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Integrated Bi2O3 nanostructure modified with Au nanoparticles for enhanced photocatalytic activity under visible light irradiation

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Progress in Natural Science:Materials International 2017年第3期27卷 289-296页

作者： hankwon lim Sher Bahadur Rawal Department of Advanced Materials and Chemical Engineering Catholic University of Daegu

An integrated Bi2O3（i-Bi2O3） nanostructure with a particle size 10 nm inducing agglomerated structure were synthesized by dissolving bismuth nitrate pentahydrate in diethylene glycol at 180 ℃ with post heat *** prepared i-Bi2O3nanostructures were employed for the construction of Au/i-Bi2O3composite system and characterized by X-ray diffraction pattern,UV-visible diffuse reflectance spectroscopy（DRS）,and transmission electron microscopy,X-ray photoemission spectroscopy（XPS） and Energy dispersive X-ray spectroscopy（EDS）.The i-Bi2O3nanostructure and Au/i-Bi2O3composite system were found to exhibit high photocatalytic activity than commercial Bi2O3in decomposing salicylic acid under visible light *** high catalytic activity of i-Bi2O3nanostructure was deduced to be caused by charge separation facilitated by electron hopping between the particles within the integrated structure and space-charge separation between i-Bi2O3and *** charge separation behavior in i-Bi2O3nanostructure was further bolstered by comparing the *** radical produced in the solution with i-Bi2O3nanostructure,commercial Bi2O3and Au/i-Bi2O3composite which readily react with 1,4-terephthalic acid（TA） inducing 2-hydroxy terephthalic acid（TAOH） that shows unique fluorescence peak at 426 *** space-charge separation between i-Bi2O3and An was confirmed by measuring the electron spin resonance（ESR） spectra.

关键词： Integrated Bi2O3 nanostructure Organic pollutant mineralization Photocatalyst Salicylic Acid(SA) Visible light

Process simulation and economic analysis of reactor systems for perfluorinated compounds abatement without HF effluent

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Frontiers of Chemical Science and Engineering 2016年第4期10卷 526-533页

作者： Boreum Lee Sunggeun Lee Ho Young Jung Shin-Kun Ryi hankwon lim Department of Advanced Materials and Chemical Engineering Catholic University of Daegu Gyeongbuk 38430 Korea Department of Environment and Energy Engineering C-honnam National University-Gwangju 61186 Korea Korea Institute of Energy Research Daejeon 34129 Korea

New and efficient proposed to treat perfluorinated reactor systems were compounds via catalytic decomposition. One system has a single reactor （S-1）, and another has a series of reactors （S-2）. Both systems are capable of producing a valuable CaF2 and eliminating toxic HF effluent and their feasibility was studied at various temperatures with a commercial process simulator, Aspen HYSYS. They are better than the conventional system, and S-2 is better than S-1 in terms of CaF2 production, a required heat for the system, natural gas usage and CO2 emissions in a boiler, and energy consumption. Based on process simulation results, preliminary economic analysis shows that cost savings of 12.37% and 13.55% were obtained in S-2 at 589.6 and 621.4℃compared to S-1 at 700 and 750 ℃, respectively, for the same amount of CaF2 production.

关键词： perfluorinated compounds (PFCs) CF4 pro-cess simulation economic analysis

Machine learning based fault detection and state of health estimation of proton exchange membrane fuel cells

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Energy and AI 2023年第2期12卷 178-186页

作者： Vijay Mohan Nagulapati SShiva Kumar Vimalesh Annadurai hankwon lim School of Energy and Chemical Engineering Ulsan National Institute of Science and Technology50 UNIST-gilEonyang-eupUlju-gunUlsan44919Republic of Korea Carbon Neutrality Demonstration and Research Center Ulsan National Institute of Science and Technology50 UNIST-gilEonyang-eupUlju-gunUlsan44919Republic of Korea Department of Mechatronics Rajalakshmi Engineering CollegeChennaiTamilnaduIndia Graduate School of Carbon Neutrality Ulsan National Institute of Science and Technology50 UNIST-gilEonyang-eupUlju-gunUlsan44919Republic of Korea

In fuel cells, chemical energy is directly converted into heat and electricity without any emissions which makes them an attractive substitute for various energy needs. Fuel cells have high energy conversion ratio and highpower densities which make them suitable for automotive applications. However, these fuel cell systems suffer with low reliability and durability as system components develop faults during operation resulting in degradation and diminished system performance. In this context, fault detection and fault mitigation strategies are being extensively developed. Diagnostic approaches like electrochemical impedance spectroscopy, cyclic voltammetry, and galvanostatic analysis offer a truthful representation of the State of Health (SOH) of the fuel cell. However, these approaches are intrusive and require pausing the operation of the fuel cell effecting its integrity. Machine learning based fault detection and SOH estimation is a non-intrusive approach where a mapping function is established between the indicators and SOH. The SOH of a fuel cell can be correlated to the patterns in sensor signals or indicators. Indicators that influence SOH are cell voltages, current density distribution, impedance spectra, acoustic emission and magnetic fields. Developing an accurate fault detection and state estimation technique through data driven machine learning approaches will allow corrective measures to avoid irreversible faults and improve the reliability and durability of fuel cells.

