Standard Operating Conditions Research Articles

Processing of manganite-based contact layers for stacking of planar solid oxide fuel cells

Quality of the solid oxide fuel cell (SOFC) stack assembly affects power density and lifetime of the power plants. In the case of planar SOFCs, assembling quality depends on electrical contact between the stainless steel current collectors and ceramic electrodes of the membrane-electrode assemblies (MEAs). Whilst Ni meshes and paste are commonly used to provide this contact at the anode side, the cathode contact materials should be stable in oxidizing atmosphere. In this work, mechanical pre-treatment conditions of submicron La0.8Sr0.2MnO3±δ (LSM) cathode powders were optimized in order to sinter mechanically stable contacts in the course of SOFC sealing procedure without use of inorganic sintering aids. Excessive ball-milling time and/or rotating speed was shown to result in partial decomposition of the perovskite phase, which increases electrical resistivity of the porous layers. Testing of the LSM contact layers in model SOFC stack with Crofer 22H current collectors under standard operating conditions demonstrated sufficient adhesion and performance.

Decontamination-Induced Modification of Bioactivity in Essential Oil-Based Plasma Polymer Coatings.

Plasma polymer coatings fabricated from Melaleuca alternifolia essential oil and its derivatives have been previously shown to reduce the extent of microbial adhesion on titanium, polymers, and other implantable materials used in dentistry. Previous studies have shown these coatings to maintain their performance under standard operating conditions; however, when used in e.g., a dental implant, these coatings may inadvertently become subject to in situ cleaning treatments, such as those using an atmospheric pressure plasma jet, a promising tool for the effective in situ removal of biofilms from tissues and implant surfaces. Here, we investigated the effect of such an exposure on the antimicrobial performance of the Melaleuca alternifolia polymer coating. It was found that direct exposure of the polymer coating surface to the jet for periods less than 60 s was sufficient to induce changes in its surface chemistry and topography, affecting its ability to retard subsequent microbial attachment. The exact effect of the jet exposure depended on the chemistry of the polymer coating, the length of plasma treatment, cell type, and incubation conditions. The change in the antimicrobial activity for polymer coatings fabricated at powers of 20–30 W was not statistically significant due to their limited baseline bioactivity. Interestingly, the bioactivity of polymer coatings fabricated at 10 and 15 W against Staphylococcus aureus cells was temporarily improved after the treatment, which could be attributed to the generation of loosely attached bioactive fragments on the treated surface, resulting in an increase in the dose of the bioactive agents being eluted by the surface. Attachment and proliferation of Pseudomonas aeruginosa cells and mixed cultures were less affected by changes in the bioactivity profile of the surface. The sensitivity of the cells to the change imparted by the jet treatment was also found to be dependent on their origin culture, with mature biofilm-derived P. aeruginosa bacterial cells showing a greater ability to colonize the surface when compared to its planktonic broth-grown counterpart. The presence of plasma-generated reactive oxygen and nitrogen species in the culture media was also found to enhance the bioactivity of polymer coatings fabricated at power levels of 10 and 15 W, due to a synergistic effect arising from simultaneous exposure of cells to reactive oxygen and nitrogen species (RONS) and eluted bioactive fragments. These results suggest that it is important to consider the possible implications of inadvertent changes in the properties and performance of plasma polymer coatings as a result of exposure to in situ decontamination, to both prevent suboptimal performance and to exploit possible synergies that may arise for some polymer coating-surface treatment combinations.

Sensitivity analysis of spacecraft in micrometeoroids and orbital debris environment based on panel method

