Journal of Chemical Engineering of Japan最新文献_第3页

Kinetics Analysis of Methylcyclohexane Dehydrogenation over Se-Modified Pt/TiO 2 Catalysts Se 改性 Pt/TiO 2 催化剂上的甲基环己烷脱氢动力学分析

IF 0.8 4区工程技术

Journal of Chemical Engineering of Japan Pub Date : 2024-02-01 DOI: 10.1080/00219592.2023.2301533

Kazumasa Oshima, Hiroya Ito, Tsuyoshi Yamamoto, Masahiro Kishida

引用次数: 0

Kinetics Analysis of Methylcyclohexane Dehydrogenation over Se-Modified Pt/TiO 2 Catalysts Se 改性 Pt/TiO 2 催化剂上的甲基环己烷脱氢动力学分析

IF 0.8 4区工程技术

Journal of Chemical Engineering of Japan Pub Date : 2024-02-01 DOI: 10.1080/00219592.2023.2301533

Kazumasa Oshima, Hiroya Ito, Tsuyoshi Yamamoto, Masahiro Kishida

引用次数: 0

Prediction of Energy Concentration during Microwave Heating with a Thermal Diffusion Dimensionless Number 利用热扩散无量纲数预测微波加热过程中的能量浓度

IF 0.8 4区工程技术

Journal of Chemical Engineering of Japan Pub Date : 2024-01-17 DOI: 10.1080/00219592.2023.2287137

Y. Asakuma, Ryohei Yakata, A. Hyde, Chi Phan

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Catalytic Performance of Solid Base Na-ZrO 2 in Subcritical Water 固体碱 Na-ZrO 2 在亚临界水中的催化性能

IF 0.8 4区工程技术

Journal of Chemical Engineering of Japan Pub Date : 2023-12-14 DOI: 10.1080/00219592.2023.2293044

Yiqi Wang, Yoshito Oshima, M. Akizuki

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Consideration on Accuracy of Numerical Analysis of Solid–Gas Two-Phase Flow with Reaction Using Energy Conservation Equation with Temperature as a Variable 使用以温度为变量的能量守恒方程对带有反应的固气两相流进行数值分析的精度考量

IF 0.8 4区工程技术

Journal of Chemical Engineering of Japan Pub Date : 2023-11-30 DOI: 10.1080/00219592.2023.2284176

Yuya Ono, Yoshiya Matsukawa, Y. Matsushita, Hideyuki Aoki

引用次数: 0

Evaluation of Solid–Liquid Equilibria for Ternary Pharmaceutical Compound + Water + Cyclodextrin or Its Derivative Systems with Wilson and Modified Chrastil Models 用 Wilson 模型和修正的 Chrastil 模型评估 "三元药物化合物 + 水 + 环糊精或其衍生物体系 "的固液平衡关系

IF 0.8 4区工程技术

Journal of Chemical Engineering of Japan Pub Date : 2023-11-29 DOI: 10.1080/00219592.2023.2285859

Hiroyuki Matsuda, Kiyofumi Kurihara, K. Tochigi

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Synthesis of Amphiphilic Triblock Copolymers by ATRP and Coalescence Suppression Effect in Suspension Polymerization ATRP法合成两亲性三嵌段共聚物及其悬浮聚合抑制聚结作用

4区工程技术

Journal of Chemical Engineering of Japan Pub Date : 2023-11-08 DOI: 10.1080/00219592.2023.2276424

Eri Fujita, Kosuke Kaneko, Tsubasa Oshima, Daiki Fujioka, Kimiyoshi Kaneko, Kiyomi Fuchigami, Tomonori Hanasaki

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Extraction of Organic Acids with Hydrophobic Eutectic Mixtures Containing Terpenoids and Decanoic Acid 含萜类和癸酸的疏水共晶混合物萃取有机酸

4区工程技术

Journal of Chemical Engineering of Japan Pub Date : 2023-11-08 DOI: 10.1080/00219592.2023.2277827

Michiaki Matsumoto, Makoto Ueda, Yoshiro Tahara

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Development of Ni Nanoparticle Encapsulated with Silicalite-1 Catalyst for High Activity Steam Reforming of Bioethanol with High Sintering Resistance and Coke Suppression 高抗烧结性、高抑焦性生物乙醇高效蒸汽重整催化剂包覆Ni纳米颗粒的研制

4区工程技术

Journal of Chemical Engineering of Japan Pub Date : 2023-10-18 DOI: 10.1080/00219592.2023.2269226

Sirintra Arayawate, Tsuki Yokosawa, Kentaro Kimura, Hiroyasu Fujitsuka, Teruoki Tago

