Group contribution (GC) approaches are based on the premise that the properties of a molecule or a mixture can be determined from the appropriate contributions of the functional chemical groups present in the system of interest. Although this is clearly an approximation, GC methods can provide accurate estimates of the properties of many systems and are often used as predictive tools when experimental data are scarce or not available. Our focus is on the SAFT-γ Mie approach [Papaioannou, V.; Lafitte, T.; Avendaño, C.; Adjiman, C. S.; Jackson, G.; Müller, E. A.; Galindo, A. Group contribution methodology based on the statistical associating fluid theory for heteronuclear molecules formed from Mie segments. J. Chem. Phys. 2014, 140, 054107–29] which incorporates a detailed heteronuclear molecular model specifically designed for use as a GC thermodynamic platform. It is based on a formulation of the recent statistical associating fluid theory for Mie potentials of variable range, where a formal statistical–mechanical perturbation theory is used to maintain a firm link between the molecular model and the macroscopic thermodynamic properties. Here we summarize the current status of the SAFT-γ Mie approach, presenting a compilation of the parameters for all functional groups developed to date and a number of new groups. Examples of the capability of the GC method in describing experimental data accurately are provided, both as a correlative and as a predictive tool for the phase behavior and the thermodynamic properties of a broad range of complex fluids.

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Biofouling in heat exchangers can be managed by regular cleaning. A mathematical framework for the optimization problem involved in selecting the best cleaning schedules for such units is presented that considers (i) an induction period associated with conditioning and colonization, which introduces complexity to the fouling kinetics, and (ii) the existence of several outcomes from cleaning, depending on the choice of cleaning method. The problem is to decide how, when, and which exchanger to clean. A mixed integer nonlinear programming approach, based on the use of a logistic function to model fouling resistance-time dynamics, is shown to give tractable results. The methodology is illustrated with a case study involving a small network of three heat exchangers. An optimized solution based on a cost/performance analysis shows that the cleaning intervals and cleaning methods differ for each exchanger.

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