Electrical Conductivity Sensor Research Articles

Laboratory evaluation of open source and commercial electrical conductivity sensor precision and accuracy: How do they compare?

Variation in the electrical conductivity (EC) of water can reveal environmental disturbance and natural dynamics, including factors such as anthropogenic salinization. Broader application of open source (OS) EC sensors could provide an inexpensive method to measure water quality. While studies show that other water quality parameters can be robustly measured with sensors, a similar effort is needed to evaluate the performance of OS EC sensors. To address this need, we evaluated the accuracy (mean error, %) and precision (sample standard deviation) of OS EC sensors in the laboratory via comparison to EC calibration standards using three different OS and OS/commercial-hybrid (OS/C) EC sensors and data logger configurations and two commercial (C) EC sensors and data logger configurations. We also evaluated the effect of cable length (7.5 m and 30 m) and sensor calibration on OS sensor accuracy and precision. We found a significant difference between OS sensor mean accuracy (3.08%) and all other sensors combined (9.23%). Our study also found that EC sensor precision decreased across all sensor configurations with increasing calibration standard EC. There was also a significant difference between OS sensor mean precision (2.85 μS/cm) and the mean precision of all other sensors combined (9.12 μS/cm). Cable length did not affect OS sensor precision. Furthermore, our results suggest that future research should include evaluating how performance is impacted by combining OS sensors with commercial data loggers as this study found significantly decreased performance in OS/commercial-hybrid sensor configurations. To increase confidence in the reliability of OS sensor data, more studies such as ours are needed to further quantify OS sensor performance in terms of accuracy and precision across different settings and OS sensor and data collection platform configurations.

Viticultural Manipulation and New Technologies to Address Environmental Challenges Caused by Climate Change

Climate change is a critical challenge for the global grape and wine industry, as it can disrupt grapevine growth, production, and wine quality. Climate change could influence the cost-effectiveness and growth of the wine industry in different wine regions since grapevine development is deeply dependent on weather (short-term) and climate (long-term) conditions. Innovation and new technologies are needed to meet the challenge. This review article addresses the impact of climate change on grapevines, such as vine phenology, pest and disease pressure, crop load, and grape and wine composition. It also reviews recent advances in the areas of viticultural manipulation and relevant technologies to potentially reduce the impact of climate change and help growers improve grape quality. Remote sensing is used for vineyard microclimate monitoring; thermal sensors combined with UAVs, aircraft, or satellites are used for water management; soil electrical conductivity sensors have been developed for soil mapping. Viticultural manipulations, such as regulated deficit irrigation for water use efficiency and berry-ripening delay for growing quality fruit, are also discussed. The review assesses future directions for further technological development, such as soil and vine water monitoring devises, precision viticulture, and artificial intelligence in vineyards.

Assimilating Low‐Cost High‐Frequency Sensor Data in Watershed Water Quality Modeling: A Bayesian Approach

AbstractUncertainty reduction in watershed water quality (WWQ) modeling remains a major challenge. One important reason is the lack of sufficient available water quality observations because traditional laboratory analysis of water samples has high labor, financial and time costs. Low‐cost high‐frequency water quality data from in‐situ sensors provide an opportunity to solve this problem. However, long‐term sensing in complex natural environments usually suffers more significant errors. This study aimed to develop a novel method to utilize in‐situ sensor data in WWQ modeling, namely, the Bayesian calibration using multisource observations (BCMSO), which can simultaneously assimilate laboratory‐based observations and in‐situ sensor data. Both synthetic and real‐world cases of nitrate modeling were used to demonstrate the methodology, and the Soil and Water Assessment Tool was employed as the WWQ model. The results indicated that direct assimilation of sensor data using traditional Bayesian calibration generated obvious deviations in parameter inference and model simulation, which could consequently bias future predictions and affect management decision correctness. However, after proper treatment of errors in sensor data, the BCMSO method could extract meaningful information from sensor data and prevent negative impacts of errors. The modeling uncertainty was also greatly reduced. In the real‐world case, with 1 yr of subhourly electrical conductivity sensor data incorporated, the modeling uncertainty of nitrate concentration and management cost of controlling nitrate pollution were reduced by 70%. The BCMSO method provides a flexible framework to accommodate nonconventional observations in environmental modeling and can be easily extended to other modeling fields.

