Simulation simultaneous modular

Simultaneous modular. The process simulator is composed of modules, but simplified, approximate, or partial representation of the modules enables solution techniques used in equation-based methods to be employed. 

Mahalec, V. H. Kluzik and L. B. Evans. Simultaneous Modular Algorithm for Steady State Flowsheet Simulation and Design. Paper presented at the 12th European Symposium on Computers in Chemical Engineering. Montreaux, Switzerland (1979). 

The older modular simulation mode, on the other hand, is more common in commerical applications. Here process equations are organized within their particular unit operation. Solution methods that apply to a particular unit operation solve the unit model and pass the resulting stream information to the next unit. Thus, the unit operation represents a procedure or module in the overall flowsheet calculation. These calculations continue from unit to unit, with recycle streams in the process updated and converged with new unit information. Consequently, the flow of information in the simulation systems is often analogous to the flow of material in the actual process. Unlike equation-oriented simulators, modular simulators solve smaller sets of equations, and the solution procedure can be tailored for the particular unit operation. However, because the equations are embedded within procedures, it becomes difficult to provide problem specifications where the information flow does not parallel that of the flowsheet. The earliest modular simulators (the sequential modular type) accommodated these specifications, as well as complex recycle loops, through inefficient iterative procedures. The more recent simultaneous modular simulators now have efficient convergence capabilities for handling multiple recycles and nonconventional problem specifications in a coordinated manner. 

Dynamic simulation, process control, real-time optimization Process synthesis, flowsheet convergence, simultaneous modular vs. equation-oriented... 

Mahalec, V. Kluzik, H. Evans, L. B., "Simultaneous Modular Algorithm for Steady State Flowsheet Simulation and Design", CACE 79, EFCE Montreux, April 8-11, 1979. 

Lin, T. D., "A Simultaneous Modular Simulator and A Sequential Block - Modular Simulator for Process Design or Simulations", Paper presented at AlChe Meeting, Houston, April 1979. 

Another potential advancement is permitted in the ASPEN system. Tear streams can be designated as desired, so that a user might define blocks or series of blocks and simulate these sets as quasi-linear blocks. The convergence method could utilize this information and solve the material (and energy) balances explicitly. In this way, a simultaneous modular architecture could be utilized. Implementation of these programs will be for later enhancements of ASPEN, not the initial version. 

Ihese difficulties vanish if the system equations are simply collected and solved for all unknown variables. Several powerful equation-solving algorithms are available in commercial programs like Maple , Mathematica , Matlab , Mathcad , and E-Z Solve that make the equation-based approach competitive with the sequential modular approach. Many researchers in the field believe that as this trend continues, the former approach will replace the latter one as the standard method for flowsheet simulation. (Engineers are also working on simultaneous modular methods, which combine features of both sequential modular and equation-based approaches. We will not deal with these refinements here, however.)... 

More recant efforts to develop computer-aided process synthesis methodologies can be cbeincterized as either sequential modular, simultaneous modular, or equation oriented, Sequeatial-modular approaches are ben for steady-state simulation where process inputs are defined and process para met ara ate available. The best feature of sequential-modular approaches is that they are flowsheet oriented, bot they are not as flexible as the other methodologies in parfonuing design and optimization tasks.39... 

L. T. Biegler, Simultaneous Modular Simulation and Optimimtion. in Proceedings of the Second... 

There are basically three types of solution algorithm for process simulators [1] sequential modular, equation solving (simultaneous nonmodular), and simultaneous modular. 

Equations-Oriented Simulators. In contrast to the sequential-modular simulators that handle the calculations of each unit operation as an iaput—output module, the equations-oriented simulators treat all the material and energy balance equations that arise ia all the unit operations of the process dow sheet as one set of simultaneous equations. In some cases, the physical properties estimation equations also are iacluded as additional equations ia this set of simultaneous equations. 

Modular simulators are frequently constructed on three levels. The lowest level consists of thermodynamics and other physical property relations that are accessed frequently for a large number of flowsheeting utilities (flash calculations, enthalpy balances, etc.). The next level consists of unit operations models as described above. The highest level then deals with the sequencing and convergence of the flowsheet models. Here, simultaneous... 

Before leaving this section we consider a slightly different optimization problem that may also be expensive to solve. In flowsheet optimization, the process simulator is based almost entirely on equilibrium concepts. Separation units are described by equilibrium stage models, and reactors are frequently represented by fixed conversion or equilibrium models. More complex reactor models usually need to be developed and added to the simulator by the engineer. Here the modular nature of the simulator requires the reactor model to be solved for every flowsheet pass, a potentially expensive calculation. For simulation, if the reactor is relatively insensitive to the flowsheet, a simpler model can often be substituted. For process optimization, a simpler, insensitive model will necessarily lead to suboptimal (or even infeasible) results. The reactor and flowsheet models must therefore be considered simultaneously in the optimization. 

Process design for continuous processes is carried out mostly using steady-state simulators. In steady-state process simulation, individual process units or entire floivsheets are calculated, such that there are no time deviations of variables and parameters. Most of the steady-state floivsheet simulators use a sequential modular approach in which the flowsheet is broken into small units. Since each unit is solved separately, the flowsheet is worked through sequentially and iteration is continued until the entire flowsheet is converged. Another way to solve the flowsheet is to use the equation oriented approach, where the flowsheet is handled as a large set of equations, which are solved simultaneously. 

In the equation-based approach, the equations for all units are collected and solved simultaneously. The natural decomposition of the system into its constituent unit operations is therefore lost. Moreover, the simultaneous solution of large numbers of equations, some of which may be nonlinear, can be a cumbersome and time-consuming problem, even for a powerful computer. For all these reasons, most commercial simulation programs were still based on the sequential modular approach when this text was written. 

When there are multiple recycles present, it is sometimes more effective to solve the model in a simultaneous (equation-oriented) mode rather than in a sequential modular mode. If the simulation problem allows simultaneous solution of the equation set, this can be attempted. If the process is known to contain many recycles, then the designer should anticipate convergence problems and should select a process simulation program that can be run in a simultaneous mode. 

In this work, we develop an algorithm based on fitting response surfaces -using a kriging metamodel- for the optimization of constrained-noise black box models. Besides, an important characteristic is that we deal with constrained problems in which the metamodel can represent either the objective function or some constraints (or both simultaneously). A typical case is the optimization of process flowsheets using modular simulators in which some units are represented by a metamodel. In these systems it is possible to include external constraints and even the result of some calculations could be constraints to the model. 

So-called plant dispersion" or extra column effects" have to be taken into account by additional mathematical models rather than including them indirectly in the model parameters of the column, e.g. by altering the dispersion coefficient. The combination of peripheral and column models is easily implemented in a modular simulation approach. In a flowsheeting approach the boundary conditions of different models are connected by streams (node balances) and all material balances are solved simultaneously. 

Sequential modular approach has some clear advantages for process flowsheeting that explain why it still dominates the technology of steady-state simulation over the simultaneous or equation-oriented approach. Table 8.2 shows a list of pros and cons about sequential modular process simulators. In order to cope with the disadvantages, a few process simulators have improved the flow of information and avoid redundant computations. As an example, Aspen HYSYS has implemented the bidirectional transmission of information technology. 

---