Commercial Meter Research Articles

Measuring methane emissions during the installation of residential and commercial natural gas meters in China

Methane is a potent greenhouse gas that accounts for one-quarter of the world's radiative forcing. Methane emissions from the natural gas sector are prevalent throughout the natural gas (NG) chain. Studies have shown that methane emissions from post-meter uses of natural gas are vastly understated. A surge in the number of natural gas users, for example, would amplify the climate impact of methane emissions during the installation of natural gas meters. Thus, quantifying methane emissions during the installation of natural gas meters is critical in light of severe global climate change and urgent reduction targets. In this study, we used a mass balance approach to calculate methane emissions during the separate installation of 1444 residential natural gas meters and 51 commercial natural gas meters. Our results revealed the methane emission had a fat tail distribution. Specifically, the estimated mean methane emissions for household users were 0.008 (0.001–0.022) kg per household and 0.192 (0.013–0.816) kg per commercial user. Extrapolating these statistics to the whole of China, total emissions from 2007 to 2021 were 3.80 million metric tons (MMt) CH4, with an annual average of 0.25 MMt. Notably, in terms of economic development and population size, the provinces with the highest methane emissions were concentrated in the southeast. Our findings close a gap in measuring CH4 emissions in China across the natural gas chain and provide data to support the reduction targets set and the development of reduction technologies.

Construction of a portable enzyme-free electrochemical glucose detection system based on the synergistic interaction of Cu-MOF and PtNPs

Three enzyme-free electrochemical glucose (GLU) sensors with bimetallic structures were constructed by electrodeposition of gold and copper nanoparticles (AuNPs/CuNPs), nickel and copper nanoparticles (NiNPs/CuNPs), and a copper-based metal-organic framework (Cu-MOF) combined with PtNPs, respectively. Amongst, the Cu-MOF/PtNPs on a gold electrode (GE) exhibited the best catalytic performance for GLU oxidation due to the synergistic amplification. The relevant electrochemical behaviour of GLU showed that two OH- and two electrons were involved in the process. The differential pulse voltammetry (DPV) enables the establishment of linearity in the concentration of 0.40–25.00 mM, with a detection limit (LOD) of 0.06 mM and a sensitivity of 158.41 µA·mM−1cm−2. In addition, the Cu-MOF/PtNPs/GE sensor was used to detect GLU in oral liquid as well as human serum samples with a recovery rate of 97.00–103.98% and RSD less than 3.59%. Finally, a portable electrochemical detection system based on the STM32F103C8T6 microcontroller, obtained a GLU concentration 5.24 mM while detecting in a diluted injection where the value 5.40 mM was recorded by a commercial meter to show the system has very broad application prospects in the point-of-care setting.

Pengujian Ketidakpastian Pengukuran Alat Ukur Debit Air Rendah Biaya Berbasis Mikro Kontroler Arduino

Commercial water flow meter costs increase when utilized in low-cost installations like pico-hydro, small-scale process industries, irrigation, and other rural or non-commercial technology applications that require measurements of water discharge. Utilizing a low-budget flow meter equipped with a freely available microcontroller is the answer to this issue. In contrast to commercial measuring devices, this low-budget measuring instrument needs to be examined and verified ahead of time using other commercial measuring devices to establish the measurement's uncertainty value and make it feasible to use. By using the Arduino Uno microcontroller and the YF-DN40 turbine sensor, this low-budget flow meter is capable of producing <5% difference in discharge readings and <1% measurement uncertainty.