Performance model evaluation of turbine flow meter in vertical gas-liquid two-phase flows

Aiming at the need for flow measurement of gas-liquid flows in domestic gas well production, this paper proposes a measurement method based on the combination of the turbine flow meter (TFM) and a rotating electric field conductance sensor (REFCS). In experiments, the REFCS is used for the measurement of the gas holdup. To verify the applicability of the TFM models investigated in the previous study, for the modeling part, the mass, momentum and torque models are evaluated in vertical upward gas-liquid two-phase flows. In our model test, the meter factor model of TFM considers the effects of the slip ratio between the gas and liquid phases and flow patterns. In particular, the gas holdup involved in calculating the slip ratio in the model evaluation is obtained from the REFCS measurements. Model test results show the torque model has better volumetric flow rate prediction accuracy than the mass and momentum models. In the present study, the ranges of the liquid and gas phases are Qw = 2–30 m3/d and Qg = 1–16 m3/d, it was found that the average absolute deviation (AAD) in the predicted volume flow rate is equal to 1.23 m3/d and the average absolute percentage deviation (AAPD) is equal to 7.69%. The evaluated results presented in this paper will allow better estimates of the volumetric flow rates of gas-liquid flows based on the combined TMF and REFCS measurements during the monitoring of gas well production.

Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine.

In this work, a visualization experiment for rectangular channels was carried out to explore gas-liquid two-phase flow characteristics. Typical flow patterns, including bubble, elastic and mixed flows, were captured by direct imaging technology and the corresponding measurements with fluctuation characteristics were recorded by using an electrical conductivity sensor. Time-domain and frequency-domain characteristics of the corresponding electrical conductivity measurements of each flow pattern were analyzed with a probability density function and a power spectral density curve. The results showed that the feature vectors can be constructed to reflect the time-frequency characteristics of conductivity measurements successfully by introducing the quantized characteristic parameters, including the maximum power of the frequency, the standard deviation of the power spectral density, and the range of the power distribution. Furthermore, the overall recognition rate of the four flow patterns measured by the method was 93.33% based on the support vector machine, and the intelligent two-phase flow-pattern identification method can provide a new technical support for the online recognition of gas-liquid two-phase flow patterns in rectangular channels. It may thus be concluded that this method should be of great significance to ensure the safe and efficient operation of relevant industrial production systems.

Development and validation of an open-source four-pole electrical conductivity, temperature, depth sensor for in situ water quality monitoring in an estuary

Most recent implementations of low-cost electrical conductivity (EC) sensors intended for water quality measurements are based on simple two-pole designs. However, in marine settings, EC often exceeds the range where two-pole sensors provide reliable results. We have developed a simple four-pole EC sensor that relies exclusively on analog-to-digital measurements made using readily available circuit boards (pyboard v.1.1 or Raspberry Pi Pico 2040) programmed using MicroPython. Other than resistors and graphite or wire electrodes, no other electronic components are required for the EC sensor. When combined with a pressure/temperature sensor (MS5803-05), an optional NTC thermistor, batteries, and a waterproof housing constructed using a PVC pipe and a 3-D-printed cap, the device becomes a working conductivity-temperature-depth sensor capable of extended field deployments. Construction is sufficiently simple that undergraduate science students can construct one during three 3-h lab periods. Lab calibrations performed on several prototypes at ECs between 0.18 and 45 mS/cm show that confidence limits as good as about ±3% of EC are possible. Re-calibration of several prototypes 1 year after initial calibration shows that long-term calibration drift is modest. Data collected by the prototypes over several tidal cycles in the Duwamish River, Washington, USA, are in agreement with data from a co-located commercial YSI-EX03 conductivity probe. When distributed across a constructed off-channel wetland in the Duwamish system, the sensors documented large amounts of spatial and temporal variability in EC, highlighting the importance of such wetlands for providing unique temperature/salinity environments potentially valuable for outmigrating juvenile salmon.

