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Comparison of UV-AOPs (UV/H2O2, UV/PDS and UV/Chlorine) for TOrC removal from municipal wastewater effluent and optical surrogate model evaluation
摘要： UV-based advanced oxidation processes (AOPs) have been widely explored to remove organic contaminants from water streams. In this lab-scale study, the removal of 17 trace organic chemicals (TOrCs) by UV/H2O2, UV/PDS and UV/Chlorine was investigated at equimolar radical promoter concentrations in municipal wastewater. Direct comparison of the UV-AOPs was conducted with eight TOrCs being resistant to direct oxidation by H2O2, PDS and chlorine and revealed a general oxidation performance following the order of UV/Chlorine > UV/H2O2 ≈ UV/PDS while UV/PDS and UV/Chlorine exhibited higher compound selectivity than UV/H2O2. However, although oxidation performance of UV/Chlorine is outstanding in comparison of the three UV-AOPs, it has to be noted that oxidation byproduct (OBP) formation potential might be substantially higher during both UV/PDS and UV/Chlorine compared to UV/H2O2 which was not investigated in this study. Evaluating potential optical surrogates to predict trace organic chemical (TOrCs) removal in UV-AOPs, nine parameters were selected representing chromophore and fluorophore features of DOM including components derived by parallel factor analysis (PARAFAC) of excitation-emission matrices. UV absorbance (UVA), total fluorescence (TF) and the selected fluorescence peak P_IV revealed highest linear correlation coefficients and were therefore identified as surrogates representing underlying mechanistic reactions of each UV-AOP. As none of the surrogates directly reacted with UV irradiation, slopes of surrogate-indicator correlations for photo-susceptible TOrCs decreased towards higher oxidant dosages. Correlations for these compounds should therefore only be determined for a limited range of oxidant dosage.
关键词： UV/HOCl,surrogate model.,UV/H2O2,wastewater,UV/PDS,trace organic chemicals
更新于2025-09-23 15:23:52
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Estimation of Uncertainty in the Lateral Vibration Attenuation of a Beam with Piezo-Elastic Supports by Neural Networks
摘要： Quantification of uncertainty in technical systems is often based on surrogate models of corresponding simulation models. Usually, the underlying simulation model does not describe the reality perfectly, and consequently the surrogate model will be imperfect. In this article we propose an improved surrogate model of the vibration attenuation of a beam with shunted piezoelectric transducers. Therefore, experimentally observed and simulated variations in the vibration attenuation are combined in the model estimation process, by using multi–layer feedforward neural networks. Based on this improved surrogate model, we construct a density estimate of the maximal amplitude in the vibration attenuation. The density estimate is used to analyze the uncertainty in the vibration attenuation, resulting from manufacturing variations.
关键词： Density estimation,neural network,uncertainty quantification,imperfect model,surrogate model
更新于2025-09-23 15:22:29
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[IEEE 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Xiamen, China (2019.12.17-2019.12.20)] 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Triple-wide-band Linear to Circular Polarization Converters Using Bi-layered Metasurfaces
摘要： Conventional EM optimization aims to use fewest possible fine model evaluations to increase the speed of optimization. In this work, we propose to use a large number of fine model evaluations to achieve an overall speedup. A large number of fine model evaluations allows us to build a surrogate model valid in a large neighborhood. In the proposed technique, these valid surrogate models are used to achieve large and effective optimization updates, thereby resulting in fewer iterations of the optimization process. Valid surrogate models uses many fine model evaluations which are realized in parallel using hybrid distributed shared memory computing platforms. Parallel computation of large number of fine model evaluations reduces the major computational time required for constructing a surrogate model. Furthermore, we exploit trust region algorithms to guarantee convergence and to re-define the fine model evaluation range in each iteration of the proposed optimization algorithm. The proposed technique aims to increase the speed of gradient based EM optimization when no coarse model (e.g., empirical or equivalent circuits) is available. Three typical examples are used to illustrate the proposed technique.
关键词： parallel computation,electromagnetic (EM) optimization,Antennas,surrogate model,passive microwave circuits,trust region,gradient based optimization
更新于2025-09-23 15:19:57
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Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics
摘要： Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.
关键词： Bayesian network,GaAs solar cells,photovoltaics,neural network surrogate model,process optimization
更新于2025-09-23 15:19:57
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Systematic approach for determining optimal processing parameters to produce parts with high density in selective laser melting process
摘要： Relying on trial-and-error methods to determine the optimal processing parameters which maximize the density of parts produced using selective laser melting (SLM) technique is costly and time consuming. With a given SLM machine characteristics (e.g., laser power, scanning speed, laser spot size, and laser type), powder material, and powder size distribution, the present study proposes a more systematic strategy to reduce the time and cost in finding optimal parameters for producing high-density components. In the proposed approach, a circle packing design algorithm is employed to identify 48 representative combinations of the laser scanning speed and laser power for a commercial Nd:YAG SLM system. For each parameter combination, finite element heat transfer simulations are performed to calculate the melt pool dimensions and peak temperature for 316L stainless steel powder deposited on a 316L substrate. The simulated results are then used to train the artificial neural networks (ANNs). The trained ANNs are used to predict the melt pool dimensions and peak temperature for 3600 combinations of the laser power and laser speed in the design space. The resulting processing maps are then inspected to determine the particular parameter combinations which produce stable single scan tracks with good adhesion to the substrate and a peak temperature lower than the evaporation point of the SS 316L powder bed. Finally, the surface roughness measurements are employed to confirm the parameter settings which maximize the SLM component density. The experimental results show that the proposed approach results in a maximum component density of 99.97 %, an average component density of 99.89%, and a maximum standard deviation of 0.03%.
关键词： Additive manufacturing,Selective laser melting,Surface roughness,Artificial neural network,Surrogate model
更新于2025-09-12 10:27:22