Article
Green & Sustainable Science & Technology
Thuy T. H. Nguyen, Takehiro Yamaki, Satoshi Taniguchi, Akira Endo, Sho Kataoka
Summary: CO2 capture and utilization is a potential solution for combating global warming, but rigorous evaluation and optimization of the entire production process are needed. This study focuses on minimizing CO2 emissions in methanol production by synthesizing intermediate syngas using DR and PO methods.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Engineering, Chemical
Oscar Ovalle-Encinia, Han-Chun Wu, Tianjia Chen, Jerry Y. S. Lin
Summary: Experimental and simulation results demonstrate the feasibility of hydrogen production with simultaneous CO2 removal through steam reforming of methane in a CO2-perm-selective membrane reactor. The mathematical model accurately describes the process, showing that adjusting permeation number, Damkohler number, reaction pressure, and sweep side conditions can enhance H2 yield and CO2 recovery.
JOURNAL OF MEMBRANE SCIENCE
(2022)
Review
Chemistry, Physical
Wan Nabilah Manan, Wan Nor Roslam Wan Isahak, Zahira Yaakob
Summary: This paper reviews the management of CO2 emissions and the recent developments in bimetallic catalysts utilizing cerium oxide in dry reforming methane and steam reforming methane from 2015 to 2021. The focus is on the identification of key trends in catalyst preparation using cerium oxide and the effectiveness of the formulated catalysts.
Article
Chemistry, Physical
Mohammad Osat, Faryar Shojaati
Summary: A novel method for methanol synthesis is proposed in this study, which reduces CO2 emission and increases CO2 conversion compared to a referenced method. The proposed method is more economical and environmentally friendly.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2022)
Article
Chemistry, Physical
Yu-Shih Lin, Jia-Yun Tu, De-Hao Tsai
Summary: This study demonstrates a facile aerosol-based method to prepare hybrid nanostructures for catalyzing steam-promoted CO2 reforming with methane. The resulting NiPdOx-CeO2 nanoparticles deposited on SiO2 nanoparticle clusters exhibit superior catalytic performance at low temperatures, with high turnover frequency, tunable H2/CO ratio, and long-term operation stability. Incorporating SiO2 nanoparticle clusters as support material helps to enhance the dispersion of active metals and suppress metal sintering.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2021)
Article
Chemistry, Multidisciplinary
Zahra Taherian, Vahid Shahed Gharahshiran, Alireza Khataee, Yasin Orooji
Summary: In this study, a surface defect-promoted Ni catalyst supported on Mg/Al hydrotalcite via a freeze-dried method was synthesized by adding samarium, leading to an increase in oxygen vacancies and high dispersion of active sites. The samarium-promoted NiMgAl catalyst showed superior catalytic activity and stability in dry and steam reforming of methane due to the scaffold structure with surface defects and oxygen vacancies, which inhibit sintering and enhance mass transportation.
JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY
(2021)
Article
Thermodynamics
Florian Pruvost, Schalk Cloete, Carlos Arnaiz del Pozo, Abdelghafour Zaabout
Summary: This study evaluates the techno-economic aspects of blue hydrogen production using steam methane reforming (SMR). The findings suggest that pre-combustion CO2 capture is a cost-effective method to reduce CO2 emissions, while post-combustion CO2 capture can be a better solution for the final 20% of emissions. Additionally, an advanced heat integration scheme and hybrid production options show potential for cost reduction.
ENERGY CONVERSION AND MANAGEMENT
(2022)
Article
Chemistry, Physical
Chien-Hung Chen, Ching-Tsung Yu, Wen-Hui Chen
Summary: Optimization of steam methane reforming reaction through CO2 sorption enhancement resulted in high-purity hydrogen production suitable for proton-exchange membrane fuel cells, demonstrating significant differences in CH4 conversion between SMR and SESMR.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2021)
Article
Chemistry, Multidisciplinary
V. I. Savchenko, Ya. S. Zimin, A. V. Nikitin, I. V. Sedov, V. S. Arutyunov
Summary: A study was conducted on the kinetic and thermodynamic aspects of CO2 utilization in the non-catalytic dry reforming of light hydrocarbons at temperatures ranging from 1400-1800 K. The results demonstrate the efficient utilization of CO2 to enhance the yield of syngas during the reforming of hydrocarbons within this temperature range.
