Development of a Co-MgO Catalyst for High-pressure Dry Reforming of Methane Based on Design of Experiment, Artificial Neural Network and Grid Search
Development of a Co-MgO Catalyst for High-pressure Dry Reforming of Methane Based on Design of Experiment, Artificial Neural Network and Grid Search
复制标题
基于实验设计、人工神经网络和网格搜索的甲烷高压干重整Co-MgO催化剂的研制
DOI：
10.1627/jpi.47.387
复制
发表时间：
2004
影响因子：
1
通讯作者：
M. Yamada
中科院分区：
文献类型：
--
作者：
K. Omata;N. Nukui;T. Hottai;Y. Showa;M. Yamada
甲烷干重整是将温室气体二氧化碳和甲烷同时转化为合成气（CO + H2）的潜在重要过程。甲烷干重整最严重的问题是积炭，因此对柠檬酸法的制备参数进行了研究，通过实验设计、人工神经网络和网格搜索，制备了低碳沉积的活性Co-MgO催化剂。根据L9正交阵列确定Co负载量、柠檬酸用量、煅烧温度和造粒压力等制备参数。在传统加压固定床反应器中设计并测量了9个参数活性数据集后，构建了人工神经网络。通过网格搜索确定最佳成分，并通过实验验证其在少量积碳的情况下具有活性。与人工神经网络和网格搜索相结合的实验设计对于催化剂的开发很有用。
Dry reforming of methane is a potentially important process to convert the greenhouse gases carbon dioxide and methane simultaneously to syngas (CO + H2). The most serious problem with the dry reforming of methane is carbon deposition, so preparation parameters of the citric acid method were surveyed to prepare an active Co-MgO catalyst with low carbon deposition using design of experiment, artificial neural network and grid search. The preparation parameters such as Co loading, amount of citric acid, calcination temperature, and pelletization pressure were determined according to an L9 orthogonal array. After 9 data sets of the parameter activity were designed and measured in a conventional pressurized fixed bed reactor, an artificial neural network was constructed. The optimum composition was determined by a grid search and verified experimentally to be active with a small amount of carbon deposition. Design of experiment combined with an artificial neural network and grid search was useful for catalyst development.
Cu、Ni和Fe纳米线氧化形成氧化物纳米管
DOI：
--
发表时间：
2009
期刊：
影响因子：
--
作者：
松林玄;仲村龍介;中嶋英雄;土谷博昭;藤本慎司
通讯作者：
藤本慎司
遗传联系、生物联系
DOI：
--
发表时间：
2018
期刊：
影响因子：
--
作者：
下山 晴彦;佐藤 隆夫;本郷 一夫;石丸 径一郎;Ken-ichi Nanbu;日比野由利
通讯作者：
日比野由利