Robust control of continuous crystallization processes

Control of Continuous Mixed‐Solution Mixed‐Product Removal Crystallization Processes

“…The continuous MSMPR crystallization is a nonlinear distributed‐parameter system, therefore, control design is a challenging task. Different strategies have been proposed including robust control , C‐control , decentralized PID, nonlinear model predictive control (NMPC) , and direct nucleation control . A Lyapunov‐function‐based approach called discrepancy‐based control was presented in and generalized to particle systems in .…”

Application of Model Predictive Control to a Continuous Multiple‐Effect Crystallizer

“…The model must represent the main dynamic response of the desired controlled variables with respect to the system disturbances. This model can be developed through a phenomenological representation, by mass and energy balance or other kind of balance necessary to represent the phenomena , . It is also possible to include stochastic models based on plant data .…”

“…In order to apply the MPC to control a crystallization process, the usual approach involves the development of a phenomenological model which includes a population balance to represent the particle size distribution (PSD) , , . However, as the population balance equation is difficult to solve, it is possible to use a simplified representation such as the moments method.…”

“…In particulate processes, the moment balances coupled with material and energy balances typically lead to systems of differential equations with nonlinear behavior , . This nonlinear behavior is also observed in evaporative multiple‐effect processes and it has led to the development of a nonlinear variant of the model predictive control (NMPC) .…”