Process simulation optimization

Keywords Process Simulators, Optimization, Kriging, Surrogate Models. 

The process view model takes a major role in defining, establishing, analyzing, and extracting the business processes of a company. It fulfills the requirements of transforming the business process, the manufacturing process, and the product-development process into a process view model. The process model is the basis for business process simulation, optimization, and reengineering. 

Computer simulation programs for process design optimization have been developed for the PUREX process utilizing these relationships (22). A subroutine has also been developed which describes the behavior of fission products (23). 

Given the first type of simulation, it is advantageous to be able to design a system of RO modules that can achieve the process objective at a minimal cost. A model has been iategrated iato a process simulation program to predict the stream matrix for a reverse osmosis process (132). In the area of waste minimization, the proper placement of RO modules is essential for achieving minimum waste at a minimum cost. Excellent details on how to create an optimal network of RO modules is available (96). 

A future goal for the integration of graphics and process design simulators is to be able to use an interactive graphics program to prepare the input to the process simulator. This capabiHty would allow tme on-line process modification, flow-sheet optimization, and process optimization, and is likely to be one of the key developments in this field in the 1990s (99). 

Many process simulators come with optimizers that vary any arbitrary set of stream variables and operating conditions and optimize an objective function. Such optimizers start with an initial set of values of those variables, carry out the simulation for the entire flow sheet, determine the steady-state values of all the other variables, compute the value of the objective function, and develop a new guess for the variables for the optimization so as to produce an improvement in the objective function. 

Spreadsheet Applications. The types of appHcations handled with spreadsheets are a microcosm of the types of problems and situations handled with fuU-blown appHcation programs that are mn on microcomputers, minis, and mainframes and include engineering computations, process simulation, equipment design and rating, process optimization, reactor kinetics—design, cost estimation, feedback control, data analysis, and unsteady-state simulation (eg, batch distillation optimization). 

Thus, methods are now becoming available such that process systems can be designed to manufacture crystal products of desired chemical and physical properties and characteristics under optimal conditions. In this chapter, the essential features of methods for the analysis of particulate crystal formation and subsequent solid-liquid separation operations discussed in Chapters 3 and 4 will be recapitulated. The interaction between crystallization and downstream processing will be illustrated by practical examples and problems highlighted. Procedures for industrial crystallization process analysis, synthesis and optimization will then be considered and aspects of process simulation, control and sustainable manufacture reviewed. 

The purification of value-added pharmaceuticals in the past required multiple chromatographic steps for batch purification processes. The design and optimization of these processes were often cumbersome and the operations were fundamentally complex. Individual batch processes requires optimization between chromatographic efficiency and enantioselectivity, which results in major economic ramifications. An additional problem was the extremely short time for development of the purification process. Commercial constraints demand that the time interval between non-optimized laboratory bench purification and the first process-scale production for clinical trials are kept to a minimum. Therefore, rapid process design and optimization methods based on computer aided simulation of an SMB process will assist at this stage. 

This interplay of the many variables is extremely complex and involves a matrix of the many variables. As an example in the molding simulation TMconcept system programmed Molding Cost Optimization (MCO) of Plastics Computer Inc., Dallas, TX, there are well over 300 variables. It is not reasonable to expect a person using manual methods to calculate these complex interactions even if molding only a modest shaped product without omissions or errors. Computerized process simulation is a practical tool to monitor the influence of design alternatives on the processability of the product and to select molding conditions that ensure the required product quality (3). 

Some recent applications have benefited from advances in computing and computational techniques. Steady-state simulation is being used off-line for process analysis, design, and retrofit process simulators can model flow sheets with up to about a million equations by employing nested procedures. Other applications have resulted in great economic benefits these include on-line real-time optimization models for data reconciliation and parameter estimation followed by optimal adjustment of operating conditions. Models of up to 500,000 variables have been used on a refinery-wide basis. 

Lang, Y. D. L. T. Biegler and I. E. Grossmann. Simultaneous Optimization and Heat Integration with Process Simulators. Comput Chem Eng 12 311-328 (1988). 

Optimization Using Equation-Based Process Simulators.525... 

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. 

Two extremes are encountered in process simulator software. At one extreme the process model comprises a set of equations (and inequalities) so that the process model equations form die constraints for optimization, exactly the same as described in previous chapters in this book. This representation is known as an equation-... 

In optimization using a process simulator to represent the model of the process, the degrees of freedom are the number of decision variables (independent variables) whose values are to be determined by the optimization, hence the results of an optimization yield a fully determined set of variables, both independent and dependent. Chapter 2 discussed the concept of the degrees of freedom. Example 15.1 demonstrates the identification of the degrees of freedom in a small process. 

In optimization using a modular process simulator, certain restrictions apply on the choice of decision variables. For example, if the location of column feeds, draws, and heat exchangers are selected as decision variables, the rate or heat duty cannot also be selected. For an isothermal flash both the temperatures and pressure may be optimized, but for an adiabatic flash, on the other hand, the temperature is calculated in a module and only the pressure can be optimized. You also have to take care that the decision (optimization) variables in one unit are not varied by another unit. In some instances, you can make alternative specifications of the decision variables that result in the same optimal solution, but require substantially different computation time. For example, the simplest specification for a splitter would be a molar rate or ratio. A specification of the weight rate of a component in an exit flow stream from the splitter increases the computation time but yields the same solution. 

Next, we need to clarify some of the jargon that you will find in the literature and documentation associated with commercial codes that involve process simulators. Two major types of optimization algorithms exist for nonlinear programming. 

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