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Conversion of carbon dioxide to value added products through anaerobic fermentation and electro fermentation: A comparative approach

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第34期47卷 15442-15455页

作者： Velvizhi, G. Sarkar, Omprakash Rovira-Alsina, Laura Puig, Sebastia Mohan, S. Venkata [a]CO Research and Green Technology Centre Vellore Institute of Technology Vellore 632014 India [b]LEQUiA. Institute of the Environment University of Girona Campus Montilivi C/Maria Aurelia Capmany 69 E-17003 Girona Spain [c]Bioengineering and Environmental Sciences Lab Department of Energy and Environmental Engineering (DEEE) CSIR-Indian Institute of Chemical Technology (CSIR-IICT) Hyderabad 500007 India [d]Biochemical Process Engineering Division of Chemical Engineering Department of Civil Environmental and Natural Resources Engineering Luleå University of Technology 971-87 Luleå Sweden

Increasing carbon footprint alters the carbon balance in nature, thereby worsening global climate change. The conversion of carbon dioxide (CO2) into value-added products through biological routes is the pathway of the future because of its ecological and sustainable character. The present study evaluated the conversion of CO2 into short-chain fatty acids (SCFA)/volatile fatty acids (VFA) and methane using four experimental conditions (R1-R4). The experimental conditions are R1 was an anaerobic fermenter (AF) operated as control, R2 consisted of an AF with electrodes operated in open circuit, R3 was an AF with electrodes operated in a closed circuit with 100 omega as load and R4 was an electro-fermentation reactor with an applied cathodic potential of-0.8 V vs. Ag/AgCl. The results were assessed in terms of production of SCFA, methane, current density and inorganic carbon reduction. Electro-fermentation (R4) setup achieved the highest production of SCFA (2050 mg/L) and methane (41.2 mL/day) compared to other reactors. R3 reported 1800 mg/L and 24 mL/day, R2 reported 1560 mg/L and 15 mL/day and R1 reported 1430 mg/L and 10 mL/day of methane and SCFA production. The study-inferred that electro-fermentation could effectively catalyse the biochemical reactions and enhance the conversion of CO2 to organic compounds in a sustainable manner. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Biocatalyst Biofuels Bioelectrochemical systems CO2 conversion Electro-fermentation

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ENERGY MANAGEMENT OF GREEN HYDROGEN-BASED POWER-TO-POWER GENERATION SYSTEMS 23

ENERGY MANAGEMENT OF GREEN HYDROGEN-BASED POWER-TO-POWER GEN...

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23rd World Hydrogen Energy Conference: Bridging Continents by H2, WHEC 2022

作者： Kafetzis, Alexandros Chatzigavriel, Stella Seferlis, Panos Panopoulos, Kyriakos Aristotle University of Thessaloniki Department of Mechanical Engineering Thessaloniki Greece Horizon S.A. Serres Greece Centre for Research and Technology Hellas Chemical Process and Energy Resources Institute Thessaloniki Greece

ISBN: (纸本)9786250008430

Post-2030 decarbonization targets, enable energy storage as a key element in the energy mix of future sustainable energy systems in Europe. Storage is supported by the high penetration of renewable energy sources (RES) in the European energy landscape. One of the most efficient, zero emission fuels is hydrogen produced by RES via electrolysis. Hydrogen production and storage is combined with fuel cells and batteries to comprise an integrated Power-to-Power (P2P) system. Such systems offer efficient, reliable, and cost-effective energy delivery that is quick in release and of high density, aspects that are necessary to counter the RES power intermittency. Stochastic RES profiles, dynamic response of sub-components, and multiple decisions inside a constantly changing environment are major challenges in P2P systems' operation. The Energy Management Strategy (EMS) developed in this work and employed in the control framework of the P2P systems ensures their efficient operation. The EMS supervises the optimal distribution of power production and consumption. A series of applications of pilot P2P systems with different characteristics showcase the system's performance, resilience and replicability potential through certain key performance indicators (KPIs). © 2022 Proceedings of WHEC 2022 - 23rd World Hydrogen Energy Conference: Bridging Continents by H2. All rights reserved.