To reduce the risk of mission failure caused by the MM/OD impact of the spacecraft, it is necessary to optimize the design of the spacecraft. Spacecraft survivability assessment is the key technology in the optimal design of spacecraft. Spacecraft survivability assessment includes spacecraft impact sensitivity analysis and spacecraft component vulnerability analysis under MM/OD environment. The impact sensitivity refers to the probability of a spacecraft encountering an MM/OD impact while in orbit. Vulnerability refers to the probability that each component of a spacecraft may fail or malfunction when impacted by space debris. Yet this paper mainly analyzes the impact sensitivity and proposes a spacecraft sensitivity assessment method under the MM/OD environment based on a panel method. Under this panel method, a spacecraft geometric model is discretized into small panels, and whether they are impacted by MM/OD or not is determined through the analysis of the shielding or shadowing relationships between panels. The number of impacts on each panel is obtained through calculation, and accordingly the probability of each spacecraft component encountering MM/OD impact can be acquired, thus generating the impact sensibility. This paper extracts data from the NASA's ORDEM2000, the ESA's MASTER8 as well as the SDEEM2015 (Space Debris Environmental Engineering Model developed by HIT), and uses the PCHIP (Piecewise Cubic Hermite Interpolating Polynomial) method to interpolate and fit the size-flux relationship of space debris. Compared with linear interpolation and cubic spline interpolation, the fitting results through the method are relatively more accurate. The feasibility of this method is also demonstrated through two actual examples shown in this paper, whose results are close to those from ESABASE, although there are some minor errors mainly due to different debris data input. Through the cross-check by three risk assessment software - BUMPER, MDPANTO and MODAOST - under standard operating conditions, the feasibility of this method is again verified.

SAR vehicle recognition via scale-coupled Incep_Dense Network (IDNet)

ABSTRACT Vehicle target recognition of synthetic aperture radar (SAR) is one of the critical tasks of image interpretation. Convolutional neural networks (CNNs) are increasingly used in SAR images and make great progress in single-scale recognition under standard operating conditions (SOCs). However, the classical CNNs have the limitation of single model design and single scale, which is unfavourable for the recognition of multi-scale targets. This paper puts forward the design idea of scale coupling with both parallel convolution and dense connection, and designs an Incep_Dense module, which can learn and express scale information from different convolution paths and different feature levels simultaneously. Based on the Incep_Dense module, we built Incep_Dense network (IDNet) for SAR image vehicle target recognition. Furthermore, we add special layers to make IDNets more standardized and practical. Experiments show that the proposed IDNet achieves the optimal accuracy of 99% in the ten classifications of MSTAR and less than 1.5% accuracy degradation on the constructed multi-scale dataset. It has excellent multi-scale learning and representation capabilities with better performance than classical CNNs. It also performs well under the extended operating conditions (EOCs) of MSTAR and Gotcha with good generalization ability and robustness.

Scale Up of ALD of Platinum Onto Carbon Powder in Fluidized Beds for Fuel Cell Applications

ALD of platinum onto carbon has been studied in the literature for use in fuel cell and catalyst applications, mostly at the small scale (<1 g). Forge Nano is scaling the chemistry up to coat large batches of powders (>10 g) in fluidized bed reactors, with the goal of ultimately producing carbon supported platinum catalysts for automotive and other fuel cell applications. We studied the deposition of Pt by ALD on EC-300J ketjenblack carbon at the 10-30 g scale (75-250 ml powder volume) in fluidized bed reactors at low vacuum (~10 torr). As is often reported, we found the handling of the carbon substrates in these systems to be challenging. High surface area carbons tend to fluidize poorly and agglomerate, which can lead to inhomogeneous dispersion of the platinum across the substrate surface. This becomes an even bigger issue for the high loadings of platinum (~50wt%) used in fuel cell applications. Under standard operating conditions, a significant fraction of the carbon would agglomerate into millimeter-sized spheres, even before the coating process began.We tested multiple fluidization aids to inhibit this agglomeration, including microjets and powder surface treatments. These aids prevented the agglomeration of the carbon substrate and improved the fluidization behavior of the carbon powder. We deposited Pt onto the carbon using both thermal- and ozone-based ALD chemistries, achieving loadings ranging from 20 to 70wt% platinum. Process monitoring and the product mass balance indicated that the oxygen or ozone used in the ALD chemistry had the potential to combust significant fractions of the substrate powder over the course of the deposition. The product powders also tended to autocatalytically ignite upon removal from the reactor, indicating the high catalytic activity of the deposited platinum. Proper choice of the deposition conditions is critical to limiting this combustion. We will discuss strategies for continuing the scale these processes to 100-1000 g batches for commercial fuel cell applications.

