Process control physical models

Intended Use The intended use of the model sets the sophistication required. Relational models are adequate for control within narrow bands of setpoints. Physical models are reqiiired for fault detection and design. Even when relational models are used, they are frequently developed bv repeated simulations using physical models. Further, artificial neural-network models used in analysis of plant performance including gross error detection are in their infancy. Readers are referred to the work of Himmelblau for these developments. [For example, see Terry and Himmelblau (1993) cited in the reference list.] Process simulators are in wide use and readily available to engineers. Consequently, the emphasis of this section is to develop a pre-liminaiy physical model representing the unit. 

Parameter Estimation Relational and physical models require adjustable parameters to match the predicted output (e.g., distillate composition, tower profiles, and reactor conversions) to the operating specifications (e.g., distillation material and energy balance) and the unit input, feed compositions, conditions, and flows. The physical-model adjustable parameters bear a loose tie to theory with the limitations discussed in previous sections. The relational models have no tie to theory or the internal equipment processes. The purpose of this interpretation procedure is to develop estimates for these parameters. It is these parameters hnked with the model that provide a mathematical representation of the unit that can be used in fault detection, control, and design. 

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 ... 

Physical fractionation, of oils, 10 813-814 Physical materials standards, 15 742 Physical metallurgy, 16 127 Physical models, for process control, 20 687 Physical netpoints, in shape-memory polymers, 22 356, 358... 

The processes controlling transfer and/or removal of pollutants at the aqueous-solid phase interface occur as a result of interactions between chemically reactive groups present in the principal pollutant constituents and other chemical, physical and biological interaction sites on solid surfaces [1]. Studies of these processes have been investigated by various groups (e.g., [6-14]). Several workers indicate that the interactions between the organic pollutants/ SWM leachates at the aqueous-solid phase surfaces involve chemical, electrochemical, and physico-chemical forces, and that these can be studied in detail using both chemical reaction kinetics and electrochemical models [15-28]. 

When thermodynamics or physics relates secondary measurements to product quality, it is easy to use secondary measurements to infer the effects of process disturbances upon product quality. When such a relation does not exist, however, one needs a solid knowledge of process operation to infer product quality from secondary measurements. This knowledge can be codified as a process model relating secondary to primary measurements. These strategies are within the domain of model-based control Dynamic Matrix Control (DMC), Model Algorithmic Control (MAC), Internal Model Control (IMC), and Model Predictive Control (MPC—perhaps the broadest of model-based control strategies). 

In recent years a great deal of applied research has centered on the study of problems related to the environment and environmental processes. In some of these studies, radiotracers have been used as primary tools to measure the dynamics of many physical and biological processes. In the best studies, the use of radiotracers to measure flow patterns, dispersion, and similar features is closely coupled to tests of theoretical models of the processes involved. This modeling is important because in environmental studies the experimental conditions are difficult to control and, in general, only a few of the many possible conditions in a given experiment will be sampled. Therefore, it is important to have some way (i.e., a model) to correlate experimental results measured under special conditions to general statements regarding an environmental process. 

The fermentation process can be performed batch-wise or continuously at a given temperature and time. The broth is further processed to remove the desired chemical. Figure 11-11 shows a schematic and an abstracted physical model of a fermenter with the liquid phase as the control region. 

A simple model of the chemical processes governing the rate of heat release during methane oxidation will be presented below. There are simple models for the induction period of methane oxidation (1,2.>.3) and the partial equilibrium hypothesis (4) is applicable as the reaction approaches thermodynamic equilibrium. However, there are apparently no previous successful models for the portion of the reaction where fuel is consumed rapidly and heat is released. There are empirical rate constants which, due to experimental limitations, are generally determined in a range of pressures or concentrations which are far removed from those of practical combustion devices. To calculate a practical device these must be recalibrated to experiments at the appropriate conditions, so they have little predictive value and give little insight into the controlling physical and chemical processes. 

Throughout the design of a chemical plant, issues relating to safety, economics and environmental impact must be considered. By doing so, the risks associated with the plant can be minimised before actual construction. The same principle applies whatever the scale of the process. The field of process control (Chapter 8) considers all these issues and is, indeed, informed by the type of hazard analyses described in Chapter 10. The objectives of an effective control system are the safe and economic operation of a process plant within the constraints of environmental regulations, stakeholder requirements and what is physically possible. Processes require control in the first place because they are dynamic systems, so the concepts covered in the earlier chapters of this book are central to process control (i.e. control models are based on mass, energy and momentum balances derived with respect to time). Chapter 8 focuses on the key aspects of control systems. 

The Great Lakes have served as a focal point for PCB research. This research has provided an understanding of the processes controlling fate and transport of PCBs, and has led to the development of models than can be applied to other contaminants and water bodies. The processes of atmospheric deposition and net sediment accumulation are described adequately in these models, but the exchange at the sediment-water interface and seasonal depositional patterns need further improvements. While concentrations have declined in air, water and sediments over the last decade, trends in fish indicate a slowing or stopping of such a decline. Thus future research efforts should address the bioaccumulation process and foodweb dynamics, and the physical processes mentioned above. 

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