Sequential modular systems

Reduce complexity and design modular systems As far as possible, use proven techniques and combine them in new ways, but let them be independent from each other, e.g., allow different synthesis methods that are independent from your reactor configuration. Furthermore, allow different sequential or parallel analysis methods. This is not a contradiction to the aforementioned integration because workflow integration does not necessarily mean technology integration. 

Partitioning and the Cycle Matrix. Sequential modular systems require an order of calculation (precedence order) be given to the modules. There are generally four steps taken to determine this ordering. 

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)... 

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.)... 

Equation-oriented approaches are based on sets of equations that are written for the units in a particalar flowsheet.36 Unlike the sequential-modular systems, which often contain the necemery information for a veriety of process units, equaiion-orianted synthesizers require the pinctirioner to develop the mode] eqon-lions. These are solved through iterative tecbaiques with staadatd numerical methods. 

Automated radioanalytical chemistry can provide near real time monitoring of reprocessing plant operations (O Hara et al. 2009). A sequential injection chromatography system for the separation and analysis of Am, Pu, and Np isotopes is integrated in a modular system that automates the complete sample analysis process, from initial sample preparation to final data reporting. 

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. 

Algorithmic stmcture of simulator EO, equation oriented HS, hybrid system SM, sequential modular and SMD, sequential modular, suitable for design. 

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. 

Validation is needed to demonstrate that the analytical method complies with established criteria for different performance characteristics [82]. When these different characteristics are being evaluated individually, this is generally done for the analytical method as such—where the input is the purified or isolated analyte and the output is the analytical result. However, MU covers the whole analytical procedure, starting from the original sample lot. The assessment of MU (see Section 8.2.2) is in line with the so-called modular validation approach. Modular validation refers to the modularity of an analytical procedure divided up into several sequential steps needed to analyze the material. These may be sample preparation, analyte extraction, and analyte determination (Figure 7). Each step in the procedure can be seen as an analytical system and can thus be validated separately and combined... 

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. 

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. 

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. 

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. 

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. 

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. 

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.)... 

Hillestad, M., and T. Hertzberg, Dynamic Simulation of Chemical Engineering Systems by the Sequential Modular Approach, Comput. Chem. Eng., v. 10, p. 377 (1986). 

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. 

Flowsheeting is still dominated by the Sequential-Modular architecture, but incorporates increasingly features of the Equation-Oriented solution mode. A limited number of systems can offer both steady state and dynamic flowsheeting simulators. 

The algorithmic treatment depends on the architecture of the flowsheeting system. In Equation-Oriented mode, the approach consists of solving all the equations describing the problem simultaneously. In Sequential-Modular approach the mathematical solution must take into account the convergence of units and tear streams, as well as of all design specifications. Supplementary equations must be added, so that the general formulation of the optimisation problem (3.10) becomes ... 

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