Plant Simulation Model

Fig. 2.4 Screenshot of a combined supply chain and plant simulation model.

A many number of modelling and simulation systems have been developed to aid in process and product engineering. In this paper the knowledge based process plant simulation model was developed. On the model development side, the issues of knowledge representation in the form of systematic component composition, ontology, and interconnections were illustrated. As a case study a plant for starch sweet syrup production was used. The system approach permits the evaluation of feasibility and global plant integration, and a predicted behavior of the reaction systems. The obtained results of the this paper have shown the variety quality of syrups simulation for different products. 

The general framework presented here on the model development side, the issues of knowledge representation in the form of systematie eomposition, ontology, and quantity representaion was involved. On the model analysis side issues involving the automatie evaluation and presentation of simulation results. The plant simulation model should mirror the behaviour of a eomplex plant subjeet to eonstraints in feedstoek, produets, equipment eapaeities, operational parameters, and utilities eonsumptions. The life eyele eoneept may lead to a reliable and maintainable tool. 

Using available flowsheeting software, it is possible to produce a computerized tool that will permit us to learn or even mirror the plant behaviour under different operating conditions or with different raw materials and product specifications. Such as tool is called the steady state plant simulation model. The steady state model, whieh is simpler to build, and has a wide variety of applications in its own right, it can be used directly in revamping and a wide variety of other engineering projeets. 

Figure 2.3 Applications of steady state and dynamic Plant Simulation Models...

Hence, the development of a Plant Simulation Model is the proper approach to deal with industrial simulation problems. The progress in software technology makes possible today the development of integrated steady state and dynamic models. However, these require significant investment in qualified staff. Recently, generic simulation products have been proposed for applications in refining and petrochemical industries, which can be customised for specific processes. 

The job is not finished with steady state controllability analysis. Only dynamic simulation enables a reliable assessment of the control problem. The solution of the dynamic modelling depends on the dynamics of units involved in the control problem. Detailed models are necessary for the key units. The simplification of the steady-state plant simulation model to a tractable dynamic model, but still able to represent the relevant dynamics of the actual problem, is a practical alternative. Steady-state models can be used for fast units, as heat exchangers, or even chemical reactors with low inventory. 

Calibration of a steady state Plant-Simulation-Model. This activity might be the most time consuming. For an existing plant, it can be combined with data reconciliation, although this can be applied with reasonable accuracy only for the main components. 

A steady-state Plant Simulation Model of an existing plant helped to calibrate the base-case model on a representative operating point. Some details of an industrial process were skipped, but the omission of these details does influence neither the plantwide material balance nor the process dynamics. The units SO, S6 and S7 may be considered black-boxes. Contrary, SI to S5 are rigorous distillation columns, modelled as sieve trays. In steady-state all the reactors are described by stoichiometric approach, but kinetic models are used for Rl and R4 in dynamic simulation. 

This investigation can be applied preferably in revamping projects, where the information about the material balance can be exploited by tuning a rigorous plant simulation model. The approach is recommended also for designing new plants, but a detailed description of the chemical reaction network producing impurities is needed. 

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. 

Once the objective and the constraints have been set, a mathematical model of the process can be subjected to a search strategy to find the optimum. Simple calculus is adequate for some problems, or Lagrange multipliers can be used for constrained extrema. When a Rill plant simulation can be made, various alternatives can be put through the computer. Such an operation is called jlowsheeting. A chapter is devoted to this topic by Edgar and Himmelblau Optimization of Chemical Processes, McGraw-HiU, 1988) where they list a number of commercially available software packages for this purpose, one of the first of which was Flowtran. 

Many HVAC system engineering problems focus on the operation and the control of the system. In many cases, the optimization of the system s control and operation is the objective of the simulation. Therefore, the appropriate modeling of the controllers and the selected control strategies are of crucial importance in the simulation. Once the system is correctly set up, the use of simulation tools is very helpful when dealing with such problems. Dynamic system operation is often approximated by series of quasi-steady-state operating conditions, provided that the time step of the simulation is large compared to the dynamic response time of the HVAC equipment. However, for dynamic systems and plant simulation and, most important, for the realistic simulation... 

Simulation models can be expensive to build and the results obtained need to be analyzed with care because they are statistical in nature. For example, two runs of the model may give different results - just as the performance on two real days in a factory can vary. Sufficiently large samples need to be taken therefore for a proper understanding of the performance of the plant. 

In the following, tiie performance of a UASB reactor with the same size of a pilot plant [7] is evaluated according to the reactor simulation model incorporated with the Monod kinetic paramet for fire hypothetical influait coirqxwiticHi for the three VFA componaits as indicated in Table 2. 

This is a further deepened work of what Samsung Total accomplished[12-14] several years ago. Several operation conditions including hardware modification which may enhance the productivity were deduced and simulated using the simulation model. Some ideas wctb alre y applied to commercial plant when they were concluded practically reasonable while some are on the waiting list One of the examples of productivity enhancement is shown in Fig. 1 and Fig. 2 which compare the conversion profiles and MWDs under original and revised operation conditions. As shown in these two figures the productivity was mhanced while MWD docs not change much. 

By using the simulation model developed in Samsung Total we applied the ideas of pFoductivily enhancement successfiiUy to LDPE plant and accomplished considerable productivity incn e. The MWD as well as the melt index and density calculated by the simulation model convinced us of applying the ideas to commercial plant. The end user property prediction capabilities of the model will be refined further by integration of phj icxjchemical and statistical approaches and be one of the next potential research items. 

L. Pages. M. D. Jourdan, and D. Picard, A simulation model of the three-dimensional architecture of the maize root system. Plant Soil II9 41 (1989). 

R. F. Grant, Simulation model of soil compaction and root growth I. Model development. Plant Soil 750 15 (1993). 

As plant surface area the layout of the existing production building was used. In addition, three existing bottling plants were incorporated in the simulation model. 

After a simulation-based design of an efficient pipeless plant had been performed, the existing standard multipurpose plant was modelled by a reference model and compared to the pipeless plant setup by determining the overall production time for different production plans. 

The bottom-up approach, which develops detailed plant simulation and optimization models, optimizes them, and translates the results from the simulations and optimization into practical operating heuristics. This approach often leads to large models with many variables and equations that are difficult to solve quickly using rigorous optimization algorithms. 

Padulles J., Ault G.W., McDonald J.R. (2000) An integrated SOFC plant dynamic model for power systems simulation. Journal of Power Sources 86, 495-500. 

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