Design and manufacturing a microcontroller based measurement device for honey adulteration detection

The aim of this study is the detection of adulteration in honey by a microcontroller measurement device. For this purpose, 18 pure honey samples from Sidr, Locoweed and Citrus honey were prepared, whose physicochemical characteristics, including moisture content and ⁰Brix, pH, color and ash content were measured. To detect impurities in honey, electrical conductivity and light refraction were used as inputs of an artificial neural network (ANN) classifier. This allowed fraud detection in honey by a portable instrument developed by enclosing the electrical conductivity and light sensors connected to a smartphone application. The results revealed that increasing impurities caused a decrease in ⁰Brix and an increase in the pH of honey. The highest value of moisture was 17.5% (for Locoweed) and the highest value of pH was 4.69 (for Sidr) at 75% impurity concentration. Citrus showed the highest value of ash and electrical conductivity of 0.36 and 415 ppm, respectively. The lowest error of training the ANN classifier of 13% was obtained with 14 neurons as the optimal number of neurons in the hidden layer. The honey samples with fructose concentrations of 0% (pure honey), 10%, 25%, 50%, 75% and 100% (pure fructose) were successfully classified, with correct classification rates of 100%, 83.3%, 80%, 87.5%, 100% and 100%, respectively. This reveals the acceptable performance of the designed device in detecting honey adulteration.

Field Trials of Microbially Induced Desaturation in Low-Plasticity Silt

Field trials of microbially induced desaturation (MID) were conducted at two sites in Portland, Oregon underlain by liquefiable fine-grained soils. MID is an emerging method for mitigating the potential for triggering liquefaction. MID treatment stimulates native denitrifying microbes with a solution containing nitrate, as well as other substrates and nutrients. An end product of the denitrification reactions is nitrogen gas, which displaces soil porewater and in turn reduces the in situ degree of saturation (Sr). Because during cyclic loading desaturated soils produce less excess porewater pressure than saturated soils, MID can mitigate the potential for triggering liquefaction. Monitoring for the two field trials was performed to evaluate the MID treatment performance and the associated subsurface desaturation. Monitoring data included seismic wave velocities measured with crosshole and downhole techniques, embedded in situ moisture and electrical conductivity sensors, water chemistry measurements, and recovery and testing of samples for changes in soil properties. Monitoring data were collected pretreatment, during treatment, and post-treatment, and then interpreted to evaluate the effectiveness of MID for reducing Sr in fine grained, low plasticity silts in the two distinct sites. Despite geotechnical site characterization data that show the field trial sites have distinct geotechnical characteristics, including interbedding, that affect liquefaction susceptibility and MID treatment application, results indicate liquefiable soil at both sites was successfully desaturated and that the desaturation persisted for at least 92 days post-treatment.

A new combined penetrometer-dielectric-low frequency acoustic-electrical conductivity sensor for measuring the soil physical characteristics