Fluorescence-Based Accurate Estimation of Chlorophyll in Tea Leaves Using Smartphone

Estimation of plant leaf chlorophyll is critical for assessment of overall health condition of plants. The existing detection methods demand relatively expensive and bulky instruments, thus restricting its use for onsite investigations. Present work demonstrates a low-cost, compact fluorescence-based sensor using a smartphone to estimate the chlorophyll content in plant leaves. The extracted plant chlorophyll samples have been optically excited by using an external LED with peak emission wavelength of 475 nm, and the corresponding emitted fluorescence signals at 679 nm have been recorded by the ambient light sensor of the phone. A 3D printed compact set-up has been designed to couple it with the phone to record the emitted fluorescence signal intensity from the sample. Chlorophyll concentrations in the range of 1-12 mg/g prepared from fresh tea leaves have been accurately estimated with a sensitivity of 0.068 AU g/mg and Limit of Detection (LoD) of 0.618 μg/L. The performance of the proposed smartphone sensor has been assessed by comparing the data with the commercial chlorophyll meter.

Miniaturized Vis–NIR handheld spectrometer for non-invasive pigment quantification in agritech applications

Advanced precision agriculture requires the objective measurement of the structural and functional properties of plants. Biochemical profiles in leaves can differ depending on plant growing conditions. By quantitatively detecting these changes, farm production processes can be optimized to achieve high-yield, high-quality, and nutrient dense agricultural products. To enable the rapid and non-destructive detection on site, this study demonstrates the development of a new custom-designed portable handheld Vis–NIR spectrometer that collects leaf reflectance spectra, wirelessly transfers the spectral data through Bluetooth, and provides both raw spectral data and processed information. The spectrometer has two preprogramed methods: anthocyanin and chlorophyll quantification. Anthocyanin content of red and green lettuce estimated with the new spectrometer showed an excellent correlation coefficient of 0.84 with those determined by a destructive gold standard biochemical method. The differences in chlorophyll content were measured using leaf senescence as a case study. Chlorophyll Index calculated with the handheld spectrometer gradually decreased with leaf age as chlorophyll degrades during the process of senescence. The estimated chlorophyll values were highly correlated with those obtained from a commercial fluorescence-based chlorophyll meter with a correlation coefficient of 0.77. The developed portable handheld Vis–NIR spectrometer could be a simple, cost-effective, and easy to operate tool that can be used to non-invasively monitor plant pigment and nutrient content efficiently.

In Situ Measurement and Reconstruction Technology of Cylindrical Shape of High-Precision Mandrel.

The technology of in situ measurement of cylindrical shapes is an important means of improving the surface machining accuracy of cylindrical workpieces. As a method of cylindricity measurement, the principle of the three-point method has not been fully studied and applied, so it is seldom used in the field of high-precision cylindrical topography measurement. Since the three-point method has the advantages of a simpler measurement structure and smaller system error compared with other multi-point methods, the research on it is still of great significance. Based on the existing research results of the three-point method, this paper proposes the in situ measurement and reconstruction technology of the cylindrical shape of a high-precision mandrel by means of a three-point method. The principle of the technology is deduced in detail and an in situ measurement and reconstruction system is built to carry out the experiments. Experiment results are verified using a commercial roundness meter and the deviation of cylindricity measurement results is 10 nm, which is 2.56% of the measurement results of commercial roundness meters. This paper also discusses the advantages and application prospects of the proposed technology.

Interlaboratory comparison of battery impedance analyzers calibration

The paper reports a results of series of interlaboratory comparisons of low impedance measurements at frequencies relevant for electrochemical impedance spectroscopy (EIS) of commercial lithium-ion cells. Two comparisons are presented. The first, bilateral comparison has focused on low impedance standards calibration in a full complex plane using digital sampling setups. The second comparison has focused on calibration and use of commercial 4-terminal battery EIS meters. Both comparisons have covered the impedance range from 50μ℧ to 100m℧ across the full complex plane in a frequency range from 0.01Hz up to 5kHz. Finally, the paper summarizes practices identified as critical for achieving measurement compatibility among various labs.

