Stelios Rigopoulos

A hybrid CFD - reaction engineering framework for multiphase reactor modelling: basic concept and application to bubble column reactors

The coupling of turbulent mixing and chemical phenomena lies at the heart of multiphase reaction engineering, but direct CFD approaches are usually confronted with excessive computational demands. In this hybrid approach, the quantification of mixing is accomplished through averaging the flow and concentration profiles resulting from a CFD flow field calculation and a computational (“virtual”) tracer experiment. Based on these results, we construct a mapping of the CFD grid into a generalised compartmental model where the chemistry calculations can be efficiently carried out. In contrast to the empirical models used in the residence time distribution (RTD) approach, the compartmental model in this methodology, owning to its CFD origins, retains the essential features of the equipment geometry and flow field. A procedure for extracting the mixing information from k–e based CFD codes is outlined, but the main concept of the approach is not restricted to any particular type of turbulence modelling, and will therefore benefit from future developments. A phenomenological model of mass transfer and chemical reaction, based on the penetration theory, is employed to simulate the interfacial phenomena in gas–liquid reactors, and a study of CO2 absorption into alkali solution is presented to demonstrate the method.

Crystallization and precipitation engineering

New computational techniques for the analysis and design of systems for the manufacture of particulate crystals have become available, and the more complex precipitation processes whereby crystallization follows fast chemical reactions have also been analysed more deeply. This progress has been aided by the growing power of the population balance and kinetic models, computational fluid dynamics (CFD) and mixing theory, respectively. These aspects are selectively reviewed and areas requiring further progress are identified.

Reduced chemistry for hydrogen and methanol premixed flames via RCCE

The method of Rate-Controlled Constrained Equilibrium (RCCE) provides a general and physically sound mathematical description of the dynamics of a chemical system, of which several species are assumed to be kinetically controlled and others equilibrated. In this paper we present an RCCE study of premixed hydrogen–air and methanol–air flames for a wide range of equivalence ratios, and burning velocity and detailed flame structure for both major and minor species are investigated. We also investigate the suitability of employing the species appearing in conventional reduced mechanisms as constraints, drawing an analogy between the constrained species in RCCE and the non-steady-state species in conventional reduced mechanisms. In particular, the RCCE mechanism is derived from a 63-species comprehensive mechanism of Lindstedt and co-workers and selection of constraints is carried out in accordance with the non-steady-state species employed in a systematically reduced mechanism for methanol derived from the same comprehensive mechanism. Most of the species are very well predicted both at stoichiometric and rich/lean limits.

Modeling of aerosol formation in a turbulent jet with the transported population balance equation-probability density function approach

Processes involving particle formation in turbulent flows feature complex interactions between turbulence and the various physicochemical processes involved. An example of such a process is aerosol formation in a turbulent jet, a process investigated experimentally by Lesniewski and Friedlander [Proc. R. Soc. London, Ser. A 454, 2477 (1998)]. Polydispersed particle formation can be described mathematically by a population balance (also called general dynamic) equation, but its formulation and use within a turbulent flow are riddled with problems, as straightforward averaging results in unknown correlations. In this paper we employ a probability density function formalism in conjunction with the population balance equation (the PBE-PDF method) to simulate and study the experiments of Lesniewski and Friedlander. The approach allows studying the effects of turbulence-particle formation interaction, as well as the prediction of the particle size distribution and the incorporation of kinetics of arbitrary comp...

Dynamic modelling of a bubble column for particle formation via a gas–liquid reaction

Abstract This paper presents a dynamic model of a bubble column reactor with particle formation, accomplished by adopting a hybrid CFD-reaction engineering approach. CFD is employed for estimating the hydrodynamics and is based on the two-phase Eulerian–Eulerian viewpoint. The reaction engineering model links the penetration theory to a population balance that includes particle formation and growth with the aim of predicting the average particle size. The model is then applied to the precipitation of CaCO 3 via CO 2 absorption into Ca(OH) 2 aq in a draft tube bubble column and draws insight into the phenomena underlying the crystal size evolution.

