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SSRN 2022年

作者： Shirzadi, Mohammadreza Fukasawa, Tomonori Fukui, Kunihiro Ishigami, Toru Chemical Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashi Hiroshima Hiroshima739-8527 Japan

A novel hybrid data-driven framework was introduced in this study for the prediction of fluid-particle dynamics of submicron particles under hydrodynamic and Brownian forces in fibrous filters. The distribution of flow parameters was predicted by a high-resolution flow surrogate model based on a deep convolutional neural network (CNN) coupled with a Lagrangian particle tracking method based on the discrete phase model (DPM). The CNN-DPM model was applied to a group of several nonuniformly distributed fibers to calculate filtration efficiency. For CNN training, ground-truth data were obtained using a two-dimensional computational fluid dynamics (CFD) model for laminar incompressible flow. A simulation speedup of three orders of magnitude was obtained by CNN for predicting the velocity components and static pressure distribution within an error range of [[EQUATION]] . The generalizability of CNN model was also confirmed for overlapped irregular fiber shapes and porosities out of the range of the training dataset with deviations in a range of [[EQUATION]] in comparison with CFD results. The collection efficiency by CNN-DPM for small particle diameters with [[EQUATION]] was overestimated by approximately 3% while agreeing well with CFD-DPM for larger particles. The errors of flow velocity predictions by CNN near the fibers added more randomness to the particle motion which was combined with the random Brownian motion of particles, consequently increasing the probability of the small particle hit number and filtration efficiency. The overall acceptable accuracy of the CNN-DPM was confirmed, which is suitable for fibrous filter design and optimization purposes that require thousands of simulations. © 2022, The Authors. All rights reserved.

关键词： Efficiency

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Performance Analysis and Optimization of Sweep Gas Membrane Distillation Module Using High-Resolution Computational Model and Machine Learning

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SSRN 2023年

作者： Shirzadi, Mohammadreza Li, Zhan Yoshioka, Tomohisa Matsuyama, Hideto Fukasawa, Tomonori Fukui, Kunihiro Ishigami, Toru Chemical Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Japan Research Center for Membrane and Film Technology Kobe University 1-1 Rokkodai Nada Kobe657-8501 Japan Graduate School of Science Technology and Innovation Kobe University 1-1 Rokkodai Nada Kobe657-8501 Japan Department of Chemical Science and Engineering Kobe University 1-1 Rokkodai Nada Kobe657-8501 Japan

Performance analysis of sweep-gas membrane distillation (SGMD) modules remains a challenging problem owing to the complex interaction and strong coupling between the flow parameters on the feed and permeate sides as well as the multiscale nature of gas permeation in the membrane pores. In this study, a framework for a practical optimization tool for SGMD was developed in which experimental data, numerical simulations, and machine learning (ML) techniques were systematically utilized. A high-resolution numerical simulation of the SGMD was introduced and validated for SGMD modules. The high-resolution numerical model was used to train an ML model with noticeable speedup and lower computational cost. The trained ML model was coupled with a multi-objective genetic algorithm to optimize the SGMD performance for a large-scale SGMD module with a novel and complex flow pattern. The SGMD, comprising five different modules with parallel sweep-gas patterns, showed interesting behavior in a sensitivity study based on modified Latin hypercube sampling. The sweep-gas temperature had the highest impact on the water permeation flux, which was due to the reheating of the feed flow as a result of the special configuration of the SGMD. Furthermore, the optimization solver successfully obtained the optimal design conditions for two different design scenarios by achieving a design target defined for an ammonia recovery ratio of 90% and an ammonia concentration of 25% at the sweep-gas outlet. The potential of the developed framework for practical engineering applications is highlighted. © 2023, The Authors. All rights reserved.

关键词： Ammonia

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Integrating chemical Information into Reinforcement Learning for Enhanced Molecular Geometry Optimization

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Journal of chemical theory and computation 2023年第23期19卷 8598-8609页

作者： Yu-Cheng Chang Yi-Pei Li Department of Chemical Engineering National Taiwan University No. 1 Sect. 4 Roosevelt Road Taipei 10617 Taiwan. Taiwan International Graduate Program on Sustainable Chemical Science and Technology (TIGP-SCST) Academia Sinica No. 128 Sec. 2 Academia Road Taipei 11529 Taiwan.