JOURNAL OF CO2 UTILIZATION
(2021)
Article
Chemistry, Physical
Jingjing Dai, Hongbo Zhang
Summary: This study systematically investigates the C-H bond activation mechanism on Pt/MgO catalyst with O-2, O-H2O, and CO2. It is found that dry reforming of CH4 (DRM) exhibits weaker activity compared to reforming or partial oxidation of CH4. Pressure dependence shows that CO has negligible effect, suggesting the involvement of CO2 in CH4 activation as a whole molecule. The reaction between adsorbed CH4 and CO2 to form formate and methyl species is kinetically relevant. Experimental results confirm that both CO2 and CH4 derivatives are the most abundant surface intermediates.
JOURNAL OF CATALYSIS
(2022)
Article
Chemistry, Physical
Ze Li, Jun Leng, Hao Yan, Dongpei Zhang, Delun Ren, Feilong Li, Yibin Liu, Xiaobo Chen, Chaohe Yang
Summary: Ni-based catalysts have been extensively studied for methane reforming, but their industrial application is limited by catalytic deactivation. In this study, La-promoted Ni/MgAl2O4 catalysts were investigated for methane bi-reforming and showed improved activity and stability. Characterizations revealed that the introduction of La increased the content and dispersion of active species (Ni0) and inhibited carbon accumulation on catalyst surface. The Ni10La5/MgAl2O4 catalyst exhibited the highest catalytic performance (93% CH4 conversion, 71% CO2 conversion, <3% activity loss) under long-term reaction conditions. This study has important guiding significance for the design of high-efficiency methane reforming catalysts.
APPLIED SURFACE SCIENCE
(2023)
Article
Chemistry, Physical
Jose L. C. Fajin, M. Natalia D. S. Cordeiro
Summary: Ni-Cu catalysts are low cost, highly selective for CO2 and H-2 in methanol steam reforming, blocking the production of methane, CO, and coke. The mechanism of methanol steam reforming on Ni-Cu surfaces involves methanol decomposition followed by the water-gas shift reaction, with a minority route for direct CO2 formation. The Ni-Cu alloy suppresses methane and coke formation and has a high desorption barrier for CO species, avoiding its production.
Article
Engineering, Chemical
Jonghun Lim, Chonghyo Joo, Jaewon Lee, Hyungtae Cho, Junghwan Kim
Summary: This study proposes a novel SMR process that utilizes desalination wastewater to reduce CO2 emissions and produce carbon-neutral hydrogen. The process model captures CO2 from the SMR process and uses mineral ions in desalination wastewater to carbonate the captured CO2. The economic feasibility of the proposed process is demonstrated through the assessment of the levelized cost of hydrogen.
Article
Chemistry, Multidisciplinary
Thaylan Pinheiro Araujo, Adrian H. Hergesell, Dario Faust-Akl, Simon Buchele, Joseph A. Stewart, Cecilia Mondelli, Javier Perez-Ramirez
Summary: This study found that copper-based systems are more active in CO hydrogenation and suitable for methanol production using CO; ZnO-ZrO2 exhibits strong resistance to deactivation in CO2-rich streams, showing good reversibility; the research emphasizes the importance of catalyst and process design in advancing CO2 utilization technologies.
Article
Thermodynamics
Dongjun Lee, Dela Quarme Gbadago, Youngtak Jo, Gyuyoung Hwang, Yeonpyeong Jo, Robin Smith, Sungwon Hwang
Summary: This study successfully developed a hydrogen liquefaction process by integrating hydrogen production, hydrogen liquefaction, and CO2 liquefaction processes together, optimizing to reduce energy consumption and CO2 emissions. Techno-economic analysis indicated that the integrated system significantly improved economic indicators and environmental friendliness.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Engineering, Chemical
Tao Shi, Wei Chun, Ao Yang, Yang Su, Saimeng Jin, Jingzheng Ren, Weifeng Shen
CHEMICAL ENGINEERING SCIENCE
(2020)
Article
Engineering, Chemical
Jingzheng Ren, Xusheng Ren, Weifeng Shen, Yi Man, Ruojue Lin, Yue Liu, Chang He, Alessandro Manzardo, Sara Toniolo, Lichun Dong
Article
Engineering, Chemical
Huaqiang Wen, Yang Su, Zihao Wang, Saimeng Jin, Jingzheng Ren, Weifeng Shen, Mario Eden
Summary: The research proposes a systematic approach to solving key problems in DNN-based QSPR modeling, including applicability domain and prediction uncertainty, using multiple machine learning technologies. The method extracts features through principal component analysis and kernel PCA, defines a detailed applicability domain using the K-means algorithm, and further analyzes prediction uncertainty.
