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Optimisation of alkene epoxidation catalysed by polymer supported Mo(VI) complexes and application of artificial neural network for the prediction of catalytic performances

Paper ID Volume ID Publish Year Pages File Format Full-Text
40301 45849 2013 11 PDF Available
Title
Optimisation of alkene epoxidation catalysed by polymer supported Mo(VI) complexes and application of artificial neural network for the prediction of catalytic performances
Abstract

•Polymer supported Mo(VI) catalysts were successfully prepared and characterised.•Epoxidations of alkenes with TBHP catalysed by polymer supported Mo(VI) catalyst.•This alkene epoxidation process is atom efficient and environmentally friendly.•Ps.AMP.Mo showed higher catalytic activity than PBI.Mo for alkene epoxidation.•Molybdenum leaching in Ps.AMP.Mo catalyst was attributed to low ligand to Mo ratio.

A greener and efficient alkene epoxidation process using heterogeneous molybdenum (Mo) based catalysts and tert-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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Keywords
Alkene epoxidation; Artificial neural network (ANN); tert-Butyl hydroperoxide (TBHP); Heterogeneous catalysis; Polymer supported Mo(VI) catalyst; 1-Hexene; 4-Vinyl-1-cyclohexene
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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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Publisher
Database: Elsevier - ScienceDirect
Journal: Applied Catalysis A: General - Volume 466, 10 September 2013, Pages 142–152
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Catalysis
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
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Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
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Any Questions? feel free to contact us