Search Results (19)

This research presents the design and implementation of an Internet of Things (IoT)-based solution to measure the percentage of Liquefied Petroleum Gas (LPG) inside domestic stationary tanks. The IoT-based sensor, in addition to displaying the percentage of the LPG level in the tank to the user through a mobile application (app), has the advantage of simultaneously sharing the acquired data with an LPG filling plant via the Internet. The design process and calculations for the selection of the electronic components of the IoT-based sensor are presented. The methodology for obtaining and calibrating the measurement of the tank filling percentage from the magnetic level measurement system is explained in detail. The operation of the developed software, and the communication protocols used are also explained so that the data can be queried both in the user’s app and on the gas company’s web platform safely. The use of the Clark and Wright savings algorithm is proposed to sufficiently optimize the distribution routes that tank trucks should follow when serving different home refill requests from customers located in different places in a city. The experimental results confirm the functionality and viability of the hardware and software developed. In addition, by having the precise location of the tank, the generation of optimized gas refill routes for thirty customers using the heuristic algorithm and a visualization of them on Google Maps is demonstrated. This can lead to competitive advantages for home gas distribution companies. Full article

This paper proposes an experimental design to publish online the measurements obtained from four sensors: one sensor inside a cistern measures the level of drinking water, another sensor in a water tank monitors its level, a third sensor measures water flow or pressure from the pipes, and a fourth sensor assesses water quality. Several tank filling and emptying tests were performed. Experimental results demonstrated that if the cistern perceived that there was no water in the tank, it turned on the water pump to fill the tank to 100% of its storage capacity; while this was happening, the water level in the cistern and tank, the flow of water from the piped water, and the quality of the water could be visualized on a dashboard. In short, this proposal monitors water levels and flows through the Internet of Things. Data collected by sensors are posted online and stored in a database. Full article

Water scarcity is a growing problem in many regions. Therefore, intelligent management of the available water is needed. Aside from using water mains directly in households and farms, hydrophores are often used for local water system creation. Water tanks are filled with water when available in the water mains, and then the water is extracted with a hydrophore. Wells are also used as alternative water sources. Filling the tank manually requires human presence and can lead to water overflow. It is also better if the hydrophore is protected by switching it on or off depending on the water level measured, current, and pressure in its balloon. This paper presents a water management system designed to address these issues. The system utilizes various sensors to monitor water levels, pressure, and current. Microcontrollers analyze sensor data and control hydrophores and valves based on user-defined parameters. The system offers remote monitoring capabilities and can be configured for different water source scenarios (tanks vs. wells). The system was successfully implemented in real-world settings, including a factory. The benefits of the water management system include hydrophore protection, autonomous water management, and valuable data on well recovery rates and hydrophore operation cycles. Full article

The growing demand for bio-pharmaceuticals necessitates improved methods for the characterization of stirred tank reactors (STRs) and their mixing heterogeneities. Traditional Eulerian measurement approaches fall short, culminating in the use of Lagrangian Sensor Particles (LSPs) to map large-scale STRs and track the lifelines of microorganisms such as Chinese Hamster Ovary cells. This study investigates the hydrodynamic characteristics of LSPs, specifically examining the effects that the size and position of the Center of Mass (CoM) have on their flow-following capabilities. Two Lagrangian Particle (LP) designs are evaluated, one with the CoM and a Geometric Center aligned, and another with a shifted CoM. The experimental study is conducted in a rectangular vessel filled with deionized water featuring a stationary circular flow. Off-center LPs exhibit higher velocities, an increased number of floor contacts, and moreover, a less homogeneous particle probability of presence within the vessel compared to LPs with CoM and Geometric Center aligned. Lattice Boltzmann Large Eddy Simulations provide complementary undisturbed fluid velocity data for the calculation of the Stokes number $St$. Building upon these findings, differences in the Stokes number $St$ between the two LP variants of $\Delta St$ = 0.01 (25 mm LP) and $\Delta St$ = 0.13 (40 mm LP) are calculated, highlighting the difference in flow behavior. Furthermore, this study offers a more representative calculation of particle response time approach, as the traditional Stokes number definition does not account for non-homogeneous particles, resulting in an alternative Stokes number ($\Delta {St}_{\mathrm{alt}}$ = 0.84 (25 mm LP) and $\Delta {St}_{\mathrm{alt}}$ = 2.72 (40 mm LP)). This study contributes to the improved characterization of STRs through the use of Lagrangian Sensor Particles. Results highlight the implications the internal mass distribution has on LSP design, offering crucial considerations for researchers in the field. Full article