Exploiting Smart Meter Water Consumption Measurements for Human Activity Event Recognition

Human activity event recognition (HAER) within a residence is a topic of significant interest in the field of ambient assisted living (AAL). Commonly, various sensors are installed within a residence to enable the monitoring of people. This work presents a new approach for HAER within a residence by (re-)using measurements from commercial smart water meters. Our approach is based on the assumption that changes in water flow within a residence, specifically the transition from no flow to flow above a certain threshold, indicate human activity. Using a separate, labeled evaluation data set from three households that was collected under controlled/laboratory-like conditions, we assess the performance of our HAER method. Our results showed that the approach has a high precision (0.86) and recall (1.00). Within this work, we further recorded a new open data set of water consumption data in 17 German households with a median sample rate of 0.083¯ Hz to demonstrate that water flow data are sufficient to detect activity events within a regular daily routine. Overall, this article demonstrates that smart water meter data can be effectively used for HAER within a residence.

An Open-Source, Low-Cost Apparatus for Conductivity Measurements Based on Arduino and Coupled to a Handmade Cell

Electrical conductivity is one of the main parameters for the characterization of water solutions and for the monitoring of water sources. In this paper, we describe a very inexpensive prototype for conductivity measurements based on Arduino UNO R3 coupled to an open-source circuit board with only passive components. We designed the printed circuit board (PCB) and the suitable handmade cell using stainless-steel electrodes and wrote the freeware management software; the assembly of the prototype, including a temperature probe, and results were relatively simple. In order to allow for replicates, the instrument design, schematics, and software are available with an open-source license. Thirty-one bottles of spring waters with conductivities of between 15.2 and 2000 µS cm−1 were tested using both this prototype and a commercial conductivity meter. Data correlation produced an equation that allowed us to obtain the conductivity value, starting with the value furnished by the Arduino apparatus in arbitrary units. The prototype is accurate enough (inaccuracy lower than 6% excluding very low conductivity values) and precise (RSD% of about 5%). Even if a lot of commercial instruments for conductivity are available, we propose a prototype built with the aim of lowering the cost of measurements, while ensuring that they remain useful for lab or in situ application, as well as for continuous water monitoring/management systems. A further aim was to propose the building of the instrument as a laboratory exercise; this can help students to better understand basic theoretical concepts regarding conductivity, electronic components, and the acquisition and treatment of analytical data.

Qualitative and Quantitative Assessments of Apple Quality Using Vis Spectroscopy Combined with Improved Particle-Swarm-Optimized Neural Networks.

Exploring a cost-effective and high-accuracy optical detection method is of great significance in promoting fruit quality evaluation and grading sales. Apples are one of the most widely economic fruits, and a qualitative and quantitative assessment of apple quality based on soluble solid content (SSC) was investigated via visible (Vis) spectroscopy in this study. Six pretreatment methods and principal component analysis (PCA) were utilized to enhance the collected spectra. The qualitative assessment of apple SSC was performed using a back-propagation neural network (BPNN) combined with second-order derivative (SD) and Savitzky-Golay (SG) smoothing. The SD-SG-PCA-BPNN model's classification accuracy was 87.88%. To improve accuracy and convergence speed, a dynamic learning rate nonlinear decay (DLRND) strategy was coupled with the model. After that, particle swarm optimization (PSO) was employed to optimize the model. The classification accuracy was 100% for testing apples via the SD-SG-PCA-PSO-BPNN model combined with a Gaussian DLRND strategy. Then, quantitative assessments of apple SSC values were performed. The correlation coefficient (r) and root-square-mean error for prediction (RMSEP) in testing apples were 0.998 and 0.112 °Brix, surpassing a commercial fructose meter. The results demonstrate that Vis spectroscopy combined with the proposed synthetic model has significant value in qualitative and quantitative assessments of apple quality.

