Simulation in Process Design and Operation

During the design of a new process, simulations provide the design engineer with predictions o f stream compositions, required equipment sizes and operating conditions, and estimates o f capital and operating costs for a potential flowsheet. As well as assessing

the feasibility o f a given flowsheet, simulations can be used to optimise a process using case studies or numerical techniques such as successive quadratic programming (Gallier and Kisala, 1987) and to perform design calculations for individual unit operations.

In most design studies, steady-state simulation is used due to the lower requirement for computer power. The unit model equations are also simplified or ideal in most cases, and sizing and costing are usually noniterative calculations performed after convergence o f the flowsheet (Biegler, 1989). With decreases in the cost o f computer power and the growing complexity o f process plants, dynamic simulation is also becoming increasingly used during the design stage to analyse the intrinsic controllability o f a process and for the design o f control systems (Pantelides and Barton, 1993). Dynamic simulation must be used for the design o f batch processes, since time dependent behaviour is involved. The information generated by process simulation during the design stage reduces the time required to explore the effect o f design changes and different operating conditions. More productive, efficient and safer processes can therefore be developed in a shorter time.

However, it should be highlighted that in existing process simulators the flowsheet topology must be specified before a simulation can be carried out. The task o f deciding exactly which unit operations to use and how they should be connected to form the flowsheet is referred to as process synthesis (Stephanopoulous and Townsend, 1986). Expert systems (computer programs which apply the technique o f logical inference to a knowledge base) can be employed to carry out the process synthesis task (Niida et al,

1986; Siletti, 1988; Asenjo et al, 1989; Turner et al, 1994). Process simulation can also be used to choose between unit operation alternatives and examine the affect o f changes in a flowsheet by using case studies to compare feasibility and perform sensitivity analysis. The development o f graphical user interfaces that allow user interaction during simulations and the inclusion o f database facilities within process simulators that can record and analyse simulation results will enable design engineers to carry out these case studies in less o f an ad-hoc manner.

Shaw (1992) has suggested integrating process simulation and other process design tasks into a single environment called an "Engineering Toolkit" that allows full electronic transfer o f data between the process simulator and other facilities using a process flow diagram representation as the interface. Stephanopoulous et a l (1987) have described an

object-orientated software support environment for process design called DESIGN-KIT which incorporates process simulation and process synthesis. However, Stephanopoulous (1990) has pointed out that computer-aided design environments containing database management systems "do not know" how the design is done and still require the guidance o f a human designer. Application o f the work o f such as that o f Pohjola et a l (1994) on a procedural model o f the design process itself may one day lead to expert systems that can actually direct the design task.

For investigations of process operation, dynamic simulation is generally used since transient behaviour often occurs. Traditional applications include the study o f the response o f a continuous process to deviations from steady-state conditions and the tuning o f controllers (Pantelides and Barton, 1993). There are increasing applications o f dynamic simulation in performing hazard and operability studies, determining appropriate start up and shut down procedures, testing o f control schemes and automatic emergency control procedures, for trouble shooting during production, and for operator training. Dynamic simulation is also used for on-line measurement and optimisation. Table 1.2 overleaf shows applications o f dynamic simulation from conception to normal operation o f a process plant (Naess et al, 1993).

One of the main obstacles for efficient application o f dynamic process simulation, especially in control and operator training when real time or faster simulation are needed, has been the lack o f computing power (Moe and Hertzberg, 1994). This has also been a problem for both steady-state and dynamic simulations involving rigorous unit models (Haley and Sarma, 1989). The use o f advanced computer architectures, such as supercomputers or parallel computers, has been one way to obtain the required computer power. Haley and Sarma (1989) found that the steady-state simulator PROCESS was an average 10.5 times faster on a Cray supercomputer than on a VAX mainframe and concluded that it was possible to transfer simulator software from a mainframe computer to a supercomputer. Moe and Hertzberg (1994), in a review o f the application o f advanced computer architectures in dynamic simulation, concluded that using parallel computers with equation-based simulators showed great potential for speeding up simulations. However, they pointed out that simulators should be programmed without attention to underlying computer architectures to increase their lifetime.

Project Phase Examples of Activities

Feasibility Screening Concepts

Operability Analysis

Conceptual Design Capacity Planning

Check Safety Margins

Basic Engineering Detailed Design Process Design Control System Equipment Design Integrated Design Scheduling Commissioning and Take Over Startup Operator Training Develop Test Procedures

Initial Tuning

Develop Operating Procedures Screening Alternatives Normal Production Trouble Shooting Revamp Studies Optimisation Increasing Operability

Table 1.2: Application o f dynamic simulation in various project phases, from Naess et al. (1993).

Another development which will enable a more realistic description o f the actual behaviour o f process plants is stochastic simulation. This form o f simulation includes a degree o f randomness in the unit model input parameters and thereby accounts for uncertainty in the design and operation o f a process. A new stochastic modelling block has been described for the ASPEN-Plus steady-state simulation package while a module providing random functions for gPROMS has been developed (Naf, 1994).

An alternate approach to describing plant behaviour which avoids the use o f rigorous dynamic models is known as qualitative simulation. This form o f simulation uses variables that can be in different states rather than having a numerical value. For example,

in the simplest one-dimensional case a variable has three possible states; -,0,+. To take into account dynamic behaviour this form o f simulation uses qualitative derivatives that describe the evolution o f a variable as increasing, decreasing or steady. The qualitative simulation then predicts trees of possible states o f the process in time (Muratet and Bourseau, 1993). Qualitative simulation has been used by Oyeleye and Kramer (1988) to simulate the effects o f equipment malfunctions on a process model.