Dynamic Simulation Models

A dynamic simulation uses the ingredients dissected from a complex system to attempt the construction of a model which exhibits dynamic attributes, i.e. emergent behaviour. Such a simulation is more complicated than the static models from which it is derived. Attempts are made to incorporate into these simulations the transactions and interactions that we know are there but which elude a precise definition. Comparisons are made with observables to ascertain some degree of the quality of a model. 

Molecular dynamics, Monte Carlo simulations (Haile, 1992), and very recently applications of cellular automata to drug research (Kier and Cheng, 

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Dynamic simulation models include fluid inertia and compressibility and exchanger shell expansion to determine the pressure spikes associated with... 

Dyer, J.A., Predicting trace-metal fate in aqueous systems using a coupled equilibrium-surface-complexation, dynamic-simulation model, in Underground Injection Science and Technology, Tsang, C.F. and Apps, J.A., Eds., Elsevier, New York, February 2007. 

Marquez, AC, Blanchar C (2004) The procurement of strategic parts. Analysis of contracts with suppliers using a system dynamics simulation model. International Journal of Production Economics 88 (1) 29-49 Mason S (2002) Simulation software buyer s guide. HE Solutions May 45-51 McAfee R, McMillan J (1987) Auctions and Bidding. Journal of Economic Literature 25 699-738... 

Completing the flowsheet allows the generation of a steady-state simulation model. A dynamic simulation model may be developed for supporting process control implementation and for the assessment of operation strategies. 

In addition to light interception, the acquisition of minerals (e.g., nitrogen, phosphorous, and sulfur) from the environment is vital for photosynthetic processes to proceed efficiently. Chemical composition is one component of dynamic simulation modeling (Denoroy, 1996). 

Pantelides [27, 28], in his work with Sargent, defined the index in a manner that exposes its potential to cause problems in initialization as well as in the integration error. They too showed that the index problem can be eliminated by differentiation. Noting that only some of the equations need to be differentiated, they use a method based on the structural properties of the equations to discover these equations. They cite several examples in which the index problem is almost certain to occur in setting up and solving dynamic simulation models, e.g., calculations of flash dynamics and problems in which the trajectory of a state variable is specified. 

The unique features of our system enable us to use three different theoretical tools — a molecular dynamics simulation, models which focus on the repulsion between atoms and a statistical approach, based on an information theory analysis. What enables us to use a thermodynamic-like language under the seemingly extreme nonequilibrium conditions are the high density, very high energy density and the hard sphere character of the atom-atom collisions, that contribute to an unusually rapid thermalization. These conditions lead to short-range repulsive interactions and therefore enable us to use the kinematic point of view in a useful way. 

Consequence analysis Once the risks have been identified, their consequences have to be analysed using an appropriate model of SC operations. The disruptions due to one particular risk or a combination of risks can be simulated and propagated through the SC model and the effects analysed. In a complex SC, there could be important domino effects. These should be explicitly considered in the analysis. Section 2.2 presents a dynamic simulation model of the integrated refinery SC which enables such analysis. 

For consequence analysis, we have developed a dynamic simulation model of the refinery SC, called Integrated Refinery In-Silico (IRIS) (Pitty et al., 2007). It is implemented in Matlab/Simulink (MathWorks, 1996). Four types of entities are incorporated in the model external SC entities (e.g. suppliers), refinery functional departments (e.g. procurement), refinery units (e.g. crude distillation), and refinery economics. Some of these entities, such as the refinery units, operate continuously while others embody discrete events such as arrival of a VLCC, delivery of products, etc. Both are considered here using a unified discrete-time model. The model explicitly considers the various SC activities such as crude oil supply and transportation, along with intra-refinery SC activities such as procurement planning, scheduling, and operations management. Stochastic variations in transportation, yields, prices, and operational problems are considered. The economics of the refinery SC includes consideration of different crude slates, product prices, operation costs, transportation, etc. The impact of any disruptions or risks such as demand uncertainties on the profit and customer satisfaction level of the refinery can be simulated through IRIS. 

Dynamic simulation is a process engineering tool that prediets how process and its controls respond to various upsets as a function of time. Dynamic simulation model leads benefits during plant start up. 

Tasks 1 and 4 receive dynamic data of the process from a dynamic simulation model. This model predicts dynamic responses of the process variables to step changes of the manipulated variables. 

Butterfield, M.H. and Thomas, P.J. (1986). Methods of quantitative validation for dynamic simulation models - Part I Theory, Trans. Inst. Measurtment and Control, 8,182-200. 

To examine the role of inactivated receptors, or reduced ability of ligand to move among receptors, Mahama and Linderman (1994b, c) have studied the two-dimensional diffusion and collision of receptors and G-proteins in the cell membrane using a dynamic simulation model. Receptor/ligand binding on the cell surface is described simply by... 

The dynamic simulation model has been adapted to meet the constraints of a large scale problem and of the equation solving mode of Aspen Dynamics. The final model contained more than 6000 equations. Since the change in material balance (inventory) takes place at long time scales, some substantial simplifications of the local control of units can be considered. Finally, the plantwide control problem is reduced to analyse a 3x7 system, where three outputs (concentration of impurities li, I2, and I3) should be controlled with three among five inputs (D2, SS2, Q2, D4, and Q4), in the presence of two disturbances (Fdce, X ). Because of decentralised control, at most three SISO controllers should be physically implemented. 

GenSim is written in Powersim Studio Enterprise 2005, a dynamic simulation-modeling software package. The model s easy-to-use policy screens allow the user to explore "what if " questions, such as... 

Towill, D. R., Naim, M. M., and Wikner, J. (1992), Industrial Dynamics Simulation Models in the Design of Supply Chains, International Journal of Physical Distribution and Logistics Management, Vol. 22, No. 5, pp. 3-13. 

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