Highly Stable and Reusable 3D Graphene-Quinizarin Voltammetric pH Sensor

A simple pH sensor has been developed employing a 3D porous graphene framework blended with quinizarin. The performance of the fabricated sensor is tested via the square wave voltammetry technique by applying different buffer solutions and real samples. The peak potential of the designed electrode varies with the change in pH of solutions due to 2e−/2H+ transfer process of pH-dependent quinone/hydroquinone redox couple. For varying pH (1–13), the designed sensor has a sensitivity of 65.6 ± 0.4 mV/pH at 25 °C. Soil pH sensing is performed for different types of soil samples prepared using 1M KCl and 0.01M CaCl2 solutions with a potential shift of 63.5 ± 0.9 mV/pH and 57.9 ± 0.3 mV/pH, respectively. The 3D graphene-quinizarin pH sensing probe demonstrates negligible hysteresis (± 0.3 pH) and long-term stability (six months and more). In comparison to the commercial pH meter, the fabricated sensor shows a relative inaccuracy of less than 5%. Moreover, a single electrode could be used to detect the pH of multiple environments by mild rinsing with deionized water and is reusable for more than 500 cycles without significant potential drift. These low-cost and reusable pH-sensitive electrodes with linear Nernstian response are promising candidates for diverse pH-sensing applications.

Design and Implementation of a Low-Cost Chlorophyll Content Meter

Chlorophyll meters are portable devices used to assess and improve plants' nitrogen management and to help farmers in the determination of the health condition of plants through leaf greenness measurements. These optical electronic instruments can provide an assessment of chlorophyll content by measuring the light passing through a leaf or by measuring the light radiation reflected from its surface. However, independently of the main principle of operation and use (e.g., absorbance vs. reflectance measurements), commercial chlorophyll meters usually cost hundreds or even thousands of euros, making them inaccessible to growers and ordinary citizens who are interested in self-cultivation, farmers, crop researchers, and communities lacking resources in general. A low-cost chlorophyll meter based on light-to-voltage measurements of the remaining light after two LED light emissions through a leaf is designed, constructed, evaluated, and compared against two well-known commercial chlorophyll meters, the SPAD-502 and the atLeaf CHL Plus. Initial tests of the proposed device on lemon tree leaves and on young Brussels sprouts plant leaves revealed promising results compared to the commercial instruments. The coefficient of determination, R2, was estimated to be 0.9767 for the SPAD-502 and 0.9898 for the atLeaf-meter in lemon tree leaves samples compared to the proposed device, while for the Brussels sprouts plant, R2 was estimated to be 0.9506 and 0.9624, respectively. Further tests conducted as a preliminary evaluation of the proposed device are also presented.

Data-Driven Model Predictive Control-Based Proactive Scheduling for Commercial Microgrid Considering Anomaly Detection

One of the challenging tasks for commercial microgrids with distributed generation is to ensure critical loads resilience during high-impact, low-frequency extreme events. This article proposes a data-driven model predictive control-based proactive scheduling strategy for microgrids subject to extreme weather events. The goal is to maximize the utility value of the building by realizing the difference between the comfort level of critical loads and the total operating cost of the commercial microgrid during the planning horizon. A two-layer framework is proposed to solve the considered problem. First, the aggregate demand load measured by smart meters is processed using a combined long short-term memory and fuzzy neural network algorithm to eliminate anomaly data. Then, the obtained real data are applied through the deep-Q network method to seek appropriate proactive scheduling solutions. However, the rewards obtained from the deep-Q network method are random because of the uncertainties in extreme events. Therefore, robust optimization namely, robust deep reinforcement learning, is incorporated to enhance the robustness of the solutions. The proposed framework ensures the optimality and feasibility of the commercial microgrid's proactive scheduling strategy within the uncertainties in extreme events and the different intensity levels of anomalous data. Extensive simulation results prove the effectiveness of the proposed optimization model and algorithm for enhancing commercial microgrid resilience in extreme events. Furthermore, it also shows good ability in detecting smart meters anomalous data, with a stable convergence value of the performance index, to secure commercial microgrid smart meter data.

Minimally Invasive Implant Type Electromagnetic Biosensor for Continuous Glucose Monitoring System: In Vivo Evaluation.

