Sequential modular modeling

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. 

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. 

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 (SM) and equation-oriented (EO) modeling difficulty versus complexity.

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 number of variations are possible with such two tiered sytems. Tearing can take place in the conventional way and the torn streams can be estimated. Each module in turn can be calculated as in the sequential modular systems. A linearized model of each module can then be generated which in turn can be used in the linearized flowsheet model. From Equation (1)... 

Umeda and Nishio ( J3) using fully linearized models compared the sequential modular and simultaneous modular approaches and concluded each architecture had its area of applicability. 

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. 

The Newton/sparse matrix methods now used by electrical engineers have become the solution method of choice. Hutchison and his students at Cambridge were among the first chemical engineers to publish this approach, in the early 1970s. They used a quasi-linear model rather than a Newton one, but the ideas were really very similar. (It appears that the COPE flowsheeting system of Exxon was Newton based it existed in the mid-1960s but slowly evolved into a sequential modular system. One must assume the Newton method failed to compete.)... 

Implementation of dynamic simulators has led to interesting research issues. For example, many have been implemented in a sequential modular format. To carry out the integration correctly from the point of view of correctly assessing integration errors, each unit model can receive as input a current estimate for the state variables (variables x), the unit input stream variables, and any independent input variables specified versus time... 

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, new developments were achieved through the use of new examples, one of which the optimisation of a real crude distillation unit involving 19 decision variables. The performance of the metamodel-based optimisation is compared with results obtained with the optimisation based on a first-principles model, embedded in a sequential-modular process simulator. It is shown that metamodel-based optimisation with adaptation of the metamodels during the optimisation procedure provides results with good accuracy and significant reduction of computational effort. The performance comparison between neural networks and kriging models for chemical processes is another contribution of this work. 

The process model was built using PETROX, a proprietary sequential-modular process simulator from PETROBRAS. The simulation comprises 53 components and pseudocomponents and 64 unit operation modules, including 7 distillation columns and a recycle stream. All modules are built with rigorous, first-principles models. For optimization applications, PETROX was linked to NPSOL, an SQP optimisation algorithm. 

The methodology was tested with an example involving the optimisation of a crude distillation unit, using the first-principles models of a sequential-modular process simulator. The solution of the corresponding optimisation problem with this rigorous model required considerable computational effort. 

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. 

The two basic flowsheet software architectures are sequential modular and equation-based. In sequential modular, we write each unit model so that it calculates output(s), given feed(s), and unit parameters. This is the most commonly used flowsheeting architecture at present, and examples include Aspen+ plus Hysys (AspenTech), ChemCAD, and PROll (SimSci). In equation-based (or open-system) architectures, all equations are written describing material and energy balances as algebraic equations in the form/(x) = 0. This is the preferred architecture for new simulators and optimization, and examples include Speedup (AspenTech) and gPROMS (PSE pic). Each is discussed in turn. 

Simultaneous-modular methods attempt to combine the features of the sequential-modular and equation-oriented approaches to make use of models developed for the forater and to include (he flexibility of the latter.3 For more information, the reader is referred to the recent review by Biegler.3 ... 

In Simultaneous-Modular approach the solution strategy is a combination of Sequential-Modular and Equation-Oriented approaches. Rigorous models are used at units level, which are solved sequentially, while linear models are used at flowsheet level, solved globally. The linear models are updated based on results obtained with rigorous models. This architecture has been experimented in some academic products. 

Equation-oriented approaches ate based on sets of equations that are written for the units in a particular flowsheet. Unlike the sequential-modular systems, which often contain the necessary information for a variety of process units, equation-oriented synthesizers require the practitioner to develop the model equations. These are solved through iterative techniques with standard numerical methods. 

A chemical process plant consists of many unit operations connected by process streams. Each process unit may be modelled by a set of equations (ODEs, PDEs, DAEs, algebraic equations), which include material, energy and momentum balances, phase and chemical equilibrium relations, rate equations and physical property correlations. These equations relate the outlet stream variables to the inlet stream variables for a given set of equipment parameters. At present, there are three approaches of flowsheet calculations the sequential modular, the equation oriented approach and the simultaneous modular strategy. 

In the simulation mode, the system of equations (8.1) may contain several thousand equations and variables. As it may be very difficult to solve a unique model that includes the entire plant, the solution procedure demands a systematic and modular approach. The strategy adopted by sequential modular process simulator is to write balance equations separately for each unit. This leads to rewrite the system of equations (8.1) in the following form ... 

In a sequential modular simulator, the unit models are encapsulated as procedures where the output streams are desired design parameters. These procedures are then solved in a sequence that generally parallels the flow of material of actual process. A remarkable exception is the process simulator HYSYS, for which most unit operations are solved as soon as all the degrees of freedom are satisfied independently of whether those specifications are introduced. 

FllnS is a modular simulator, and each module is activated in a sequential way to recreate a spectro-spatial measurement via a core program. The easiest way to understand how FllnS works is through a flow diagram, as shown in Fig.4.1. Each box in the flow diagram indicates a FllnS Module. Pink boxes correspond to the Sky Simulator, blue boxes indicate modules inside the Instrument Model, and the green box represents the output of the simulator. 

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