Rollover prevention of partially filled tank trucks is an ongoing challenge in the road transportation industry, with the core challenge being real-time perception and observation of the liquid state inside the tank. In order to realize reliable observation of a sloshing liquid, this article first proposes a sloshing modeling method based on a multi-degree-of-freedom pendulum model and derives the double mass trammel pendulum model (DMTP, 2DOF) accordingly, which accurately reflects the sloshing dynamics under wider operating conditions. Second, a free surface fluctuation sensor is designed based on magnetostriction, capable of measuring the inclination and height of the liquid level inside tanks filled with hazardous chemicals. Finally, the unscented Kalman filter (UKF) is utilized to synthesize the information of the two, establishing a credible real-time observation of the sloshing liquid. Verified using a vehicle–fluid coupled co-simulation, under the condition of a consecutive double lane change, the observation error of the proposed method is only 25.9% of that of the open-loop calculation, providing a secure guarantee for the observation of the state variables of the single pendulum model (SP) used for most kinds of anti-rollover control. Full article

Propellant mass gauging under micro-gravity conditions is a challenging task due to the unpredictable position and shape of the fuel body inside the tank. Micro-gravity conditions are common for orbiting satellites and rockets that operate on limited fuel supplies. Capacitance sensors have been investigated for this task in recent years; however, the effect of various positions and shapes of the fuel body is not analyzed in detail. In this paper, we investigate this with various fill types, such as annular, core-annular, and stratified fills at different positions. We compare the performance among several curve-fitting-based approaches and a machine-learning-based approach, the latter of which offers superior performance in estimating the fuel content. Full article

Maintaining a high standard of indoor air quality (IAQ) is vital to ensuring good human health. The concentration of CO₂ in air is a good proxy for IAQ, while high levels of CO₂ have been shown to cause cognitive or physiological impairment. Work environments that generate CO₂ as an inherent part of their business present a unique and significant risk in terms of poor IAQ. Craft breweries generate CO₂ and, unlike larger breweries, often lack the technology to capture and re-use the fermentation CO₂ for beer carbonation. The purpose of this study is to demonstrate that the venting of fermentation CO₂ and the unintentional venting of CO₂ during the filling of CO₂ storage tanks can cause the indoor CO₂ levels to rise significantly. This is shown by monitoring CO₂ levels inside an Australian craft brewery using a newly developed system containing three Internet of Things (IoT) sensor nodes positioned strategically in different sections of the brewery. The maximum CO₂ level recorded was in excess of 18,000 ppm, with the maximum time period levels exceeding 1000 and 10,000 ppm being equivalent to 425 and 26 min, respectively. The identification of differences in measured CO₂ at different times and locations throughout the brewery reveals that a single hard-wired CO₂ sensor may be inadequate to support IAQ monitoring. For this purpose, a network of portable or wearable CO₂ sensor nodes may be most suitable. The battery life of the sensors is a key consideration, and the current sensor battery life is too short. Low-power sensors and communication protocols are recommended for this task. Full article

Sensing technologies using optical fibers have been studied and applied since the 1970s in oil and gas, industrial, medical, aerospace, and civil areas. Detecting ultrasound acoustic waves through fiber-optic hydrophone (FOH) sensors can be one solution for continuous measurement of volumes inside production tanks used by these industries. This work presents an FOH system composed of two optical fiber coils made with commercial single mode fiber (SMF) working in the sensor head of a Michelson’s interferometer (MI) supported by an active stabilization mechanism that drives another optical coil wound around a piezoelectric actuator (PZT) in the reference arm to mitigate external mechanical and thermal noise from the environment. A 1000 mL glass graduated cylinder filled with water is used as a test tank, inside which the sensor head and an ultrasound source are placed. For detection, amplitudes and phases are measured, and machine learning algorithms predict their respective liquid volumes. The acoustic waves create patterns electronically detected with resolution of 1 mL and sensitivity of 340 mrad/mL and 70 mvolts/mL. The nonlinear behavior of both measurands requires classification, distance metrics, and regression algorithms to define an adequate model. The results show the system can determine liquid volumes with an accuracy of 99.4% using a k-nearest neighbors (k-NN) classification with one neighbor and Manhattan’s distance. Moreover, Gaussian process regression using rational quadratic metrics presented a root mean squared error (RMSE) of 0.211 mL. Full article

