Standard Sensors Research Articles

Broadband cavity-enhanced optical flux monitoring

This work describes a new type of sensor for growth process monitoring named broadband cavity-enhanced optical flux monitoring sensor (BBCE-OFM). Like existing optical flux monitoring (OFM) solutions, it relies on absorption spectroscopy. However, the implementation of an optical cavity reduces the measurement uncertainty, enabling efficient operation even at very low growth rates. Using the BBCE-OFM sensor mounted in our solid-source oxide molecular beam epitaxy reactor, we achieved an uncertainty of ±2% on the measurement of Sr and Ti growth rates in SrTiO3 at around 1 Ml/min, to be compared to the ±16% obtained in the same conditions using a conventional OFM setup. Furthermore, our sensor architecture, based on an echelle monochromator and LEDs replacing the hollow cathode lamps used in standard OFM sensors, is more robust against drift.

Design and performance evaluation of embedded micro stiffness sensor for On-Orbit service tools

On-orbit service of the space station is a vital mission. The space station will be in orbit for many years, and parts would break down and instruments could fail in tight space. This research focuses on the needs of on-orbit maintenance operations in tight space and designs an end tool with embedded micro-sensors. The pressure & deformation sensing module is designed based on the principle of polyurethane flexible plate and metal strain film. The sensor module uses alloy as the housing to form a stiffness sensor, built into the end tool to achieve a single sensor envelope size in the range of 15*15*13 mm3. Calibration results indicate that the overall stiffness of this sensing unit is approximately 12000 N/mm, capable of sensing material stiffness within the range of 50 N/mm ∼ 12000 N/mm. Through multiple measurements and root mean square error (RMSE) analysis, it is shown that the uncertainty of different sensors is within the acceptable range compared with the standard data. The results of the fatigue test and simulated temperature test in the space station show that the performance of the stiffness sensor can meet the normal operation requirements in the space station environment. Comparisons with geometric dimensions, accuracy, sensitivity, and measurement precision show that the uncertainty between different position sensors, metal strain film sensors, and standard sensors does not exceed 1.8 %. The research results confirm the potential of this stiffness-sensing unit in future space missions and dexterous operations applications.

Fault Detection on Short-Haul or Highly Dynamic Flights Using Transient Flight Segments

Abstract A machine learning-based approach is presented, which allows to detect persistent engine faults after a single flight. It utilizes transient in-flight measurements and a transient engine model. The time series of the residuals between the measured data and the data resulting from performance synthesis is evaluated using moving windows containing at least one transient segment. A continuous wavelet transformation and a pretrained convolutional neural network are utilized on the residuals for feature extraction. The fault detection is carried out via a one-class support vector machine, trained exclusively on nominal engine operation data. Therefore, the approach requires no a-priory knowledge of the effects of engine faults on the in-flight measurements. Under the assumption of persistent faults, all windows of a single flight, which contain at least one transient segment, are considered in order to improve the reliability of the fault detection. This approach is validated using measured data of a small helicopter engine that replicates the dynamic flight of the corresponding model helicopter on a ground test bed. Consequently, step changes as well as complex variations of the shaft power output are considered. Four standard gas path sensors are considered. The one-class support vector machine is used successfully to detect two types of total pressure sensor anomalies. Assuming a typical number of transient segments for an average short haul flight, it turns out that persistent faults can be detected within one flight with a probability of above 90%.

Wafer-Scale Replication of Plasmonic Nanostructures via Microbubbles for Nanophotonics.

Quasi-3D plasmonic nanostructures are in high demand for their ability to manipulate and enhance light-matter interactions at subwavelength scales, making them promising building blocks for diverse nanophotonic devices. Despite their potential, the integration of these nanostructures with optical sensors and imaging systems on a large scale poses challenges. Here, a robust technique for the rapid, scalable, and seamless replication of quasi-3D plasmonic nanostructures is presented straight from their production wafers using a microbubble process. This approach not only simplifies the integration of quasi-3D plasmonic nanostructures into a wide range of standard and custom optical imaging devices and sensors but also significantly enhances their imaging and sensing performance beyond the limits of conventional methods. This study encompasses experimental, computational, and theoretical investigations, and it fully elucidates the operational mechanism. Additionally, it explores a versatile set of options for outfitting nanophotonic devices with custom-designed plasmonic nanostructures, thereby fulfilling specific operational criteria.

Single Molecule Thermodynamic Penalties Applied to Enzymes by Whispering Gallery Mode Biosensors.

