The history of process simulation is strongly related to the development of computing and hardware and programming languages. Simple and early implementations of aspects of chemical processes were introduced in the 1970s when suitable hardware and software were available (here mainly the FORTRAN and C programming languages ​​\u200b\u200bC (programming language)). Modeling of chemical properties began much earlier, particularly the cubic equation of states and Antoine's equation were precursor developments of the 19th century.

Initially, process simulation was used to simulate steady-state processes. Steady-state models perform a mass and energy balance of a stationary process (a process in an equilibrium state) that does not depend on time.

Dynamic simulation is an extension of steady-state process simulation, in which time dependence is built into the models through derived terms, i.e., mass and energy accumulation. The advent of dynamic simulation means that time-dependent description, prediction and control of real processes in real time have become possible. This includes describing plant startup and shutdown, changes in conditions during a reaction, delays, thermal changes, and more.

Dynamic simulations require longer computation time and are mathematically more complex than a steady-state simulation. It can be viewed as a multiplied repeated steady state simulation (based on a fixed time step) with constantly changing parameters.

Dynamic simulation can be used both online and offline. The online case is the predictive control model, where real-time simulation results are used to predict the changes that would occur for a change in control input, and control parameters are optimized based on the results. Offline process simulation can be used in process plant design, troubleshooting, and optimization, as well as in conducting case studies to evaluate the impacts of process modifications. Dynamic simulation is also used for operator training.

The history of process simulation is strongly related to the development of computing and hardware and programming languages. Simple and early implementations of aspects of chemical processes were introduced in the 1970s when suitable hardware and software were available (here mainly the FORTRAN and C programming languages ​​\u200b\u200bC (programming language)). Modeling of chemical properties began much earlier, particularly the cubic equation of states and Antoine's equation were precursor developments of the 19th century.

Initially, process simulation was used to simulate steady-state processes. Steady-state models perform a mass and energy balance of a stationary process (a process in an equilibrium state) that does not depend on time.

Dynamic simulation is an extension of steady-state process simulation, in which time dependence is built into the models through derived terms, i.e., mass and energy accumulation. The advent of dynamic simulation means that time-dependent description, prediction and control of real processes in real time have become possible. This includes describing plant startup and shutdown, changes in conditions during a reaction, delays, thermal changes, and more.

Dynamic simulations require longer computation time and are mathematically more complex than a steady-state simulation. It can be viewed as a multiplied repeated steady state simulation (based on a fixed time step) with constantly changing parameters.

Dynamic simulation can be used both online and offline. The online case is the predictive control model, where real-time simulation results are used to predict the changes that would occur for a change in control input, and control parameters are optimized based on the results. Offline process simulation can be used in process plant design, troubleshooting, and optimization, as well as in conducting case studies to evaluate the impacts of process modifications. Dynamic simulation is also used for operator training.