Continuous glucose monitoring system (CGMS) is growing popular and preferred by diabetes over conventional methods of self-blood glucose monitoring (SBGM) systems. However, currently available commercial CGMS in the market is useful for few days to few months. This paper presents a durable, highly sensitive and minimally invasive implant type electromagnetic sensor for continuous glucose monitoring that is capable of tracking minute changes in blood glucose level (BGL). The proposed sensor utilizes strong oscillating nearfield to detect minute changes in dielectric permittivity of interstitial fluid (ISF) and blood due to changes in BGL. A biocompatible packaging material is used to cover the sensor. It helps in minimizing foreign body reactions (FBR) and improves stability of the sensor. The performance of the proposed sensor was evaluated on live rodent models (C57BL/6J mouse and Sprague Dawley rat) through intravenous glucose and insulin tolerance tests. Biocompatible polyolefin was used as the sensor packaging material, and the effect of packaging thickness on the sensitivity of sensor was examined in in-vivo test. Proposed sensor could track real-time BGL change measured with a commercial blood glucose meter. High linear correlation (R2 > 0.9) with measured BGL was observed during in vivo experiments. The experimental results demonstrate that the proposed sensor is suitable for long term CGMS applications with a high accuracy. Present work offers a new perspective towards development of long term CGM system using electromagnetic based implant sensor. The in vivo evaluation of the sensor shows excellent tracking of BGL changes.

Investigation of NIR spectroscopy and electrical resistance-based approaches for moisture determination of logging residues and sweet sorghum

Techniques based on electrical resistance and near-infrared (NIR) spectroscopy were used to determine the moisture content (MC) of logging residues and sweet sorghum. The MC of biomass is a factor to be controlled that can affect the quality of final products. To accurately measure the moisture in fragmented materials, it is essential to increase the bulk density of the materials by compression. The low bulk density increased the error from the oven-drying MC and the variation between repeated measurements. The calculated correction factor made it possible to use a commercial wood moisture meter for biomass materials. Ordinary least squares regression models built with the electrical resistance data achieved coefficients of determination (R2) of 0.933 and 0.833 with root mean square errors (RMSE) of 0.505 and 0.891, respectively, for the MC predictions of logging residue and sweet sorghum. Partial least squares regression models combined with NIR spectroscopy achieved R2 of 0.942 and 0.958 with RMSE of 1.318 and 3.681 for logging residue and sweet sorghum, respectively. In contrast to the electrical resistance-based models, the NIR-based models could predict the MC regardless of the bulk density of the materials. Data transformation by the second derivative and removal of outliers contributed to the improvement of the prediction of the NIR-based models.

Apparatus Development for the Measurement of the Thermal Conductivity of Geothermal Backfill Materials

This paper describes the design, construction, validation, and calibration of a thermal conductivity measuring apparatus for geothermal backfill materials in the range from 0.13–2.80 W/m·K. The developed apparatus is based on the Transient Hot Wire (THW) method whose mathematical basis is the Infinite Linear Source (ILS) model. The apparatus consists of a nichrome hot wire, an adjustable direct current power supply, a temperature sensor (K-type thermocouple), and a datalogger. For the validation and calibration of the developed apparatus, four standard samples have been used with a known thermal conductivity, to 3.0 W/m·K. Furthermore, the thermal conductivity of four geothermal backfill materials of common use (bentonite, neat cement, cement–sand mortar, and cement–bentonite mortar) has been measured using both the developed apparatus and a commercial meter.

Dual Ion-Selective Membrane Deposited Ion-Sensitive Field-Effect Transistor Integrating a Whole Blood Processing Microchamber for In Situ Blood Ion Testing.