Development of simple and economical sensors to measure the soil physical properties is of great importance in precision agriculture. Building on a previous study with a fusion of penetrometer, dielectric and high-frequency acoustic sensors, the present work was aimed to develop a combined penetrometer-dielectric-microphone-apparent electrical conductivity (ECa) sensor to present a cost-effective instrument for measuring the soil physical properties. The multi-sensor probe consists of a cone connected to a load cell, an integrated dielectric-ECa sensor and a microphone inside the cone to record the passive sound produced at the soil-cone interface. To evaluate the multi-sensor probe, soils with different textural compositions (clay content from 142 to 537 g kg-1) were remoulded at three levels of water content and three levels of bulk density. The probe was tested in vertical penetration tests using a hydraulic loading frame. The results of acoustic signal processing using principal component analysis showed satisfactory differentiation between coarse and fine-grained texture classes. The output of the dielectric sensor was related to soil volumetric water content with R2 = 0.81 with some texture dependency. With fusion of penetrometer, dielectric and microphone sensors, R2 increased to 0.9 with RMSE= 0.031 m3 m-3. The soil relative density was predicted from data fusion of penetrometer, dielectric and microphone sensors with a regression model with R2 = 0.86 and RMSE= 3.11%. The ECa sensor showed rational responses to the concentration of NaCl solution as well as decreased the RMSE of soil water content and relative density prediction when fused with the other sensors. The multi-sensor probe developed is an economical instrument for simultaneously measuring the soil physical properties. Field evaluations are needed to further evaluate the sensor in wider range of soil texture and soil characteristics as well as in on-the-go measurement to be applied in precision agriculture.

Effects of saline irrigation on the physiology and ornamental quality of Euphorbia Ascot Rainbow and its relationship with salinity indexes based on the bulk electrical conductivity

• Moderate salinity (<3 dS m −1 ) did not compromise the quality of euphorbia. • High salinity (>5 dS m −1 ) produced plants unsuitable for commercial purposes. • Measuring the bulk EC just after irrigating was an accurate substrate salinity index. • Leaching when this index exceeded 1.9 dS m −1 controlled salt stress effectively. Water scarcity and the global demand for better water management policies are leading to a more intensive use of brackish water in plant production. Irrigating with saline water, farmers seek a certain leaching fraction to prevent salt‐induced damage which will depend on the plant tolerance to salts. In this sense, soil electrical conductivity (EC) and moisture sensors are essential for optimizing the leaching fraction in irrigation systems by automatically monitoring the water content and the bulk EC (EC b ). However, since the EC b depends on both salt and moisture content, indexes are needed to account for this interaction to estimate the actual salinity conditions in the soil solution. This experiment was carried out in a polycarbonate greenhouse in which plants of Euphorbia Ascot Rainbow (Euphorbia) were irrigated at three salinity levels [1.8 (control), 3.3 (EC3), and 4.9 (EC5) dS m −1 )]. The effects of the irrigation treatments on the physiology and morphology of Euphorbia and the reliability of three salinity indexes derived from the EC b were evaluated. The results showed that EC3 plants reduced their growth without being affected in terms of physiological and photochemical performance. The salinity index based on the EC b measured immediately after irrigation (EC bai ) showed the greatest sensitivity to salinity changes. A threshold of 1.9 dS m −1 for this index should not be exceeded when producing Euphorbia plants.

Effect of emotion induction on potential consumers’ visual attention in beer advertisements: a neuroscience study

PurposeThe study aims to evaluate the effect of inducing emotions (neutral, joy and fear) on the level of visual attention in beer advertisements.Design/methodology/approachA between-subject experimental study with a multi-method design was carried out using three neuroscience equipment concomitantly. The electroencephalogram and the electrical conductance sensor on the skin were used to assess the emotions induced in the individuals, while eye-tracking was used to assess the visual attention to beer advertisements. Three independent groups were formed. Each group was induced to one emotion (neutral, joy or fear), and then the level of visual attention was observed in ten stimuli of beer advertisements.FindingsThe results revealed that the induction of joy increased the visual attention to the brand name, while the induction of fear increased the visual attention to both the brand name and product packaging but reduced the visual attention to human faces within the ads.Research limitations/implicationsThis paper extends the literature, and to the best of the authors’ knowledge, it is the first study to indicate that induced emotions before ad viewing influence potential consumers’ visual attention.Practical implicationsThe findings can serve as a basis for developing advertising campaigns that use emotion induction before ad viewing to increase the visual attention of potential consumers.Originality/valueTo the best of the authors’ knowledge, this is the first study to investigate whether the emotion induction that happens before ad viewing can impact the level of visual attention to advertisements. The study also provides clear and comprehensible implications from marketing practices to improve visual attention to ads.

