Reactive extraction of muconic acid by hydrophobic phosphonium ionic liquids - Experimental, modelling and optimisation with Artificial Neural Networks
Alexandra Cristina Blaga,
Elena Niculina Dragoi,
Alexandra Tucaliuc,
Lenuta Kloetzer,
Adrian-Catalin Puitel,
Dan Cascaval,
Anca Irina Galaction
Affiliations
Alexandra Cristina Blaga
''Gheorghe Asachi'' Technical University of Iasi, ''Cristofor Simionescu'' Faculty of Chemical Engineering and Environmental Protection, Iasi, Romania; Corresponding author. ''Gheorghe Asachi'' Technical University of Iasi, ''Cristofor Simionescu'' Faculty of Chemical Engineering and Environmental Protection, D. Mangeron Av., no 67, Iasi, Romania.
Elena Niculina Dragoi
''Gheorghe Asachi'' Technical University of Iasi, ''Cristofor Simionescu'' Faculty of Chemical Engineering and Environmental Protection, Iasi, Romania
Alexandra Tucaliuc
''Gheorghe Asachi'' Technical University of Iasi, ''Cristofor Simionescu'' Faculty of Chemical Engineering and Environmental Protection, Iasi, Romania
Lenuta Kloetzer
''Gheorghe Asachi'' Technical University of Iasi, ''Cristofor Simionescu'' Faculty of Chemical Engineering and Environmental Protection, Iasi, Romania
Adrian-Catalin Puitel
''Gheorghe Asachi'' Technical University of Iasi, ''Cristofor Simionescu'' Faculty of Chemical Engineering and Environmental Protection, Iasi, Romania
Dan Cascaval
''Gheorghe Asachi'' Technical University of Iasi, ''Cristofor Simionescu'' Faculty of Chemical Engineering and Environmental Protection, Iasi, Romania; Corresponding author.
Anca Irina Galaction
''Grigore T. Popa'' University of Medicine and Pharmacy, Faculty of Medical Bioengineering, Iasi, Romania
Muconic acid is a six-carbon dicarboxylic acid with conjugated double bonds that finds extensive use in the food (additive), chemical (production of adipic acid, monomer for functional resins and bio-plastics), and pharmaceutical sectors. The biosynthesis of muconic acid has been the subject of recent industrial and scientific attention. However, because of its low concentration in aqueous solutions and high purity requirement, downstream separation presents a significant problem. Artificial Neural Networks and Differential Evolution were used to optimize process parameters for the recovery of muconic acid from aqueous streams in a system with n-heptane as an organic diluent and ionic liquids as extractants. The system using 120 g/L tri-hexyl-tetra-decyl-phosphonium decanoate dissolved in n-heptane, pH of the aqueous phase 3, 20 min contact time, and 45 °C temperature assured a muconic acid extraction efficiency of 99,24 %. Low stripping efficiency compared to extraction efficiency was observed for the optimum conditions on the extraction step (120 g/L ionic liquids dissolved in heptane). However, re-extraction efficiencies obtained for the recycled organic phase in three consecutive stages were close to the first extraction stage. The mechanism analysis proved that the analysed phosphonium ionic liquids (PILSs) extracts only undissociated molecules of muconic acid through H-bonding.
