Process simulation—batch equipment models

Mathematically speaking, a process simulation model consists of a set of variables (stream flows, stream conditions and compositions, conditions of process equipment, etc) that can be equalities and inequalities. Simulation of steady-state processes assume that the values of all the variables are independent of time a mathematical model results in a set of algebraic equations. If, on the other hand, many of the variables were to be time dependent (m the case of simulation of batch processes, shutdowns and startups of plants, dynamic response to disturbances in a plant, etc), then the mathematical model would consist of a set of differential equations or a mixed set of differential and algebraic equations. 

The simulation of the model requires the speciftcation of the maximum availability of all the resources within the plant. These conditions cause constraints to be placed on the simulation flow in the model based on the availability of resources. User specified the purchase cost/cost per use of the resources or built-in cost model based on costestimating factors for biopharmaceutical process equipment were used (Remer Idrovo, 1991). The series of process steps and ancillary operations to manufacture the product, prepare the equipment, test the sample and document the batch were then defined in their respective hierarchical workspaces. Finally, the factors for mass balance calculations were input to determine the characteristics (i.e. mass, volume) of the process streams from each process step. 

BATCHES and Aspen Batch Process Developer can be used to model multiple batches of multiple products. However, they take a long time to generate solutions because they do detailed material and energy balances for all the simulated batches. Furthermore, these tools cannot easily accormt for equipment failures, delays, work shift patterns, downtime for equipment maintenance, holidays, etc. Consequently, they cannot be used for day-to-day scheduling of multiproduct plants. 

Process models are unfortunately often oversold and improperly used. Simulations, by definition, are not the actual process. To model the process, assumptions must be made about the process that may later prove to be incorrect. Further, there may be variables in the material or processing equipment that are not included in the model. This is especially true of complex processes. It is important not to confuse virtual reality with reality. The claim is often made that the model can optimize a cure cycle. The complex sets of differential equations in these models cannot be inverted to optimize the multiple properties they predict. It is the intelligent use of models by an experimenter or an optimizing routine that finds a best case among the ones tried. As a consequence, the literature is full of references to the development of process models, but examples of their industrial use in complex batch processes are not common. 

As in the steady-state simulation of continuous processes, it is convenient to convert from a process flowsheet to a simulation flowsheet. To accomplish this, it is helpful to be familiar with the library of models (or procedures) and operations provided by the simulator. For example, when using SUPERPRO DESIGNER to simulate two fermentation reactors in series, the process flowsheet in Figure 4.25a is replaced by the simulation flowsheet in Figure 4.25b. In BATCH PLUS, however, this conversion is accomplished without drawing the simulation flowsheet, since the latter is generated automatically on the basis of the recipe specifications for each equipment item. 

A substantial number of chemical, food, pharmaceutical and metallurgical industry rely on batch-wise production processes. The dynamic and complicated character of the recipe steps and the equipment can lead the process to reach unsafe conditions. The accurate and appropriate modeling of the batch process and the recipe is the focus of this research. In particular, the goal of this paper is to present the modeling for simulation and optimization of safety constraints related to a polymer batch process. 

Finally, it should be noted that the validity of the system model can be confirmed by testing it with known scenarios. In other words, the equipment states and process conditions of every component at different stage of the batch operation should be simulated and compared with the expected system behavior before actual implementation of the proposed hazard identification procedure. 

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