Characterization of Large Format Pouch Cells by Electrochemical Impedance Spectroscopy for Automotive Applications

The influence of operating conditions such as temperature and state of charge on the performance of a high-power commercial lithium-ion pouch cell is explored using electrochemical impedance spectroscopy. State-of-charge and temperature ranges were selected based on relevance to automotive applications, such as DC Fast Charging, cold start, and high rate discharge events. Typical vehicle operation varies on the order of a few seconds and is therefore comparable with impedance values at frequencies higher than 0.1 Hz. Therefore, vehicle designers can utilize EIS as a tool to probe available power under standard operating conditions, as well as low temperatures and SOC in the vehicle at the battery cell level, offering the ability to decrease the amount of battery pack or vehicle level testing.Here, the full cell impedance is used to calculate electrochemical modeling parameters. Simulations using the parameters obtained from EIS are then compared to pulse tests and drive profiles for a large format pouch cell. Trends in the diffusion coefficients, reaction kinetics, and electrode conductivity as a function of state-of-charge and temperature will be discussed.

EIS as a Rapid Performance Diagnostic Tool in Automotive Li-Ion Batteries

The influence of operating conditions such as temperature and state of charge on the performance of a high-power commercial lithium-ion pouch cell is explored using electrochemical impedance spectroscopy. State-of-charge and temperature ranges were selected based on relevance to automotive applications, such as DC Fast Charging, cold start, and high rate discharge events. Typical vehicle operation varies on the order of a few seconds and is therefore comparable with impedance values at frequencies higher than 0.1 Hz. Therefore, vehicle designers can utilize EIS as a tool to probe available power under standard operating conditions, as well as low temperatures and SOC in the vehicle at the battery cell level, offering the ability to decrease the amount of battery pack or vehicle level testing. Here, the full cell impedance is presented and analyzed. Clear trends in the high frequency resistance, charge transfer resistance, and mass transport resistances as a function of state of charge and temperature are evident.

Combination of ELM and BEMD for Target Recognition of SAR Images

For the problem of target recognition of synthetic aperture radar (SAR) images, a method based on the combination of bidimensional empirical mode decomposition (BEMD) and extreme learning machine (ELM) is proposed. BEMD performs feature extraction for SAR images, producing multi-layer bidimensional intrinsic mode functions (BIMF). These BIMFs covey the discrimination of the original target while effectively eliminating the noises. ELM conducts the classification of each BIMF with high efficiency and robustness. Finally, the decisions from different BIMFs are fused using a linear weighting strategy to reach a reliable decision on the target label. The proposed method compensates the relatively low adaptivity of ELM to noise corruption by BEMD feature extraction. Moreover, the multi-layer BIMFs provide more discriminative information for correct decision. Hence, the overall recognition performance can be improved. As an efficient recognition algorithm, the proposed method can be used in an embedded system for wide applications. Experiments are designed and implemented on the moving and stationary target acquisition and recognition (MSTAR) dataset. The proposed method is tested under both the standard operating condition (SOC) and extended operating conditions (EOCs). The results reflect its effectiveness and robustness via quantitative comparisons.

The influence of in-cylinder flows and bulk gas density on early Spray G injection in an optical research engine

A well-characterized multi-hole gasoline injector, the Engine Combustion Network’s (ECN) Spray G injector, was investigated in an optically accessible research engine under four motored operating conditions with early injection. The experiments were conducted at intake pressures of [Formula: see text] and [Formula: see text], nearly matching the ECN’s standard early injection operating conditions, Spray G2 (flash boiling) and Spray G3 (early injection), respectively. This was combined with two engine speeds at [Formula: see text] and [Formula: see text]. Using particle image velocimetry and volumetric Mie scatter imaging, the in-cylinder flows were evaluated and the effects on the spray morphology were characterized. The in-cylinder flow was evaluated to understand the spray-flow interaction, including the turbulent kinetic energy. Little effect on turbulent energy was observed in the region examined near the exit of the fuel injector nozzle shortly after injection. Mie scatter imaging was used to characterize the spray morphology and wall wetting was clearly visible on the spark plug. Cyclic variability of the sprays was found to be insignificant; and major differences in spray morphology are attributed to the in-cylinder velocity and intake pressure at the time of injection. Decreasing the bulk gas density by decreasing the intake pressure had a number of effects on the evolution of the spray including faster evaporation, increased axial liquid penetration, and decreased spray angle. Increasing the in-cylinder flow magnitudes by increasing the engine speed had a similar effect on spray morphology by also increasing the evaporation rate, increasing the axial penetration, and decreasing the spray opening angle. Comparison of the motored spray cases with a no-flow case (when the fuel is sprayed into the engine without the piston present) further illustrated the extent to which the intake flow influenced the spray shape.

