Steady-state chemical process simulation

Steady State Chemical Process Simulation A State-of-the-Art Review... 

Perspective, The use of a mathematical model on a computer to simulate a chemical process is now approximately two decades old. The field, which has been referred to as steady state chemical process simulation, flowsheeting or computer aided chemical process design to emphasize various shadings and meanings has had a major impact on moving chemical process design from essentially an art form of the 1950 s to an accepted engineering science today. 

Rosen, E. M., 1980, Steady State Chemical Process Simulation A State-of-the-Art Review, Computer Applications to Chemical Engineering, ACS Symposium Series 124, p. 115-13... 

Additionally, there is process simulation steady-state flow sheet simulators and dynamic flow sheet simulators. Steady-state flow sheet simulators have been widely used in chemical process engineering since the 1960s. Steady-state simulators describe the process as a set of modules connected by flows of material and energy between them. The modules correspond to mass and energy balances together with physical and thermodynamic data necessary for calculations. The calculations may be performed using one of two basic techniques. The sequential approach computes modules one by one, in a direction that generally follows that of the physical flows in the system (Leiviska, 1996). 

PLOW 1 RAN was made available in 1974 by Monsanto Co. for steady-state simulation of chemical processes based on sequential modular technology. It requires specification of feed streams and topology of the system. In 1987, an optimization enhancement was added. 

Classification Process simulation refers to the activity in which mathematical models of chemical processes and refineries are modeled with equations, usually on the computer. The usual distinction must be made between steady-state models and transient models, following the ideas presented in the introduction to this sec tion. In a chemical process, of course, the process is nearly always in a transient mode, at some level of precision, but when the time-dependent fluctuations are below some value, a steady-state model can be formulated. This subsection presents briefly the ideas behind steady-state process simulation (also called flowsheeting), which are embodied in commercial codes. The transient simulations are important for designing startup of plants and are especially useful for the operating of chemical plants. 

Distillation is a well-known process and scale-up methods have been well established. Many computer programs for the simulation of continuous distillation columns that are operated at steady state are available. In fine chemicals manufacture, this concerns separations of products in the production of bulk fine chemicals and for solvent recovery/purification. In the past decade, software for modelling of distillation columns operated at non-steady state, including batch distillation, has been developed. In the fine chemicals business, usually batch distillation is applied. 

A plant simulation is the set of equations necessary to approximate the response of a chemical plant to various changes. A steady- state plant simulation is one that predicts the eventual outputs when the inputs and all the internal variables are held constant. It does not say how the outputs are reached. A dynamic plant simulation is one that predicts how the outputs of a plant will change when a known change in the input occurs. It gives the path the process follows in going from one steady state to another. 

NN applications, perhaps more important, is process control. Processes that are poorly understood or ill defined can hardly be simulated by empirical methods. The problem of particular importance for this review is the use of NN in chemical engineering to model nonlinear steady-state solvent extraction processes in extraction columns [112] or in batteries of counter-current mixer-settlers [113]. It has been shown on the example of zirconium/ hafnium separation that the knowledge acquired by the network in the learning process may be used for accurate prediction of the response of dependent process variables to a change of the independent variables in the extraction plant. If implemented in the real process, the NN would alert the operator to deviations from the nominal values and would predict the expected value if no corrective action was taken. As a processing time of a trained NN is short, less than a second, the NN can be used as a real-time sensor [113]. 

Steady-state process simulation or process flowsheeting has become a routine activity for process analysis and design. Such systems allow the development of comprehensive, detailed, and complex process models with relatively little effort. Embedded within these simulators are rigorous unit operations models often derived from first principles, extensive physical property models for the accurate description of a wide variety of chemical systems, and powerful algorithms for the solution of large, nonlinear systems of equations. 

Gupta, A.K., Steady State Simulation of Chemical Processes, Ph.D. Thesis, University of Calgary, Alberta (1990). 

PK modeling can take the form of relatively simple models that treat the body as one or two compartments. The compartments have no precise physiologic meaning but provide sites into which a chemical can be distributed and from which a chemical can be excreted. Transport rates into (absorption and redistribution) and out of (excretion) these compartments can simulate the buildup of chemical concentration, achievement of a steady state (uptake and elimination rates are balanced), and washout of a chemical from tissues. The one- and two-compartment models typically use first-order linear rate constants for chemical disposition. That means that such processes as absorption, hepatic metabolism, and renal excretion are assumed to be directly related to chemical concentration without the possibility of saturation. Such models constitute the classical approach to PK analysis of therapeutic drugs (Dvorchik and Vesell 1976) and have also been used in selected cases for environmental chemicals (such as hydrazine, dioxins and methyl mercury) (Stem 1997 Lorber and Phillips 2002). As described below, these models can be used to relate biomonitoring results to exposure dose under some circumstances. 

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