关键词： Renewable energy resources

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Two-dimensional Hilbert-Huang transform-based thermographic data processing for non-destructive material defect detection

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Quantitative InfraRed Thermography Journal 2024年

作者： Hsiao, Tung-Yu Sfarra, Stefano Liu, Yi Yao, Yuan Department of Chemical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila Italy Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China

Previous research has established the Hilbert-Huang transform (HHT) as a powerful tool for processing and analysing one-dimensional time series signals. Its extension into multi-dimensional Euclidean spaces further demonstrates its versatility. In this study, we leverage a two-dimensional (2D) HHT to enhance non-destructive testing, utilising active infrared thermographic data to improve the detection of material defects. The proposed methodology incorporates a 2D HHT, which synergises multidimensional ensemble empirical mode decomposition with the Riesz transform (RT). This combination is adept at uncovering both global (i.e. intrinsic mode functions) and local (i.e. phase angle and spatial frequency) information. In particular, RT is integrated with a Gaussian filter, which allows for a more thorough analysis of the inherent characteristics of defects through the monogenic signal produced by RT. The experimental application of this method on a mosaic sample, intentionally embedded with defects, has yielded promising results. The approach effectively distinguishes critical defect signals from the surroundings, leading to more accurate and clearer results. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Hilbert-Huang transform

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Event-Triggered SMC for Networked Markov Jumping Systems With Channel Fading and Applications: Genetic Algorithm

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IEEE Transactions on Cybernetics 2023年第10期53卷 6503-6515页

作者： Qi, Wenhai Zhang, Ning Zong, Guangdeng Su, Shun-Feng Yan, Huaicheng Yeh, Ruey-Huei Qufu Normal University School of Engineering Rizhao276826 China Chengdu University School of Information Science and Engineering Chengdu610106 China Tiangong University School of Control Science and Engineering Tianjin300387 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education Shanghai200237 China National Taiwan University of Science and Technology Department of Industrial Management Taipei106 Taiwan

The event-triggered sliding-mode control (SMC) for discrete-time networked Markov jumping systems (MJSs) with channel fading is investigated by means of a genetic algorithm. In order to reduce resource consumption in the transmission process, an event-triggered protocol is adopted for networked MJSs. A key feature is that the signal transmission is inevitably affected by fading phenomenon due to delay, random noise, and amplitude attenuation in a networked environment. With the aid of a common sliding surface, an event-triggered SMC law is designed by adjusting the system network mode. Under the framework of stochastic Lyapunov stability, sufficient conditions are constructed to ensure the mean-square stability of the closed-loop networked MJSs, and the sliding region is reached around the specified sliding surface. Moreover, based on the iteration optimizing accessibility of objective function, an effective SMC approach under genetic algorithm is proposed to minimize the convergence region around the sliding surface. Finally, the effectiveness of the proposed method is proved by the F-404 aircraft model. © 2013 IEEE.

关键词： Markov processes

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Functional meniscus reconstruction with biological and biomechanical heterogeneities through topological self-induction of stem cells

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Bioactive Materials 2024年第6期36卷 358-375页

作者： Mingze Du Kangze Liu Huinan Lai Jin Qian Liya Ai Jiying Zhang Jun Yin Dong Jiang Department of Sports Medicine Peking University Third HospitalInstitute of Sports Medicine of Peking UniversityBeijing Key Laboratory of Sports InjuriesEngineering Research Center of Sports Trauma Treatment Technology and DevicesMinistry of EducationBeijingChina The State Key Laboratory of Fluid Power Transmission and Control Systems Key Laboratory of 3D Printing Process and Equipment of Zhejiang ProvinceSchool of Mechanical EngineeringZhejiang UniversityZhejiang310058China Department of Engineering Mechanics Key Laboratory of Soft Machines and Smart Devices of Zhejiang ProvinceZhejiang UniversityZhejiang310058China School of Chemistry Chemical Engineering and BiotechnologyNanyang Technological University639798Singapore

Meniscus injury is one of the most common sports injuries within the knee joint,which is also a crucial pathogenic factor for osteoarthritis(OA).The current meniscus substitution products are far from able to restore meniscal biofunctions due to the inability to reconstruct the gradient heterogeneity of natural meniscus from biological and biomechanical ***,inspired by the topology self-induced effect and native meniscus microstructure,we present an innovative tissue-engineered meniscus(TEM)with a unique gradient-sized diamond-pored microstructure(GSDP-TEM)through dual-stage temperature control 3D-printing system based on the mechanical/biocompatibility compatible high M_(w) poly(ε-caprolactone)(PCL).Biologically,the unique gradient microtopology allows the seeded mesenchymal stem cells with spatially heterogeneous differentiation,triggering gradient transition of the extracellular matrix(ECM)from the inside ***,GSDP-TEM presents excellent circumferential tensile modulus and load transmission ability similar to the natural *** implantation in rabbit knee,GSDP-TEM induces the regeneration of biomimetic heterogeneous neomeniscus and efficiently alleviates joint *** study provides an innovative strategy for functional meniscus *** self-induced cell differentiation and biomechanical property also provides a simple and effective solution for other complex heterogeneous structure reconstructions in the human body and possesses high clinical translational potential.