Blood ion testing is one of the methods that is commonly used for monitoring the immune status and providing physiological information for disease diagnosis. However, traditional blood ion sensing methods often require well-trained operators to process the whole blood sample and perform the measurements using bulky instruments, making real-time and continuous blood ion sensing at the bedside difficult. To address the above issues, we proposed a dual ion-selective membrane deposited ion-sensitive field-effect transistor (DISM-ISFET) sensor integrating a microchamber to enable on-chip serum extraction and in situ Na+/K+ ion sensing. As a proof of concept, we sequentially dispensed NaCl and KCl solutions at various concentrations on the DISM-ISFET to find out the highest ion sensitivity and selectivity. Next, we also confirm the high red blood cell sedimentation and serum purity using a microchamber. Finally, we evaluated the system performance using nine clinical whole blood samples and compared their Na+/K+ ion-sensing results with a commercial pocket ion meter. To sum up, our results showed that a DISM-ISFET system can successfully extract 200 μL serum from 500 μL whole blood sample and simultaneously achieve Na+/K+ ion sensing. All the sample processes and measurements can be finished within 10 min in a single chip. We envision this compact and easy-to-use system can be potentially used for various medical environments requiring real-time and continuous blood ion monitoring, such as in a hemodialysis room, operation room, and intensive care unit.

Experimental Evaluation of Smart Electric Meters’ Resilience Under Cyber Security Attacks

For the first time, commercial grade smart meters have been subjected to cyber security attacks to understand their operation and security resilience under different attack scenarios. Cyber security is a matter of top concern for utility companies installing smart meters for remote collection of power usage data from customer premises. Keeping power-usage data secure and to maintain system’s resiliency under cyber security attacks is very important. In Smart electric grids, the power usage data from smart meters are periodically reported to the utility company. Reporting and remote monitoring of power usage data requires the use of data network protocols, which introduces security vulnerabilities. Cyber security attacks can impact reporting mechanisms in the smart grid, which may result in alteration or complete loss of power usage data as reported to the utility company. Despite the wide deployment of smart meters, there has not been much experimental work done to evaluate resiliency and data integrity of smart meters under security attacks. It is not clear how the operation of smart meters or the remote collection of power-usage data can be affected under security attacks. In this paper, we present our experimental work to test security resiliency of the commercial grade smart meters. We conducted real experiments, using smart electric meters from leading manufacturers to investigate data integrity under common data security attacks in a lab environment using real network equipment. Based on the experimental observations, it was discovered that the common cybersecurity attacks were able to adversely impact the data reporting operation of the smart electric meters. Cybersecurity attacks in some cases were found to cause complete loss of reporting of the power-usage data to the remote monitoring station.

Lyapunov Optimization in Online Battery Energy Storage System Control for Commercial Buildings

The high instantaneous discharging capability of battery energy storage systems (BESSs) make them ideal candidates for reducing peak loads in commercial buildings. An efficient online BESS control algorithm can be beneficial for reducing the monthly electricity bill of individual commercial buildings. Conventional model-based BESS control algorithms rely heavily on accurate long-horizon net load forecast, which is difficult to obtain. To address this problem, we develop a Lyapunov optimization-based online BESS control algorithm and derive its theoretical performance bound. Comprehensive numerical study results using real world commercial building smart meter data in southern California show that our proposed Lyapunov optimization-based online control algorithm with time-varying weighting parameters yields higher savings in electricity cost and requires less computation time in comparison to the state-of-the-art baseline algorithms.

An Intelligent Food Salt Tester Using Cross-Conductive Sensor

Salt is a mineral used in various industries/foods to fulfil different needs. Its utilization for food preparation is most common and its unmonitored consumption leads to many diseases. In this article, two sensors S1 and S2 have been designed and simulated using COMSOL MULTIPHYSICS 5.6 software to measure the salt concentration of various salt samples. The performances of the sensor structure have been evaluated by varying geometrical parameters and electrode materials. The sensor works on the extension of Thompson and Lampard Theorem, a well-known principle used to fabricate a primary standard of capacitor. Sensor S2 is fabricated, and experiments are conducted with various concentrations of salt samples. The experimental results closely match the results of the simulation and the commercial conductivity meter. A highly linear response with a sensitivity of 1.87 mV/(mg/100 ml) and with an average repeatability index of ±0.17% is observed. Furthermore, the Gaussian Naïve Bayes-based machine learning algorithm has been used to categorize salt samples into eight different salt taste types. The data for salt concentration samples are generated using 100 volunteers. The machine learning algorithm achieved an accuracy of 93.33% for predicting salt taste type.