On dynamic simulation in chemical engineering

Although steady-state simulation allows us to gain a rational understanding of chemical processes as well as some intuitive insights, since a steady state cannot be achieved in actual production processes, it is necessary to use some empirical data during such simulations. I believe this can be regarded as an average state, or an ideal operating condition. However, in the actual production process, parameters are in constant fluctuation; the most ideal state is also a dynamic equilibrium, and it is impossible for steady-state simulation tools to replicate such a dynamic state. Therefore, I believe that dynamic simulation is more instructive for actual production. This can be specifically illustrated by, 1) the simulation of the start-up and shutdown processes. By simulating the start-up and shutdown processes, it is possible to determine how many defective products will be generated during these processes, as well as how much time it will take to complete them. This allows one to figure out what size of containers are needed to store these defective products, and thus to determine how much high-quality product needs to be mixed with them in order to obtain products that can be sold as Qualified ones. 2) The impact of parameter fluctuations during the production process. Parameters such as changes in feed composition, temperature fluctuations, and pressure fluctuations require some time for the system to return to its normal state. Through dynamic simulation, it is possible to determine the range within which changes in feed composition can occur without affecting the production parameters, how long it takes for the system to return to normal, and the resulting fluctuations in product quality. Motion simulation tools can provide practical guidance for the production process.

Dynamic simulation is essentially an analysis of control and an understanding of the dynamic characteristics of equipment.

For situations where steady-state simulation is not feasible, dynamic simulation can help identify many problems; however, due to certain assumptions and simplifications, its computational accuracy is reduced

Everyone has their strengths and weaknesses. I agree with what was said above; although my understanding of dynamics is not deep, I feel that dynamic simulations cannot replicate real-world situations 100% accurately.

There is a significant gap between dynamic simulations and reality; for example, in devices with complex geometries such as heat exchangers, it is difficult to accurately simulate time-related effects

Personally, I think the differences between simulation and reality should be analyzed from the following aspects: 1. The limitations of dynamic simulation models; 2. The functional limitations of the software used; 3. The capabilities of the users

Dynamic simulation comes in many different levels, and the choice depends on the intended use. If your goal is dynamic research, then dynamic simulation is carried out from a design perspective, with high requirements regarding the accuracy of the dynamic responses – though these requirements are certainly not 100%. It’s simple: even for steady-state simulations, the data shown in the PFD diagrams produced by design firms usually come from their empirical calculations combined with steady-state simulations (using software such as PROII, HYSYS, Aspen+). But do you really think that the results obtained after actual operation can match the data in those PFD diagrams? ? :Lol, steady-state simulation takes into account far fewer factors than dynamic simulation; hence it’s clear that dynamic simulation is very difficult to carry out. Yet, when done properly, it provides great assistance and guidance for design corrections, process improvements, APC control, DCS and SIS adjustments, as well as operator training. To carry out effective dynamic simulation, it’s not the case that just because you use a sophisticated platform you can produce good results; what matters is the individual skills of the user, including their theoretical knowledge, engineering experience, and level of commitment, among other factors.