The present work is framed inside a broader activity aimed at improving the accuracy of numerical models in predicting the crashworthiness behavior of flexible fuel tanks. This paper describes a comprehensive experimental and numerical study aimed at estimating the impact force of a test article, consisting of a soft nylon bag filled with water, subjected to crash impact tests. In order to understand and improve response predictions, the test article drops freely from different heights, and then strikes onto a rigid plate which is instrumented with different types of sensors. Strain gauges, piezoceramic sensors, and fiber optics are used to measure the strain induced by the impact force during the experiments. To tune the test matrix and the measurement chain parameters, numerical computations are carried out to predict the dynamics of drop impact through FE explicit analyses. Through analysis and comparison with experimental results, a relationship between strain and impact energy correlated with the drop height is established, and the overall accuracy of the entire measurement chain is assessed to determine the effectiveness of such a methodology in a full-scale test on a flexible fuel tank structure. Full article

Pneumatic transmission is a technology that uses compressed air as a power source to drive and control various mechanical equipment to realize the mechanization and automation of production processes. With the development of industrial mechanization and automation, pneumatic technology, represented by pneumatic muscle, is increasingly becoming more widely used in various fields. The current standards for research are more complex for the measurement of flow without a flowmeter, some of them do not consider the influence of temperature change on flow measurement, and some of them are simplified as adiabatic or isothermal models, which are inaccurate measurement methods in actual practical application. This paper describes a method to determine flow rate by measuring the pressure change in the process of gas tank inflation. This study used the method of temperature compensation to eliminate the influence of temperature in the isothermal formula. The measurement structure was simple and the calculation was accurate, which has a certain practical significance. Based on this method, charging experiments were carried out with a gas tank that had a volume of 3 L or 5 L with or without copper wire filling, and the experimental results were used in the processed research. The temperature compensation parameters were identified with or without an isothermal environment and in different sizes of tanks. This method identified the different parameters of the 5 L tank and 3 L tank. Finally, the flow compensation was completed for the gas tank filled with copper wire. After verification, the results of the quasi-isothermal calculation formula and temperature compensation formula were close to those measured by a high-precision flow sensor in the experiment. The method introduced in this study is a novel flow calculation method that is simple in structure and accurate in calculation compared with the conventional isothermal calculation method; furthermore, it can be used in real world situations without the need for a high-precision flow sensor. Full article

Remote sensing techniques currently used to detect oil spills have not yet demonstrated their applicability to dispersed forms of oil. However, oil droplets dispersed in seawater are known to modify the local optical properties and, consequently, the upwelling light flux. Theoretically possible, passive remote detection of oil droplets was never tested in the offshore conditions. This study presents a field experiment which demonstrates the capability of commercially available sensors to detect significant changes in the remote sensing reflectance R_rs of seawater polluted by six types of dispersed oils (two crude oils, cylinder lubricant, biodiesel, and two marine gear lubricants). The experiment was based on the comparison of the upwelling radiance L_u measured in a transparent tank floating in full immersion in seawater in the Southern Baltic Sea. The tank was first filled with natural seawater and then polluted by dispersed oils in five consecutive concentrations of 1–15 ppm. After addition of dispersed oils, spectra of R_rs noticeably increased and the maximal increase varied from 40% to over three-fold at the highest oil droplet concentration. Moreover, the most affected R_rs band ratios and band differences were analyzed and are discussed in the context of future construction of algorithms for dispersed oil detection. Full article

