Process control, correlation feedback

Automatization of all stages of the analytical process is a trend that can be discerned in the development of modern analytical methods for chemical manufacture, to various extents depending on reliability and cost-benefit considerations. Among the elements of reliability one counts conformity of the accuracy and precision of the method to the specifications of the manufacturing process, stability of the analytical system and closeness to real-time analysis. The latter is a requirement for feedback into automatic process-control systems. Since the investment in equipment for automatic online analysis may be high, this is frequently replaced by monitoring a property that is easy and inexpensive to measure and correlating that property with the analyte of interest. Such compromise is usually accompanied by a collection of samples that are sent to the analytical laboratory for determination, possibly at a lower cost. 

In-line/on-line feedback control of color of food during processing can improve not only color quality but also color related quality such as texture and appearance. To do this, there are three major aspects development of an in-line/on-line color sensor understanding of color change kinetics and establish correlations between instrumental measured and sensory panel perceived colors of foods. In this research, we have chosen color machine vision technology for the measurement of colors of food due to its superior spatial resolution over conventional instruments such as colorimeter or spectrophotometer. Relationships between measured colors and corresponding principal chemical markers were established for the model food systems. We have also found excellent correlations between the color machine vision system (CMVS) measured and a sensory panel determined colors of food samples (Ling and Tepper, 1995). We believed that a CMVS can be used for food process control to ensure color quality as perceived by consumers. 

Assumption 6.5. In view of Remark 6.2, it is assumed that all the material flow rates associated with lo1 are determined by appropriate functions of the process state variables (e.g., via feedback control laws, constitutive relations or pressure-flow correlations). 

The controller receives the on-line composition measurement of the product outlets (extract and raffinate) as feedback data from the plant. These measurements are filtered through a periodic Kalman filter and used together with the simplified SMB model results to estimate the state of the system and to remove the possible moidel errors. The formulation of RMPC is based on the assumption that possible errors or disturbances are likely to repeat and will have a periodic effect on the output, which is the most likely correlation between disturbances and output in a SMB unit. The estimated future concentration profile in the SMB is used to optimize the future behaviour of the plant over a predefined prediction horizon. The controller implements the calculated optimal plant input by changing the external flow rates in order to control the internal flow rates, which are the manipulated variables. Time lags, e.g. between online concentration measurements and optimizer or between optimizer and SMB plant, are insignificant relative to the process dynamics and sampling time for the planned scheme. 

In the past decades, nuclear magnetic resonance (NMR) spectroscopy has been used extensively to study various aspects of polymer chemistry and engineering. Fig. 1 shows the relationship among polymerization conditions, polymer structure, and the material s physical structure and end uses. Solution, solid state, and imaging NMR techniques contribute to imderstanding the physical and chemical aspects of the route from raw materials to final product. Solution NMR provides information about all aspects of the polymerization reactions and the final structure of the synthesized polymer. This information can be correlated with the material s final properties and provide feedback to control the initial polymerization process so that the fraction of structures responsible for desirable properties can be controlled in a systematic way. 

Analysis of full sheet data is useful for process performance evaluations and product value calculations. For feedback control or any other on-line application, it is necessary to continuously convert scanner data into a useful form. Consider the data vector Y ,k) for scan number k. It is separated into its MD and CD components as Y( , A ) = yM )( )+Yc )( , k) where Ymd ) s the mean of Y ,k) as a scalar and YcD -,k) is the instantaneous CD profile vector. MD and CD controllers correspondingly use these calculated measurements as feedback data for discrete time k. Univariate MD controllers are traditional in nature with only measurement delay as a potential design concern. On the other hand, CD controllers are multivariate in form and must address the challenges of controller design for large dimensional correlated systems. 

Suppose that the process is well known and that an adequate mathematical model for it is available. If there is an auxiliary process variable which correlates well with the changes in process dynamics, we can relate ahead of time the best values of the controller parameters to the value of the auxiliary process variable. Consequently, by measuring the value of the auxiliary variable we can schedule or program the adaptation of the controller parameters. Figure 22.1 shows the block diagram of a programmed adaptive control system. We notice that it is composed of two loops. The inner loop is an ordinary feedback control loop. The outer loop includes the parameter adjustment (adaptation)... 

The establishment of the development program, and in particular the development program of the fertilizer industry, requires not only the collection of data to be processed using a specific methodology but also requires self-control in the feedback system. There are numerous examples of development programs established on the basis of future or expected demand, which are not correlated with the macroeconomic options and features of the country s economy. 

During plant tests, the input variables arc sometimes adjusted by the operator in order to maintmn the product at its specification. Different corrective actions are taken in response to a particular control situation in order to avoid correlation between the independent variables. However, this type of operator intervention introduces feedback into the test data that can lead to significant bias in the estimated process models. To remove this efifect, noise... 

Improved product quality and consistency It is a big focus for many manufacturers, especially in specialty polymers where there can be high variance in production lots. Even within a company, different plants can produce the same product with very different end properties which then creates negative feedback from customers. Using feedback control, predictive models, and historical databases, manufacturers will be able to share reaction data between their plants and will be able to build correlations to polymer properties based on process and ACOMP data, thereby creating the most efficient process to manufacture a uniform product. 

---