Geometry optimization is a crucial step in computational chemistry, and the efficiency of optimization algorithms plays a pivotal role in reducing computational costs. In this study, we introduce a novel reinforcement-learning-based optimizer that surpasses traditional methods in terms of efficiency. What sets our model apart is its ability to incorporate chemical information into the optimization process. By exploring different state representations that integrate gradients, displacements, primitive type labels, and additional chemical information from the SchNet model, our reinforcement learning optimizer achieves exceptional results. It demonstrates an average reduction of about 50% or more in optimization steps compared to the conventional optimization algorithms that we examined when dealing with challenging initial geometries. Moreover, the reinforcement learning optimizer exhibits promising transferability across various levels of theory, emphasizing its versatility and potential for enhancing molecular geometry optimization. This research highlights the significance of leveraging reinforcement learning algorithms to harness chemical knowledge, paving the way for future advancements in computational chemistry.

关键词： Algorithms Geometry optimization Mathematical methods Molecules Optimization

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Performance characterization of a novel compact dust collector with pleated filter cartridges

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Separation and Purification Technology 2023年 305卷

作者： Huang, A.N. Hsu, W.Y. Fukasawa, T. Ishigami, T. Fukui, K. Kuo, H.P. Green Technology Research Center Chang Gung University Taoyuan33302 Taiwan Department of Otolaryngology - Head & Neck Surgery Linkou Chang Gung Memorial Hospital Taoyuan33305 Taiwan Chemical Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Hiroshima Higashi-Hiroshima739-8527 Japan Department of Chemical Engineering National Taiwan University Taipei10617 Taiwan

A compact dust collector loaded with four pleated filter cartridges is numerically studied by Computational Fluid Dynamics (CFD). The performance of the dust collector is evaluated during filtration and during backwashing, and the predictions are compared with the experimental results. In the filtration operation, the upper right corner entry design effectively guides the airflow to the two sides of the four cartridges through the air opposite-side-wall impacting and rebounding. A relatively poor filtration is found at the right side of the left lower cartridge. The CFD predictions agree well with the experimental data in terms of showing better backwash cleaning performances of the lower cartridges and at the cartridge front positions. During the 50 ms pulse-jet backwashing, the difference between the maximum and minimum air mass flow rates through a total of 16 × 2 inject holes is less than 4.0%. The high pressure of 0.5 MPa draws the air flowing from the normal exit, which acts as the majority of the backwashing air (ca. 73.7%). The hydrostatic pressure acting on the four cartridges is greater than 1750 Pa in 80% of the pulse-jet cleaning time. © 2022 Elsevier B.V.

关键词： Computational fluid dynamics

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Optimal Planning of Hybrid Energy Storage Systems Using Curtailed Renewable Energy Through Deep Reinforcement Learning

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SSRN 2023年

作者： Kang, Dongju Kang, Doeun Hwangbo, Sumin Niaz, Haider Lee, Won Bo Liu, J. Jay Na, Jonggeol School of Chemical and Biological Engineering Seoul National University Gwanak-ro 1 Gwanak-gu Seoul08826 Korea Republic of Department of Chemical Engineering and Materials Science Ewha Womans University Seoul03760 Korea Republic of Graduate Program in System Health Science and Engineering Ewha Womans University Seoul03760 Korea Republic of Department of Chemical Engineering Pukyong National University Busan48513 Korea Republic of

Energy management systems (EMS) are becoming increasingly important in order to utilize the continuously growing curtailed renewable energy. Promising energy storage systems (ESS), such as batteries and green hydrogen should be employed to maximize the efficiency of energy stakeholders. However, optimal decision-making, i.e., planning the leveraging between different strategies, is confronted with the complexity and uncertainties of large-scale problems. Here, we propose a sophisticated deep reinforcement learning (DRL) methodology with a policy-based algorithm to realize the real-time optimal ESS planning under the curtailed renewable energy uncertainty. A quantitative performance comparison proved that the DRL agent outperforms the scenario-based stochastic optimization (SO) algorithm, even with a wide action and observation space. Owing to the uncertainty rejection capability of the DRL, we could confirm a robust performance, under a large uncertainty of the curtailed renewable energy, with a maximizing net profit and stable system. Action-mapping was performed for visually assessing the action taken by the DRL agent according to the state. The corresponding results confirmed that the DRL agent learns the way like what a human expert would do, suggesting reliable application of the proposed methodology. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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MOF-catalyzed hydroxyalkylation-alkylation reaction for the controlled synthesis of furan oligomers

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Applied Catalysis A: General 2024年 669卷

作者： Atayde, Eduardo C. Matsagar, Babasaheb M. Wang, Yu-Cheng Wu, Kevin C.-W. Molecular Science and Technology Program Taiwan International Graduate Program Academia Sinica No. 128 Sec. 2 Academia Road Taipei11529 Taiwan Department of Chemistry National Tsing Hua University No. 101 Sec. 2 Kuang-Fu Road Hsinchu30013 Taiwan Department of Chemical Engineering National Taiwan University No. 1 Sec. 2 Roosevelt Road Taipei10617 Taiwan Department of Chemical Engineering and Materials Science Yuan Ze University Taoyuan Chung-Li Taiwan

