Process model 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. 

In many process design applications like polymerization and plasticization, specific knowledge of the thermodynamics of polymer systems can be very useful. For example, non-ideal solution behavior strongly governs the diffusion phenomena observed for polymer melts and concentrated solutions. Hence, accurate modeling of... 

To address these challenges, chemical engineers will need state-of-the-art analytical instruments, particularly those that can provide information about microstmctures for sizes down to atomic dimensions, surface properties in the presence of bulk fluids, and dynamic processes with time constants of less than a nanosecond. It will also be essential that chemical engineers become familiar with modem theoretical concepts of surface physics and chemistry, colloid physical chemistry, and rheology, particrrlarly as it apphes to free surface flow and flow near solid bormdaries. The application of theoretical concepts to rmderstanding the factors controlling surface properties and the evaluation of complex process models will require access to supercomputers. 

Stamovlasis, D., Tsaparlis, G. (2001). Application of complexity theory to an information processing model in seience education. Nonlinear Dynamics in Psychology and Life Sciences, 3, 267-286. 

Neither method will achieve a bumpless startup for complex kinetic schemes such as fermentations. There is a general method, known as constant RTD control, that can minimize the amount of off-specification material produced during the startup of a complex reaction (e.g., a fermentation or polymerization) in a CSTR. It does not require a process model or even a realtime analyzer. We first analyze shutdown strategies, to which it is also applicable. 

Chan K-W, W Chu (2006) Model applications and intermediates quantification of atrazine degradation hy UV-enhanced Fenton process. J Agric Food Chem 54 1804-1813. 

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... 

The next two steps after the development of a mathematical process model and before its implementation to "real life" applications, are to handle the numerical solution of the model s ode s and to estimate some unknown parameters. The computer program which handles the numerical solution of the present model has been written in a very general way. After inputing concentrations, flowrate data and reaction operating conditions, the user has the options to select from a variety of different modes of reactor operation (batch, semi-batch, single continuous, continuous train, CSTR-tube) or reactor startup conditions (seeded, unseeded, full or half-full of water or emulsion recipe and empty). Then, IMSL subroutine DCEAR handles the numerical integration of the ode s. Parameter estimation of the only two unknown parameters e and Dw has been described and is further discussed in (32). 

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 sewer model is designed from a conceptual point of view and has potential for further applications. In Section 4.3.3, it was concluded that the occurrence of sulfide can be used as a pragmatic measure of malodors. Therefore, the sewer process model also has potential for the prediction of odor problems. Furthermore, as dealt with in Section 8.5.2, the model also predicts the aerobic transformations of suspended sediment particles in sewers (Vollertsen and Hvitved-Jacobsen, 1998, 1999 Vollertsen et al 1998, 1999). The model is also a potential tool for simulation of the impacts from combined sewer overflows. 

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 ... 

In this chapter different aspects of data processing and reconciliation in a dynamic environment were briefly discussed. Application of the least square formulation in a recursive way was shown to lead to the classical Kalman filter formulation. A simpler situation, assuming quasi-steady-state behavior of the process, allows application of these ideas to practical problems, without the need of a complete dynamic model of the process. 

Dohrmann also supply an automated nitrogen analyser with video display and data processing (model DN-1000) based on similar principles which is applicable to the determination of down to O.lmg L 1 nitrogen in solid and liquid samples. 

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... 

Step 4 Verification - here, the selected candidates are further analyzed in terms of their performance when they are applied for their designed use. Models capable of simulating their performance in their process of application are needed. These models may be process simulation models (for example, ICASSIM or ICAS-utility) as well as product application models (such as delivery of an active ingredient). 

It is the purpose of this article to present in its entirety one of the early applications of reaction engineering, which was well under way in 1952 before the name Reaction Engineering was even coined. We will describe the laboratory kinetic experiments, the diffusional analysis, the integration of these phenomena into a mathematical process model, its field testing and validation, and subsequent use in process design, modification, control, and optimization. 

---