https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/147 Tue, 21 Jul 2026 10:02:19 GMT 2026-07-21T10:02:19Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/169983 Title: Bayesian Uncertainty Estimation for Deep Learning Inversion of Electromagnetic Data Authors: Oh, S.; Byun, Joong moo Abstract: With the recent progress in deep learning (DL), DL inversion, which reconstructs subsurface physical properties from geophysical data using DL techniques, has been widely applied. For decision-making and risk management related to the application of DL inversion, assessing the reliability of a prediction is essential, and such assessment can be achieved through uncertainty estimation. However, most geophysical studies have focused on deterministic prediction that does not provide uncertainty estimates. In this letter, a practical uncertainty estimation method based on the Bayesian framework is introduced for DL inversion of electromagnetic data. More specifically, iterative estimation by a convolutional neural network with dropout provides epistemic and aleatoric uncertainties as well as a resistivity model. Using numerical tests, we observed that aleatoric uncertainty indicates the nonuniqueness of the inverse problem, showing which parts of the resistivity model are less sensitive to the data. In addition, we proposed an empirical criterion for determining whether new data are similar to training data using estimated epistemic and aleatoric uncertainties. Based on this criterion, out-of-distribution data were identified; these data showed larger data misfit, indicating that the predictions would be unreliable. The applicability of uncertainty estimation and the empirical criterion derived from uncertainties were demonstrated using field data. Bayesian uncertainty estimation and the criterion established here may help to achieve more reliable prediction via DL inversion. https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/169983 https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/169985 Title: Biomethane enhancement via plastic carriers in anaerobic co-digestion of agricultural wastes Authors: Faisal, Shah; Salama, El-Sayed; Hassan, Sedky H. A.; Jeon, Byong Hun; Li, Xiangkai Abstract: Two types of plastic carriers low-density polyethylene (LDPET) and high-density polyethylene (HDPET) were used as a support material for biofilm formation during anaerobic co-digestion of agricultural wastes. LDPET and HDPET were added separately to different reactors containing binary substrates: corn straw and cauliflower leaves (G 1), corn straw and cow dung (G 2), while ternary substrates corn straw, cauliflower leaves, and cow dung were used in G 3. Reactors containing either HDPET or LDPET carriers supported the enhancement of biogas and biomethane. Maximum daily biomethane (333.43 and 368.35 mL/day) was achieved after HDPET addition to G1 and G2 at day 10 and 12, respectively. The accumulative biomethane were significantly enhanced (p < 0.05) by 17.14% and 23.52%, compared with reactors having LDPET carriers 11.89% and 5.53%, respectively. HDPET addition to ternary substrates (G 3) resulted in highest biomethane production (31.61%) and total solids (31.70%) and volatile solid (61.63%) removal. The major short-chain fatty acids (SCFAs) detected in all groups were acetic acid (4-5 g/L) and propionic acid (2-3 g/L), and their conversion to biomethane was the highest with HDPET. Scanning electron microscopy (SEM) analysis of the supporting materials showed that the plastic carriers support the biofilm formation especially in the case of HDPET. This study demonstrated that addition of cost-effective plastic carrier (HDPET) to anaerobic digestion system supported the formation of biofilm, leading to significantly increase in substrate utilization and biomethane production. https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/169985 https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/217633 Title: Hydrogen-rich producer gas from air- and steam-blown co-gasification of waste polypropylene pyrolysis oil and cashew nut shell in a two-stage downdraft gasifier Authors: Yoon, Joo-Hyeong; Kim, Jong-Su; Kwon, Eilhann E.; Jeong, Soo-Hwa Abstract: The co-gasification of waste polypropylene pyrolysis oil (WPPO) and cashew nut shell (CNS) was investigated in a two-stage downdraft gasifier to produce hydrogen-rich, low-tar gas. Experiments were conducted under air (ER = 0.3) and steam (S/C = 2.5) conditions, with selected runs incorporating activated carbon (AC) in the secondary reactor. Steam gasification achieved a maximum hydrogen concentration of 77.9 vol%. The WPPO/CNS mixing ratio affected performance, with the 1:1 blend increasing CGE from 70.3 to 89.7% to 108.9% and CCE from 68.2 to 71.5% to 83.9%, along with enhanced gas yield. The use of AC reduced gas-phase tar, and a downstream dried AC impinger further decreased tar concentration to 0.45 mg/Nm3. In contrast, the composition of condensed tar showed a shift toward heavier species during steam gasification. GC–MS analysis at a WPPO/CNS ratio of 1:1 indicated that steam gasification promoted the formation of heavier PAHs due to enhanced thermal cracking and subsequent polymerization of aromatic intermediates. Fri, 01 Jan 2027 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/217633 2027-01-01T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/218037 Title: Key factor governing transient maldistribution in proton exchange membrane fuel cells: A numerical study on decoupling modeling framework and sorption rate asymmetry Authors: Lee, Sumin; Sohn, Young-Jun; Choi, Yoon-Young; Lim, In Seop; Um, Sukkee; Oh, Hwanyeong Abstract: Accurate transient modeling of proton exchange membrane fuel cells (PEMFCs) requires careful treatment of ionomer water sorption and desorption kinetics. To address uncertainties in modeling approaches, this study utilizes a transient, three-dimensional, two-phase, non-isothermal model under 50% relative humidity conditions. We first compared widely used representative sorption-rate models, which differ in modeling frameworks (equation-based vs. constant-rate) and sorption-rate coefficient symmetry (symmetric vs. asymmetric). Their intertwined characteristics were then systematically decoupled to assess the isolated effect of each factor on transient dynamics. Within the load-step protocols and operating conditions investigated in this study, the modeling framework has a secondary influence on predicted transient behaviors and spatial distributions, as a constant-rate model with matched time-averaged coefficients captures the main trends of the equation-based results. In contrast, sorption-rate coefficient symmetry plays a decisive role. Desorption-dominant asymmetry in the sorption-rate coefficients causes severe local dehydration and a redistribution of current density during galvanostatic transients. Among water phases, the ionomer water content shows the greatest sensitivity to the sorption-rate model, with the most direct link to the current density distribution. This 3D analysis provides guidance for future modeling by showing how sorption-rate model selection and coefficient parameterization influence the prediction of transient performance and spatial nonuniformity, which are not observable in lower-dimensional models. Fri, 01 Jan 2027 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/218037 2027-01-01T00:00:00Z