Real-world Operating Conditions Research Articles

Analysis of the Operating Parameters of Wood Transport Vehicles from the Point of View of Operational Reliability

The aim of the research was to create a universal system for monitoring and evaluating the operating parameters of the haulage vehicles used for the haulage of wood with self-maintenance. The article presents partial results from the entire research. Data for research into the operational reliability of IVECO, SCANIA, and TATRA vehicles were obtained from the real-world operating conditions of two companies dealing with the mining/transportation process. Information from the operating conditions was obtained according to the test plan [n, R, t], according to which n objects were simultaneously tested, and the objects that were damaged during the tests were replaced with new ones; the tests ended after the test time t for each of the n positions. Based on the results and statistical analyses, it can be said that the best operational reliability is achieved by IVECO, followed by SCANIA, and only then by TATRA. The resolution of the above conclusions in operating conditions will contribute to the efficiency of the operation of the investigated facilities and the extension of the technical life of the means of transportation.

Digital twin real-time hybrid simulation platform for power system stability

This paper offers a comprehensive exploration of the application of digital twin technology for power system stability. Leveraging both physics-based and data-driven modeling techniques, we propose a novel digital twin framework that provides high fidelity simulations of power systems. Several case studies, including voltage stability assessment, dynamic stability analysis, predictive maintenance of power transformers, and renewable energy integration optimization, substantiate the efficacy of the framework under different real-world operating conditions. Our research conclusively indicates that the digital twin framework significantly enhances prediction accuracy, accelerates analysis speed, improves system reliability, and aids in efficient risk management in power systems. As a significant outcome, the framework has demonstrated its potential to revolutionize power system management and operations by facilitating predictive maintenance and efficient renewable energy integration. The paper concludes by emphasizing that strategic partnerships between academia and industry, robust cybersecurity measures, and the development of comprehensive standards and guidelines are key to unlocking the full potential of digital twin technology in power systems.

Review on PCM Heat Sink for Electronic Thermal Management Application

A significant challenge in thermal management has arisen as a result of the rising demand for high-performance electronic devices. The efficiency, size, and weight of conventional cooling methods like liquid and air cooling are constrained. Due to their high storage of latent heat capacity and isothermal phase transition behavior, phase change materials (PCMs) have become a promising thermal management solution. The purpose of this experimental study is to evaluate the performance of a PCM heat sink for electronic thermal regulation. The PCM heat sink is made up of a PCM module attached to a typical heat sink. To improve heat dissipation capabilities, the PCM module uses a PCM material with a suitable phase change temperature and encapsulation. The experimental setup involves simulating real-world operating conditions by applying controlled heat loads to electronic components. By observing temperature changes, thermal resistance, and transient response, the PCM heat sink’s thermal performance is assessed. To evaluate the superiority of the PCM heat sink, a comparison is made with traditional air-cooled and liquid-cooled heat sink configurations. The experimental investigation’s findings show that the PCM heat sink performs better in terms of thermal management than traditional cooling techniques. The phase change process used by the PCM efficiently absorbs and stores extra heat produced by electronic parts, improving temperature regulation and lowering temperature gradients. Lower component temperatures and higher operational reliability are the results of the PCM heat sink’s improved thermal resistance and heat dissipation efficiency. A further benefit of the PCM heat sink’s isothermal behavior during the phase transition is that it prevents temperature spikes and lessens the effects of heat stress on the electronic devices. The long cooling times provided by the PCM material’s high latent heat storage capacity allow for prolonged operation without affecting device performance. This experimental study concludes by demonstrating the efficiency of a PCM heat sink for electronic thermal management. The design of heat sinks with PCM integration offers notable enhancements in temperature control, thermal resistance, and system overall reliability. The results of this research help to advance thermal management strategies, which makes it easier to create efficient electronic devices with better cooling capacities.