关键词： Tissue-engineered meniscus Functional reconstruction Mesenchymal stem cells Heterogeneity Topology structure

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Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Fu, Lijiang Xia, Qian Tang, Hao Chen, Junqing Tan, Jinglu Guo, Ya Jiangnan University Ministry of Education Key Laboratory of Advanced Process Control for Light Industry Wuxi China Changshu Institute of Technology School of Electrical Engineering and Automation Changshu China University of Missouri Department of Chemical and Biomedical Engineering ColumbiaMO United States

Long-term reconstructed solar-induced chlorophyll fluorescence (SIF) derived from raw gridded SIF has been used for the estimation of gross primary production (GPP), but the robustness of the spatial relationship may vary from location to location. We examined the often-used linear relationship between GPP and SIF in terms of R2 values for varied locations globally using three GPP datasets (FLUXCOM, VPM, PML) and three long-term reconstructed monthly SIF datasets (CSIF, SIF005, and RTSIF). The results show that the R2 value is a concave function of vegetation greenness level (NDVI) on an annual or seasonal basis. The average R2 is over 0.8 in areas where the annual average NDVI is in the range of 0.4 to 0.6, whereas the R2 is much lower where the annual average NDVI is less than 0.2 or greater than 0.8. Prediction of GPP or SIF by three methods from five major environmental variables revealed greater uncertainties in GPP and/or SIF at low or high greenness levels as an apparent cause of the low R2. The results offer useful insights into how global GPP may be effectively estimated from multi-satellite measured SIF. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Optical correlation

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Variational Adversarial Active Learning Assisted process Soft Sensor Method 4

Variational Adversarial Active Learning Assisted Process Sof...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Dai, Yun Zhang, Ying Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Taizhou Vocational and Technical College Department of Mechanical and Electrical Engineering Taizhou318000 China National Tsing Hua University Department of Chemical Engineering Hsinchu30013 Taiwan

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Soft sensor methods have been widely applied in process industries to predict key quality variables that cannot be measured online. However, labeled samples to construct models are often limited because quality variables are difficult to be obtained. Additionally, due to the instrument of redundant sensors, the process data is high-dimensional with strong correlations. In this paper, an active learning soft sensor framework named variational adversarial active learning (VAAL) is developed to select informative unlabeled samples to enhance prediction performance. The sampling strategy of VAAL learns a latent space using a variational autoencoder (VAE) and an adversarial network trained in a way of minimax game. The VAE tries to trick the adversarial network into predicting that all samples are from the labeled pool, while the adversarial network learns how to discriminate between dissimilarities in the latent space. The Gaussian process regression model is adopted in VAAL as a base soft sensor. The prediction results of an industrial debutanizer column demonstrate the advantages of VAAL as compared to the existing active learning strategies. © 2022 IEEE.

关键词： Forecasting

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Dynamic Graph Learning Soft Sensor in process Industry

Dynamic Graph Learning Soft Sensor in Process Industry

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Data Driven control and Learning Systems (DDCLS)

作者： Mingwei Jia Danya Xu Tao Yang Yuan Yao Yi Liu Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Department of Chemical Engineering National Tsing Hua University Taiwan

The development of an accurate soft sensor modeling method in the process industry remains a great challenge because the coupling relationship between variables is always intricate and difficult to model. In this work, a dynamic graph learning (DGL) soft sensor is proposed to alleviate this problem. The proposed model realizes the ability of the soft sensor to perceive the coupling relationship in real time by automatically learning the dynamic graph. Then, a causal convolutional mechanism and a multi-hop graph attention mechanism are used to systematically construct the dependencies of variables in the spatial-temporal dimension and model their variation patterns effectively. Finally, the proposed method is tested on the penicillin fermentation process and shown to be feasible and effective. The results showed that the change of the dynamic graph in the spatial-temporal dimension was in line with the process mechanism.