SensorTalk: Extending the Life for Redundant Electrical Conductivity Sensor

Air and soil sensors are used in smart agriculture to autonomously collect the soil-related data. It is important that the measurements of the sensors are accurate. This article uses electrical conductivity (EC) as an example to show how to avoid the inaccuracy due to EC sensor aging. We consider a promising solution that packages <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> redundant EC sensors in one device ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}&gt;{1}$ </tex-math></inline-formula> ) called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> -redundant EC sensor package. The redundant-sensor package outputs the measured data based on a voting process among <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> sensors, which makes soil measurement more accurate and reliable. Based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> -redundant EC sensor package, this article invents the first out-of-the-laboratory automatic calibration procedure in the farm field. We also propose the SensorTalk mechanism to extend the life of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> -redundant EC sensor package through calibration. Most people have the illusion that the life of a redundant-sensor package is longer than a nonredundant sensor. This article proves that the life of a redundant-sensor package is shorter than its nonredundant counterpart. Then, we propose the self-calibration process of the redundant-sensor package to extend its operation life. For example, if the life of a sensor in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> -redundant package is six months with the aging-after-calibration factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\gamma }={0}.{9}$ </tex-math></inline-formula> , then SensorTalk may extend its life to 52.2 months for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}={9}$ </tex-math></inline-formula> . We also shed some nontrivial insights. In many smart farming solutions, the sensors typically report the measured data with a frequency lower than one per 10 min. Our study indicates that by increasing the sampling frequency up to one per 20 s, calibration for an EC value can be sped up by 19 times. Our study also suggests that an odd <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> number should be chosen. If we add one more sensor to make an even <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula> number, the life of the redundant-sensor package is actually shortened.

Towards globally relevant, small-footprint dewatering solutions: Optimal conditioner dose for highly variable blackwater from non-sewered sanitation

Globally, the sanitation needs of three billion people are met by non-sewered sanitation. Small-footprint treatment technologies are needed that are appropriate for dense urban areas. Blackwater (BW) (or fecal sludge), contains more than 95% liquid, and dewatering it without conditioning requires large footprints. Chemically-enhanced dewatering with conditioners is a promising option to increase dewatering performance and reduce required footprints. However, before implementation of this solution there is a need for increased knowledge on selection and dosing of conditioners. This study evaluated bio-based and synthetic conditioners (chitosan, tannin-, and starch-based, synthetic with and without poly-acrylamide) with 14 types of BW from five countries. The supernatant after settling with jar-tests was analyzed to quantify optimal dose and dewatering performance. The reduction of total chemical oxygen demand (COD) was >55%, achieved by removal of particulate constituents with mainly soluble COD remaining in the supernatant. A reduction in particulate COD could lead to increased efficiency of soluble COD in supernatant treatment. Bio-based conditioners are as effective as synthetic conditioners, and when performance was variable, it was due to differing properties of TSS, TS, EC and pH. Optimal conditioner dose for synthetic conditioners and chitosan could be predicted using concentrations of total solids (TS) (R2 > 0.7), whereas optimal dose for starch- and tannin-based conditioners could be predicted with electrical conductivity (EC) (R2 > 0.8), and colloid titration (R2 > 0.8). In addition, real-time optical TSS and EC sensors could accurately predict chitosan dose for fresh BW treated at source (R2 = 0.97, R2 = 0.95). This study validates that use of conditioners for dewatering with highly variable BW can be implemented with real-time measurements for optimal dose, in globally relevant implementations.