Optical microcavities, particularly whispering gallery mode (WGM) microcavities enhanced by plasmonic nanorods, are emerging as powerful platforms for single-molecule sensing. However, the impact of optical forces from the plasmonic near field on analyte molecules is inadequately understood. Using a standard optoplasmonic WGM single-molecule sensor to monitor two enzymes, both of which undergo an open-to-closed-to-open conformational transition, the work done on an enzyme by the WGM sensor as atoms of the enzyme movethrough the electric field gradient of the plasmonic hotspot during conformational change has been quantified. As the work done by the sensor on analyte enzymes can be modulated by varying WGM intensity, the WGM microcavity system can be used to apply free energy penalties to regulate enzyme activity at the single-molecule level. The findings advance the understanding of optical forces in WGM single-molecule sensing, potentially leading to the capability to precisely manipulate enzyme activity at the single-molecule level through tailored optical modulation.

The Helicopter Turboshaft Engine’s Reconfigured Dynamic Model for Functional Safety Estimation

This research substantiates the necessity for developing and implementing structural reconfiguration methods for automatic control systems in the event of a parametric sensor failure to enhance the helicopter turboshaft engine’s overall reliability and safety. The research aim is the substantiation of the helicopter turboshaft engine’s mathematically reconfigured automatic control system in the event of the failure of a standard sensor, which will ensure the helicopter turboshaft engine’s stable operation under failure conditions, minimizing the impact on engine control and performance. A theorem was developed and proven concerning the reconfiguration of the helicopter turboshaft engine’s automatic control system structure, defining the system’s new mathematical form using nonlinear thermogas-dynamic parameters. A method was proposed to determine the values of these parameters that keep the reconfigured control system stable. This method uses numerical optimization to find the best thermogas-dynamic parameters to ensure system stability. Experimental results showed that for slow changes, using parameters from the previous step works best, while for fast changes, restarting is more effective due to significant differences in the system states. The accuracy of the proposed mathematical model for the reconfigured control system was confirmed through mean square error analysis (within 0.4% and 0.77% under white noise), regression analysis (with a determination coefficient of 0.986), and cross-validation (with a metric deviation from the maximum mean square error of 3.88%).

Transport Effects in Electrochemical Biochip Sensors with Redox Cycling Amplification: Computational and Experimental Analysis

In the last few decades, and especially after the COVID-19 pandemic, point-of-care diagnostics and field-deployable biosensors have attracted significant attention. Key components of these instruments are microchips that integrate microfluidics with a sensor (e.g., optical or electrochemical) to provide rapid, low-cost, real-time detection of analyte molecules in very small volumes. Next-generation electrochemical sensors have been miniaturized on solid-state platforms (e.g., arrays and vectors) where the area is limited. However, as the active surface area of the sensors decreases from a few square microns to sub-micron and nanometric scales, the measured currents drop, hence affecting the limit of detection. As the concentration of many analytes in the physiological environment is low, low signal-to-noise ratios have limited the miniaturization of standard electrochemical sensors. One way to increase signal at the sensor output is by redox amplification. To achieve amplification, a second working electrode must be incorporated close to the first working electrode and proper bias must be applied to both. Micrometer-sized interdigitated electrode arrays have been fabricated using robust and scalable processes based on optical lithography and reactive ion-etching techniques. In some cases, IDAs have been integrated into microfluidics devices. The physical and electrochemical phenomena that occur in microfluidic channels under flow have been previously evaluated and showed an increase of current under flow. The effect of redox cycling in microfluidic and nanofluidic systems has also been qualitatively demonstrated. Some studies measured the amplification in the cases with and without flow and showed that the flow itself amplifies the measured electrochemical current, though in most cases, it reduces the amplification factor.Towards a more quantitative investigation, analytical and computational modeling were used, providing meaningful insights into the physics of IDAs without requiring numerous device fabrications. In this work, we used COMSOL Multiphysics to create a full three-dimensional simulation-based model of a real device in order to study the effect of laminar flow on redox cycling, as well as a simplified one-dimensional model for the analysis of transition velocity between different amplification regimes. IDAs with 5-10 mm for both electrodes width and gap were studied numerically and experimentally. The results of the 3D and 1D simulations were consistent between themselves, and also with those of the physical experiments. The relationship between the two mass transfer methods — diffusion and convection — were analyzed at different flow velocities and for different electrode geometries.Here we report a study of transport effects on a microfluidic sensor-chip in which electrochemical sensing is amplified by redox cycling using an interdigitated electrode array (IDA). Our models indicate that there exist two dominant regimes: one which is limited by redox cycling diffusion—a unique feature of IDAs— which is dominant at relatively low flow rates, and another which is limited by convection and has dominant flow effects which are effective at high flow rates (see Figure). The transition velocity between the two sensing regimes depends on the width of the electrode and spacing. It was found to be Vflow [mm/sec]=0.335/L[μm] + 0.04 which is similar to the prediction of Vflow∝1/L obtained from the one-dimensional model.An understanding of the combined effects of redox cycling and convection flow is critical as microfluidic chips with electrochemical sensing amplified by redox cycling using IDAs are becoming widely used in diagnostic assays. The method for prediction of current under any flow rate described here will support technological development of more efficient and accurate devices. Figure 1

