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. and Patel, D. and Mbeleck, R. and Niyogi, D. and Sherrington, D.C. and Saha, B. (2013) Optimisation of alkene epoxidation catalysed by polymer supported Mo(VI) complexes and application of artificial neural network for the prediction of catalytic performances. Applied Catalysis A: General, 466. pp. 142-152. ISSN 0926-860X (https://doi.org/10.1016/j.apcata.2013.06.055)

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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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