An LES-PBE-PDF approach for modeling particle formation in turbulent reacting flows

Many chemical and environmental processes involve the formation of a polydispersed particulate phase in a turbulent carrier flow. Frequently, the immersed particles are characterized by an intrinsic property such as the particle size, and the distribution of this property across a sample population is taken as an indicator for the quality of the particulate product or its environmental impact. In the present article, we propose a comprehensive model and an efficient numerical solution scheme for predicting the evolution of the property distribution associated with a polydispersed particulate phase forming in a turbulent reacting flow. Here, the particulate phase is described in terms of the particle number density whose evolution in both physical and particle property space is governed by the population balance equation (PBE). Based on the concept of large eddy simulation (LES), we augment the existing LES-transported probability density function (PDF) approach for fluid phase scalars by the particle numb...

Modelling of soot formation in laminar diffusion flames using a comprehensive CFD-PBE model with detailed gas-phase chemistry

A discretised population balance equation (PBE) is coupled with an in-house computational fluid dynamics (CFD) code in order to model soot formation in laminar diffusion flames. The unsteady Navier–Stokes, species and enthalpy transport equations and the spatially-distributed discretised PBE for the soot particles are solved in a coupled manner, together with comprehensive gas-phase chemistry and an optically thin radiation model, thus yielding the complete particle size distribution of the soot particles. Nucleation, surface growth and oxidation are incorporated into the PBE using an acetylene-based soot model. The potential of the proposed methodology is investigated by comparing with experimental results from the Santoro jet burner [Santoro, Semerjian and Dobbins, Soot particle measurements in diffusion flames, Combustion and Flame, Vol. 51 (1983), pp. 203–218; Santoro, Yeh, Horvath and Semerjian, The transport and growth of soot particles in laminar diffusion flames, Combustion Science and Technology, Vol. 53 (1987), pp. 89–115] for three laminar axisymmetric non-premixed ethylene flames: a non-smoking, an incipient smoking and a smoking flame. Overall, good agreement is observed between the numerical and the experimental results.

Reduced flame kinetics via rate-controlled constrained equilibrium

The dynamical evolution of a chemically reacting system is governed by the equations of chemical kinetics, which exhibit a wide range of time scales thus giving rise to stiff equations. In the rate-controlled constrained equilibrium method (RCCE), the dynamical evolution of the system is governed by the kinetics of the species associated with the slower timescales (kinetically controlled), while the remaining species are calculated via a constrained minimisation of the Gibbs free energy of the mixture. This permits the derivation of a general set of differential-algebraic equations, which apply to any reduced system given a particular selection of kinetically controlled species. In this paper, it is shown how the differential-algebraic formulation of RCCE can be derived from first principles, in the form of an extension of the computation of chemical equilibrium via miminisation of the free energy. Subsequently, RCCE is employed to reduce a comprehensive combustion mechanism and to calculate the burning velocity of premixed H2-O2 and CH4-air flames under a range of pressures and equivalence ratios.

Development of novel process designs for simultaneous oxidation and denitrification of wastewaters

Publisher Summary A superstructure-based stochastic reaction-separation network optimization methodology has been applied to investigate and optimize the activated sludge process for combined nitrogen and chemical oxygen demand removal. The method indicated a new concept for process design; instead of employing separate aerobic and anoxic reactors, it is suggested that both processes are be carried out within the same unit. This can be accomplished by the use of very low dissolved oxygen concentrations that allow both aerobic and anoxic reactions to proceed at reasonable rates. The new design policy yielded a significant improvement in nitrogen removal. The ability of the optimization methodology to deal with complex problems was demonstrated and its potential to detect novel designs based on concepts radically different from those of conventional processes. The optimization has, however, employed the reaction model within the whole domain of possible configurations, and the optimum did not occur in the region where the kinetic parameters were determined. The new designs feature significantly improved nitrogen removal as compared to conventional processes. A detailed activated sludge model with a robust synthesis methodology for multiphase reaction–separation networks is combined to gain insights into the complex process tradeoffs and to seek pathways toward improved designs.