Enhancing source separation quality via optimal sensor placement in noisy environments

The paper aims to bridge a part of the gap between source separation and sensor placement studies by addressing a novel problem: “Predicting optimal sensor placement in noisy environments to improve source separation quality”. The structural information required for optimal sensor placement is modeled as the spatial distribution of source signal gains and the spatial correlation of noise. The sensor positions are predicted by optimizing two criteria as measures of separation quality, and a gradient-based global optimization method is developed to efficiently address this optimization problem. Numerical results exhibit superior performance when compared with classical sensor placement methodologies based on mutual information, underscoring the critical role of sensor placement in source separation with noisy sensor measurements. The proposed method is applied to actual electroencephalography (EEG) data to separate the P300 source components in a brain-computer interface (BCI) application. The results show that when the sensor positions are chosen using the proposed method, to reach a certain level of spelling accuracy, fewer sensors are required compared with standard sensor locations.

Exploring the Significance of Carbon Quantum Dots and Graphene Quantum Dots in Sarcosine Sensing: A Key Biomarker for Prognosticating Prostate Cancer

AbstractLuminescent carbon nanoparticles, such as carbon quantum dots (CQDs) and graphene quantum dots (GQDs), are increasingly utilized for sarcosine detection due to their exceptional water permeability, sustained luminescence, and biocompatibility. Sarcosine as a biomarker holds significance for therapeutic purposes and disease identification. Carbon‐based sensors offer eco‐friendliness, recyclability, and affordability, making them sustainable for sensing applications across various domains. Despite the widespread use of standard sensors for sarcosine measurement, they face limitations. This paper presents a focused review of recent developments in biosensors utilizing luminescent carbon quantum dots and graphene quantum dots for sarcosine detection. Key processes are characterized by their selectivity and sensitivity to different forms of sarcosine and interfering substances like metallic ions and amino acids. The significant reaction mechanisms and detection methods for sarcosine are outlined and compared. Additionally, the challenges in developing functionalized CQDs and GQDs for further investigation are explored. In summary, luminescent carbon nanoparticles offer a promising avenue for sarcosine detection, addressing limitations of standard sensors through their cost‐effectiveness, sensitivity, and rapid response, thus holding potential for diverse sensing applications.

A Smart Visual Sensor for Smoke Detection Based on Deep Neural Networks.

The automatic detection of smoke by analyzing the video stream acquired by traditional surveillance cameras is becoming a more and more interesting problem for the scientific community thanks to the necessity to prevent fires at the very early stages. The adoption of a smart visual sensor, namely a computer vision algorithm running in real time, allows one to overcome the limitations of standard physical sensors. Nevertheless, this is a very challenging problem, due to the strong similarity of the smoke with other environmental elements like clouds, fog and dust. In addition to this challenge, data available for training deep neural networks is limited and not fully representative of real environments. Within this context, in this paper we propose a new method for smoke detection based on the combination of motion and appearance analysis with a modern convolutional neural network (CNN). Moreover, we propose a new dataset, called the MIVIA Smoke Detection Dataset (MIVIA-SDD), publicly available for research purposes; it consists of 129 videos covering about 28 h of recordings. The proposed hybrid method, trained and evaluated on the proposed dataset, demonstrated to be very effective by achieving a 94% smoke recognition rate and, at the same time, a substantially lower false positive rate if compared with fully deep learning-based approaches (14% vs. 100%). Therefore, the proposed combination of motion and appearance analysis with deep learning CNNs can be further investigated to improve the precision of fire detection approaches.