107 Assessment of in Vitro Toxicity of Inhalable Incidental Nanoparticles Originating in the Ceramic Industry

Abstract Assessing exposure to nanomaterials in industrial workplaces is a widespread challenge. Nanoparticle release was evaluated, under real-world operating conditions, in one thermal spraying and one tile firing facility. Nanoparticles were characterised in terms of mass and number concentrations (µg/m3; /cm3), particle size distribution, chemical composition and morphology, and in vitro toxicity. The present work targets especially the toxicological characterisation of the aerosols sampled. Particles <2 microns in size were sampled in cell-culture medium using an SKC Biosampler connected to a pump operating at 12 lpm. Samples were conserved frozen at -20ºC until analysis. Submerged-cell cultures using the commercially-available A549 cell line were used for the in-vitro assays, where cell viability and generation of reactive-oxygen species (ROS) were assessed. Preliminary results evidenced a relatively large variability across samples in terms of cell viability, with reductions ranging between <5% and up to 60% after 2-hour exposures. Results suggested that the initial INP concentration in the samples may play a relevant role in the results, as in highly concentrated samples nanoparticles may interfere with the cellular uptake of the Alamar Blue dye used and, subsequently, on fluorescence results. ROS generation was detected increasingly over time during the test period (1-4 hours). Readings obtained after 3-4 hour became mostly stable. Finally, these results provide the baseline for the subsequent assessment of the expected reduction in INP concentrations and toxicity resulting from the implementation of risk mitigation measures, which will focus on a custom-designed air filtration system developed in the framework of this project.

Multislice Time-Frequency image Entropy as a feature for railway wheel fault diagnosis

In machinery fault diagnosis, most of the entropy features used were proposed for time series signals, while much key information of vibration signals, including fault information, can only be disclosed on the Time-Frequency Image (TFI). Besides, in the TFI of a signal, the first region to be noticed is generally the region with a large amplitude, but the difference between many signals lies in these details whose amplitude is small. Aiming at this issue, in this paper, we propose a novel entropy feature, named Multislice Time-Frequency Image Entropy (MTFIE), to capture the low-amplitude details embedded in a signal and to characterize the frequency stability and frequency mutability of the signal. The proposed MTFIE feature, combined with a Neural Network (NN)-based classifier, is constructed as a fault diagnostic method for railway wheels. The results show that the MTIFIE-NN method can achieve excellent results under both ideal and real-world operating conditions.

Data-Driven Parameterized Corner Synthesis for Efficient Validation of Perception Systems for Autonomous Driving

Today's automotive cyber-physical systems for autonomous driving aim to enhance driving safety by replacing the uncertainties posed by human drivers with standard procedures of automated systems. However, the accuracy of in-vehicle perception systems may significantly vary under different operational conditions (e.g., fog density, light condition, etc.) and consequently degrade the reliability of autonomous driving. A perception system for autonomous driving must be carefully validated with an extremely large dataset collected under all possible operational conditions in order to ensure its robustness. The aforementioned dataset required for validation, however, is expensive or even impossible to acquire in practice, since most operational corners rarely occur in a real-world environment. In this paper, we propose to generate synthetic datasets at a variety of operational corners by using a parameterized cycle-consistent generative adversarial network (PCGAN) . The proposed PCGAN is able to learn from an image dataset recorded at real-world operational conditions with only a few samples at corners and synthesize a large dataset at a given operational corner. By taking STOP sign detection as an example, our numerical experiments demonstrate that the proposed approach is able to generate high-quality synthetic datasets to facilitate accurate validation.

A review on fault diagnosis in TCSC compensated transmission lines using various techniques

This study presents a comparative analysis of fault detection and categorization methods in TCSC (Thyristor Controlled Series Capacitor)-compensated power systems. The analysis explores different techniques for extracting fault features from current data obtained from TCSC-compensated transmission lines. Machine learning algorithms, namely kNearest Neighbor (k-NN) and Support Vector Machine (SVM) are employed to categorize the extracted features, enabling fault detection and identification. To evaluate the efficacy of the proposed method, simulation tests were conducted on a test system, considering various fault scenarios. The simulations accounted for the presence of noise and TCSC correction, reflecting real-world operating conditions. The results demonstrate the efficiency of multiple approaches in accurately and swiftly detecting and classifying faults. By leveraging the capabilities of machine learning algorithms and extracting relevant features from current data, the proposed method offers a promising solution for fault detection and categorization in TCSC-compensated power systems. The ability to operate effectively even in the presence of noise and TCSC correction highlights the robustness and reliability of the proposed approach.