A novel conductive sensor-based test method to measure longitudinal wicking of fabrics

This paper reports the development of novel vertical wicking instrument which is specially designed to measure the wicking behavior of textile fabrics precisely. The instrument is designed using T-shaped test frame fabricated with tribo-electric fibre glass and electrical conductivity sensors. The developed electrical conductive sensors are capable to measure the time taken for the vertical wicking of water through inter-fibre capillaries with respect to height. The wet fabric allows the electrical current flow between two conductive points of sensor and enables the IoT controller circuit to monitor the time taken for wicking. To improve the accuracy of measuring the wicking behavior, tribo-electric fibre glass is used. The tribo electric fibre glass has electrostatic charges on its surface and induces static cling effect. Static cling is the tendency of light objects such as fabrics to stick (cling) to other objects owing to static electricity. The static cling effect attracts the fabric test sample to make it in contact with conductivity sensor array. The wicking process is carried out without causing obstruction to the movement of water through inter-fibre capillaries. The accuracy of the measured data obtained from the novel instrument is compared with the data of manual standard test procedure (R2> 0.97). The comparison shows that the developed instrument produces more reliable results.

Comparative Evaluation of Digital Soil Moisture and Electrical Conductivity Sensors Based on Time-Domain Electrometry

Comparative Evaluation of Digital Soil Moisture and Electrical Conductivity Sensors Based on Time-Domain Electrometry

A portable device based on an electrical conductivity sensor for the detection of monosodium glutamate (MSG) in soupy foods

In everyday life, food is made with various taste images, including the savory taste obtained from monosodium glutamate (MSG). Although it is allowed as a food ingredient, excessive use of MSG and continuous consumption will have adverse effects on health. The effects of consuming MSG excessively on the body's organs include brain, ovarian, testicular, liver, kidney, heart, and respiratory disorders. MSG detection using technology is needed to control the consumption of MSG in the body. Hence, this study was focused on creating a portable device for detecting monosodium glutamate (MSG) in soupy foods: meatball soup and chicken soup. 3 mg of MSG was applied to each solution sample and four MSG brands, including SS, MW, AM, and MR. The concept of this prototype is based on the conductivity value and total dissolved solids (TDS) in the MSG solution. These solutions can produce an electrolyte because there is a sodium salt content in the MSG solution and water. The electrolyte solution can conduct electricity. An electrical conductivity sensor was installed in this prototype and these sensors included two electrode plates, a positive electrode (anode) and a negative electrode (cathode) with a distance of ± 1 cm. The conductivity sensor begins by starting a sensor into an electrolyte solution and, given an electric voltage input, changes to the value of the electric voltage. The sensor can be read and detect the MSG level in the solution. Then, the data are received and recorded in the DRF analog which is to process the signal from the sensor into analog form, and the data is sent to the Arduino Nano as a microcontroller. From the experiment result, the average conductivity value and TDS value of meatball soup are 21.98 mS and 7.91 Mg/I, while the average conductivity value and TDS value of Chicken Soto are 16.92 mS and 6.08 mg/I. This prototype was successfully created and implemented for the MSG detection in soupy foods.

Using Top-of-Froth Conductivity to Infer Water Overflow Rate in a Two-Phase Lab-Scale Flotation Column

The metallurgical performance of a flotation machine is largely defined by phenomena occurring in the froth zone. The water content in the froth affects recovery by influencing froth stability and mobility and, at the same time, reduces grade by mechanical entrainment of gangue particles in the overflow water. Efficient operation requires a compromise between the water carried by bubbles from the collection zone and that which overflows. It is believed that the most suitable operating strategy could be based on the measurement of froth water content, as a strong correlation with water overflow is anticipated. This work reports the testing results of an in situ electrical conductivity sensor continuously measuring the froth zone water content in a laboratory-scale flotation column. The test program included simultaneous measurement of froth conductivity and water overflow rates for changes in gas flow rate and frother concentration. The results show a stronger dependence of the measured top-of-froth water content on frother concentration than on the gas flow rate. A relatively linear trend was shown between top-of-froth water content and water overflow rate for a given air rate and frother.

