Model predictive control for continuous lactide ring‐opening polymerization processes

Abstract

Polylactic acid (PLA) is an attractive environment‐friendly thermoplastic that is bio‐sourced and biodegradable. PLA is industrially produced by the ring‐opening polymerization of lactide. This reaction is sensitive to drifts in the operating conditions and impurities in the raw materials that may affect the reaction rate as well as the polymer properties, which can be very costly in continuous processes. It is therefore crucial to employ a control strategy that allows recovering the nominal conditions and maintaining the desired properties and conversion level in case of drift. Three control strategies are discussed in this paper: proportional‐integral (PI) controller, dynamic optimization, and model predictive control (MPC). The proposed approaches are validated by simulation of a continuous PLA process constituted of three cascade reactors including one loop reactor in the middle. Besides the coupling of inputs and outputs, the process model is highly nonlinear, and the control is done only on the boundaries. The results show that the open‐loop optimization strategy provides better performance compared to the PI controller if the disturbance is assumed to be measured. The MPC also shows superior performances provided that the disturbance is first estimated. A polynomial model is developed to predict the nonmeasured disturbance based on the measured outputs.

Model Predictive Control with Integrated Model Reduction for a Continuous Lactide Ring-Opening Polymerization Process

“…The LMPC controller is compared to the modified PI controller ( eqs 32 and 33 ) 8 and to the NLMPC (eq 31). 17 In the case of the modified PI controller, we assume a perfectly measured disturbance and that the computed inputs are implemented each minute ( N s = 1 min). However, for the cases of the LMPC and the NLMPC, both of these methods are not based on any measured disturbance.…”

“…Various levels of positive and negative perturbations were supposed to occur in the inlet flows. 17 We added positive perturbations in the range 1–10 mmol kg –1 together with different negative perturbation levels (−1 to −5 mmol kg –1 ) to the nominal value which is equal to 5 mmol kg –1 . The responses of the PI controller, the NLMPC, and the LMPC strategy to these perturbations were studied.…”

“…A nonlinear prediction model of the process was employed in our work previously, 17 which leads to nonlinear MPC (NLMPC). At each sample time N S , the future sequence of manipulated variables was determined through solving the optimization eq ( eq 22 ).…”

“…The structure of this paper is as follows: In the Mathematical PLA Model section, we provide the nonlinear mathematical model of the PLA process. In the Controller Design section, we present a summary of the control objectives, along with a presentation of the principles of the three control strategies employed: (i) the novel LMPC strategy based on a discrete-time linear reduced state-space model, (ii) the NLMPC proposed in our previous work, 17 and (iii) the PI controller-based method proposed in ref ( 8 ). A comparison of the simulation results of the three used strategies is presented in the Results and Discussion section.…”

Design and Economic Assessment of Alternative Evaporation Processes for Poly-Lactic Acid Production

“…In the pre-polymer step, crude LA is placed into the evaporator to remove water, and after evaporating the water, the concentrated LA is introduced into the pre-polymer reactor for reaction [ 6 , 7 , 8 ]. After the reaction has been completed, in the lactide step, the pre-polymer is placed into the lactide reactor for polymerization [ 9 , 10 , 11 ]. In particular, the evaporation process of the pre-polymer section, which is a process of concentrating low-purity LA into high-purity LA, is considered a high-cost process, as a large amount of steam energy is consumed in the evaporation of low-purity LA [ 12 ].…”