Improved Spatiotemporal Framework for Human Activity Recognition in Smart Environment.

The rapid development of microsystems technology with the availability of various machine learning algorithms facilitates human activity recognition (HAR) and localization by low-cost and low-complexity systems in various applications related to industry 4.0, healthcare, ambient assisted living as well as tracking and navigation tasks. Previous work, which provided a spatiotemporal framework for HAR by fusing sensor data generated from an inertial measurement unit (IMU) with data obtained by an RGB photodiode for visible light sensing (VLS), already demonstrated promising results for real-time HAR and room identification. Based on these results, we extended the system by applying feature extraction methods of the time and frequency domain to improve considerably the correct determination of common human activities in industrial scenarios in combination with room localization. This increases the correct detection of activities to over 90% accuracy. Furthermore, it is demonstrated that this solution is applicable to real-world operating conditions in ambient light.

Enhanced Impedance Measurement to Predict Electromagnetic Interference Attenuation Provided by EMI Filters in Systems with AC/DC Converters

Due to the widespread integration of renewable energy sources connected to AC and DC power systems by means of power electronics converters, electromagnetic noise propagates along lines, and metallic earth-return structures. EMI filters are commonly used to mitigate the common mode and differential mode noise at the interface of distribution lines, and their suppression characteristics are usually assessed in standard test setups, the impedances of which are, however, scarcely representative of real-world applications. In this paper, an online, inductively coupled impedance measurement method is proposed. A sensitivity analysis to highlight the benefits of the proposed setup and experimental verification is performed. The proposed method enables non-intrusive impedance measurement in energized systems, including power converters. These measures, in turn, allow the evaluation of modal insertion losses of EMI filters in real-world operating conditions. The three-phase example considered in this study shows significant deviations from manufacturer specifications, thus justifying the need for more advanced estimation techniques.

Flash Characterization of Smartphones Used in Point-of-Care Diagnostics.

Rapidly growing interest in smartphone cameras as the basis of point-of-need diagnostic and bioanalytical technologies increases the importance of quantitative characterization of phone optical performance under real-world operating conditions. In the context of our development of lateral-flow immunoassays based on phosphorescent nanoparticles, we have developed a suite of tools for characterizing the temporal and spectral profiles of smartphone torch and flash emissions, and their dependence on phone power state. In this work, these tools are described and documented to make them easily available to others, and demonstrated by application to characterization of Apple iPhone 5s, iPhone 6s, iPhone 8, iPhone XR, and Samsung Note8 flash performance as a function of time and wavelength, at a variety of power settings. Flash and torch intensity and duration vary with phone state and among phone models. Flash has high variability when the battery charge is below 10%, thus, smartphone-based Point-of-Care (POC) tests should only be performed at a battery level of at least 15%. Some output variations could substantially affect the results of assays that rely on the smartphone flash.

Power control strategy and economic analysis using physics-based battery models in standalone wind–battery systems

Lithium-ion battery storage is coupled with wind power systems to address the issue of power imbalance. Operating wind turbines continuously at a maximum power point (MPP) might dump excess power based on the load demand. Manufacturer provided battery life is for lab conditions and might not replicate under real-life conditions. To address these issues, we (a) propose a power management and control strategy to avoid dumping of excess power, (b) calculate yearly capacity-fade using a physics-based electrochemical–thermal battery model for real-world operating conditions, and (c) study the financial impact of storage on the cost of wind energy generation. The power management and control strategy is designed to maintain a power balance in the system irrespective of the variations in wind speed, load demand and battery state of charge and can track the MPP or deviate from it to guarantee 0% dumping power. The formation and growth of the solid-electrolyte interface layer are modelled to calculate the battery degradation rate. Finally, seven reliability indices, including loss of energy expectation, annualised life cycle cost, and battery sufficiency, are compared for three locations with varying wind potential. The techno-economic analysis reveals that battery life and wind potential significantly impact the energy generation cost.

Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines

Prognostics and health management (PHM) is an important part of ensuring reliable operations of complex safety- critical systems. System-level remaining useful life (RUL) estimation is a much more complex problem than making estimations at the component level, and system-level RUL methodologies remain sparse in the literature. Model-based approaches have traditionally worked in the past for components such as capacitors, MOSFETs, batteries, or hard-drives (to name a few examples), but developing high fidelity dynamics models of cyber physical systems that can be used to study the effects of multiple degrading components in the system remains a challenging task. Some initial work on model-based System RUL predictions was demonstrated in Khorasgani, et al [1], but, to generalize the system-level prognostics problem, we have to resort to pure data driven and hybrid approaches. In this work, we propose an end-to-end data- driven framework for developing deep learning models to predict remaining useful life of cyber physical systems operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process anddown stream data analysis tasks. Due to the unknown faulty conditions, the raw sensor data is transformed into signals that expose the underlying degradation processes, which are then used for model development. Bayesian Optimization is used to tune the model parameters prior to training and validation. We show that this approach results in accurate predictions within 3 cycles to end of life (EOL). We demonstrate the effectiveness of our approach by applying it to the N-CMAPSS turbofan engine dataset recently released by NASA, which includes high fidelity degradation modeling, real world operating conditions, and a large set of fault operating modes.

Simulation of Cathode Catalyst Durability Under Fuel Cell Vehicle Operation - the Effect of Temperature

Fuel cell vehicles (FCVs) emerge to be promising candidates to produce clean power for the transportation sector in order to tackle climate change issues. Although the commercialization of polymer electrolyte membrane fuel cells (PEMFCs) has progressed in the past few decades, there are still obstacles to address, including high cost and limited hydrogen infrastructure. For heavy duty transportation applications, the PEMFC durability is also not yet proven, and extrapolating from lab data to real-world field operating conditions remains a significant challenge [1].In this work, the cathode catalyst degradation in PEMFCs is studied to make an estimation of fuel cell durability in the FCV application. The well-known Butler-Volmer approach is utilized to model platinum dissolution and redeposition, platinum oxidation and platinum ion formation during the fuel cell operation [2]. First, the model is calibrated with the results presented by Ferreira et al. [3] at 80℃; the calibrated model is then validated with Kocha’s results [4] to demonstrate the model’s capability of generating reliable results at different temperatures. In order to realistically predict the cathode degradation pattern and lifetime, the real-life fuel cell operating conditions should be obtained. Therefore, a drive cycle recorded based on a real-life transit bus operation in the city of Victoria is utilized to calculate the input fuel cell voltage profile. The procedure to calculate fuel cell operating voltage profile using the drive cycle has been thoroughly explained by Ahmadi and Kjeang [5]. This procedure employs Newton’s second law to calculate the required cell power density considering the air flow drag force counteracting the vehicle thrust. Finally, polarization curves representing the fuel cell performance are used to calculate the fuel cell voltage profile with the determined required cell power density. Polarization curves measured under a range of temperatures are employed to adequately reflect the effect of temperature on the fuel cell performance. The cell active area is considered to be 500 cm2 and the stack is assumed to contain 225 cells, representing a nominal stack power of 74 kW at 80℃. The fuel cell operation and degradation are simulated at three different temperatures: 60, 70, and 80℃. The change of remaining electrochemically active surface area (ECSA) with time is calculated as the output of the model.Temperature highly affects the electrochemical reactions of platinum dissolution in several ways. First, it affects the Tafel slope in the platinum dissolution reaction. Second, it substantially impacts on platinum degradation reaction rate constant [6]. The Arrhenius approach is taken in this study to apply the effect of temperature on the reaction rate constants. Fuel cell voltage loss over time is determined by assuming simple Tafel kinetics. The cathode lifetime is calculated at 0.6 A/cm2 and is estimated by considering 10% voltage drop as the failure criterion. Fig. 1 shows the simulated change of remaining ECSA over time and the resulting fuel cell lifetime for the three different cell temperatures. The results indicate that the cathode lifetime increases 168% when the cell temperature drops from 80℃ to 60℃ due to a significantly lower platinum degradation rate at 60℃. The results show relatively low cathode catalyst lifetimes for a transit bus. The reason is attributed to the bus drive cycle used as the model input. The drive cycle contains a great portion of deceleration and idling time which leads to a fuel cell operation close to open circuit voltage (OCV), thus experiencing a high degradation rate.In this regard, the present modeling framework could become a useful tool for fuel cell and FCV developers to predict lifetime for new products and systems prior to commercial release. Scientifically, the present model can also be used to further explore key influencing factors on fuel cell durability and develop more durable cell, stack, and system designs for a targeted FCV application. Acknowledgements This research was supported by the Natural Sciences and Engineering Research Council of Canada, Canada Research Chairs, and Simon Fraser University Community Trust Endowment Fund.

