Optimisation of alkene epoxidation catalysed by polymer supported Mo(VI) complexes and application of artificial neural network for the prediction of catalytic performances
Optimisation of alkene epoxidation catalysed by polymer supported Mo(VI) complexes and application of artificial neural network for the prediction of catalytic performances
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聚合物负载Mo(VI)配合物催化烯烃环氧化的优化及人工神经网络在催化性能预测中的应用
DOI：
10.1016/j.apcata.2013.06.055
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Mohammed M
中科院分区：
文献类型：
--
作者：
Mohammed M
以非均相钼（Mo）基催化剂和过氧化叔丁基（thbhp）为氧化剂，研究了一种绿色高效的烯烃环氧化工艺。一种多苯并咪唑负载Mo（VI）配合物，即PBI。成功制备了Mo和聚苯乙烯2-（氨基甲基）吡啶负载Mo（VI）配合物，即Ps.AMP.Mo催化剂。在夹套搅拌间歇式反应器中测试了聚合物负载Mo（VI）催化剂对1-己烯和4-乙烯基-1-环己烯环氧化反应的催化活性。通过间歇式实验研究了不同类型催化剂、催化剂负载、烯烃与三必和二必的进料摩尔比（FMR）和反应温度对1-己烯和4-乙烯基-1-环己烯两种烯烃环氧化物收率的影响。PBI的长期稳定性。Mo和Ps.AMP.Mo催化剂通过多次循环使用催化剂进行批量实验，以形成连续环氧化过程的基础。在去除非均相催化剂后，从反应上清溶液中分离出任何残留物，并将残留物作为环氧化的潜在催化剂，研究了每种聚合物负载催化剂的Mo浸出程度。采用人工神经网络模型对PBI的催化性能进行了预测。Mo和Ps.AMP.Mo催化剂的所有批次实验结果。人工神经网络的预测值与批量实验结果吻合较好。批处理实验和人工神经网络建模的结果为在FlowSyn和反应精馏塔（RDC）等多功能反应器中进行连续环氧化实验提供了有用的信息。
A greener and efficient alkene epoxidation process using heterogeneous molybdenum (Mo) based catalysts andtert-butyl hydroperoxide (TBHP) as an oxidant has been developed. A polybenzimidazole supported Mo(VI) complex, i.e. PBI.Mo and polystyrene 2-(aminomethyl) pyridine supported Mo(VI) complex, i.e. Ps.AMP.Mo catalysts have been successfully prepared and characterised. The catalytic activities of the polymer supported Mo(VI) catalysts have been tested for epoxidation of 1-hexene and 4-vinyl-1-cyclohexene in a jacketed stirred batch reactor. Batch experiments have been conducted to study the effect of different types of catalysts, catalyst loading, feed mole ratio (FMR) of alkene to TBHP and reaction temperature on the yield of epoxide for both alkenes, i.e. 1-hexene and 4-vinyl-1-cyclohexene. The long-term stability of PBI.Mo and Ps.AMP.Mo catalysts has been evaluated by recycling the catalyst several times for batch experiments using conditions that will form the basis of a continuous epoxidation process. The extent of Mo leaching from each polymer supported catalyst has been investigated by isolating any residue from reaction supernatant solutions after the removal of the heterogeneous catalyst and using the residue as potential catalyst for epoxidation. An artificial neural network (ANN) model has been employed to predict the catalytic performance of PBI.Mo and Ps.AMP.Mo catalysts for all batch experimental results. The ANN predicted values are in good agreement with the batch experimental results. The results obtained from batch experiments and ANN modelling provided useful information for conducting continuous epoxidation experiments in multi-functional reactors such as FlowSyn and reactive distillation column (RDC).
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