Dynamic Simulation of the Process

Process Safety Considerations. Unit optimization studies combined with dynamic simulations of the process may identify operating conditions that are unsafe regarding fire safety, equipment damage potential, and operating sensitivity. Several instances of fires and deflagrations in ethylene oxide production units have been reported in the past (160). These incidents have occurred in both the reaction cycle and ethylene oxide refining areas. Therefore, ethylene oxide units should always be designed to prevent the formation of explosive gas mixtures. 

We will first concentrate on studying the process dynamics, so let us consider a numerical experiment that consists of starting a dynamic simulation of the process from initial conditions that are slightly perturbed from the nominal, steady-state values of the state variables. Although material holdups are stabilized using the proportional controllers in Equation (4.40), in view of the process-level operating objective stated above, this can be considered an open-loop simulation. 

A periodic adsorption process is operated as a series of sequential steps on one or more a otbers packed with either single or multiple layers of adsorbent. Although the operation of each bed is batch-wise, the whole system is continuous owing to the use of multiple adsorbers operated in phased cycles. The traditional approach for CSS determination is to execute a dynamic simulation of the process, starting with a specified set of initial conditions and then simulated over a large number of cycles until a CSS condition is eventually obtained with a pre-defined criteria, i.e., the cycle initial state at to must be identical with the cycle end state at tn (see Figure 1). 

The stream conditions shown in Figure 14.1 are from the dynamic simulation of the process at steady-state conditions with the recycle of solvent loop closed. This loop did not converge in the steady-state Aspen Plus simulation. Other simulation issues are discussed in the next section. 

A natural and obvious way to calculate a periodic state of a cyclic process is to simply simulate the dynamic behaviour of the process. The simulation will approach, as does the physical system, a periodic state. The disadvantage of this strategy is that the initial transient phase may be very long, in particular in the presence of large capacity terms and slow kinetic terms. All extunple reactor types given above are based on packed beds, where a column is filled with a solid sorbent and/or catalyst. Typically the solids inventory is very high and so is the buffer capacity in terms of the adsorption capacity or heat capacity. For such systems the dynamic simulation of the process may need tens of thousands of cycles in order to converge to a periodic state. 

This work briefly shows some findings about the influence of RTE parameters on the process economical performance. As a result, a methodology for an adequate tuning of such parameters is proposed. It is shown how the parameters related to control variables can be tuned just by using the steady state model. On the other hand, the time parameter needs both, the characterisation of the disturbance in terms of amplitude and frequency and a further testing over a dynamic simulation of the process. In addition, a periodical characterisation of the disturbances allows an on-line adaptation of the parameters. 

Dynamic simulation of the process was performed to evaluate control performance. Temperature transmitter span was 50 F, and maximum cooling water flow was 4 times the design value. Relay-feedback tests were run to find the ultimate gain and ultimate period for each design case with two 15-second lags in the temperature loop. Then the TL controller settings were used. 

Certainly the bio-process with heat integration considered here would be a challenging test for many of these approaches in terms of the size and scope of what must be considered, the number of potential design alternatives, and the type of control objectives and disturbances that must be considered. A typical industrial approach would be to work through systematically all of the control objectives using a nonlinear dynamic simulation of the process to assess alternatives and to analyze performance (Fig. 11). 

To check these findings, dynamic simulations of the process, using PI controllers, are performed with HYSYS.Plant. At steady state, the hot stream of n-octane at 2,350 Ibmol/h is cooled from 500 to 300°F using n-decane as the coolant, with F2 = 3,070 Ibmol/h and F3 = 1,200 Ibmol/h. Note that these species and flow rates are chosen to match the heat-capacity flow rates defined by [10], with Fi slightly increased to avoid temperature crossovers in the heat exchangers due to temperature variations in the heat capacities. Additional details of the HYSYS.Plant simulation are ... 

The solution to this problem was carried out by using the developed problem-oriented software [11]. The program is based on the molecular dynamic simulation of the processes that accompany the formation of nanoheterostraetuies. The use of the methods of molecular dynamics for... 

While the classical approach to simulation of slow activated events, as described above, has received extensive attention in the literature and the methods are in general well established, the methods for quantum-dynamical simulation of reactive processes in complex systems in the condensed phase are still under development. We briefly consider electron and proton quantum dynamics. 

In addition to enhancing surface reactions, water can also facilitate surface transport processes. First-principles ab initio molecular dynamics simulations of the aqueous/ metal interface for Rh(l 11) [Vassilev et al., 2002] and PtRu(OOOl) alloy [Desai et al., 2003b] surfaces showed that the aqueous interface enhanced the apparent transport or diffusion of OH intermediates across the metal surface. Adsorbed OH and H2O molecules engage in fast proton transfer, such that OH appears to diffuse across the surface. The oxygen atoms, however, remained fixed at the same positions, and it is only the proton that transfers. Transport occurs via the symmetric reaction... 

The source material will release excess silicon in the beginning of the growth cycle and be more carbon-rich in the end due to preferential depletion of silicon. This is a known problem and it is a matter of detailed control and an understanding of the dynamic transport mechanisms in combination with thermodynamics. Nevertheless, the result is invariably that SiC boules grown by seeded sublimation growth are Si-rich in the beginning and C-rich near the end, which creates yield issues. Simulation of the process is necessary to improve the situation. 

In a later section we show results of rigorous dynamic simulations of this process. However, it may be useful at this point to show the predictions of a linear model of this type of FEHE-reactor system. 

II, for the simulation of molecular decomposition. The aim of the present work is to reveal step by step the mechanism of explosion and to find the way of reliable characterization of explosive materials using classical molecular dynamics simulations of the uni-molecular decomposition process. The structure of four molecules selected for dynamic simulations are reported in Ref. 12-15, for RDX, P-HMX, DADNE and NQ. Molecular structures are shown in the Fig. la-d. 

In all dynamical simulations presented so far, it has been assumed that the electrons stay in their ground state throughout the whole process, i.e. the simulations have been based on the Born-Oppenheimer approximation. Still, at metal surfaces with their continuous spectrum of electronic states at the Fermi energy electron-hole (e-h) pair excitations with arbitrarily small energies are possible. However, the incorporation of electronically nonadiabatic effects in the dynamical simulation of the interaction dynamics of molecules with surface is rather difficult [2, 109, 110]. Hence the role of electron-hole pairs in the adsorption dynamics as an additional dissipation channel is still unclear [4],... 

A. Woinaroschy, 1986, A New Model for the Dynamic Simulation of the Rectification Processes. 

Dynamic simulations represent the temporal and the spatial behavior of a chemical process unit in the presence of perturbations or at process startup. There is a natural division in the types of numerical methods used to solve the equations describing the dynamic behavior of the process. In lumped parameter descriptions of the process units, the resulting equations are ordinary time evolution differential equations, whereas for distributed parameter descriptions of process units the resulting equations are parabolic partial differential equations. The numerical methods used to solve these equations are very different and necessitate a separate discussion. Numerical methods used to solve ordinary differential equations describing the dynamics are considered first followed by a discussion of the methods employed to solve evolution equations of the parabolic type. 

Tasks 1 and 4 receive dynamic data of the process from a dynamic simulation model. This model predicts dynamic responses of the process variables to step changes of the manipulated variables. 

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