Process simulation—dynamic

Figure 4-78. Dynamic process simulation test arrangement used by the Elliott Company.

Holl, P.. Marquardt, W., and Gilles, E. D., Diva—a powerful tool for dynamic process simulation. Comput. Chem. Eng. 12, 421 (1988). 

Marquardt, W., Dynamic process Simulation-recent progress and future challenges. In Chemical Process Control, CPC-IV (Y. Arkun, and W. H. Ray, eds.) CACHE, AIChE Publishers, New York, 1991. 

Development of computer code system for analyzing the heat/mass balance, dynamic process simulation, supporting the component design works, etc. 

I.n W. Marquardt, Dynamic Process Simulation Recent Trends and Future Challenges, in Chemical Process Control CPC-IV, Y. Arkun, H. W. Ray, (Eds.), pp. 131-188, CACHE, Austin, 1991. 

Unsteady-state or dynamic simulation accounts for process transients, from an initial state to a final state. Dynamic models for complex chemical processes typically consist of large systems of ordinary differential equations and algebraic equations. Therefore, dynamic process simulation is computationally intensive. Dynamic simulators typically contain three units (i) thermodynamic and physical properties packages, (ii) unit operation models, (hi) numerical solvers. Dynamic simulation is used for batch process design and development, control strategy development, control system check-out, the optimization of plant operations, process reliability/availability/safety studies, process improvement, process start-up and shutdown. There are countless dynamic process simulators available on the market. One of them has the commercial name Hysis [2.3]. 

Throughout this book, we have seen that when more than one species is involved in a process or when energy balances are required, several balance equations must be derived and solved simultaneously. For steady-state systems the equations are algebraic, but when the systems are transient, simultaneous differential equations must be solved. For the simplest systems, analytical solutions may be obtained by hand, but more commonly numerical solutions are required. Software packages that solve general systems of ordinary differential equations— such as Mathematica , Maple , Matlab , TK-Solver , Polymath , and EZ-Solve —are readily obtained for most computers. Other software packages have been designed specifically to simulate transient chemical processes. Some of these dynamic process simulators run in conjunction with the steady-state flowsheet simulators mentioned in Chapter 10 (e.g.. SPEEDUP, which runs with Aspen Plus, and a dynamic component of HYSYS ) and so have access to physical property databases and thermodynamic correlations. 

A reviewer has kindly provided references to other programs for solving differential equations by numerical methods (13-21). These will be useful to individuals that do not have IBM equipment but would like to do calculations of the sort outlined above. The general topic has been discussed (13). Sebastian, et al. (14) discuss DPS (Dynamic Process Simulator) and provide references to descriptions of MIMIC (15), ACSL (16), ISIS (17), BEDSOCS (18), DSL/77 (19), DARE (20) and PMSP (21). References to other programs can be found in papers that accompany reference 2 and in other Proceedings of Conferences on Applications of Continuous System Simulation. Because of the simplicity and power of system simulation programs such as are discussed herein, one could expect that every major computer producer would have available software with the capability of CSMP. 

The balance equations described in the previous sections include both space and time derivatives. Apart from a few simple cases, the resulting set of coupled partial differential equations (PDE) cannot be solved analytically. The solution (the concentration profiles) must be obtained numerically, either using self-developed programs or commercially available dynamic process simulation tools. The latter can be distinguished in general equation solvers, where the model has to be implemented by the user, or special software dedicated to chromatography. Some providers are given in Tab. 6.3. 

Marquardt, W. Dynamic process simulation - recent progress and future challenges. In Arkun, Y., Ray, W.H. (eds.) Proceedings of the Fourth International Conference on Chemical Process Control, Padre Island, Texas, February 17-22, 1991, pp. 131-180 (1991)... 

Table 6.3 Examples of dynamic process simulation tools.

Because of the computational complexities associated with dynamic process simulation for rrmltiunit processes, there is still much to be done before simulators of this type become available for general application. Another problem complicating their development is that process nnodels for even individual separation units ate usually for steady-state cases this is the result of both incomplete understanding of the chemical and physical nciples involved and computational difficulties. This is one of the main reasons why process control considerations ate difficult to incorporate into chemical process simulation and thesis and why on-line plant optimization is still far away in most instances. 