Research and Simulation of Partial Discharge Phenomenon Sensing Technology for Substation Inspection Robots

Abstract This paper thoroughly investigates the phenomenon of partial discharge in substations and proposes a set of simulation experimental apparatus to replicate various types of partial discharge defects in switchgear. By modifying the switchgear, incorporating models for different types of partial discharge, and equipping various standard partial discharge sensors, accurate simulations of phenomena such as point discharge, suspended discharge, gap discharge, and particle discharge are achieved. Through this system, the effectiveness of different detection methods, such as ultrasonic, high-frequency, and transient voltage methods, is validated for various defect models. Furthermore, utilizing an indoor inspection robot guide rail, the paper simulates the environment of partial discharge in substations, enabling the validation of the robot’s partial discharge detection performance and typical patterns under different conditions. This work is dedicated to providing situational awareness technology for partial discharge phenomenon detection for substation inspection robots, so that the inspection robot can detect various abnormal partial discharge phenomena in transformers or other substation equipment through micro sensors to promote the stable operation of the substation.

Rethinking Instrumental Interface Design: The MetaBow

Abstract Although extensive research has been conducted over the past two decades on the development and implementation of sensors using inertial movement units for violin bows, most of these new interfaces share a common drawback—bulky and user-unfriendly design. Despite the advancements in sensor data processing for composition, performance, and pedagogy, interface design remains a critical bottleneck for the real-world implementation of these sensor-embedded devices. Our study introduces MetaBow, a low-cost, nonintrusive frog design for violin bows that can accommodate either a standard sensor kit or a custom-designed board. This interface eliminates the need for additional physical training and maintains the integrity of traditional violin performance mechanisms. We thus view MetaBow as heralding a new era in digital music interface design for the violin family with the potential for seamless human-computer and human-machine collaboration in music practice and performance.

A Compact, Low-Cost, and Low-Power Turbidity Sensor for Continuous In Situ Stormwater Monitoring.

Turbidity stands as a crucial indicator for assessing water quality, and while turbidity sensors exist, their high cost prohibits their extensive use. In this paper, we introduce an innovative turbidity sensor, and it is the first low-cost turbidity sensor that is designed specifically for long-term stormwater in-field monitoring. Its low cost (USD 23.50) enables the implementation of high spatial resolution monitoring schemes. The sensor design is available under open hardware and open-source licences, and the 3D-printed sensor housing is free to modify based on different monitoring purposes and ambient conditions. The sensor was tested both in the laboratory and in the field. By testing the sensor in the lab with standard turbidity solutions, the proposed low-cost turbidity sensor demonstrated a strong linear correlation between a low-cost sensor and a commercial hand-held turbidimeter. In the field, the low-cost sensor measurements were statistically significantly correlated to a standard high-cost commercial turbidity sensor. Biofouling and drifting issues were also analysed after the sensors were deployed in the field for more than 6 months, showing that both biofouling and drift occur during monitoring. Nonetheless, in terms of maintenance requirements, the low-cost sensor exhibited similar needs compared to the GreenSpan sensor.

Flexible anisotropic magnetoresistive sensors for novel magnetic flux leakage testing capabilities

Rigid magnetic field sensors such as anisotropic magnetoresistive (AMR), giant magnetoresistive (GMR) and Hall sensors have been used for years and have become industry standard for electromagnetic non-destructive testing (NDT). Recent technological developments in the field of flexible electronics allow for the fabrication of reshapeable magnetic field sensors on flexible substrates via thin-film deposition or printing. The magnetic properties of these sensors have comparable characteristics to industry-standard rigid magnetic field sensors, with the added ability of adapting to the surface of complex components and scanning in contact with the sample surface. This improves defect detectability and magnetic signal strength by minimizing the scanning lift-off (LO) distance. In this article flexible AMR sensors mounted on a rotative mechanical holder were used to scan a semi-circular ferromagnetic sample with 3 reference defects via magnetic flux leakage (MFL) testing, thus demonstrating the applicability of this type of sensors for the scanning of curved samples. In order to benchmark the performance of these sensors in comparison to industry standard rigid magnetic field sensors, a ferromagnetic sample with 10 reference defects of different depths was scanned employing flexible AMR and rigid GMR sensors. Defects with depths ranging from 110μm up to 2240μm were detected with an signal-to-noise ratio (SNR) of 2.7 up to 27.9 (for flexible AMR sensors) and 6.2 up to 72.3 (for rigid GMR sensors), respectively. A 2D magnetometer mapping of the sample with a spatial scanning step of 10×50μm2 (flexible AMR) and 16×100μm2 (rigid GMR) was obtained. The results show that this type of sensor can be used for high-resolution and high-detail mapping of defects on the surface of planar and non-planar ferromagnetic samples since the scanning lift-off distance is equal to the substrate thickness of 20μm for in-contact scanning. The SNR comparison between flexible and rigid sensors shows that the performance of the flexible AMR sensors employed is not very far behind the performance of the rigid GMR sensors used.