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Rheological and Filtration Property Enhancement of a Water-Based Drilling Mud Using Modified Graphene Nanoplatelets Derived from Kenaf Stem

Rheological and Filtration Property Enhancement of a Water-B...

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2024 SPE Nigeria Annual International Conference and Exhibition, NAIC 2024

作者： Yahya, M.N. Norddin, M.N.A.M. Ismail, I. Rasol, A.A.A. Oseh, J.O. Mohammed, U. Yakasai, F. Umunnawuike, C. Gbonhinbor, J. Agi, A. Department of Petroleum Engineering Faculty of Chemical and Energy Engineering Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Chemical and Petroleum Engineering Faculty of Engineering Bayero University Nigeria Advanced Membrane Technology Research Centre AMTEC Nanostructured Materials Research Group NMRG - MD - Frontier Materials Universiti Teknologi Malaysia Johor Bahru Malaysia Malaysia Petroleum Resources Corporation Institute for Oil and Gas MPRC-UTM Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Petroleum Engineering School of Energy and Energy Engineering Federal University of Technology Imo State Owerri Nigeria Faculty of Chemical and Process Engineering Technology College of Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Gambang Malaysia Department of Petroleum Engineering Niger Delta University Wilberforce Island Amassoma Yenagoa Nigeria Centre for Research in Advanced Fluid and Processes Fluid Centre Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Gambang Malaysia

ISBN: (纸本)9781959025474

Drilling fluids' rheological and filtration qualities have been discovered to be improved by graphene nanoplatelets (GNPs). Research has demonstrated that adding GNPs to water-based mud (WBM) can enhance its plastic viscosity (PV), yield point (YP), gel strength (GS), filtration loss (FL), and filter cake thickness (FCT). Their small particle size, excellent material dispersion, and thermal stability are the reasons behind the usage of GNPs in drilling fluid. Here, GNP from Kenaf stem was synthesized, and its surface was modified with nonionic surfactant triton-X100 to improve its dispersion in aqueous media. The synthesized nanomaterials were subjected to particle size distribution (PSD), energy dispersive X-ray analysis (EDX), and zeta potential analysis to confirm their elemental content, stability, and particle size. Critical analysis and a comparison of the properties of surface modified GNP (GNT) and synthesized GNP from Kenaf (GNK) were conducted in WBM formulations. The materials are stable, according to the zeta potential study, although the EDX results showed that surface modification reduced the oxygen-containing group in GNT. Better rheological properties were obtained by adding the synthesized materials to WBM as an additive. PV increased from 13.5 to 17cp, YP increased from 31 to 40 lb/100ft2, FL control performance improved from 12mL to 6.5mL, and FCT decreased from 2.60 to 1.20mm. It has been discovered that adding GNT to WBM enhances its rheological and filtration properties more effectively than adding GNK, which makes it a viable inclusion for drilling operations. Copyright © 2024, Society of Petroleum Engineers.

关键词： Drilling fluids

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Generative Convolutional Monitoring Method for Online Flooding Recognition in Packed Towers

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Journal of the Taiwan Institute of chemical Engineers 2024年 165卷

作者： Liu, Yi Jiang, Yuxin Gao, Zengliang Liu, Kaixin Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou 310023 China The State Key Lab for Electronic Testing Technology North University of China Taiyuan 030051 China Department of Chemical Engineering National Tsing Hua University Hsinchu 300044 Taiwan

Background: Data-driven methods play an important role in monitoring the liquid flooding process for ensuring the efficient and safe operation of packed towers. However, their online recognition performance is often limited due to the imbalanced and nonlinear nature of the flooding data. Method: In this work, a generative convolutional monitoring (GCM) method is proposed for online flooding recognition. Firstly, a generative model by integrating variational autoencoder with Wasserstein generative adversarial networks is designed to generate information-rich flooding images for enlarging the diversity of the dataset. Secondly, the convolutional neural network is employed for the online recognition of flooding. Finally, feature visualization explains the details of the GCM method in terms of feature extraction. Consequently, the proposed method extracts nonlinear characteristics while overcoming the difficulties associated with unbalanced data. Significant findings: Experiments on a lab-scale packed tower demonstrate the feasibility of the proposed approach. The flooding state in packed towers can be online detected. © 2024 Taiwan Institute of chemical Engineers

关键词： Generative adversarial networks

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