wear mechanism of surfaces.

Characterization of Particle Shape with an Improved 3D Light Scattering Sensor (3D-LSS) for Aerosols

To characterize fine particulate products in industrial gas–solid processes, insights into the particle properties are accessible via various measurement techniques. For micron particles, online imaging techniques offer a fast and reliable assessment of their size and shape. However, for the shape analysis of submicron particles, only offline techniques, such as SEM and TEM imaging, are available. In this work, an online sensor system based on the principle of elastic light scattering of particles in the gas phase is developed to measure the shape factor of non-spherical particles in the size range of 500 nm to 5 µm. Single aerosol particles are guided through a monochromatic circularly polarized laser light beam by an aerodynamic focusing nozzle, which was developed based on the CFD simulation of the flow and particle movement. The intensity of the scattered light is measured at several discrete positions in the azimuthal direction around the particles. An algorithm computes the sphericity of the particles based on the distribution of the intensity signals. The sensor construction, data processing and analysis are described. Model aerosols with particles of different shapes are investigated to test the developed sensor and show its performance in the determination of the sphericity distribution of particles.