SAR Target Recognition Based on Joint Representation of Multimode Representations

For the problems of feature extraction and decision making in synthetic aperture radar (SAR) image target recognition, a method based on multimode clustering and decision fusion is proposed. The bidimensional variational mode decomposition (BVMD) is used to decompose the SAR image to obtain multiple modes, which provide multilevel descriptions of the target characteristics. Clustering is performed based on the intrinsic correlation of multiple modes, and several subsets with different modes are selected. Based on the joint sparse representation (JSR), each mode subset is classified, and the corresponding reconstruction error vector is obtained. The linear weighted fusion is employed to fuse the results from different mode subsets. Finally, a decision is made based on the fused results. Experiments are carried out based on the MSTAR dataset. The results show the effectiveness of the method under the standard operating condition (SOC) and robustness under extended operating conditions (EOCs).

A SAR Target Recognition Method Based on Decision Fusion of Multiple Features and Classifiers

A synthetic aperture radar (SAR) target recognition method combining multiple features and multiple classifiers is proposed. The Zernike moments, kernel principal component analysis (KPCA), and monographic signals are used to describe SAR image features. The three types of features describe SAR target geometric shape features, projection features, and image decomposition features. Their combined use can effectively enhance the description of the target. In the classification stage, the support vector machine (SVM), sparse representation-based classification (SRC), and joint sparse representation (JSR) are used as the classifiers for the three types of features, respectively, and the corresponding decision variables are obtained. For the decision variables of the three types of features, multiple sets of weight vectors are used for weighted fusion to determine the target label of the test sample. In the experiment, based on the MSTAR dataset, experiments are performed under standard operating condition (SOC) and extended operating conditions (EOCs). The experimental results verify the effectiveness, robustness, and adaptability of the proposed method.

Environmental assessment of underground coal gasification in deep-buried seams

This study presents a preliminary assessment of the environmental impact of field-scale underground coal gasification (UCG) targeting deep-buried seams by studying the fate of the UCG products in the reactor vicinity. A series of simulation scenarios are conducted in three deep (900 m) areas of specific interest – namely, South Wales coalfield (UK), Upper Silesian coal basin (Poland) and Ruhr mining district (Germany) – to investigate the transport of gaseous and dissolved chemicals. The results indicate that the syngas propagation is limited to 2.0 m in the vicinity of the reactor after 30 days, except that it is limited to 4.4 m in shale (overburden) of the UK site. Transport of the dissolved chemicals, through diffusion, is limited to 2.0 m without considering adsorption and less than 0.5 m when adsorption is considered after 10 years. Moreover, the effects of hydrology condition, stratum adsorption properties and the thermally induced change in rock porosity and permeability on the propagation of UCG products are studied. The results suggest that unacceptable risks to the environment are unlikely to arise if standard operating conditions are applied, offering a great prospect of deep coal seams to be considered for UCG. This study also provides insights into the environmental evaluation for other potential UCG projects.

Anhysteretic Magneto-Elastic Behaviour of Terfenol-D: Experiments, Multiscale Modelling and Analytical Formulas.

Giant magnetostrictive materials such as Terfenol-D and Galfenol are used to design actuators and sensors, converting magnetic input into a mechanical response, or conversely, mechanical input into a magnetic signal. Under standard operating conditions, these materials are subjected to stress. It is therefore important to be able to measure, understand and describe their magneto-mechanical behaviour under stress. In this paper, a comprehensive characterisation of the anhysteretic magneto-mechanical behaviour of Terfenol-D was performed. An energy-based multiscale approach was applied to model this behaviour. Finally, it was shown that the strain behaviour of Terfenol-D can be satisfactorily described using an analytical model derived from the full multiscale approach.