Article
Engineering, Chemical
Jun Zhang, Qin Wang, Yang Su, Saimeng Jin, Jingzheng Ren, Mario Eden, Weifeng Shen
Summary: This study developed an accurate and interpretable deep neural network (AI-DNN) model for predicting lipophilicity. A hybrid method of molecular representation, combining directed message passing neural networks and fixed molecule-level features, was employed to capture the local and global features of molecules. The proposed model demonstrated promising predictive accuracy and discriminative power in structural and stereoisomers. The use of Monte Carlo Tree Search allowed for interpretation of the model, which is important in fields with a high demand for interpretable deep networks, such as green solvent design and drug discovery.
Article
Engineering, Chemical
Binxin Huang, Yu Tong, Yong Chen, Ali Eslamimanesh, Weifeng Shen, Shun'an Wei
Summary: This study combines genetic algorithm with random forest algorithm to select suitable molecular feature combination, significantly reducing the number of descriptors required for model development, and utilizes a combination of backpropagation neural network and Bayesian optimization for intelligent tuning of relevant model parameters.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2022)
Article
Engineering, Chemical
Qi Lu, Jinlong Li, Ao Yang, Xiuguang Yi, Weifeng Shen
Summary: This work systematically studies the sustainable separation process for recovering acetonitrile from wastewater and proposes an energy-efficient extractive distillation process. By analyzing the separation performances of candidate entrainers, determining the boundaries of decision variables for optimization procedures, and evaluating the economic, environmental, and energy efficiency performances of conventional and heat-integrated schemes, the optimal operating parameters for the heat-integrated scheme using [EMIM][OAC] are obtained.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2022)
Article
Chemistry, Multidisciplinary
Yue Liu, Tao Shi, Ao Yang, Jingzheng Ren, Weifeng Shen, Chang He, Sara Toniolo
Summary: This paper proposes a decision-making framework based on process simulation and the fuzzy PROMETHEE II method to evaluate the performances of waste management alternatives. The framework can handle the problems of lacking data and uncertainty, and provides useful references and suggestions for decision-makers through establishing criteria system, conducting sustainability assessment, and performing sensitivity and uncertainty analysis.
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
(2022)
Article
Engineering, Chemical
Zhengtao Zhou, Mario Eden, Weifeng Shen
Summary: QSPR modeling is a widely used method for estimating molecular properties based on structural information, and it has been applied in exploring new solvents, pharmaceuticals, and materials with desired properties. SMILES is considered as a chemical language, and a deep pyramid convolutional neural network architecture is constructed to extract information from SMILES sentences. The effectiveness of this approach is proven through a case study of predicting the logarithm values of the octanol-water partition coefficient, showing better performance compared to a precedent reference model and providing insights for molecular information mining and exploration of chemical property space through natural language processing technologies.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Energy & Fuels
Yousaf Ayub, Jianzhao Zhou, Jingzheng Ren, Tao Shi, Weifeng Shen, Chang He
Summary: The biomass gasification process was predicted and optimized by integrating Aspen Plus simulation with the HDMR method. The results showed that temperature and biomass to air ratio had significant effects on the process. The HDMR models had high performance in predicting various parameters and the optimal solution increased gasification yield while maintaining energy efficiency.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2023)
Article
Engineering, Chemical
Ao Yang, Zong Yang Kong, Shirui Sun, Jaka Sunarso, Jingzheng Ren, Weifeng Shen
Summary: This work presents the development of two novel intensified energy-efficient extractive distillation configurations that offer superior performance for the separation of ethyl acetate and methanol from waste effluent, prioritizing economic, environmental, and safety performances.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Engineering, Chemical
Shirui Sun, Ao Yang, Chenglin Chang, Guanqing Hua, Jingzheng Ren, Zhigang Lei, Weifeng Shen
Summary: This study proposes a multiobjective optimization framework that combines the particle swarm algorithm and the technique for order preference by similarity to the ideal solution for improving the economic performance of the extractive dividing wall column (EDWC). The framework introduces particle mutation and linearly decreasing inertia weight strategies to increase population diversity and feasible solutions. The results demonstrate the unique advantages of the improved MOPSO in maintaining population diversity and reducing total annual cost compared to sequential iterative optimization.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Chemistry, Multidisciplinary
Ao Yang, Yang Su, Zihao Wang, Saimeng Jin, Jingzheng Ren, Xiangping Zhang, Weifeng Shen, James H. Clark
Summary: This study introduces a novel deep learning approach for predicting multiple flammability-related properties simultaneously with higher accuracy compared to traditional methods.
Article
Chemistry, Physical
Chaofang Deng, Yang Su, Fuhua Li, Weifeng Shen, Zhongfang Chen, Qing Tang
JOURNAL OF MATERIALS CHEMISTRY A
(2020)
Article
Chemistry, Multidisciplinary
Zihao Wang, Yang Su, Saimeng Jin, Weifeng Shen, Jingzheng Ren, Xiangping Zhang, James H. Clark