Influent pollutants monitoring in decentralised treatment facilities using electrical conductivity sensors: Experimental and modelling insights

Electrical conductivity (EC) is a common and cheap water monitoring sensor, and to some extent reflects the water body pollution level qualitatively. In this study, the characteristic analysis of domestic...

Measurement of Oil–Gas–Water Flows in Vertical Pipes Using Electromagnetic Flowmeter and Dual-Conductance Sensors

Three-phase flows are significantly complicated in comparison to single-phase flows and gas&#x2013;liquid two-phase flows. So far, faced with the presence of two nonconductive dispersed phases in oil&#x2013;gas&#x2013;water three-phase flows, the measurement characteristics of electromagnetic flowmeter (EMF) in three-phase flows are an unresolved problem. The flowrate of three-phase flows is difficult to measure due to the nonuniformity arising from interactions between dispersed phases. Aiming at the difficult problem of production logging in oil wells of three-phase flows, this article reports an experimental study on measuring three-phase flows via the EMF and the dual-conductance sensors. Among them, the phase holdups of three-phase flow in vertical pipe are extracted through newly designed distributed coaxial double-ring conductance sensor (DRCS) and rotating electric field conductance sensor (REFCS). A three-phase flow loop test in a pipe of 20 mm inner diameter is carried out. The results show that the EMF has the ability to substantively measure the flowrate of water phase under three flow patterns (bubble, slug, and churn flows) when the water cut is above 60&#x0025;, and the average absolute percentage deviation (AAPD) for prediction of superficial water velocity is 3.55&#x0025;. The measurement of superficial gas and oil velocities is implemented by solving the drift-flux models of three-phase flow based on the phase holdups and superficial water velocity, and the AAPDs for predictions of superficial gas and oil velocities are 4.33&#x0025; and 5.95&#x0025;, respectively. This research provides a new measurement strategy for production logging of oil, gas, and water three-phase flow production profile by combining the EMF with dual-conductance sensors.

Aquaphotomics determination of nutrient biomarker for spectrophotometric parameterization of crop growth primary macronutrients using genetic programming

Water quality assessment is currently based on time-consuming and costly laboratory procedures and numerous expensive physicochemical sensors deployment. In response to the trend of device minimization and reduced outlays in sustainable aquaponic water monitoring, the integration of aquaphotomics and computational intelligence is presented in this paper. This study used the combination of temperature, pH, and electrical conductivity sensors in predicting crop growth primary macronutrient concentration (nitrate, phosphate, and potassium (NPK)), thus, limiting the number of deployed sensors. A total of 220 water samples collected from an outdoor artificial aquaponic pond were temperature perturbed from 16 to 36 °C with 2 °C increments to mimic ambient range, which varies water compositional structure. Aquaphotomics was applied on ultraviolet, visible light, and near-infrared spectral regions, 100 to 1 000 nm, to determine NPK compounds. Principal component analysis emphasized nutrient dynamics through selecting the highly correlated water absorption bands resulting in 250 nm, 840 nm, and 765 nm for nitrate, phosphate, and potassium respectively. These activated water bands were used as wavelength protocols to spectrophotometrically measure macronutrient concentrations. Experiments have shown that multigene symbolic regression genetic programming (MSRGP) obtained the optimal performance in parameterizing and predicting nitrate, phosphate, and potassium concentrations based on water physical properties with an accuracy of 87.63%, 88.73%, and 99.91%, respectively. The results have shown the established 4-dimensional nutrient dynamics map reveals that temperature significantly strengthens nitrate and potassium above 30 °C and phosphate below 25 °C with pH and electrical conductivity ranging between 7 and 8 and 0.1 to 0.2 mS cm−1 respectively. This novel approach of developing a physicochemical estimation model predicted macronutrient concentrations in real-time using physical limnological sensors with a 50% reduction of energy consumption. This same approach can be extended to measure secondary macronutrients and micronutrients.