Volatility of aircraft exhaust ultrafine particles inferred from field measurements at Narita International Airport

We conducted field measurements of aerosols near a runway at Narita International Airport (NRT) in February 2018 to characterize the physical and chemical properties of aircraft exhaust ultrafine particles (UFPs; diameter <100 nm). An ultrafine condensation particle counter (UCPC; d50 = 2.5 nm), a condensation particle counter (CPC; d50 = 10 nm), and an engine exhaust particle sizer (EEPS; 6.0–523 nm) were used to measure the particle number concentrations and size distributions in ambient air. The sample flow for the UCPC and CPC was passed through an evaporation tube for heated measurements (150 °C, 250 °C, or 350 °C) or through a bypass tube for unheated measurements. The evaporation tube temperature (Ttube) of 350 °C was used as a threshold point for the separation of volatile and non-volatile particles. The removal efficiency of the volatile particles at intermediate temperatures (<350 °C) was evaluated in the laboratory using triacontane (C30) and tetracontane (C40) as test compounds. The critical temperature at which the laboratory-generated 30-nm C30 and C40 particles were almost entirely removed was ∼130 °C and ∼250 °C, respectively. The particle number concentrations measured by the UCPC, CPC, and EEPS at NRT were used to investigate the volatility of aircraft exhaust UFPs under real-world operating conditions. The number fraction of the non-volatile particles strongly depended on the geometric mean diameter (GMD) of the unheated particle number size distributions, whereas the vaporization temperature of the volatile particles did not exhibit significant dependency on GMD. The particle number fraction remaining, calculated for the selected evaporation tube temperature (Ttube), indicates that ∼50% and ≳ 90% of the volatile particles were removed at Ttube = 150 °C and 250 °C, respectively, on a number basis. These results suggest that a large fraction (more than half by number) of the volatile UFPs emitted from aircraft was equally or less volatile than the 30 nm pure C30 particles and only a small fraction of the volatile UFPs was equally or less volatile than the 30 nm pure C40 particles.

Temperature-Dependent Optical Modeling of Perovskite Solar Cells

Comprehensive temperature-dependent optical modeling of perovskite solar cells (PSCs) and modules is essential to accurately predict their energy yield and quantify their energy losses under real-world operating conditions, where devices are subject to different irradiance spectra and intensities as well as operating temperatures. These models require the accurate determination of the temperature-dependent optical constants of perovskites. Here, we report on these data, empirically determined via spectroscopic ellipsometry, for triple-cation perovskites with band gaps ranging between 1.58 and 1.77 eV at temperatures between 25 and 75 °C. Using this data set, we develop a simple empirical model to obtain the temperature-dependent optical constants of perovskites of an arbitrary band gap. We validate our empirical model by comparing the measured temperature-dependent short-circuit current densities and external quantum efficiency data of single-junction PSCs with simulated results using the modeled optical constants.

Simulation of Cathode Catalyst Durability Under Fuel Cell Vehicle Operation - the Effect of Fuel Cell Stack Size

