Feedback concentration control implementation

There are different approaches to implementing the feedback concentration control for the direct design. Various schemes to implement the concentration control for direct design are described in the literature for cooling and antisolvent crystallizations. " The basic steps are as follows (i) the solution concentration is estimated from IR absorbances and temperature or solvent-antisolvent ratio using the calibration model that relates IR spectra to concentration and (ii) the temperature or antisolvent flow rate setpoint is calculated from the concentration, solubility curve, and the user-specified supersaturation setpoint. 

If all the state variables are not measured, an observer should be implemented. In the Figure 14, the jacket temperature is assumed as not measured, but it can be easily estimated by the rest of inputs and outputs and based on the separation principle, the observer and the control can be calculated independently. In this structure, the observer block will provide the missing output, the integrators block will integrate the concentration and temperature errors and, these three variables, together with the directly measured, will input the state feedback (static) control law, K. Details about the design of these blocks can be found in the cited references. 

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. 

Next we consider the equipment that is used to implement control strategies. For the stirred-tank mixing system under feedback control in Fig. 1.4, the exit concentration X is controlled and the flow rate iV2 of pure species A is adjusted using proportional control. To consider how this feedback control strategy could be implemented, a block diagram for the stirred-tank control system is shown in Fig. 1.6. Operation of the concentration control system can be summarized for the key hardware components as follows ... 

The available data from emulsion polymerization systems have been obtained almost exclusively through manual, off-line analysis of monomer conversion, emulsifier concentration, particle size, molecular weight, etc. For batch systems this results in a large expenditure of time in order to sample with sufficient frequency to accurately observe the system kinetics. In continuous systems a large number of samples are required to observe interesting system dynamics such as multiple steady states or limit cycles. In addition, feedback control of any process variable other than temperature or pressure is impossible without specialized on-line sensors. This note describes the initial stages of development of two such sensors, (one for the monitoring of reactor conversion and the other for the continuous measurement of surface tension), and their implementation as part of a computer data acquisition system for the emulsion polymerization of methyl methacrylate. 

The design, optimization, and control of the EMR for the decolorization of Orange II require the implementation of a control system. Two control systems were developed (a) a feedforward system based on the knowledge of kinetics and reactor hydraulics and (b) a feedback system based on the concentration of DO into the reactor, which was observed to be a main variable that provides extensive information about the development of the process. 

Chaotic behavior requires a nonhnearity in the equations of motion. For conservative mechanical systems, of which computing classical trajectories is, for us, the prime example. Section 5.2.2.1, the nonlinearity is due to the anharmonicity of the potential. In chemical kinetics" there are two sources of nonlinearity. One is when the concentrations are not uniform throughout the system so that diffusion must be taken into account. The other is if there is a feedback so that, for example, formation of products influences the reaction rate, see Problem H. As we shall see, this type of nonlinearity occurs naturally in many surface reactions and this is why we chose catalytic processes as an example. In both mechanical and chemical kinetics systems there is one more way to add nonlinear terms and this is by an external perturbation. For surface reactions this additional control can be implemented, for example, by modulating the gas-phase pressures of reactants and/or products."... 

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