Feedback control schemes for common unit operations. Developing and solving dynamic process models, including the application of Laplace transforms and transfer functions as well as the use of numerical simulation tools. Frequency response analysis and Bode plots. Modelling of closed-loop control systems and PID controllers. Closed-loop stability analysis and controller tuning. Advanced single-loop control strategies and multiloop control systems.
Due to the needs of high production meetingproduct quality, process safety and environmental regulation, control systems play a key role inchemical and biochemical plants operation (Santos et al., 2005). However, in order to have awell regulated system, a suitable and reliable mathematical model must be available. Firstprinciple models are usually better than empirical models not only because of the theoreticalbackground, but also due to the extrapolation capability allowed by proper parameter estimation(Levenspiel, 2002). On the other hand, fundamental models may be too large for real timeapplication, besides, some difficulties may arise during the modeling task, such as the choice ofconstitutive relations for example the kinetic equation or thermodynamic equation of state (Lenziet al., 2005). A commonly used alternative employed to overcome these difficulties is theuse of empirical models, which can be obtained by process identification techniques (Pearson,2006). Modeling and identification of biochemical reactors represent a broad research field withseveral successful applications already reported in literature. Results concern fundamental andempirical modeling (Kirdar et al., 2008), applications to membrane reactors (Ng and Kim,2007), solid-state fermentation (Mitchell et al., 2004), gas-lift reactors (Petersen andMargaritis, 2001).
11. Kirdar, A.O., K.D. Green and A.S. Rathore, "Application of multivariate data analysis for identification and successful resolution of a root cause for a bioprocessing application", Biotechnol. Prog., 24, 720-726 (2008). 2b1af7f3a8