The concerns regarding climate change have made the researchers seek a clean alternative for the fossil fuel vehicles. Fuel cell vehicles (FCVs) are considered to be promising candidates owing to their efficient energy conversion and zero-carbon emission. However, a number of obstacles such as high cost and limited hydrogen infrastructure have made the FCVs commercialization process challenging. Polymer electrolyte membrane fuel cells (PEMFCs) have been proven promising for transportation applications. For heavy duty transportation applications, the PEMFC durability is also not yet proven, and extrapolating from lab data to real-world field operating conditions remains a significant challenge [1].In this work, the cathode catalyst degradation in PEMFC is studied to estimate the effect of stack size on fuel cell durability in the FCV application. Platinum dissolution and redeposition, platinum oxidation and platinum ion formation during the fuel cell operation are modeled using the Butler-Volmer approach presented in [2]. A drive cycle recorded based on a real-life transit bus operation in the city of Victoria is utilized to calculate the input fuel cell voltage profile based on the methodology presented by Ahmadi and Kjeang [3]. According to this methodology, the required cell power density is calculated using Newton’s second law considering the air flow drag force as a counteracting force against the vehicle movement. Then, the required voltage cycle is obtained by employing a polarization curve characterizing the fuel cell performance. Finally, the change of remaining electrochemically active surface area (ECSA) with time is calculated as the output of the model. The fuel cell is assumed to operate at 80 ℃ and the cell active area is considered to be 500 cm2. Simple Tafel kinetics is then used to determine the fuel cell voltage loss. A 10% voltage drop at 0.6 A/cm2 is considered as the failure criterion for the cathode lifetime. Moreover, the effect of the fuel cell stack size is studied. By increasing fuel cell stack size, the required cell power density drops, leading to a decrease in the voltage cycle amplitude while the voltage cycle period remains the same. According to the empirical kinetic rate equation, the catalyst degradation exponentially increases with increasing the voltage. Therefore, a higher degradation rate is observed for a catalyst operating on a voltage cycle with a lower amplitude while the period and the upper potential limit (UPL) are maintained the same, causing a significant platinum ion generation. Fig. 1 shows the change of remaining ECSA over time and resulting fuel cell lifetime for three stack sizes which are represented by the stack nominal powers. The results show that the fuel cell lifetime will be roughly doubled when the stack size is reduced by half. Stack sizing is thus an important consideration for fuel cell durability in the FCV application.In this regard, predicting fuel cell lifetime is a crucial step in commercializing FCVs. The present modeling framework could be utilized by FCV developers to predict lifetime for new products instead of carrying out time-consuming lifetime experiments. The factors influencing fuel cell durability can also be investigated using the present model framework to develop durables cells and stacks for a targeted FCV application. Acknowledgements This research was supported by the Natural Sciences and Engineering Research Council of Canada, Canada Research Chairs, and Simon Fraser University Community Trust Endowment Fund.

Constructing robust health indicators from complex engineered systems via anticausal learning

In prognostics and health management (PHM), the task of constructing comprehensive health indicators (HI) from huge amounts of condition monitoring data plays a crucial role. HIs may influence both the accuracy and reliability of remaining useful life (RUL) prediction, and ultimately the assessment of system’s degradation status. Most of the existing methods assume apriori an oversimplified degradation law of the investigated machinery, which in practice may not appropriately reflect the reality. Especially for safety–critical engineered systems with a high level of complexity that operate under time-varying external conditions, degradation labels are not available, and hence, supervised approaches are not applicable. To address the above-mentioned challenges for extrapolating HI values, we propose a novel anticausal-based framework with reduced model complexity, by predicting the cause from the causal models’ effects. Two heuristic methods are presented for inferring the structural causal models. First, the causal driver is identified from complexity estimate of the time series, and second, the set of the effect measuring parameters is inferred via Granger Causality. Once the causal models are known, off-line anticausal learning only with few healthy cycles ensures strong generalization capabilities that helps obtaining robust online predictions of HIs. We validate and compare our framework on the NASA’s N-CMAPSS dataset with real-world operating conditions as recorded on board of a commercial jet, which are utilized to further enhance the CMAPSS simulation model. The proposed framework with anticausal learning outperforms existing deep learning architectures by reducing the average root-mean-square error (RMSE) across all investigated units by nearly 65%.

A 3D Transient CFD Simulation of a Multi-Tubular Reactor for Power to Gas Applications

