Simulate / Steady State
It is a well-known fact that a process simulation is a proven tool and when applied correctly, helps to solve process problems and boosts the quality and efficiency of systems and operations. A successful simulation project is one that delivers useful information or a result at the appropriate time to support a meaningful decision or a task.
25 Reasons Why Chemical Engineers Should Know and Apply Process Modeling List of Most Important Applications of Process Modeling
Although we are mostly unaware of this, models are an integral part of any kind of human activity. Discussion about modeling so often goes into the direction of complex mathematical expressions. However, this isn't always the case, although most models in engineering are qualitative in nature.
Modelling is also an art and a very creative process! It is an important learning process.
We wanted to list most important activities of chemical engineering which are impossible without mathematical modeling. Check the list! Did we forget anything?
Application of Simulation Through The Life-cycle of a Process Modeling and Simulation Through Different Phases of a Process
The use of models is important because of their capacity to assist the solution of many important and fundamental problems in chemical engineering and as such, they are inseparable part through the life-cycle of a process. The models are an explicit way of describing the knowledge of the process and related phenomena. They provide a systematic approach to the problems in all the stages of the process life-cycle.
How much detail is sufficient in a simulation to capture the essence of the real world process being modeled? Is my model performing as I expected it to perform?
Regardless of the level of detail included, a simulation will always be an approximation of the real system; an arbitrarily high degree of fidelity is generally not possible. There are many potential pitfalls in answering these question when performing simulation work. This article discusses some of those most commonly seen.
Some Observations on the Practical Use of Modeling and Simulation Trends in Application of Mathematical Modeling and Simulation in Industry, Research and Innovation
The advances in basic knowledge and model-based process engineering methodologies are resulting with an increasing demand for models. The observations given here are commentaries and considerations about some aspects of modeling with the focus on reliability of models and simulations, their role in the industry and innovations, their role in technology transfer and knowledge management and the role of the universities in modeling and simulation development.
Process simulation as the key discipline of chemical engineering Application of process simulation in disciplines of chemical processing
Process simulation is today applied in almost all disciplines of chemical engineering and engineering in general and for any process engineer is important to understand at least the basics. It is the inevitable part of process design, research and development, production planning, optimization, training and education to the decision-making process which makes it one of the most important disciplines of engineering.
Thermodynamic basics for process modeling Basic guidance to help you avoid problems caused by selection of wrong thermodynamic model
When developing a process model using any process simulator, one of the first steps for a user is to select the proper thermodynamic system. The choice of a thermodynamics will have a significant impact on the results of the model, such as accuracy, calculation complexity and abilities of convergence. These are all reasons why this step should be done with attention and taking into account characteristics of the system that is modelled. This article is sharing with you basic guidance to help you simplify this step and select the proper thermodynamic system.
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What does the term of „mathematical model“ represent in chemical engineering and engineering in general? A broad definition of a model is - „a virtual version of reality“, which means that any of us can observe it differently. For this reason, every model must be accompanied with clearly defined assumptions and goals.
A good model should reflect the important factors affecting a process and must not be crowded with minor, secondary factors that will complicate the mathematical analysis and might render the investigation.