Introduction

Dynamic process models differ from steady-state models in their ability to capture time-dependent behavior. The distinction goes beyond simply adding a time dimension: dynamic models incorporate material and energy accumulation terms, control-loop dynamics, and equipment holdup, providing a more realistic representation of actual plant operation.

While steady-state analysis is mainly used for process flowsheet design, usually to determine mass and energy balances and approximate equipment sizes, or perhaps stream properties, the ability of dynamic models to model transient behavior opens up a whole new world of application. Typical applications of dynamic models are as follows:

  • analysis of transient behavior, including performance during start-up, shutdown, and load change;

  • regulatory (i.e., PID) control scheme analysis and design;

  • design of optimal operating procedures, for example to optimize transition between product grades;

  • design of batch processes;

  • design of inherently dynamic continuous processes;

  • fitting data from non steady-state operations, for example dynamic experiments, which contain much more information than steady-state experiments, or estimation of process parameters from transient plant data;

  • safety analysis;

  • inventory accounting and reconciliation of plant data;

  • online or offline parameter re-estimation to determine key operating parameters;

  • online soft-sensing;

  • operator training.

Dynamic simulation is more computationally demanding and mathematically complex than steady-state analysis. Conceptually, it can be viewed as a sequence of quasi-steady-state evaluations at discrete time steps, with state variables updated at each step according to the accumulated material and energy changes.

The dynamic model shares the same physical property packages as the steady-state model, simulating the behavior of the chemical system in a similar manner. On the other hand, the dynamic model uses a different set of conservation equations which account for changes occurring over time.

The equations for material, energy, and composition balances include an additional accumulation (volume) term which is differentiated with respect to time. Numerical integration is used to determine the process behavior at sequential time steps. The smaller the step, the more closely the calculated solution matches the analytic solution. However, this gain in rigor is offset by the additional calculation time required to simulate the same amount of elapsed real time. A reasonable compromise is achieved by using the largest possible step size, while maintaining an acceptable degree of accuracy without becoming unstable.

In DWSIM, dynamic modeling is handled by the Dynamics Manager. Dynamic modeling components include Events and Event Sets, Cause-and-Effect Matrices, Integrators, Monitored Variables and Schedules.