Model process optimization

A generic form of a process optimization model is provided as... 

Development of Process (Matfiematical) Models Constraints in optimization problems arise from physical bounds on the variables, empirical relations, physical laws, and so on. The mathematical relations describing the process also comprise constraints. Two general categories of models exist ... 

The first modeling software which allowed for the optimization of nonlinear separations by SMB was presented in the early 1990s [46]. Today, numerous publications from academia allows one to have a better understanding of the SMB system [47-51]. Industry now has the practical tools for modeling SMB for quick and efficient process optimization [41, 52]. 

Curing of Polyimlde Resin. Thermoset processing involves a large number of simultaneous and interacting phenomena, notably transient and coupled heat and mass transfer. This makes an empirical approach to process optimization difficult. For instance, it is often difficult to ascertain the time at which pressure should be applied to consolidate the laminate. If the pressure is applied too early, the low resin viscosity will lead to excessive bleed and flash. But if the pressure is applied too late, the diluent vapor pressure will be too high or the resin molecular mobility too low to prevent void formation. This example will outline the utility of our finite element code in providing an analytical model for these cure processes. 

Overall, catalytic processes in industry are more commonly described by simple power rate law kinetics, as discussed in Chapter 2. However, power rate laws are simply a parameterization of experimental data and provide little insight into the underlying processes. A micro-kinetic model may be less accurate as a description, but it enables the researcher to focus on those steps in the reaction that are critical for process optimization. 

Based on the experimental data kinetic parameters (reaction orders, activation energies, and preexponential factors) as well as heats of reaction can be estimated. As the kinetic models might not be strictly related to the true reaction mechanism, an optimum found will probably not be the same as the real optimum. Therefore, an iterative procedure, i.e. optimization-model updating-optimization, is used, which lets us approach the real process optimum reasonably well. To provide the initial set of data, two-level factorial design can be used. 

Complex steam systems usually feature many important degrees of freedom to be optimized. To establish the steam costs for retrofit of site processes requires an optimization model to be developed. This allows the steam loads for process heating to be gradually decreased and the steam system reoptimized at each setting. The result in cost... 

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. 

A product can be a real product that is producible on a set of resources. A description for a process is stored as a product, because processes often produce a product. As an abstraction a product also represents a production step/step in a work flow in the optimization model. Important data are, for example ... 

MILP Optimization Models for Short-term Scheduling of Batch Processes... 

It is the objective of this paper to provide a comprehensive review of the state-of-the art of short-term batch scheduling. Our aim is to provide answers to the questions posed in the above paragraph. The paper is organized as follows. We first present a classification for scheduling problems of batch processes, as well as of the features that characterize the optimization models for scheduling. We then discuss representative MILP optimization approaches for general network and sequential batch plants, focusing on discrete and continuous-time models. Computational... 

With the main purpose of developing more efficient optimization models for batch sequential processes, especially those involving sequence-dependent changeovers, different approaches were proposed based on the concept of batch precedence. 

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