A 3D stationary CFD study was conducted in our previous work, resulting in a novel reactor design methodology oriented to upgrading biogas through CO2 methanation. To enhance our design methodology incorporating relevant power to gas operational conditions, a novel transient 3D CFD modelling methodology is employed to simulate the effect of relevant dynamic disruptions on the behaviour of a tubular fixed bed reactor for biogas upgrading. Unlike 1D/2D models, this contribution implements a full 3D shell cooled methanation reactor considering real-world operational conditions. The reactor’s behaviour was analysed considering the hot-spot temperature and the outlet CH4 mole fraction as the main performance parameters. The reactor start-up and shutdown times were estimated at 330 s and 130 s, respectively. As expected, inlet feed and temperature disruptions prompted “wrong-way” behaviours. A 30 s H2 feed interruption gave rise to a transient low-temperature hot spot, which dissipated after 60 s H2 feed was resumed. A 20 K rise in the inlet temperature (523–543 K) triggered a transient low-temperature hot spot (879 to 850 K). On the contrary, a 20 K inlet temperature drop resulted in a transient high-temperature hot spot (879 to 923 K), which exposed the catalyst to its maximum operational temperature. The maximum idle time, which allowed for a warm start of the reactor, was estimated at three hours in the absence of heat sources. No significant impacts were found on the product gas quality (% CH4) under the considered disruptions. Unlike typical 1D/2D simulation works, a 3D model allowed to identify the relevant design issues like the impact of hot-spot displacement on the reactor cooling efficiency.

Controller Evaluation for Solar-Latent Thermal Energy Applications

In this study, the performance of a self-sufficient controller used for a solar-latent heat domestic hot water (DHW) production unit under real-world operating conditions was analyzed. The unit consists of a flat-plate solar collector and a latent heat storage tank. The controller is powered by a small solar panel and governs the charging and discharging of the system, ensuring maximum solar energy absorption, desired hot water temperature, and constant monitoring of the heat-storage tank’s capacity. The system is compact and can be installed on flat and curved roofs as a direct replacement of conventional solar collectors with heat-energy storage tanks. During testing, all internal and external parameters were monitored using a monitoring system that was also used for emulating user profiles. The controller uses self-learning techniques to adjust its parameters and improves its performance by fine-tuning the control equations to the peculiarities of the specific system and installation location. The system was installed and operated for an extended period to allow for the learning equations to train the system. The results for the first, fifth, and twentieth days of operation are presented in this paper. On the 20th operating day, the controller effectively regulated the heat transfer fluid temperature difference in the charging circuit within the efficient band of 2°C–5°C following the irradiance conditions at the testing area. During discharge, the DHW temperature was regulated between 37°C–40°C, with the user’s set temperature as 38°C. The regulation hysteresis time for the DHW temperature regulation was approximately 5 min. The tests were conducted under real-world operating conditions for the charging of the system, while for the discharging, the user profile was emulated using a test rig.

E-waste dismantling as a source of personal exposure and environmental release of fine and ultrafine particles

Electronic waste (WEEE; from TV screens to electric toothbrushes) is one of the fastest growing waste streams in the world. Prior to recycling, e-waste components (metals, wood, glass, etc.) are processed by shredding, grinding and chainsaw cutting. These activities generate fine and ultrafine particle emissions, containing metals as well as organics (e.g., flame retardants), which have high potential for human health impacts as well as for environmental release. In this work, release of fine and ultrafine particles, and their exposure impacts, was assessed in an e-waste recycling facility under real-world operating conditions. Parameters monitored were black carbon, particle mass concentrations, ultrafine particles, and aerosol morphology and chemical composition. Potential health impacts were assessed in terms of cytotoxicity (cell viability) and oxidative stress (ROS) on <2 μm particles collected in liquid suspension. Environmental release of WEEE aerosols was evidenced by the higher particle concentrations monitored outside the facility when compared to the urban background (43 vs.11 μgPM2.5/m3, respectively, or 2.4 vs. 0.2 μgCa/m3). Inside the facility, concentrations were higher in the top than on the ground floor (PM2.5 = 147 vs. 78 μg/m3, N = 15.4 ∗ 104 vs. 8.7 ∗ 104/cm3, BC = 12.4 vs. 7.2 μg/m3). Ventilation was a key driver of human exposure, in combination with particle emissions. Key chemical tracers were Ca (from plastic fillers) and Fe (from wiring and other metal components). Y, Zr, Cd, Pb, P and Bi were markers of cathode TV recycling, and Li and Cr of grinding activities. While aerosols did not evidence cytotoxic effects, ROS generation was detected in 4 out of the 12 samples collected, associated to the ultrafine fraction. We conclude on the need for studies on aerosol emissions from WEEE facilities, especially in Europe, due to their demonstrable environmental and human health impacts and the rapidly growing generation of this type of waste.