Process simulation strategies

For modular-based process simulators, the determination of derivatives is not so straightforward. One way to get partial derivations of the module function(s) is by perturbation of the inputs of the modules in sequence to calculate finite-difference substitutes for derivatives for the tom variables. To calculate the Jacobian via this strategy, you have to simulate each module (C + 2) nT + nF + 1 times in sequence, where C is the number of chemical species, nT is the number of tom streams, and nF is the number of residual degrees of freedom. The procedure is as follows. Start with a tear stream. Back up along the calculation loop until an unperturbed independent variable xI t in a module is encountered. Perturb the independent variable,... 

The success of this control strategy depends largely on the accuracy of the model prediction, which is often imperfect as models can rarely exactly predict the effects of process disturbances. For this reason, an additional feedback loop is often used as a backup or to trim the main feedforward action, as shown in Fig. 2.25. Many of the continuous process simulation examples in this book may be altered to simulate feedforward control situations. 

Sorensen and Skogestad (1994) developed control strategies for BREAD processes by repetitive simulation strategy using a simple model in SPEEDUP package. Wilson and Martinez (1997) developed EKF (Extended Kalman Filter) based composition estimator to control BREAD processes. The estimator was found to be quite robust and was able to estimate composition within acceptable accuracy, even in the face of process/model mismatches. Balasubramhanya and Doyle III... 

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]. 

Two means of developing a sound RVOP strategy are (1) to learn as you go during the initial application of EVOP to an industrial process, and (2) to practice the technique by optimizing a process simulation. Because of the conservative nature of EVOP, there is little risk in allowing a strategy to... 

Dynamic simulation is more computationally intensive than steady-state simulation. Dynamic simulation is usually applied to parts of a process (or even single unit operations) rather than an entire process. Different simulation strategies are needed to give a robust dynamic model. Good introductions to dynamic simulation are given in the books by Luyben (2006), Ingham et al. (2007), Seborg et al. (2003), and Asprey and Machietto (2003) and the paper by Pantelides (1988). 

The advantage of process simulator calculations comes when the sequence of process steps has been set. The process simulator draws the process flow sheet specified and uses it to fix the computer algorithm for the process. There are optimization strategies to select the best conditions of the feed streams to the equipment and operating conditions for the whole process. 

Maroudas and co-workers have described a hierarchical scheme for atomistic simulations involving the use of electronic structure calculations to develop and test semiempirical potentials that are in turn used for MD simulations. These results can sometimes be used to develop elementary step transition probabilities for use in dynamic Monte Carlo schemes. With Monte Carlo techniques, the well-known length and time scale limitations of MD can be greatly extended. This hierarchical approach appears to have great promise for the development of simulation strategies that will allow studies of a wide range of practical surface and thin-film chemical and physical processes. 

CHEOPS obtains this setup file in XML format from ModKit-l-. Tool wrappers are started according to this XML file. The input files required for the modeling tools Aspen Plus and gPROMS are obtained from the model repository ROME. CHEOPS applies a sequential-modular simulation strategy implemented as a solver component because all tool wrappers are able to provide closed-form model representations. The iterative solution process invokes the model evaluation functionality of each model representation, which refers to the underljdng tool wrapper to invoke the native computation in the modeling tool the model originated from. Finally, the results of all stream variables are written to a Microsoft Excel table when the simulation has terminated. 

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