The aim of this work is to show that the equations developed by Sovova, based on the concept of broken and intact cells for describing the supercritical extraction curves from plants and vegetables, can be applied for the modelling of the supercritical CO2 extraction curves obtained with microalgae. Experimental and modelling results of supercritical carbon dioxide extraction of lipids from four different microalgae: Nannochloropsis oculata, Cylindrotheca closterium, Chlorella vulgaris and Spirulina platensis, are presented. The experimental setup is a laboratory scale apparatus which allows an accurate monitoring of the mass loss of the microalgae sample during the extraction. The experimental data were obtained at a pressure of 40 MPa, a temperature of 333 K and CO2 flow rates from 0.3 to 0.5 kg h(-1). The extraction experiments were performed on samples having undergone different pretreatments: (i) after harvesting and centrifugation, microalgae were dried either by freeze-drying or air flow drying) and (ii) they were ground and sieved at different particle sizes (particle diameters ranging from 160 to 1000 pm). The complete extraction of neutral lipids was performed leading to mass losses up to 30% depending on the samples and on the operating conditions. Extracts were mainly composed of triglycerides (more than 90 wt% of extracts). The mathematical model published in 2005 and the simplified equations of extraction curves (using the characteristic times characterizing each extraction step) published in 2012 by Sovova were chosen to fit the experimental data. Among the hypotheses proposed by Sovova, we considered that the flow pattern of supercritical CO2 in the extraction autoclave was plug flow and the extraction process was supposed to occur with negligible solute-matrix interactions. The adjustable parameters were calculated by minimizing the sum of least squares between experimental and calculated values of the extraction yield. Good agreement between the two models and our experimental measurements was obtained. The average absolute relative deviation ranges between 0.5 and 10.2%. (C) 2013 Elsevier B.V. All rights reserved.
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Inst Agr & Food Res & Technol IRTA, Aquat Ecosyst Program, E-43540 San Carlos de la Rapita, Catalonia, SpainInst Agr & Food Res & Technol IRTA, Aquat Ecosyst Program, E-43540 San Carlos de la Rapita, Catalonia, Spain
Trobajo, Rosa
Ibanez, Carles
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Inst Agr & Food Res & Technol IRTA, Aquat Ecosyst Program, E-43540 San Carlos de la Rapita, Catalonia, SpainInst Agr & Food Res & Technol IRTA, Aquat Ecosyst Program, E-43540 San Carlos de la Rapita, Catalonia, Spain
Ibanez, Carles
Clavero, Ester
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Catalonia Inst Energy Res IREC, Bioenergy & Biofuels Div, E-43007 Tarragona, Catalonia, SpainInst Agr & Food Res & Technol IRTA, Aquat Ecosyst Program, E-43540 San Carlos de la Rapita, Catalonia, Spain
Clavero, Ester
Salvado, Joan
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Catalonia Inst Energy Res IREC, Bioenergy & Biofuels Div, E-43007 Tarragona, Catalonia, Spain
Univ Rovira & Virgili, Dept Chem Engn, E-43007 Tarragona, Catalonia, SpainInst Agr & Food Res & Technol IRTA, Aquat Ecosyst Program, E-43540 San Carlos de la Rapita, Catalonia, Spain
Salvado, Joan
Jorgensen, Sven Erik
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Univ Copenhagen, Fac Hlth, Inst A, Sect Environm Chem, DK-2100 Copenhagen O, DenmarkInst Agr & Food Res & Technol IRTA, Aquat Ecosyst Program, E-43540 San Carlos de la Rapita, Catalonia, Spain