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How can oil and gas coexist with products derived from biomass? IFPEN develops predictive models

April 2017

The public is increasingly conscious of the benefits of using renewable resources, both in terms of new fuels and raw materials for the chemicals sector. Consequently, plant-based materials, or bio-resources, are increasingly sought-after, particularly second-generation resources from non-food agricultural residues such as wood and straw. IFPEN has been exploring this avenue for a number of years and is already involved in industrial demonstration activities, while pursuing research programs aimed at acquiring the knowledge and models/tools required to improve the processes concerned.

The bio-based substances being considered for use as substitutes for fossil resources cover molecular families that are very different from those generally encountered in the oil sector, and which have been employed for decades in the refining and petrochemicals sector. Unlike "fossil" hydrocarbons (molecules based solely on carbon and hydrogen), bio-based substances include "oxygenated” molecules, containing, for example, alcohol, acid, ether or ester functions.
The latter create polarities and hydrogen bonds, which modify affinities between species, in a very different way than in hydrocarbon mixtures.

             Creation of "hydrogen bonds" in a mixture of oxygenated molecules.

The consequences of these phenomena on the properties of the mixtures are important, since they have an impact on the implementation of existing industrial processes. Typically, it is observed that some products are non-miscible in the liquid state (as with oil and water), while others may see their volatilities altered. Determining such behavior via experimentation, for the entire range of situations encountered, is very costly in terms of both resources and time, because the actual challenge involves constructing an entire new reference system.

Within the context of the Tuck Foundation's "Thermodynamics for biofuels" chair, a research program was thus launched in 2010 to develop predictive models making it possible to use calculations to determine the behaviors of such systems.

The tool developed (Statistical Associating Fluid Theory, SAFT, equation of state) was evaluated on various product families, and its use was demonstrated in the research of invited Professor C. Gambini Pereira, from Rio Grande do Norte, in Brazil, during her scientific visit to IFPEN in 2013-2014.

                        Molecular representation with the SAFT equation of state

Within the context of this scientific collaboration, early research consisted in studying gas solubility in representative pyrolysis oil mixtures, using experimentation and predictive modeling [1].

More recently, the model developed has been employed for computing phenomena occurring during the production of fuel esters, which are long-chain molecules, derived from a chemical reaction between fatty acids and alcohols. In addition to the target ester, this reaction produces glycerol as a by-product, which has to be separated from the mixture.
However, in certain conditions, glycerol can decant in a separate phase, thereby facilitating separation. An accurate description of this behavior is thus required to enable the appropriate dimensioning of the process and the calculation tool has proved particularly reliable and descriptive from this point of view [2].

             Comparison between the SAFT model, molecular simulation and experimental data
                              (example of the ethyl laurate + ethanol phase diagram)

Scientific contact :


[1]  C.G. Pereira, L. Grandjean, S. Betoulle, N. Ferrando, C. Féjean, R. Lugo, J.C. de Hemptinne, P. Mougin, Phase equilibria of systems containing aromatic oxygenated compounds with CH4, CO2, H2, H2S, CO and NH3: Experimental data and predictions, Fluid Phase Equilibria, 382, (2014), 219–234.

[2] C.G. Pereira, N. Ferrando, R. Lugo, P. Mougin, J.C. de Hemptinne, Predictive evaluation of phase equilibria in biofuel systems using molecular thermodynamic models, J. of Supercritical Fluids 118 (2016) 64–78.

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