Computational Process Modeling

Simulation, Modeling, and Design Feasibility Because reaction and separation phenomena are closely coupled in a reactive distillation process, simulation and design are significantly more complex than those of sequential reaction and separation processes. In spite of the complexity, however, most commercial computer process modeling packages offer reliable and flexible routines for simulating steady-state reactive distillation columns, with either equilibrium or kinetically controlled reaction models... 

Process Modeling. The complexity of emulsion polymerization makes rehable computer models valuable. Many attempts have been made to simulate the emulsion polymerization process for different monomer systems (76—78). 

There are special numerical analysis techniques for solving such differential equations. New issues related to the stabiUty and convergence of a set of differential equations must be addressed. The differential equation models of unsteady-state process dynamics and a number of computer programs model such unsteady-state operations. They are of paramount importance in the design and analysis of process control systems (see Process control). 

N. Bhat and T. J. McAvoy, "Dynamic Process Modeling via Neural Computing," paper presented atMJOE National Meeting, San Francisco, 1989. 

MAPPS (NUREG/CR-3634) is a task-oriented, computer-based model for simulating actual processes in maintenance activities. It includes environmental,... 

Traditional control systems are in general based on mathematical models that describe the control system using one or more differential equations that define the system response to its inputs. In many cases, the mathematical model of the control process may not exist or may be too expensive in terms of computer processing power and memory. In these cases a system based on empirical rules may be more effective. In many cases, fuzzy control can be used to improve existing controller systems by adding an extra layer of intelligence to the current control method. 

A chemical process must be designed to operate under a chosen set of conditions, each of which must be controlled within specified limits if the process is to operate rehably and yield a product of specified quality. Accurate, complex, computer-solvable models of chemical processes will incorporate features of the controls that are needed to maintain the desired process conditions. Such models will be able to... 

Airlift loop reactor (ALR), basically a specially structured bubble column, has been widely used in chemical industry, biotechnology and environmental protection, due to its high efficiency in mixing, mass transfer, heat transfer etc [1]. In these processes, multiple reactions are commonly involved, in addition to their complicated aspects of mixing, mass transfer, and heat transfer. The interaction of all these obviously affects selectivity of the desired products [2]. It is, therefore, essential to develop efficient computational flow models to reveal more about such a complicated process and to facilitate design and scale up tasks of the reactor. However, in the past decades, most involved studies were usually carried out in air-water system and the assumed reactor constructions were oversimplified which kept itself far away from the real industrial conditions [3] [4]. 

In the present study, we propose a tuning method for PID controllers and apply the method to control the PBL process in LG chemicals Co. located in Yeochun. In the tuning method proposed in the present work, we first find the approximated process model after each batch by a closed-loop Identification method using operating data and then compute optimum tuning parameters of PID controllers based on GA (Genetic Algorithm) method. 

If the final structure either deviates from the refined model or does not match the NMR restraints (8) one has to revise the experimental data and the parameters used in the DG and MD computations (9). In many cases, mistakes are made when preparing and performing the computational processes (10) or even experimental errors might be present (11). Those errors include a wrong NMR peak assignment, no precise calibration of the NOE/ROE signals, an incorrect conversion of the experimental data to constraints, and a nonfactual parameterization of the rMD and fMD trajectories. In such cases either new calculations or new experiments must be performed. 

Linear models with respect to the parameters represent the simplest case of parameter estimation from a computational point of view because there is no need for iterative computations. Unfortunately, the majority of process models encountered in chemical engineering practice are nonlinear. Linear regression has received considerable attention due to its significance as a tool in a variety of disciplines. Hence, there is a plethora of books on the subject (e.g., Draper and Smith, 1998 Freund and Minton, 1979 Hocking, 1996 Montgomery and Peck, 1992 Seber, 1977). The majority of these books has been written by statisticians. 

Fate of Chemicals in Aquatic Systems Process Models and Computer Codes... 

HSPF. The Hydrologic Simulation Program (FORTRAN) ( 1, 42) is based on the Stanford Watershed Model. Version 7 of HSPF incorporates the process models of SERATRA in its aquatic section, with several (user-selectable) options for sediment transport computations. HSPF includes the generation of transformation products, each of which is in turn subject to volatilization, phototransformation, biolysis, etc. 

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