Industrial process models applications

Since the advent of efficient and robust simulation and optimization solution engines" and flowsheeting software packages that allow for relatively easy configuration of complex models, numerous integrated, high fidelity, and multiscale process model applications have been deployed in industrial plants to monitor performance and to determine and capture improvements in operating profit. 

N. Y. Chen, William E. Garwood, and Frank G. Dwyer Alpha Olefins Applications Handbook, edited by George R. Lappin and Joseph L. Sauer Process Modeling and Control in Chemical Industries, edited by Kaddour Najim... 

Off-line analysis, controller design, and optimization are now performed in the area of dynamics. The largest dynamic simulation has been about 100,000 differential algebraic equations (DAEs) for analysis of control systems. Simulations formulated with process models having over 10,000 DAEs are considered frequently. Also, detailed training simulators have models with over 10,000 DAEs. On-line model predictive control (MPC) and nonlinear MPC using first-principle models are seeing a number of industrial applications, particularly in polymeric reactions and processes. At this point, systems with over 100 DAEs have been implemented for on-line dynamic optimization and control. 

Perhaps a major factor is the handling of batches. For instance, pharmaceutical plants usually handle fixed sizes for which integrity must be maintained (no mix-ing/splitting), while solvent or polymer plants handle variable sizes that can be split and mixed. Similarly, different requirements on processing times can be found in different industries depending on process characteristics. For example pharmaceutical applications might involve fixed times due to FDA regulations, while solvents or polymers have times that can be adjusted and optimized with process models. 

The electrostatic precipitator in Example 2.2 is typical of industrial processes the operation of most process equipment is so complicated that application of fundamental physical laws may not produce a suitable model. For example, thermodynamic or chemical kinetics data may be required in such a model but may not be available. On the other hand, although the development of black box models may require less effort and the resulting models may be simpler in form, empirical models are usually only relevant for restricted ranges of operation and scale-up. Thus, a model such as ESP model 1 might need to be completely reformulated for a different size range of particulate matter or for a different type of coal. You might have to use a series of black box models to achieve suitable accuracy for different operating conditions. 

In principle, any type of process model can be used to predict future values of the controlled outputs. For example, one can use a physical model based on first principles (e.g., mass and energy balances), a linear model (e.g., transfer function, step response model, or state space-model), or a nonlinear model (e.g., neural nets). Because most industrial applications of MPC have relied on linear dynamic models, later on we derive the MPC equations for a single-input/single-output (SISO) model. The SISO model, however, can be easily generalized to the MIMO models that are used in industrial applications (Lee et al., 1994). One model that can be used in MPC is called the step response model, which relates a single controlled variable y with a single manipulated variable u (based on previous changes in u) as follows ... 

Solubility modelling with activity coefficient methods is an under-utilized tool in the pharmaceutical sector. Within the last few years there have been several new developments that have increased the capabilities of these techniques. The NRTL-SAC model is a flexible new addition to the predictive armory and new software that facilitates local fitting of UNIFAC groups for Pharmaceutical molecules offers an interesting alternative. Quantum chemistry approaches like COSMO-RS [25] and COSMO-SAC [26] may allow realistic ab-initio calculations to be performed, although computational requirements are still restrictive in many corporate environments. Solubility modelling has an important role to play in the efficient development and fundamental understanding of pharmaceutical crystallization processes. The application of these methods to industrially relevant problems, and the development of new... 

Many industrial processes involve mass transfer processes between a gas/vapour and a liquid. Usually, these transfer processes are described on the basis of Pick s law, but the Maxwell-Stefan theory finds increasing application. Especially for reactive distillation it can be anticipated that the Maxwell-Stefan theory should be used for describing the mass transfer processes. Moreover, with reactive distillation there is a need to take heat transfer and chemical reaction into account. The model developed in this study will be formulated on a generalized basis and as a consequence it can be used for many other gas-liquid and vapour-liquid transfer processes. However, reactive distillation has recently received considerable attention in literature. With reactive distillation reaction and separation are carried out simultaneously in one apparatus, usually a distillation column. This kind of processing can be advantageous for equilibrium reactions. By removing one of the products from the reactive zone by evaporation, the equilibrium is shifted to the product side and consequently higher conversions can be obtained. Commercial applications of reactive distillation are the production of methyl-... 

However fascinated by the achievements in catalysis, one has always to keep in mind, that all those successes were made possible by the extensive research into the synthesis of new ligands and metal complexes, their structural characterization, and the meticulous studies on reaction kinetics with the new catalysts in model systems and in the desired applications. Only the synthetic and catalytic work, hand in hand, can lead to development of new, efficient and clean laboratory and industrial processes. 

One important use of experimental designs is to achieve optimum operating conditions of industrial processes. For a discussion of this application, see Box and Wilson (1951). This paper is extraordinarily rich in response surface concepts. What is the steepest ascent technique discussed in this paper What models are assumed, and what experimental designs are used ... 

The best practices for the successful implementation of process analytics applications are common to all industries. There are, however, differences in the business model (driver), technical and particularly regulatory areas that are unique to the pharmaceutical industry. The following section seeks to briefly outline these distinctions. 

Environmental release estimates are critical inputs for models that calculate indirect human exposures via the environment such as through ambient air or drinking water. They are also critical to modeling exposures to nonhuman aquatic and terrestrial species. Release estimates may be site-specific or they may be generic for a particular industrial process or industrial use. Releases from consumer and commercial products should also be estimated if applicable. 

In this section, we present two examples with different scenarios. The first example illustrates the performance of the model on a single site total refinery planning problem where we compare the results of the model to an industrial scale study from Favennec et al. (2001). This example serves to validate our model and to make any necessary adjustments. The second example extends the scale of the model application to cover three complex refineries in which we demonstrate the different aspects of the model. The refineries considered are of large industrial-scale refineries and actually mimic a general set-up of many areas around the world. The decisions in this example include the selection of crude blend combination, design of process integration network between the three refineries, and decisions on production units expansion options and operating levels. 

Separation processes (both liquid-liquid and gas-liquid) are a key element in many industrial processes. For this application, solvent molecules are built from UNIFAC submolecular groups, and the relevant properties of the new molecules such as distribution coefficients and selectivities are estimated. Strategies for the design of solvents for separation processes were initially formulated and later extended to better model the processes of solvent synthesis, solvent evaluation, and solvent screening. A method for solvent design for liquid-liquid extraction has been developed. 

Kinetic- information is acquired lor two different purposes. Hirst, data are needed lor specific modeling applications that extend beyond chemical theory. These arc essential ill the design of practical industrial processes and are also used io interpret natural phenomena such as Ihe observed depletion of stratospheric ozone. Compilations of measured rate constants are published in the United Stales by the National Institute of Standards and Technology (NISTt. Second, kinetic measurements are undertaken to elucidate basic mechanisms of chemical change, simply to understand the physical world The ultimate goal is control of reactions, but the immediate significance lies in the patients of kinetic behavior and the interpretation in terms of microscopic models. 

In the chemical engineering domain, neural nets have been applied to a variety of problems. Examples include diagnosis (66,67), process modeling (68,69), process control (70,71), and data interpretation (72,73). Industrial application areas include distillation column operation (74), fluidized-bed combustion (75), petroleum refining (76), and composites manufacture (77). 

Industrial Applications of Plant-Wide Equation-Oriented Process Modeling—2010... 

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