LQG applications in the process industries

Multivariable control of anaerobic reactor by using external recirculation and bypass ratio

“…17 It is noted that there are some specific features making the present application different from the conventional LQT applications. First, the LQT method is a model-based approach, so that its performance depends on the accuracy of the model.…”

“…Also, the steady state operating point is not precisely known, which further worsens model accuracy. 17 Second, as mentioned earlier, the state space model in this work is different from the general model equation used in the normal LQR/LQT approach because of the additional terms for disturbance X in and control input u in Equation (8). Therefore, using the normal LQT control law in Equation (12) for the output tracking problem would produce a steady offset.…”

“…Moreover, careful assessment of the system robustness may be necessary by using additional techniques such as loop transfer recovery (LTR) or a true Kalman filter in the LQR controller. 17 Finally, using a more detailed mechanical model of the anaerobic digestion by including biogas and pH dynamics or using a comprehensive model that includes more components might improve the accuracy and practical reliability of a controller design.…”

“…Using a traditional LQR/LQT method usually results in a steady state offset. 17 LQR/LQT with integral action is a practical method to eliminate the control offset. 13,16,18,19,22 In this study, an advanced multivariable control strategy is proposed for an anaerobic reactor using both external recirculation-to-feed ratio and bypass-to-feed ratio.…”

“…Studies have found that advanced control approaches such as linear quadratic regulation or tracking (LQR/LQT), the Generic Predictive Controller (GPC) and the model predictive controller (MPC) were superior to the proportional-integral (PI) control method in terms of the transient response performance and disturbance rejection capability, 13 -16 especially for problems with complex multiple and coupling variables. During the past three decades the theory of LQR/LQT has been well developed to deal with many important issues, such as stability, nonlinearity and robustness, 17 and it has been widely applied to different complex industrial control processes. 13,14,16,18 -23 An LQR/LQT controller can optimize a cost function that is a compromise between output error and control effort.…”