Poly(lactic acid) fibers (PLA)

Farrington, David; Lunt, Jim; Davies, Stephen N.; Blackburn, Richard S.

doi:10.1533/9781845694609.1.140

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Polylactic acid (PLA) is a thermoplastic synthesized from renewable resources (corn starch, cassava roots, sugarcane, etc). It is becoming competitive as a green alternative of nonbiodegradable polymers in various fields like fibers [1], medical implants [2], and biomedical applications [3]. PLA is industrially produced by the ring‐opening polymerization (ROP) of lactide monomer to produce a polymer with high molecular weight as required in most applications [4].…”

Section: Introductionmentioning

confidence: 99%

Model predictive control for continuous lactide ring‐opening polymerization processes

Afsi

Othman

Bakir

et al. 2020

Asian Journal of Control

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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.

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Section: Introductionmentioning

confidence: 99%