PV temperature and performance prediction in free-standing, BIPV and BAPV incorporating the effect of temperature and inclination on the heat transfer coefficients and the impact of wind, efficiency and ageing

A novel compact model is developed to predict the PV temperature Tpv, coefficient f which relates Tpv with the in-plane solar irradiance IT, and power output Pm. The Tpv, IT, ambient temperature Ta, and wind velocity vw on a sun-tracking pc-Si PV and c-Si BIPV were monitored. f depends explicitly on vw, PV efficiency, heat losses coefficient, and implicitly on Tpv, IT, Ta, loosely on the module inclination at low vw, while this effect weakens at high vw. Tpv prediction is provided by means of 5 functions, which cater for the deviation of the environmental conditions from the Standard Operating Conditions, the operating efficiency, the natural ageing, PV geometry and cell technology. The Tpv prediction for the sun-tracking system has relative error 2.6% for PV operating temperatures around the NOCT, and may overestimate by up to 1.4 °C. Similarly, the relative error for the BIPV system is −2.1% for PV temperatures around the NOCT, with underestimation up to 1.6 °C. The model predicted Pm with relative error 1.9% for PV operating near its nominal value. The model is compared to 3 well-known models and also applied to other BIPV/BAPV configurations in various countries proving its wide applicability, high accuracy and universality.

Deep learning for pH prediction in water desalination using membrane capacitive deionization

The pH of a solution has a large influence on the ion removal efficiency of the membrane capacitive deionization (MCDI) process, an electrochemical ion separation process. We developed a convolutional neural network linked with a long short-term memory (CNN-LSTM) model based on an artificial intelligence algorithm to predict the effluent pH of MCDI, as effluent pH is difficult to predict using conventional numerical modeling. The model accurately predicted effluent pH (R2≥0.998) based on the analysis of five input variables (current, voltage, influent conductivity and pH, and effluent conductivity) under standard operating conditions of MCDI using either constant-current or constant-voltage conditions. The developed model predicted effluent pH using only limited input variables, current and voltage, with high accuracy (R2≥0.997). Thus, the CNN-LSTM model can be used in practical applications as only the current and voltage of MCDI cells are often monitored in field applications.

Recognition of occluded targets in SAR images based on matching of attributed scattering centers

ABSTRACT A local optimal matching scheme of attributed scattering centres (ASC) is proposed for occluded target recognition in synthetic aperture radar (SAR) images. Considering the number of matching pairs and differences between different matching pairs, the local optimal matching is established between the ASC sets from the test and template samples. Accordingly, the properties of occluded targets are used to define a robust similarity measure. The similarities between the test sample and different template samples are calculated to determine the target label. Experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The proposed method could achieve an average recognition rate of 99.18% for 10 classes of targets under the standard operating condition (SOC). Its performance under random occlusion and directional occlusion is also superior over the reference methods.

Development and qualification of a representative scale-down model of automated Ficoll-based processing of a cell-based therapeutic according to quality by design principles

Background aimsThis article describes the development of a small-scale model for Ficoll-based cell separation as part of process development of an advanced therapy medicinal product and its qualification. Because of the complexity of biological products, their manufacturing process as well as characterization and control needs to be accurately understood. Likewise, scale-down models serve as an indispensable tool for process development, characterization, optimization and validation. This scale-down model represents a cell processor device widely used in advance therapies. This approach is inteded to optimise resources and to focus its use on process characterisation studies under the paradigm of the Quality by design. A scale-down model should reflect the large manufacturing scale. Consequently, this simplified system should offer a high degree of control over the process parameters to depict a robust model, even considering the process limitations. For this reason, a model should be developed and qualified for the intended purpose. MethodsProcess operating parameters were studied, and their resulting performance at full scale was used as a baseline to guide scale-down model development. Once the model was established, comparability runs were performed by establishing standard operating conditions with bone marrow samples. These analyses showed consistency between the bench and the large scale. Additionally, statistical analyses were employed to demonstrate equivalence. ResultsThe process performance indicators and assessed quality attributes were equivalent and fell into the acceptance ranges defined for the large-scale process. ConclusionsThis scale-down model is suitable for use in process characterization studies.