关键词： PEM fuel cell Data driven prognostics Fault detection Dynamic load test Gaussian process regression Support vector machine Artificial neural networks

Prognostics and health management of alkaline water electrolyzer: Techno-economic analysis considering replacement moment

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Energy and AI 2023年第3期13卷 160-168页

作者： Hyunjun Lee Jiwon Gu Boreum Lee Hyun-Seok Cho hankwon lim School of Energy and Chemical Engineering Ulsan National Institute of Science and Technology50 UNIST-gilEonyang-eupUlju-gunUlsan 44919Republic of Korea Hydrogen Research Department Future Energy Research DivisionKorea Institute of Energy Research152 Gajeong-roYuseongDaejeonRepublic of Korea Graduate School of Carbon Neutrality Ulsan National Institute of Science and Technology50 UNIST-gilEonyang-eupUlju-gunUlsan 44919Republic of Korea Carbon Neutrality Demonstration and Research Center Ulsan National Institute of Science and Technology50 UNIST-gilEonyang-eupUlju-gunUlsan 44919Republic of Korea

Recently,considerable attention has been paid to the installation of renewable energy capacity to mitigate global CO_(2) emissions.H_(2) produced using water electrolysis and renewable energy is regarded as a clean energy carrier,generating electricity without CO_(2) emissions,called‘Green H 2’.In this paper,a prognostics and health man-agement model for an alkaline water electrolyzer was proposed to predict the load voltage on the electrolyzer to obtain the state of health *** prognostics and health management model was developed by training historical operating data via machine learning models,support vector machine and gaussian process regression,showing the root mean square error of 1.28×10^(−3) and 8.03×10^(−6).In addition,a techno-economic analysis was performed for a green H_(2) production system,composed of 1 MW of photovoltaic plant and 1 MW of alkaline water electrolyzer,to provide economic insights and feasibility of the system.A levelized cost of H_(2) of$6.89 kgH_(2)−1 was calculated and the potential to reach the levelized cost of H_(2) from steam methane reforming with carbon capture and storage was shown by considering the learning rate of the photovoltaic module and ***,the replacement of the alkaline water electrolyzer at around 10 years was preferred to increase the net present value from the green H_(2) production system when capital expenditure and replacement cost are low enough.

关键词： Green H_(2) Alkaline water electrolysis Prognostics and health management Voltage degradation Techno-economic analysis Replacement

A novel combined multi-battery dataset based approach for enhanced prediction accuracy of data driven prognostic models in capacity estimation of lithium ion batteries

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Energy and AI 2021年第3期5卷 141-149页

作者： Vijay Mohan Nagulapati Hyunjun Lee DaWoon Jung SalaiSargunan S Paramanantham Boris Brigljevic Yunseok Choi hankwon lim School of Energy and Chemical Engineering UNISTUlsanRepublic of Korea Xynergypower Corp Gyeonggi-doRepublic of Korea Department of Energy Engineering UNISTUlsanRepublic of Korea

To ensure smooth and reliable operations of battery systems,reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost ***,battery degradation is a complex challenge involving many electrochemical reactions at anode,separator,cathode and electrolyte/electrode ***,there is significant effect of the operating conditions on the battery *** machine learning tech-niques have been applied to estimate the capacity and State of Health of lithium ion batteries to ensure reliable operation and timely *** this paper,we study the Gaussian Process Regression(GPR)and Support Vector Machine(SVM)model-based approaches in estimating the capacity and State of Health of ***-tery capacity and State of Health estimations are carried out using GPR and SVM models and the predictions comparatively studied for accuracy based on RMSE *** prediction accuracy is further compared with re-spect to single sensor and multi sensor ***,a combined multi battery data set model is used to improve the prediction *** the data of multiple batteries with similar operating conditions for training a model resulted in higher prediction accuracy.

关键词： Capacity estimation State of health Gaussian process regression Support vector machine Lithium ion battery

Sorption enhanced catalytic CF4 hydrolysis with a three-stage catalyst-adsorbent reactor

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Frontiers of Chemical Science and Engineering 2017年第4期11卷 537-544页

作者： Han, Jae-Yun Kim, Chang-Hyun Nam, Sung-Chan Ryi, Shin-Kun Lee, Boreum lim, hankwon Jung, Ho-Young Lee, Kwan-Young Korea Inst Energy Res Adv Mat & Devices Lab Daejeon 34129 South Korea Catholic Univ Daegu Dept Adv Mat & Chem Engn Gyeongbuk 38430 South Korea Chonnam Natl Univ Dept Environm & Energy Engn Gwangju 61186 South Korea Korea Univ Dept Chem & Biol Engn Seoul 136701 South Korea

In this study, we developed a three-stage catalyst-adsorbent reactor for the catalytic hydrolysis of CF4. Each stage is composed of a catalyst bed followed by an adsorbent bed using Ca（OH）2 to remove HF. The three stages are connected in series to enhance the hydrolysis of CF4 and eliminate a scrubber to dissolve HF in water at the same time. With a 10 wt-% Ce/AlaO3 catalyst prepared by the incipient wetness method using boehmite and a granular calcium hydroxide as an adsorbent, the CF4 conversion in our proposed reactor was 7%-23% higher than that in a conventional single-bed catalytic reactor in the temperature range of 923-1023 K. In addition, experimental and numerical simulation （Aspen HYSYS） results showed a reasonable trend of increased CF4 conversion with the adsorbent added and these results can be used as a useful design guideline for our newly proposed multistage reactor system.

关键词： PFCs catalytic hydrolysis calcium hydro-xide sorption enhanced process simulation