Design and Control

Long time constants in the system and zone-to-zone interaction of the heaters complicated the controller design and tuning. The time available for experimental measurements was limited by the schedule of other experimental work to be performed by the extruder. The classic step response methods of tuning controllers would take on the order of hours to perform, and frequently disturbances in the polymer feed or in the ambient room conditions would invalidate the test. Consequently, a mathematical rather than an empirical approach was desirable. 

We have given up the pretense that we can cover controller design and still have time to do all the plots manually. We rely on MATLAB to construct the plots. For example, we take a unique approach to root locus plots. We do not ignore it like some texts do, but we also do not go into the hand sketching details. The same can be said with frequency response analysis. On the whole, we use root locus and Bode plots as computational and pedagogical tools in ways that can help to understand the choice of different controller designs. Exercises that may help such thinking are in the MATLAB tutorials and homework problems. 

The idea of a root locus plot is simple—if we have a computer. We pick one design parameter, say, the proportional gain Kc, and write a small program to calculate the roots of the characteristic polynomial for each chosen value of as in 0, 1, 2, 3,., 100,..., etc. The results (the values of the roots) can be tabulated or better yet, plotted on the complex plane. Even though the idea of plotting a root locus sounds so simple, it is one of the most powerful techniques in controller design and analysis when there is no time delay. 

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. 

We will use this simple system in many subsequent parts of this book. When we use it for controller design and stability analysis, we will use an even simpler version. If the throughput F is constant and the holdups and temperatures ate the same in all three tanks, Eqs. (3.3) become... 

Chemical reactors intended for use in different processes differ in size, geometry and design. Nevertheless, a number of common features allows to classify them in a systematic way [3], [4], [9]. Aspects such as, flow pattern of the reaction mixture, conditions of heat transfer in the reactor, mode of operation, variation in the process variables with time and constructional features, can be considered. This work deals with the classification according to the flow pattern of the reaction mixture, the conditions of heat transfer and the mode of operation. The main purpose is to show the utility of a Continuous Stirred Tank Reactor (CSTR) both from the point of view of control design and the study of nonlinear phenomena. 

In regard dynamics and control scopes, the contributions address analysis of open and closed-loop systems, fault detection and the dynamical behavior of controlled processes. Concerning control design, the contributors have exploited fuzzy and neuro-fuzzy techniques for control design and fault detection. Moreover, robust approaches to dynamical output feedback from geometric control are also included. In addition, the contributors have also enclosed results concerning the dynamics of controlled processes, such as the study of homoclinic orbits in controlled CSTR and the experimental evidence of how feedback interconnection in a recycling bioreactor can induce unpredictable (possibly chaotic) oscillations. 

Annaswamy, A. M., M. Fleifil, J. W. Rumsey, R. Prasanth, J.P. Hathout, and A. F. Ghoniem. 2000. Thermoacoustic instability Model based optimal control designs and experimental vahdation. IEEE Transactions on Control Systems Technology 8(6). 

Model Predictive Control Design and Implementation Using MATLAB Liuping Wang... 

Often analytical solutions are permitted. Moreover, on today s high-speed computers they impose only minor CPU requirements, and therefore could find considerable use in process control, design and optimization. They also become very attractive models for solution on inexpensive personal computers which could be placed in a remote location or refinery. 

Assist with controller design and selection of tuning parameters for system start-up... 

Also, the design practice includes P ID documentation, database specification and verification of purchased equipment, control design and performance analysis, software configuration, real-time simulation for DCS system checkout and operator training, reliability studies, interlock classification and risk assessment of safety instrumented systems (SIS), and hazard and operability (HAZOP) studies. 

The aim of this project is to construct mathematical models for char conversion in a fixed-bed gasifier. The understanding of the conditions during char gasification will facilitate a better control, design and the scaling up of gasification plants. 

G. Tao, Adaptive Control Design and Analysis. New york Wiley-lnterscience, 2003. 

In order to maintain the furnace running in a safe, stable, and high-efficiency state it is necessary to control the outlet temperatures of the multiple passes to be the same. Traditional control methods usually have difficulties in controlling these temperatures, and some advanced control methods are too complex for a convenient use. In this paper, a control technique is proposed to distribute the inlet flowrates so that the outlet temperatures are as identical as possible. The principle of the proposed method is firstly explained and demonstrated, then the system analysis, the controller design, and the simulation experiments are presented, and finally the results of application to an industrial refinery furnace are reported. This technique has the following advantages it does not need complicated design procedures, the controller structure is simple, it is easy to apply and it can be extended to furnaces with different number of passes. 

Fig. 5 shows good agreement between the experimental and simulation results of dynamic liquid bulk concentrations. Because of its complexity the rate-based model is not suitable for controller design and optimization of the RD process. Therefore, an extended equilibrium stage model, which includes a reaction kinetic, is used for these tasks. Fig. 6 shows comparisons of simulation results of the rate-based model (RBA) and the equilibrium stage model for a typical trajectory of input variables. The dynamic behavior is covered well by the simplified model and the deviations between the absolute values are acceptable for control purposes. The advantage of substantially reduced computing time motivates the use of the simplified model for control and optimization purposes. 

Step 1 Postulate a model for the process. The unknown process is not completely a black box. Some information about its dynamic behavior is known from basic principles and/or plant experience. Therefore, some estimate of its model s order and some initial values for the unknown parameters will be available. The more we know about the process, the more effective the postulated model will be. Consequently, we should use for its development all available information. Remember, though, that complex models of high order will not necessarily produce better controller designs and will burden the computational effort without tangible results. 

We start with the performance criteria since we need to establish some basis for the comparison of alternative controller designs, and because its selection constitutes the principal difficulty during the design of a feedback system. 

Instrumentation and control—study of control loops for proper process control design and operation (Chap, 9). 

As in the case of reboilers and condensers, distillation control is too wide a topic to be adequately covered in a handful of chapters. Entire texts (68, 89, 301, 332, 362) deal exclusively with distillation control. Most of these strike a balance between theory, practice, controls design, and controls optimization. In contrast, the coverage here emphasizes operational aspects what various control schemes can and cannot do, how to put together a control system (not necessarily optimum, but one that works), how to recognize and avoid a troublesome system, what are the ill effects of various poor control schemes, and what corrective action can restore trouble-free operation. 

Industrial uses of textile composites include conveyor belting, tires, and hoses. These are not considered here specifically, other than that several of the test methods may well be applicable where the textile element is of relevance. These are products in their own right and have been extensively reviewed elsewhere [4. 5]. It is the intention in this chapter to review the test methods available for coated fabrics, their practicability and their application in the areas of quality control, design, and specifications relevant to product end use. Some knowledge of processing is assumed, but clarification is given as necessary. 

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