Process sequential modular

PLOW 1 RAN was made available in 1974 by Monsanto Co. for steady-state simulation of chemical processes based on sequential modular technology. It requires specification of feed streams and topology of the system. In 1987, an optimization enhancement was added. 

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. 

Historically, sequential-modular simulators were developed first. They were also developed primarily ia iadustry. They coatiaue to be widely used. la terms of unit operatioas, each module can be made as simple or complex as needed. New modules can be added as needed. Equation-oriented simulators, on the other hand, are able to handle arbitrary specifications and limitations for the entire process dow sheet more dexibly and conveniendy than sequential-modular simulators, and process optimization can also be carried out with less computer effort. 

The second classification is the physical model. Examples are the rigorous modiiles found in chemical-process simulators. In sequential modular simulators, distillation and kinetic reactors are two important examples. Compared to relational models, physical models purport to represent the ac tual material, energy, equilibrium, and rate processes present in the unit. They rarely, however, include any equipment constraints as part of the model. Despite their complexity, adjustable parameters oearing some relation to theoiy (e.g., tray efficiency) are required such that the output is properly related to the input and specifications. These modds provide more accurate predictions of output based on input and specifications. However, the interactions between the model parameters and database parameters compromise the relationships between input and output. The nonlinearities of equipment performance are not included and, consequently, significant extrapolations result in large errors. Despite their greater complexity, they should be considered to be approximate as well. 

Sequential-modular programs in which the equations describing each process unit (module) are solved module-by-module in a stepwise manner and iterative techniques used to solve the problems arising from the recycle of information. 

In the past, most simulation programs available to designers were of the sequential-modular type. They were simpler to develop than the equation based programs, and required only moderate computing power. The modules are processed sequentially, so essentially only the equations for a particular unit are in the computer memory at one time. Also, the process conditions, temperature, pressure, flow-rate, are fixed in time. 

Sequential modular. Refers to the process simulator being based on modules, and the modules solved in a sequential precedence order imposed by the flowsheet information flow. 

Commercial process simulators mainly use a form of SQP. To use LP, you must balance the nonlinearity of the plant model (constraints) and the objective function with the error in approximation of the plant by linear models. Infeasible path, sequential modular SQP has proven particularly effective. 

Typical process modules used in sequential modular-based flowsheeting codes with their subroutine names. 

Kisala, T. P. R. A. Trevino-Lozano J. F. Boston H. I. Britt et al. Sequential Modular and Simultaneous Modular Strategies for Process Flowsheet Optimization. Comput Chem Eng 11 567-579 (1987). 

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. 

FIGURE 2 Sequential modular solution of a chemical process with recycle. 

The solution of a chemical process simulation problem using the sequential modular technique is represented in Fig. 2. Here, the modeling equations can be written such that the outlet stream from each unit is a function of the inlet streams to each unit ... 

An alternative to the sequential modular approach is to solve the equations modeling all of the units in a process flowsheet simultaneously this is known as the equation-based approach. Advantages to the sequential modular approach include (1) specialized numerical techniques tailored to each unit operation can be used, and (2) the numerical failure of one unit operation may still yield usable flowsheet information. Advantages to the equation-based... 

A major academic effort has been mounted to reevaluate system architectures. This has been motivated by the limitations of the sequential modular method for design and optimization (21). This in turn has led to a strong research effort in equation solving methods tailored to meet the needs of process simulation. 

Sequential Modular. By far the most experience with flowsheeting systems has been with the sequential modular architecture (59- 3). It is this architecture that is most easily understood by the process engineer. Each module calculates all output streams from input streams subject to module parameters. Generally, the stream variables consist of component flows, temperature (or enthalpy) and pressure as the independent variables. Other dependent variables such as total flow, fraction vapor and total enthalpy (or temperature) are often carried in the stream. 

Lin (100) suggested breaking the process flowsheet into one or more blocks of modules. Each block of modules contains one or more modules and all of the modules in the same block are solved simultaneously. The whole process flowsheet is then solved by conventional sequential modular approach by treating each block as a module. 