Andreas Kroner, P. HoU, W. M2irqucirdt, cuid E. D. Gilles. Diva - an open circhitecture for dynamic process simulation. Comp. Chem. Engng., 14, 1289-1295 (1990). 

P. Deuflhard, J. Lang and U. Nowcik. Adaptive Algorithms in Dynamical Process Simulation. Preprint SC-95-16, Konrad-Zuse-Zentrum, Berlin, 1995. 

We compared our algorithm GSPAR with FRONTAL, the frontal method of SPEEDUP, using four example matrices arising from dynamic process simulation of chemical plants. The computation time (in CPU sec.) for a Cray Y-MP is given in table 4.1. 

Adaptive computations of nonlinear systems of reaction-diffusion equations play an increasingly important role in dynamical process simulation. The efficient adaptation of the spatial and temporal discretization is often the only way to get relevant solutions of the underlying mathematical models. The corresponding methods are essentially based on a posteriori estimates of the discretization errors. Once these errors have been computed, we are able to control time and space grids with respect to required tolerances and necessary computational work. Furthermore, the permanent assessment of the solution process allows us to clearly distinguish between numerical and modelling errors - a fact which becomes more and more important. 

Operator Training and Operator Support using Multiphase Pipeline Models and Dynamic Process Simulation Sub-Sea Production and On-Shore Processing... 

Novel multiphase technology has been coupled with dynamic process simulation, open control system interfaces and a custom user interface to provide a necessary tool for process operators. We hope to be able to report interesting experience and observations from the commissioning of the system. 

Minimal representations in any discipline are of great importance both from a theoretical and practical point of view. They are known to have no redundant elements and that is why they are easier to handle and to analyse for characteristic model properties. Lumped process models are the most important and widespread class of process models for control and diagnostic applications. The majority of CAPM tools and dynamic process simulators deal only with lumped process models. Therefore we also restrict ourselves to this case. 

Moe, H.I. (1995), Dynamic process simulation studies on modeling and index reduction, PhD thesis, Norwegian Institute of Technology, Trondheim, Norway. 

Utilizing dynamic process simulation as a tool for decision support in the process industry has both short and long term advantages. Once the new upgrades were commissioned, the mill decided to utilize the model to solve specific problems or optimization issues. To achieve this, the mill simulation and operational costs were modeled to enable some degree of manual optimization. A credible decision support system requires real process data, which in this case was achieved by connecting the simulation model to the PI data historian through a DDE link. 

The goal of plantwide control structure synthesis is to develop feasible control structures that address the objectives of the entire chemical plant and account for the interactions associated with complex recycle and heat integration schemes, and the expected multivariate nature of the plant. Many strategies have been proposed for accomplishing this task, and the majority of them have been demonstrated using dynamic process simulations. However, none have been accepted as the universal approach, in a manner similar to the steady-state process design synthesis hierarchy of Douglas [1]. 

Jurgensen L, Ehimen EA, Bom J, Holm-Nielsen JB (2015) Dynamic biogas upgrading based on the Sabatier process Thermodynamic and dynamic process simulation. Biores Technol 178 323-329... 

If a dynamic process simulator is available, it should be used to evaluate the proposed plantwide control strategy and to determine recommended initial controller settings. It also should be used to evaluate the assumptions behind the core model analysis—namely, that the pressure and thermal control loops can be considered to be substantially decoupled from the flow/ level/composition loops. 

PCs), the development of Windows-based systems software, and the development of object-oriented programming languages. This combination of inexpensive hardware and system tools has led to the proliferation of exceptionally user-friendly and robust software tools for steady-state process simulation and design. Dynamic simulation naturally developed along with the steady-state simulators [24]. Figure 1.1 presents a sunamary of the growth of dynamic process simulation. 

The hrst attempts to provide a modular-based dynamic process simulator were DYFLO and DYNSYS [28]. These two early modular simulators differed in their approach. DYFLO provided the simulator with a suite of FORTRAN routines that were linked via a program written by the user. Hence, it was to some extent cumbersome, but useable. DYNSYS [28], on the other hand, provided a key word stmcture much like the steady-state simulators of the era allowing the user to build a dynamic simulation. Both simulators found limited use owing to the difficulty of producing a simulation, and the actual run times on the computer hardware of the time were often greater than real time. 

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