Hydroxyalkylation-alkylation (HAA) reaction is a type of C-C coupling technique utilized in the production of furan oligomers, which are potential biofuel precursors. In this study, we present the first application of an acidic MOF, Sulfated MOF-808, in catalyzing the HAA reactions of a series of furan oligomers with high yield and selectivity. Specifically, we focused on the optimization of the HAA between 5-methylfurfural (5-MFUR) and 2-methylfuran (2-MF) by varying different reaction conditions, which afforded a quantitative yield reaching 97% while preventing the self-condensation of 2-MF. The catalyst was also found to be recyclable and can be reused without significant loss in its activity. Lastly, we extended the substrate scope to include benzene-based electrophiles, thereby further proving the versatility of the Sulfated MOF-808 catalyst in the HAA reactions of a variety of starting materials. © 2023 Elsevier B.V.

关键词： Catalysis

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Cyclodextrin-modified layered double hydroxide thin-film nanocomposite desalination membrane for boron removal

Cyclodextrin-modified layered double hydroxide thin-film nan...

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2023 AIChE Annual Meeting

作者： Ee, Liang Ying Zhao, Qipeng Gao, Jie Lim, Chit Kai Xue, Kai Chin, Sze Yuet Li, Sam Fong Yau Chung, Tai-Shung Chen, Shing Bor Department of Chemical and Biomolecular Engineering College of Design and Engineering National University of Singapore 4 Engineering Drive 4 Singapore117585 Singapore Department of Chemistry College of Humanities and Sciences National University of Singapore 3 Science Drive 3 Singapore117543 Singapore Chemical Engineering Program Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Centre of High Field NMR Spectroscopy and Imaging School of Physical and Mathematical Sciences College of Science Nanyang Technological University 21 Nanyang Link Singapore637371 Singapore NUS Environmental Research Institute National University of Singapore T-Lab Building 5A Engineering Drive 1 Singapore117411 Singapore Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei106335 Taiwan

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Acid-catalyzed hydrothermal treatment of sewage sludge: effects of reaction temperature and acid concentration on the production of hydrolysis by-products

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Biomass Conversion and Biorefinery 2023年第9期13卷 7533-7546页

作者： Pham, Van Toi Guan, Chung-Yu Han, Po-Chun Matsagar, Babasaheb M. Wu, Kevin C. W. Ahamad, Tansir Chang, Ching-Yuan Yu, Chang-Ping Graduate Institute of Environmental Engineering National Taiwan University Taipei106 Taiwan Faculty of Environmental Engineering National University of Civil Engineering 55 Giai Phong Hai Ba Trung Hanoi100000 Viet Nam Department of Environmental Engineering National Ilan University Yilan260 Taiwan Ph.D. Program of Green Materials and Precision Devices National Taiwan University Taipei10617 Taiwan Department of Chemical Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei10617 Taiwan Department of Chemistry College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia Water Innovation Low Carbon and Environmental Sustainability Research Center National Taiwan University Taipei106 Taiwan

Sewage sludge (SS) is an organic biomass from wastewater treatment plants, but the use of SS to produce bio-based chemicals under acid-catalyzed hydrolysis (ACH) processes is not well investigated. In this study, ACH processes were performed at 120, 150, and 180 °C using sulfuric acid (H2SO4) with concentrations of 0–0.5 M within a reaction time of 90–180 min to produce bio-based chemicals from SS. The results showed that the reaction temperature, reaction time, and concentrations of H2SO4 affected the yields of sugars, levulinic acid (LA), and 5-hydroxymethylfurfural (HMF). The highest xylose and glucose yields were 7.69 mol% and 5.22 mol% at 120 °C with 0.5 M H2SO4 during the 180 min of reaction time, respectively. Besides, under the ACH process at 180 °C and 180 min, the yield of LA reached a maximum value of 0.48 mol% at 0.5 M H2SO4, and the highest yield of HMF was 1.66 mol% at 0.1 M H2SO4. The data obtained also indicated that the ACH process led to an increase in soluble chemical oxygen demand, dissolved organic carbon, and total dissolved nitrogen. Fluorescence excitation-emission matrix of dissolved organic matter and thermogravimetric analysis of the treated SS further supported the enhanced solubilization of SS after ACH process, especially under higher reaction temperature and concentrations of H2SO4. Although the lower yields of bio-based chemicals might restrict the downstream recovery of the target products, the largely improved SS solubilization by ACH suggests that acid-catalyzed hydrothermal hydrolysis process has potential as an alternative pretreatment method of SS. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sewage sludge

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Fabrication of copper- and silver-coated triply periodic minimal surface structures through 3D printing, electroplating and electroless processes

