Industrial process models variables

Since both the on-line dynamic optimization and the model-based control strategy rely on process models, the knowledge of current states and/or model parameters is required. However, in most industrial processes, state variables are not all measurable and some parameters are not known exactly. As a consequence, there is a need for estimating these states and parameters. In this work, two Extended Kalman Filters (EKF) are implemented. The first one is applied to predict the reactant concentration, which will be used for on-line dynamic optimization, from its delayed measurement. The other one is applied to estimate the unknown heat of reaction, which will be used for model-based controller, from the frequently available measurements of temperature. 

In the context of chemometrics, optimization refers to the use of estimated parameters to control and optimize the outcome of experiments. Given a model that relates input variables to the output of a system, it is possible to find the set of inputs that optimizes the output. The system to be optimized may pertain to any type of analytical process, such as increasing resolution in hplc separations, increasing sensitivity in atomic emission spectrometry by controlling fuel and oxidant flow rates (14), or even in industrial processes, to optimize yield of a reaction as a function of input variables, temperature, pressure, and reactant concentration. The outputs ate the dependent variables, usually quantities such as instmment response, yield of a reaction, and resolution, and the input, or independent, variables are typically quantities like instmment settings, reaction conditions, or experimental media. 

Introduction The model-based contfol strategy that has been most widely applied in the process industries is model predictive control (MFC). It is a general method that is especially well-suited for difficult multiinput, multioutput (MIMO) control problems where there are significant interactions between the manipulated inputs and the controlled outputs. Unlike other model-based control strategies, MFC can easily accommodate inequahty constraints on input and output variables such as upper and lower limits or rate-of-change limits. 

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. 

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 ... 

The flexibility in the petrochemical industry production and the availability of many process technologies require adequate strategic planning and a comprehensive analysis of all possible production alternatives. Therefore, a model is needed to provide the development plan of the petrochemical industry. The model should account for market demand variability, raw material and product price fluctuations, process yield inconsistencies, and adequate incorporation of robustness measures. 

Process models are unfortunately often oversold and improperly used. Simulations, by definition, are not the actual process. To model the process, assumptions must be made about the process that may later prove to be incorrect. Further, there may be variables in the material or processing equipment that are not included in the model. This is especially true of complex processes. It is important not to confuse virtual reality with reality. The claim is often made that the model can optimize a cure cycle. The complex sets of differential equations in these models cannot be inverted to optimize the multiple properties they predict. It is the intelligent use of models by an experimenter or an optimizing routine that finds a best case among the ones tried. As a consequence, the literature is full of references to the development of process models, but examples of their industrial use in complex batch processes are not common. 

In the first report about immobilization of peroxidases on mesoporous materials, Takahashi and coworkers shed light on different parameters that affect the process. Using horseradish peroxidase (HRP) as a model, the authors reported that higher stability to temperature and organic solvent, important variables on industrial processes, were obtained when the size of the pore match the size of the enzyme, in such a way that the encapsulated enzyme was located in a restricted space that slowed down its free movement, preventing its denaturation [4],... 

The data obtained from many processes are multivariate in nature, and have an empirical or theoretical model that relates the variables. Such measurements can be denoised by minimizing a selected objective function subject to the process model as the constraint. This approach has been very popular in the chemical and minerals processing industries under the name data rectification, and in electrical, mechnical and aeronautical fields under the names estimation or filtering. In this chapter all the model-based denoising methods are referred to as data rectification. 

These issues, positive and negative, are reflected in the available correlations. These correlations are both highly useful and also limited. Some are useful because the inputs are easily measured and adjusted as needed however, correlations are mostly empirical or semi-empirical, which means that they are not widely applicable but, rather, are bioreactor design dependent at best. Hence, geometric similarity is very important. Furthermore, most studies are performed in air-water systems while most industrial processes use much more complicated and time-variant liquids. In other words, the airhft bioreactor correlations have similar problems as those for stirred-tank bioreactors and bubble columns and are due to the fact that they share the problem source bubble-bubble interactions. Bubble-bubble interactions are highly variable and lead to hydrodynamics which, in turn, are difficult to quantify and predict. Hence, the result has been that the airlift bioreactor correlations and models are either system dependent or not adequately constrained. 

---