GPROMS

The optimisation problem was solved using CVP approach of Vassiliadis et al. (1994a,b) as implemented within gPROMS process modelling tool (Process Systems Enterprise Ltd., 1998). 

The gPROMS-modeling tool was used to obtain the numerical solution of dynamic simulation. And the results of simulation were stable for the range of conditions used in this work. 

For the study of the process, a set of partial differential model equations for a flat sheet pervaporation membrane with an integrated heat exchanger (see fig.2) has been developed. The temperature dependence of the permeability coefficient is defined like an Arrhenius function [S. Sommer, 2003] and our new developed model of the pervaporation process is based on the model proposed by [Wijmans and Baker, 1993] (see equation 1). With this model the effect of the heat integration can be studied under different operating conditions and module geometry and material using a turbulent flow in the feed. The model has been developed in gPROMS and coupled with the model of the distillation column described by [J.-U Repke, 2006], for the study of the whole hybrid system pervaporation distillation. 

The model of the task based crystallizer having two compartments has been implemented in gPROMS Modelbuilder 3.0.0. (PSE Enterprise, London). A base case simulation has been done with the settings as given in Table 1. The initial values correspond in both vessels to a clear saturated liquid. 

Note, although Furlonge et al. (1999) reported that variable hold-ups in the vessels of MultiVBD reduces energy consumption, in this work, we distributed the feed in different vessels according to the product profiles calculated a priori. Also, for conventional column piecewise constant reflux ratio with two intervals were used for each cut. The above optimisation problem is solved using gPROMS software. Note, for CBD column, two reflux intervals were considered for each cut and the reflux ratio in each interval was assumed to be piecewise constant (Mujtaba, 2004). 

The full nonlinear model is developed using Aspen Dynamics. For obtaining the redueed model, the same procedure presented in [6] is used. However, in this case the reduced model will be developed using gProms. 

Since the reactor has a strong nonlinear behaviour, the model simplifieation is used. A dynamic model is written using gProms, consisting of five eomponent balances, and considering constant temperature and physical properties. 

For the distillation columns, linear model-order reduction will be used. The linear model is obtained in Aspen Dynamics. Some modifications to the previous study have been done to the linear models, in order to have the reboiler duty and the reflux ratio as input or output variables of the linear models. This is needed to have access to those variables in the reduced model, for the purpose of the dynamic optimization. A balanced realization of the linear models is performed in Matlab. The obtained balanced models are then redueed. The redueed models of the distillation columns are further implemented in gProms. When all the reduced models of the individual units are available, these models are further connected in order to obtain the full reduced model of the alkylation plant. The outeome of the model reduction procedure is presented in Table 1, together with some performances of the reduced model. 

After the redueed model is obtained, the dynamie optimization problem (equations (1) -(7)) is implemented in gProms. The single shooting method is used. 

The equation set in this example was solved by using a differential-algebraic equation solver called gPROMS from Process Systems Enterprises (www.pse. com). It can also be solved with other software and programming languages such as FORTRAN. Example 16 is too complicated to be done on a spreadsheet. 

As suitable analytical solutions are not available for most of the column models, simulation-based parameter estimation using simulation software is recommended. This method is very versatile in terms of the number and complexity of models. One example for an estimation task is the gEST tool included in the gPROMS program package (PSEnterprise, UK). An additional advantage of the simulation based approach is the consistency of the obtained data, if the same models and simulation tools are used for subsequent process analysis and optimization. 

The following examples illustrate a few effects encountered in model validation based on our research (Epping, 2005 and Jupke, 2004). All process simulations are based on the transport dispersive model. Model equations were solved by the gPROMS Software (PSenterprise, UK) using the OCFE method (Section 6.4). 

In the following some examples based on our own research are given for the validation of the SMB transport dispersive model, using an on-line detection system in the recycle stream. All flowsheet, column and plant models were implemented in the gPROMS (PSenterprise, UK) simulation tool and solved with OCFE methods (Section 6.4). 

A control vector parameterisation approach [66,67] implemented with the gPROMS process modeling tool was employed to solve this dynamic optimization process [68]. The optimum values of the switching time, fj, and of the final time, tf, were determined. The optimal operating conditions were foimd for different numbers of operating cycles for either TTB or PHL as the main product. The optimum number of cycles, and hence the effective column length, is thereby determined. 

Process System Enterprise Ltd., UK, gProms Advanced User Guide Release 1.7 (1999). 

The design support software tools employed in the case study are of a completely different nature. They include commercial as well as legacy tools. Examples are Microsoft Excel, various process simulators such as Polymers Plus from Aspen Technology, gPROMS from PSE, MOREX, BEMFlow and BEMView from Institut filr Kunststoffverarbeitung at RWTH Aachen University, the project database Comos PT from innotec, the document manage-... 

CHEOPS couples different kinds of simulators to perform plant-wide simulations and therefore uses MOREX, gPROMS, and Polymers Plus. 

A plant-wide simulation is performed with the help of CHEOPS, which couples different kinds of simulators (MOREX, gPROMS, and Polymers Plus) at run time. The input data for CHEOPS are generated by an integration tool which queries the flowsheet for the components and their connections, retrieves the respective simulation models via the AHEAD system, generates an input file for CHEOPS, and also passes the simulation models to CHEOPS. The integrator cannot run completely automatically, since the user still has to select the simulation models to be used from sets of candidate models. 

After having simulated the components of the chemical process individually, a simulation of the overall process is performed. To this end, different kinds of simulators have to be coupled to form a heterogeneous simulator. We have shown that MOREX has been used for ID simulation of the extruder. Furthermore, reaction and separation simulations have been performed with the help of the commercial simulators Polymers Plus and gPROMS, respectively. 

The call back queries used in these steps are not purely queries to source databases. For example, the needed simulation results of the reactor are results of an aggregation function. In this sense the results are the results of a (highly complex) query on the data warehouse store. As simulating is a time consuming and expensive task we also store the results in the data warehouse for reuse. To gain access to the units the DB trader contains meta information about the CAPE-OPEN components. As a result of the usage of the CAPE-OPEN compliant units we do not need to handle very different simulators such as Aspen Plus, Pro/II or gPROMS, but only have to create the CAPE-OPEN objects used by the units. This especially concerns the material object for each substance contained in the input ports of the unit. The process data warehouse produces these CORBA objects and is then able to start the simulation of the unit. 

Tasks like controller design or the assessment of start-up procedures require dynamic simulation studies, which are supported by dynamic simulators such as gPROMS [916]. 

Furthermore, in order to simplify the technical realization and maintenance of the environment, abstract interfaces for tool wrappers have been introduced to render models from external tools, including gPROMS and Aspen Plus (cf. Fig. 5.19). These abstract interfaces permit the development of generic functionality in the environment. Furthermore, future changes in the simulation tools only influence the corresponding wrapper implementation they do not need to be accounted for in the implementation of the functional modules of the overall environment. 

Fig. 5.20, Model flowsheet of the process CSTR, Split, WFE, and Extruder refer to model types, while Polym.ers Plus, gPROMS, and MOREX denote available simulation tools...

The reaction section is followed by a separation section which separates unconverted monomer from the effluent of the reaction section. Two alternative realizations using either a leacher or, as shown in Fig. 5.20, a wiped-film evaporator (WFE) are described in [99]. Models of an appropriate level of detail are neither available for the leacher nor for the wiped-film evaporator in standard libraries of process modeling tools. Therefore, customized models have been developed for both apparatuses by means of the gPROMS modeling environment. 

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