Flowsheet optimization using process

Rigorous Flowsheet Optimization Using Process Simulators and Surrogate Models... 

Major equipment estimates based on a more detailed given flowsheet that includes all of the equipment of significance roughly sized with approximate costs. Optimization using process flow simulators (refer to Chapter 15) can be employed. Figure B.2 illustrates a typical analysis for a tank. Refer to Brown (2000) for additional details. 

We first considered applications of this approach within process engineering. Steady-state flowsheeting or simulation tools are the workhorse for most process design studies the application of simultaneous optimization strategies has allowed optimization of these designs to be performed within an order of magnitude of the effort required for the simulation problem. An application of this strategy to an ammonia synthesis process was presented. Currently, flowsheet optimization is widely available commercially and has also been installed on the FLOWTRAN simulator for academic use. 

Optimize each application of PUREX and the overall process to attain sufficiently improved performance by refinement of flowsheet conditions using reliable and accurate software (i.e., database and simulation code) and by sophistication of process-control methods. 

At the process level, efficient flowsheet optimization strategies based on lumped parameter models are now widely used in practice (Biegler et al., 1997). At this scale, the PEFC is embedded within a power plant flowsheet model, as shown in Figure 3. The process comprises... 

At the end of the Level 4, the result is a close-to-optimum process flowsheet together with a consistent material balance. The next levels will have as a goal the solution of the problems related with the optimal use of energetic resources and material utilities, as well as with waste minimization and plantwide process control. 

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. 

In all of these alternatives, the design team selects acceptable temperature levels and flow rates of the recirculating fluids. These are usually limited by the rates of reaction, and especially the need to avoid thermal runaway or catalyst deterioration, as well as the materials (rf construction and the temperature levels of the available cold process streams and utilities, such as cooling water. It is common to assign temperatures on the basis of these factors earily in process synthesis. However, as optimization strategies are perfected, temperature levels are varied within bounds. See Chapters 10 and 18 for discussions of the use of optimization in process synthesis and optimization of process flowsheets, as well as Example 6.3 to see how constrained optimization is applied to design an ammonia cold-shot converter. 

Improved heat recovery is the most common for improved energy efficiency. However, a recent work (Zhu et al., 2011) has pointed to the areas of process and equipment iimovations, which are less commonly explored, that may provide significant opportunities for both process and energy improvements. Implementing process innovations often results in combined benefits in process yields, throughput, and energy efficiency. Many of these areas include process condition, flowsheet optimization, as well as use of advanced equipment. Some examples can be listed as below... 

The strategy for optimizing a process flowsheet using response surface concepts from the statistical design of experiments was introduced. The method was then illustrated using the DME flowsheet from... 

The initial aim of the procedure is to generate a reasonable base case design that can be used for preliminary economic evaluation of the process. This can subsequently be optimized and/or compared with any process alternatives that are identified. The complete process is always considered at each decision level, but additional fine detail is added to the structure of the flowsheet at any stage. Established heuristics and equipment selection procedures are used together with new process synthesis insights to guide each flowsheet decision. 

In Chapter 1, two alternative ways were discussed that can be used to develop the structure of a flowsheet. In the first way, an irreducible structure is built by successively adding new features if these can be justified technically and economically. The second way to develop the structure of a flowsheet is to first create a superstructure. This superstructure involves redundant features but includes the structural options that should be considered. This superstructure is then subjected to optimization. The optimization varies the settings of the process parameters (e.g. temperature, flowrate) and also optimizes the structural features. Thus to adopt this approach, both structural and parameter optimization must be carried out. So far, the discussion of optimization has been restricted to parameter optimization. Consider now how structural optimization can be carried out. 

Process simulators contain the model of the process and thus contain the bulk of the constraints in an optimization problem. The equality constraints ( hard constraints ) include all the mathematical relations that constitute the material and energy balances, the rate equations, the phase relations, the controls, connecting variables, and methods of computing the physical properties used in any of the relations in the model. The inequality constraints ( soft constraints ) include material flow limits maximum heat exchanger areas pressure, temperature, and concentration upper and lower bounds environmental stipulations vessel hold-ups safety constraints and so on. A module is a model of an individual element in a flowsheet (e.g., a reactor) that can be coded, analyzed, debugged, and interpreted by itself. Examine Figure 15.3a and b. 

Although, as explained in Chapter 9, many optimization problems can be naturally formulated as mixed-integer programming problems, in this chapter we will consider only steady-state nonlinear programming problems in which the variables are continuous. In some cases it may be feasible to use binary variables (on-off) to include or exclude specific stream flows, alternative flowsheet topography, or different parameters. In the economic evaluation of processes, in design, or in control, usually only a few (5-50) variables are decision, or independent, variables amid a multitude of dependent variables (hundreds or thousands). The number of dependent variables in principle (but not necessarily in practice) is equivalent to the number of independent equality constraints plus the active inequality constraints in a process. The number of independent (decision) variables comprises the remaining set of variables whose values are unknown. Introduction into the model of a specification of the value of a variable, such as T = 400°C, is equivalent to the solution of an independent equation and reduces the total number of variables whose values are unknown by one. 

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