3D Printed Hydrogel Sensor for Rapid Colorimetric Detection of Salivary pH.

Salivary pH is one of the crucial biomarkers used for non-invasive diagnosis of intraoral diseases, as well as general health conditions. However, standard pH sensors are usually too bulky, expensive, and impractical for routine use outside laboratory settings. Herein, a miniature hydrogel sensor, which enables quick and simple colorimetric detection of pH level, is shown. The sensor structure was manufactured from non-toxic hydrogel ink and patterned in the form of a matrix with 5 mm × 5 mm × 1 mm individual sensing pads using a 3D printing technique (bioplotting). The authors' ink composition, which contains sodium alginate, polyvinylpyrrolidone, and bromothymol blue indicator, enables repeatable and stable color response to different pH levels. The developed analysis software with an easy-to-use graphical user interface extracts the R(ed), G(reen), and B(lue) components of the color image of the hydrogel pads, and evaluates the pH value in a second. A calibration curve used for the analysis was obtained in a pH range of 3.5 to 9.0 using a laboratory pH meter as a reference. Validation of the sensor was performed on samples of artificial saliva for medical use and its mixtures with beverages of different pH values (lemon juice, coffee, black and green tea, bottled and tap water), and correct responses to acidic and alkaline solutions were observed. The matrix of square sensing pads used in this study provided multiple parallel responses for parametric tests, but the applied 3D printing method and ink composition enable easy adjustment of the shape of the sensing layer to other desired patterns and sizes. Additional mechanical tests of the hydrogel layers confirmed the relatively high quality and durability of the sensor structure. The solution presented here, comprising 3D printed hydrogel sensor pads, simple colorimetric detection, and graphical software for signal processing, opens the way to development of miniature and biocompatible diagnostic devices in the form of flexible, wearable, or intraoral sensors for prospective application in personalized medicine and point-of-care diagnosis.

Fabrication of PTFE + TiO2/Ag coatings on 316L/polydopamine with advanced mechanical, bio-corrosion, and antibacterial properties for stainless steel Catheters

This study explores the corrosion resistance and antibacterial properties of a PTFE + TiO2/Ag coating applied to 316 L stainless steel. To enhance adhesion, a polydopamine interlayer was chemically deposited onto the steel surface. The PTFE + TiO2 coating was subsequently applied through immersion, followed by the deposition of silver nanoparticles using a chemical method. Optimization of the polydopamine interlayer involved varying temperature, time, stirring speed, and drying parameters. The optimal conditions for the polydopamine interlayer were determined to be 60 °C for 1 h, 300 rpm stirring, and 24-h drying in a freeze dryer. Analytical results demonstrated that both the PTFE + TiO2 and PTFE/PTFE + TiO2/Ag coatings exhibited exceptional corrosion resistance, with corrosion currents of 3.3 × 10−5 and 3.2 × 10−4 μA/cm2, respectively. Antibacterial assessments showcased the remarkable ability of the PTFE/PTFE + TiO2/Ag coating, containing 5% silver content, to effectively inhibit bacterial penetration within a 6.5 mm radius. Furthermore, this coating displayed a water contact angle of 143°, classifying it as a hydrophobic coating. The photocatalytic efficiency (Rs) was determined to be 3.18 × 10−3 A/W, a performance level comparable to that of a standard UV sensor. These findings underscore the substantial enhancements in corrosion resistance, antibacterial performance, and hydrophobic characteristics achieved with the PTFE + TiO2/Ag coating, particularly through the novel optimization of the polydopamine interlayer. This coating exhibits great promise for multifunctional protective applications in diverse fields, particularly demonstrating its suitability for implants and bio-coatings.