The computational architecture is a sequential modular approach with advanced features. To model a process, each equipment module is simulated by a program module. The overall process is simulated by connecting the models together in the same way as the equipment in the flow sheet. When the input streams are known then the outputs can be calculated. The entire flowsheet can be calculated "sequentially" in this manner. Advanced features are discussed below in connection with an example. 

Simulation techniques suitable for the description of phenomena at each length-scale are now relatively well established Monte Carlo (MC) and Molecular Dynamics (MD) methods at the molecular length-scale, various mesoscopic simulation methods such as Dissipative Particle Dynamics (Groot and Warren, 1997), Brownian Dynamics, or Lattice Boltzmann in the colloidal domain, Computational Fluid Dynamics at the continuum length-scale, and sequential-modular or equation-based methods at the unit operation/process-systems level. 

Chemical processes more often than not contain recycle, a feature that complicates their analysis. Recycle often occurs, as in the styrene process where unreacted ethylbenzene is recovered and recycled back to the reactor as a physical mass flow. Recycle also occurs in the form of heat exchange (again in the styrene process) and sometimes as information, e.g., a specification that two variable temperatures must equal each other. The sequential-modular solution strategy is based upon knowing all inputs to a module and using these to calculate all outputs. When an input stream to a module is the output of a downstream module (i.e., there is recycle), calculations cannot be performed for the upstream module because one of its inputs is not yet known. This is illustrated in Fig. 4.7 unit 1 cannot be calculated because input stream 4 is the output of unit 2 nor can unit 2 be calculated because input stream 2 is an output of unit 1. This same problem of circular reasoning was encountered in Example 1. This dilemma in the sequential modular solution scheme can be... 

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. 

Now that the problem is formulated we turn our attention to solving the equations. One solution method that we could use is the sequential modular method. For this method, select one of the process units as the starting point for the calculation. Then, assume values for some of variables to reduce the degrees of freedom to zero for that rniit. Next, precede unit-by-unit through the flow sheet... 

Given a description of a multiple-unit process, determine the number of degrees of freedom, identify a set of feasible design variables, and if there are cycles in the flowchart, identify reasonable tear stream variables and outline the solution procedure. Draw a sequential modular block diagram for the process, inserting necessary convergence blocks. 

As we noted at the beginning of this chapter, there are two broad approaches to the automated solution of the balance equations for a process system the sequential modular approach and the equation-based approach. This section outlines the first of these methods. The balance equations (and any other equations that may arise from physical considerations or process specifications) for each unit are written and solved. If there are no recycle streams, the calculation moves from one unit to another, until all units have been covered. If there is a cycle (the conventional term for a recycle loop in a process flowchart), a trial-and-error procedure is required values of one or more stream variables in the cycle are assumed the balance equations for units in the cycle are solved, one unit at a time, until the values of the assumed variables are recalculated new variable values are assumed and the procedure is repeated until the assumed and calculated values agree. 

The first step in setting up a process for the sequential modular approach is to reconstruct the process flowchart in terms of blocks or modules (process units or operations) and streams connecting them. Several types of blocks and names that might be given to them are as follows ... 

The next example illustrates the structuring of a sequential modular process simulation using blocks of the types just described. 

If the calculations were to be done by hand, overall system and subsystem balances would eventually yield n equations in unknowns, and the equations could then in principle be solved for all the desired process variables. It would be difficult to write a sequential modular program to implement this method for an arbitrary process, however. Instead, the following iterative approach is used. 

Equations could be derived and solved for all of the unknown process variables, making trial-and-error solution unnecessary. However, for illustrative purposes we will set up the spreadsheet to parallel the sequential modular solution procedure of part 2. 

An example of a sequential modular simulation of a relatively large process is given in Example 10.3-3, following a discussion of the second broad approach to process simulation. 

The sequential modular approach to process simulation solves system equations in blocks corresponding to the unit operations that make up the process. The block diagram for the process looks very much like the traditional process flowchart. Since engineers are accustomed to viewing chemical processes as sequences of unit operations, they lend to feel comfortable with this approach. 

The final example illustrates the analysis of a multiple-unit process with several internal cycles, using both the sequential modular and equation-based approaches. 

Set up a sequential modular simulation of the process, using the following blocks ... 

Figure lOJ-2 Block diagram for sequential modular simulation of ammonium nitrate process. 

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