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

M.L. Mohammed, D. Patel, R. Mbeleck, D. Niyogi, D.C. Sherrington, B. Saha

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

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).
LanguageEnglish
Pages142-152
Number of pages11
JournalApplied Catalysis A: General
Volume466
DOIs
Publication statusPublished - 10 Oct 2013

Fingerprint

Molybdenum
Epoxidation
Alkenes
Olefins
Polymers
Neural networks
Catalysts
Adenosine Monophosphate
tert-Butylhydroperoxide
Experiments
Epoxy Compounds
Polystyrenes
Distillation columns
Batch reactors
Oxidants
Catalyst supports
Pyridine
Leaching
Recycling
Catalyst activity

Keywords

  • alkene epoxidation
  • artificial neural network
  • tert-butyl hydroperoxide
  • heterogeneous catalysis
  • 1-Hexene
  • 4-Vinyl-1-cyclohexene

Cite this

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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 = "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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Optimisation of alkene epoxidation catalysed by polymer supported Mo(VI) complexes and application of artificial neural network for the prediction of catalytic performances. / Mohammed, M.L.; Patel, D.; Mbeleck, R.; Niyogi, D.; Sherrington, D.C.; Saha, B.

In: Applied Catalysis A: General, Vol. 466, 10.10.2013, p. 142-152.

Research output: Contribution to journalArticle

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