Model-based control algorithms

Hence, this paper focuses on the slave-loop control and presents a detailed case study where the impact of the slave-loop is illustrated by the temperature control of a pilot plant presented in Section 2. The tendency model of the jacket of the reactor is given in Section 3, while in Section 4 this model is utilized in a model-based control algorithm. Based on the real-time control results presented also in this section, some conclusions will be drawn in Section 5. 

A new model-based control algorithm that interacts with an industrial batch digester was developed and has been implemented. The control predictions made by the new controller led to improved control compared with the current controller which is based... 

Improved control performance through the use of process model-based control algorithms. 

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). 

The details of the control strategy have received much less attention. The theoretical (159) and experimental (160) analyses of the transfer function for CZ growth are notable exceptions. Algorithms for model-based control are just being developed. 

A second approach to the problem of difficult to obtain measurements is knowledge-based or model-based control. Knowledge-based systems attempt to use various types of knowledge of the biological process (rules etc.) to supplement traditional mathematical control approaches.16 Expert systems are one type of knowledge-based control. Model-based control systems use a model of the process as part of the control algorithm their reliability depends on the accuracy of the model. 

The considerations about the target temperature ( Tb.sp) and the control interval made for the feedback algorithm can be extended to the model-based controller. 

The drying time, resulting when the model-based controller is used, is slightly higher than that obtained through the feedback algorithm, but the simpler mathematical formulation, since no optimization is involved in the calculation, and the smaller computation time make the model-based approach more suitable for in-line control (Pisano et al, 2010a). 

Multiscale process identification and control. Most of the insightful analytical results in systems identification and control have been derived in the frequency domain. The design and implementation, though, of identification and control algorithms occurs in the time domain, where little of the analytical results in truly operational. The time-frequency decomposition of process models would seem to offer a natural bridge, which would allow the use of analytical results in the time-domain deployment of multiscale, model-based estimation and control. 

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