Alexander Zinser

A Dynamic Method for Computing Thermodynamic Equilibria in Process Simulation

Abstract A generalized approach for the calculation of complex chemical and phase equilibria is presented which is based on the simulation of the dynamic evolution of a mixture from arbitrary nonequilibrium initial composition towards the final equilibrium composition. The proposed method is able to deal with pure chemical or pure phase equilibria as well as with simultaneous chemical/phase equilibria. The unique advantage of our approach, compared to conventional equilibrium calculations, is the fact that the simulation algorithm always converges towards the thermodynamic equilibrium state regardless which initial composition is chosen.

Dynamic method for computation of chemical and phase equilibria

Abstract A generalized approach for the calculation of complex chemical and phase equilibria is presented that is based on the simulation of the dynamic evolution of a mixture from non-equilibrium initial composition towards the final equilibrium composition. The proposed method is able to solve pure chemical or phase equilibria as well as simultaneous chemical/phase equilibria. The advantage of our approach compared to conventional equilibrium calculations is the fact that the approach is physically motivated and can handle chemical and phase equilibria as well as simultaneous chemical and phase equilibria.

Computationally Efficient Steady-State Process Simulation by Applying a Simultaneous Dynamic Method

Abstract In process simulation, different types of thermodynamic equilibria have to be solved. In a previous work (Zinser et al., 2015), we introduced a dynamic method to solve general thermodynamic equilibrium problems which is based on the solution of a set of ordinary differential equations (ODE). In this contribution, we extend our approach to the simultaneous solution of an overall process flowsheet in one iteration step. This is done by the coupling of the ODE systems of the single units according to the flowsheet connectivity. It is shown, that this leads to a significant improvement of the computational costs compared to conventional methods. A systematic comparison with established algorithms is performed with respect to convergence, initialization and computational costs.

Process Optimization by Applying a Simultaneous Dynamic Method

Abstract In process optimization, an objective function such as the energy demand of a process has to be minimized subject to the equations of the process model. In a previous work (Zinser et al., 2016a) the conditions for thermodynamic equilibria on the unit level were combined with the mass balances on the process level. This approach is based on the solution of a set of ordinary differential equations from an arbitrary initial guess towards its steady state. In this contribution this dynamic approach is extended to optimization problems by introducing additional evolution equations which describe the evolution of some process parameters from an initial guess into the optimal process parameters subject to the energy demand of the process. Since the presented method does not require any iterative interaction between optimization problem and process simulation, it is shown that the computational costs are in the same order of magnitude as in a conventional process simulation at fixed process parameters.