Anomaly Detection in Photovoltaic Production Factories via Monte Carlo Pre-Processed Principal Component Analysis

This paper investigates a use case of robust anomaly detection applied to the scenario of a photovoltaic production factory—namely, Enel Green Power’s 3SUN solar cell production plant in Catania, Italy—by considering a Monte Carlo based pre-processing technique as a valid alternative to other typically used methods. In particular, the proposed method exhibits the following advantages: (i) Outlier replacement, by contrast with traditional methods which are limited to outlier detection only, and (ii) the preservation of temporal locality with respect to the training dataset. After pre-processing, the authors trained an anomaly detection model based on principal component analysis and defined a suitable key performance indicator for each sensor in the production line based on the model errors. In this way, by running the algorithm on unseen data streams, it is possible to isolate anomalous conditions by monitoring the above-mentioned indicators and virtually trigger an alarm when exceeding a reference threshold. The proposed approach was tested on both standard operating conditions and an anomalous scenario. With respect to the considered use case, it successfully anticipated a fault in the equipment with an advance of almost two weeks, but also demonstrated its robustness to false alarms during normal conditions.

Impact of injection timings and exhaust gas recirculation rates on the characteristics of diesel engine operated with neat tamarind biodiesel

ABSTRACT The extensive usage of petroleum products in the power sector, industries, and transportation may lead to the depletion of fossil resources. Also, the most important human challenges of the 21st century are global warming and climate changes due to increased pollution levels. Nowadays, the need for new energy sources is one of the foremost problems of all countries in the world. The current experimental work is primarily focusing on the exploration of neat tamarind seed methyl ester (TSME) as a sustainable energy source for diesel engines. All the physicochemical properties were determined experimentally with concern to ASTM standards and compared with diesel fuel. Initially, tests were performed on a diesel engine with diesel and neat tamarind biodiesel in standard operating conditions 23º CA bTDC. Later, the injection timing was varied like 19º CA bTDC and 27º CA bTDC for the neat tamarind biofuel. A test result found with TSME biofuel that enhancement in brake thermal efficiency (BTE) by 4.07% was achieved with the retardation of engine injection timing when compared to standard injection timing. Also, it was found that drastic reduction of exhaust emissions by 9.7%of CO, 15.8% of HC and 6% of SO have resulted in the retarded injection timing of TSME when compared to standard conditions of TSME at full load. However, the oxides of nitrogen emissions were increased. To control the oxides of nitrogen emissions, exhaust gas recirculation (EGR) at two levels such as 10% and 20% were used when the engine was operated at retarded injection timing (IT) with TSME. It was found that TSME with 10% EGR inferred a drastic reduction of nitrogen oxide emissions by 30% and 37.7% over diesel and TSME, respectively, at full load.

Simulation Study to Evaluate the Hybrid Photovoltaic - Thermoelectric Energy Generation System with Heat Recovery Mechanism

ABSTRACT Electricity is one of the most common and widely used form of energy. It plays a vital role in improving the life of humans along with the economic growth of a country. Excessive heating of solar photovoltaic (PV) panels in a sunny hot weather is the main cause of power loss in solar PV system. The major purpose of this research is to minimize power loss in PV system due to increase in temperature of PV panels beyond standard operating conditions. Rise in the temperature of PV panels has severely affected the efficiency and voltage of the solar PV system. Thermo-electric Cells (TEC’s) are used to generate electricity by reutilizing the recovered waste heat from the PV panels due to rise in temperature. Seebeck effect demonstrates that the loss of electrical power due to heat is compensated by creating a temperature difference across TEC’s either through air-cooling or water-cooling mechanism. Electrical power generated from both the sources is common pooled. This hybrid power system is capable of feeding electricity to the grid. It is observed that the performance ratio of Solar PV system is 78.64% if this energy recovery technique is not utilized. Whereas with the addition of Water-Cooled Solar-TEC Hybrid system 53.5% energy is recovered and with Air Cooled Solar-TEC hybrid system 16.6% energy is recovered.