Centralised monitoring and control of buildings using open standards

Abstract. Due to the necessary heat turnaround, buildings are increasingly seen as part of districts, e. g. Positive Energy Districts. Optimizing the energy efficiency of buildings is an essential part of implementing the heat turnaround. As a result, sensor/actuator systems are becoming increasingly important, both at building level and district level. Various proprietary and open standards for monitoring and control are used at building level. This heterogeneity makes it difficult to jointly optimize energy efficiency in buildings. OGC Sensorthings API (STA) is an open standard that defines a common language for sensor data and even tasking processes. In addition, KNX and LoRaWAN are available as standards for building monitoring and control. This article therefore proposes a solution that combines these standards. As proof of concept, two different buildings are equipped with KNX and LoRaWAN. Sensor data is collected in both buildings and merged centrally in a so-called building data lake based on OGC STA Sensing Part, e.g. temperature or indoor air quality. In addition, OGC STA Tasking Part is used to trigger actuator commands centrally and independently of the communication standard used in the controlled building, e. g. temperature setting of thermostats. As part of the proof of concept, it is shown how standardized sensor data can be visualized based on available visualization software, e. g. Grafana, and how the standardized actuator control level can be used for optimizations such as the night setback of heating systems.

Noninvasive Real-Time Glucose Monitoring Is in the Near Future.

Objective: Since the introduction of continuous glucose monitoring (CGM) technology, developers have rigorously researched the feasibility of creating a noninvasive glucose monitoring device. In a recent pilot study, investigators reported a strong correlation between glucose values obtained from novel noninvasive monitoring device (GWave) values to venous and capillary glucose measurements. Research Design and Methods: We investigated whether the level of accuracy observed in the pilot study could be reproduced in a larger cohort, using a smaller third-generation manufacturable device (Gen III GWave) containing a standardized sensor chip that can be mass produced for commercial use. The evaluation assessed concordance with capillary blood glucose, reproducibility between two Gen III devices, and accuracy during insulin-induced hypoglycemia. Results: Assessment of samples from 75 subjects (type 2 diabetes, n = 6; type 1 diabetes, n = 28; nondiabetic pregnant subjects, n = 10; and nondiabetic, n = 31) showed that 97% of values were in Zone A with 3% in Zone B of the Clarke Error Grid, with a mean absolute relative difference of 6.7% from reference blood glucose. Comparison between two independent Gen III GWave devices demonstrated reproducibility between the sensors (R2 = 0.95), with 100% of values within Zone A. In the hypoglycemia assessment, measurements from the Gen III sensor tightly followed the capillary glucose measurements down to 42 mg/dL (2.3 mmol/L), whereas the CGM measurements from two different CGM only converged with the GWave and capillary glucose readings after 90 min of decreasing glucose levels. Conclusion: Our results show promise as potentially the first noninvasive technology. Future studies will focus on larger number of people in all glucose ranges. Real-time noninvasive blood glucose monitoring is possible using GWave technology.

A Case Study of a Tiny Machine Learning Application for Battery State-of-Charge Estimation

Growing battery use in energy storage and automotive industries demands advanced Battery Management Systems (BMSs) to estimate key parameters like the State of Charge (SoC) which are not directly measurable using standard sensors. Consequently, various model-based and data-driven approaches have been developed for their estimation. Among these, the latter are often favored due to their high accuracy, low energy consumption, and ease of implementation on the cloud or Internet of Things (IoT) devices. This research focuses on creating small, efficient data-driven SoC estimation models for integration into IoT devices, specifically the Infineon Cypress CY8CPROTO-062S3-4343W. The development process involved training a compact Convolutional Neural Network (CNN) and an Artificial Neural Network (ANN) offline using a comprehensive dataset obtained from five different batteries. Before deployment on the target device, model quantization was performed using Infineon’s ModusToolBox Machine Learning (MTB-ML) configurator 2.0 software. The tests show satisfactory results for both chosen models with a good accuracy achieved, especially in the early stages of the battery lifecycle. In terms of the computational burden, the ANN has a clear advantage over the more complex CNN model.

Enhanced monitoring and dynamic analysis of a concentrated solar thermal experimental plant for steam production

Solar heat for industrial processes (SHIP) is an increasingly interest option as a key strategy to decarbonise the industrial sector. Linear Fresnel Collectors are a promising technology due to their cost-effectiveness and efficiency. As part of the SOLPINVAP project, an experimental facility was constructed to prove the use of modular Linear Fresnel collectors for steam production in industrial processes. SHIP plants commonly use standard industrial sensors, which can result in high uncertainty when measuring absorbed and produced heat. This can be problematic when selling energy to customers through an energy purchase agreement. To address this issue, an uncertainty analysis was conducted in the experimental plant, and more precise instrumentation was added to the installation. This reduced the uncertainty in the absorbed energy in the solar field from 12 % to 3 % and obtaining an uncertainty of 3 % in the generated energy. Additionally, the operation of the plant was studied through experimental tests conducted during the summer of 2022, and the system dynamics were analysed during the period of peak heat absorption.