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Surfaces and Interfaces 2025年 69卷

作者： Melo, Leonardo G.T.C. de Menezes, Frederico Duarte Peixoto da Costa, José Ângelo Silva, Karla de Morais Chiang, Yi Wai Santos, Rafael M. School of Engineering University of Guelph Guelph N1G 2W1 ON Canada Graduate Program in Chemical Engineering (PPGEQ) Federal University of Pernambuco Pernambuco Recife 50740-540 Brazil Department of Higher Education Courses (DACS) Federal Institute of Education Science and Technology of Pernambuco Pernambuco Recife 50740-545 Brazil

This research presents an innovative approach that offers an accessible method for directly coating metallic layers onto 3D-printed parts for point-of-use (POU) water treatment applications. This provides more control over the final geometry while concentrating the disinfecting agent on the surface—rather than using a massive piece—ensuring effective treatment while reducing material costs and minimizing waste of expensive metallic resources. TPMS (Triply Periodic Minimal Surface) infill meshes were generated via a Schwarz P function coded in Python and then fabricated with a commercial-grade PLA-based composite filament with conductive properties. This made the electrochemical deposition of copper ions onto the printed infill surfaces possible via a DC-powered electroplating process. Initially, four different approaches were tested under four different electrolyte bath concentrations—sixteen experiments (four under static conditions and twelve under agitation)—with the outcomes evaluated through visual inspection, digital microscopy, and scanning electron microscopy (SEM), which was also used as a tool for quantifying the fraction of the surface area with metal deposition. Energy-dispersive spectroscopy (EDS) and ImageJ-based surface quantification were employed to validate coating composition and coverage. The optimal electroplating conditions were then refined by incorporating a postprint annealing treatment and reconfiguring the disposition of the components to enhance coating uniformity. The deposition process was monitored by measuring the electric current, allowing for the quantification of copper mass deposited through Faraday's law of electrolysis. Finally, a silver-over-copper (Ag@Cu) deposition was successfully performed, with the resulting silver mass assessed via inductively coupled plasma‒mass spectrometry (ICP‒MS). Application tests demonstrated >99 % disinfection efficiency against Escherichia coli within a calculated exposure of minutes, with statis

关键词： Additive manufacturing Copper Disinfection Electroplating Process intensification Purification TPMS

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Investigating the impact of metal ion variations in terephthalate metal–organic frameworks on the organic solvent nanofiltration performance of mixed matrix membranes

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Journal of Membrane Science 2024年 700卷

作者： Ajebe, Eyasu Gebrie Hu, Chien Chieh Lugito, Graecia Hu, Chun-Po Hung, Wei-Song Lee, Kueir-Rarn Lai, Juin-Yih Graduate Institute of Applied Science and Technology Advanced Membrane Materials Research Center National Taiwan University of Science and Technology Taipei Taiwan Chemical Engineering Study Program Faculty of Industrial Technology Institut Teknologi Bandung West Java Indonesia Nanyang Technological University 1 Cleantech Loop Singapore R&D Center for Membrane Technology Chung Yuan University Chungli Taoyuan Taiwan Department of Chemical Engineering and Materials Science Yuan Ze University Chungli Taoyuan Taiwan

Nano-fillers integrated into mixed matrix membranes (MMMs) present promising alternatives for organic solvent nanofiltration (OSN). In this work, our objective is to enhance OSN performance by incorporating terephthalate metal-organic frameworks (M-BDC, where M = Zr or Cu) into a P84 matrix to fabricate M-BDC/P84 composite MMMs. This work focuses on the influence of metal cluster variation on particle size, pore size distribution, and specific surface area of M-BDC, while systematically investigating the impact of M-BDC addition on OSN performance and underlying separation mechanisms. The synthesized membranes underwent comprehensive characterization using SEM, XRD, ATR-FTIR, contact angle, zeta potential, and thermogravimetric analysis. Significantly, all MMMs exhibited superior permeance and methyl blue (MB) rejection compared to P84 membranes. This enhancement is attributed to the improved interfacial contact between M-BDC and P84, as well as their effective dispersion within the membrane matrix. Interestingly, comparative analysis between Cu-BDC/P84 and Zr-BDC/P84 MMMs revealed the latter's superior permeance, attributed to its larger pore size and surface area. The resulting MMMs demonstrated outstanding MB rejection (>96.7%) with ethanol permeance of 21.5 and 19.0 LMH/Bar for Zr-BDC/P84 and Cu-BDC/P84 MMMs, respectively. Furthermore, the stable performance of M-BDC-based MMMs during prolonged MB/ethanol operation underscores their potential significance in the field of OSN, providing valuable insights for future membrane design and application. © 2024 Elsevier B.V.

关键词： Organometallics

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