Site Sobre Simulação de Processos

Pessoal,

Existe um site bem bacana, que possui literatura sobre simulação de processos. É claro que é um site de um fornecedor, mas vale a pena ir na seção “literature”. Lá, tem bastante coisa interessante.

http://hpsweb.honeywell.com/Cultures/en-US/Products/ControlApplications/simulation/UniSimDesign/default.htm

Process simulation and modelling

(Taken from http://www.psenterprise.com/concepts/process_simulation_vs_modelling.html)

Process simulation and process modelling refer to different things.

Process modelling is the art or activity of building a mathematical model of the process (or of a product, for that matter) by describing its fundamental physical and chemical relationships – without specifying how they are to be solved.

Process simulation is merely one of the activities that you can perform with that process model.

Process simulation is often an exercise in ‘molecule accounting’, and as such is often performed by relatively junior engineers.

Construction of a high-accuracy process model, on the other hand, requires deep modelling and process expertise, and is usually performed by an experienced specialist – sometimes working in conjunction with R&D personnel.

What does this mean in practice?

Applying the many different capabilities of a true process modelling tool such as UniSim to first-principles models allows you to gain high-accuracy predictive information for a unit or process.

By contrast, much process simulation is carried out using “off-the-shelf” models that provide little competitive advantage, or purely steady-state models that do not capture the complexity of process operation.

Having said that, process simulation is a valuable and essential activity, which can be significantly enhanced by using high-accuracy customer models of the process to capture corporate knowledge and gain true competitive advantage.

What can you do with a true process model that you can’t with a simulation?

Consider, or example, a detailed model of a fluidised bed reactor and its surrounding flowsheet.

With this model you can of course perform steady-state and dynamic simulation runs to see what happens if feed conditions are varied. What is more, you can do that with a high-accuracy custom model that closely reflects your actual process rather than somebody else’s.

However with the model you can also:

• estimate parameters using model-based data analysis and validation techniques on that model, against experimental data. This can enhance predictive accuracy significantly, and provides information that can be used in formal risk analysis.
• design experiments to refine the parameter estimations and reduce the risk associated with measurement inaccuracy.
• perform optimisations – dynamic or steady-state – on the model, to directly calculate optimal trajectories or values rather than undertaking lengthy trial-and-error invetigations.
• generate linearised models for use in control design applications or Model-based Predictive Control (MPC), gain scheduling or any other activity that requires linear models.
• because this is a model and not a simulation, simulate ‘backwards’ to find out what feed or unit values give rise to the desired product qualities, at no additional cost in terms of execution time or complexity of model.
• generate an equation-set object (ESO) for other software – for example, plant-wide optimisers – to use

Only with a process model will you be able to perform all the activities required to model across the process lifecycle, from conceptual design and laboratory experimentation through detailed engineering design to operation.

What is Simulation?

(Taken from Wikipedia)

Simulation is the imitation of some real thing, state of affairs, or process. The act of simulating something generally entails representing certain key characteristics or behaviours of a selected physical or abstract system.

Simulation is used in many contexts, including the modeling of natural systems or human systems in order to gain insight into their functioning. Other contexts include simulation of technology for performance optimization, safety engineering, testing, training and education. Simulation can be used to show the eventual real effects of alternative conditions and courses of action.

Key issues in simulation include acquisition of valid source information about the referent, selection of key characteristics and behaviours, the use of simplifying approximations and assumptions within the simulation, and fidelity and validity of the simulation outcomes.

Historically, simulations used in different fields developed largely independently, but 20th century studies of Systems theory and Cybernetics combined with spreading use of computers across all those fields have led to some unification and a more systematic view of the concept.

Physical simulation refers to simulation in which physical objects are substituted for the real thing (some circles^[2] use the term for computer simulations modelling selected laws of physics, but this article doesn’t). These physical objects are often chosen because they are smaller or cheaper than the actual object or system.

Interactive simulation is a special kind of physical simulation, often referred to as a human in the loop simulation, in which physical simulations include human operators, such as in a flight simulator or a driving simulator.

Human in the loop simulations can include a computer simulation as a so-called synthetic environment.^[3]

Main article: Computer simulation

A computer simulation (or “sim”) is an attempt to model a real-life or hypothetical situation on a computer so that it can be studied to see how the system works. By changing variables, predictions may be made about the behaviour of the system.^[1]

Computer simulation has become a useful part of modeling many natural systems in physics, chemistry and biology^[4], and human systems in economics and social science (the computational sociology) as well as in engineering to gain insight into the operation of those systems. A good example of the usefulness of using computers to simulate can be found in the field of network traffic simulation. In such simulations, the model behaviour will change each simulation according to the set of initial parameters assumed for the environment.

Traditionally, the formal modeling of systems has been via a mathematical model, which attempts to find analytical solutions enabling the prediction of the behaviour of the system from a set of parameters and initial conditions. Computer simulation is often used as an adjunct to, or substitution for, modeling systems for which simple closed form analytic solutions are not possible. There are many different types of computer simulation, the common feature they all share is the attempt to generate a sample of representative scenarios for a model in which a complete enumeration of all possible states would be prohibitive or impossible.

Several software packages exist for running computer-based simulation modeling (e.g. Monte Carlo simulation and stochastic modeling) that makes the modeling almost effortless.

Modern usage of the term “computer simulation” may encompass virtually any computer-based representation.