{"title":"Development of Ni Nanoparticle Encapsulated with Silicalite-1 Catalyst for High Activity Steam Reforming of Bioethanol with High Sintering Resistance and Coke Suppression","authors":"Sirintra Arayawate, Tsuki Yokosawa, Kentaro Kimura, Hiroyasu Fujitsuka, Teruoki Tago","doi":"10.1080/00219592.2023.2269226","DOIUrl":"https://doi.org/10.1080/00219592.2023.2269226","url":null,"abstract":"Steam reforming of bioethanol is a potential reaction for H2 production. The development of active Ni catalysts with sintering resistance, coking resistance and thermal stability is indispensable for practical application. In this study, the encapsulation structure of Ni nanoparticles inside Silicalite-1 was proposed as a catalyst (Ni@Silicalite-1). The catalyst was prepared by hydrothermal synthesis using Ni-phyllosilicate as a Ni precursor. An investigation of catalytic activity was conducted at 500–800 °C, along with an investigation of catalyst properties, including surface morphology, Ni properties, and coke formation on the spent catalysts, compared to a conventional Ni/Silicalite-1. Ni@Silicalite-1 exhibits a higher ethanol conversion, hydrogen production, and selectivity towards C1 products. Especially in a kinetic control condition (500–600 °C), Ni@Silicalite-1 exhibits 46.8% in ethanol conversion at 500 °C and two times higher in C2-to-C1 conversion at 600 °C than that of Ni/Silicalite-1. Moreover, a significant coke suppression of 2–5 times reduction form that of impregnation catalyst is obtained for Ni@Silicalite-1 in various reaction temperatures. It also shows a sintering resistance as maintaining the Ni size at 4.0–4.1 nm after ESR at high temperature (800 °C 4 h). Therefore, the encapsulation structure of ultrafine Ni nanoparticles inside Silicalite-1 is promising for bioethanol steam reforming.","PeriodicalId":15331,"journal":{"name":"Journal of Chemical Engineering of Japan","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast Prediction of Heat Flux Distribution in Boilers Using Computational Fluid Dynamics Simulation Data via Multi-Extreme Learning Machines 基于多极限学习机的计算流体力学模拟数据快速预测锅炉热流密度分布

4区工程技术

Journal of Chemical Engineering of Japan Pub Date : 2023-10-04 DOI: 10.1080/00219592.2023.2260416

Zhenhao Tang, Yuan Yang, Luyin Pan, Mengxuan Sui, Shengxian Cao

{"title":"Fast Prediction of Heat Flux Distribution in Boilers Using Computational Fluid Dynamics Simulation Data via Multi-Extreme Learning Machines","authors":"Zhenhao Tang, Yuan Yang, Luyin Pan, Mengxuan Sui, Shengxian Cao","doi":"10.1080/00219592.2023.2260416","DOIUrl":"https://doi.org/10.1080/00219592.2023.2260416","url":null,"abstract":"The heat transfer and hydrodynamic safety of the water-cooled wall in coal-fired boilers are important guarantees for the safe and stable operation of thermal power units. In recent years, influenced by coal quality changes and load fluctuation, the ash deposition and slagging of the water wall and the fluctuation of heat load bring great challenges to the furnace heat transfer safety. It is urgent to monitor the heat flux distribution of the water-cooled wall in the furnace to ensure the safety of the heating surface. In this study, a calculation method for heat flux distribution of 350 MW boiler based on computational fluid dynamics (CFD) and extreme learning machine (ELM) is proposed. First, 120 typical operating conditions in boiler were simulated using ANSYS Fluent to characterize heat flux distribution. Next, to circumvent the high computational cost of CFD simulations, a heat flux distribution prediction model based on multi-ELM was developed by integrating the simulation data and the actual operating parameters of the boiler. Two parameters, namely, separated over-fire air (SOFA) damper opening and main burner secondary air damper opening, were applied to divide the 120 operating conditions into six categories, and use K-means algorithm to find the central operating condition of each category. In each category, the heat flux data of a specific operating condition were randomly selected as the verification set, and the validity of the heat flux distribution prediction models was verified. Finally, the prediction models of heat flux distribution based on multi-ELM were compared with the prediction models based on four contemporary neural network algorithms. Results showed that for the multi-ELM model, the prediction error was <10%, showing that it can accurately predict the heat flux distribution of a boiler and provide guidance for the regulation of the actual production process.","PeriodicalId":15331,"journal":{"name":"Journal of Chemical Engineering of Japan","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135597423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0