The present work focused on the experimental study of the performance of a scaled system of adsorbed natural gas (ANG) storage and transportation based on carbon adsorbents. For this purpose, three different samples of activated carbons (AC) were prepared by varying the size of coconut shell char granules and steam activation conditions. The parameters of their porous structure, morphology, and chemical composition were determined from the nitrogen adsorption at 77 K, X-ray diffraction (XRD), small-angle X-ray scattering (SAXS), and scanning electron microscopy (SEM) measurements. The methane adsorption data measured within the temperature range from 178 to 360 K and at pressures up to 25 MPa enabled us to identify the most efficient adsorbent among the studied materials: AC-90S. The differential heats of methane adsorption on AC-90S were determined in order to simulate the gas charge/discharge processes in the ANG system using a mathematical model with consideration for thermal effects. The results of simulating the charge/discharge processes under two different conditions of heat exchange are consistent with the experimentally determined temperature distribution over a scaled ANG storage tank filled with the compacted AC-90S adsorbent and equipped with temperature sensors and heat-exchanger devices. The amounts of methane delivered from the ANG storage system employing AC-90S as an adsorbent differ from the model predictions by 4–6%. Both the experiments and mathematical modeling showed that the thermal regulation of the ANG storage tank ensured the higher rates of charge/discharge processes compared to the thermal insulation. Full article

The paper presents the results of research on the hybrid industrial tomograph electrical impedance tomography (EIT) and ultrasonic tomography (UST) (EIT-UST), operating on the basis of electrical and ultrasonic data. The emphasis of the research was placed on the algorithmic domain. However, it should be emphasized that all hardware components of the hybrid tomograph, including electronics, sensors and transducers, have been designed and mostly made in the Netrix S.A. laboratory. The test object was a tank filled with water with several dozen percent concentration. As part of the study, the original multiple neural networks system was trained, the characteristic feature of which is the generation of each of the individual pixels of the tomographic image, using an independent artificial neural network (ANN), with the input vector for all ANNs being the same. Despite the same measurement vector, each of the ANNs generates its own independent output value for a given tomogram pixel, because, during training, the networks get their respective weights and biases. During the tests, the results of three tomographic methods were compared: EIT, UST and EIT-UST hybrid. The results confirm that the use of heterogeneous tomographic systems (hybrids) increases the reliability of reconstruction in various measuring cases, which is used to solve quality problems in managing production processes. Full article

This paper reports on high-accuracy simulation of a grating structure based fiber optic plasmonic sensor for salivary cortisol sensing. Gratings of SiO₂ and SiC (one at a time) in combination with a thin Ag layer are considered to be in direct contact with analyte medium (solutions containing different concentrations of cortisol) considering that the groove regions are also filled with analyte. The optimization of Ag layer thickness is carried out to achieve maximum power loss (PL) corresponding to cortisol concentration variation. The variation of PL (in dB) spectra with the angle of incidence (α) is the sensing mechanism of the proposed scheme. Sensing performance is extensively analyzed in terms of sensitivity, limit-of-detection (LOD) and figure-of-merit (FOM) that incorporates both the sensitivity and the width of the corresponding PL curves. While the sensitivity and FOM values are significantly large, the results also reveal that in angular interrogation mode (AIM), an average LOD of 9.9 pg/mL and 9.8 pg/mL is obtained for SiO₂ and SiC-based sensor designs, respectively. When the intensity interrogation method (IIM) in place of AIM is considered, an average LOD of 22.6 fg/mL and 68.17 fg/mL is obtained for SiO₂ and SiC-based sensor designs, respectively. LOD (with IIM, in particular) is considerably better than the present-state-of-art related to cortisol monitoring. Pragmatic model for possible practical implementation of sensor scheme is also discussed. The involvement of optical fiber in the proposed sensor design makes it possible to implement it as a flexible sensor or for wearable solution for cortisol detection via sweat monitoring as well as for measuring cortisol level in aquaculture tanks where concentration levels are much lower than 10 ng/mL. Full article

Supervisory Control And Data Acquisition (SCADA) systems currently monitor and collect a huge among of data from all kind of processes. Ideally, they must run without interruption, but in practice, some data may be lost due to a sensor failure or a communication breakdown. When it happens, given the nature of these failures, information is lost in bursts, that is, sets of consecutive samples. When this occurs, it is necessary to fill out the gaps of the historical data with a reliable data completion method. This paper presents an ad hoc method to complete the data lost by a SCADA system in case of long bursts. The data correspond to levels of drinking water tanks of a Water Network company which present fluctuation patterns on a daily and a weekly scale. In this work, a new tensorization process and a novel completion algorithm mainly based on two tensor decompositions are presented. Statistical tests are realised, which consist of applying the data reconstruction algorithms, by deliberately removing bursts of data in verified historical databases, to be able to evaluate the real effectiveness of the tested methods. For this application, the presented approach outperforms the other techniques found in the literature. Full article