The biomass‐derived polyesters poly(1,3‐propylene 2,5‐furandicarboxylate) (PPF), poly(1,3‐propylene succinate) (PPS) and poly(1,3‐propylene 2,5‐furandicarboxylate‐co‐1,3‐propylene succinate) (PPFPS) have been synthesized via a two‐step process involving polycondensation and azeotropic distillation. The kinetic parameters were obtained by fitting the experimental data from a batch polymerization reactor to three different kinetic models for polyesterification reactions. The activation energies of the all monomer systems were obtained by Arrhenius plots. Given the increasing availability of biomass‐derived monomers their use in renewable polyesters as substitutes for fossil fuel derived chemicals becomes a distinct possibility. The kinetic modeling of the uncatalyzed polyesterification reactions will enable further integrative process simulation of the studied bioderived polymers and provide a reference for future practical study or industrial applications of catalyzed polyesterification reactions and other bioderived monomer systems. © 2016 The Authors. Journal of Polymer Science Part A: Polymer Chemistry Published by Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2016, 54, 2876–2887

For the first time, we present the process simulation and multiobjective optimization of the batch-reactor polyesterification of a library of biomass-derived renewable polyesters: poly(1,5-pentylene 2,5-furandicarboxylate) (PTF), poly(1,5-pentylene succinate) (PTS), and poly(1,5-pentylene 2,5furandicarboxylate-co-1,5-pentylene succinate (PTFTS). The simulation environment was implemented in Aspen Plus, and the ε-constraint method was employed for the optimization problem, considering two objective functions that maximize the number-average degree of polymerization (DPN) and minimize the heat duty Q. The performance of the biobased polyesters was compared to that of poly(ethylene terephthalate) (PET). The kinetic rate expressions were defined following the functional-group approach, and the parameters were estimated by fitting a polyesterification model found in the literature to the experimental data. The present work provides comprehensive fundamental information toward a feasible process design and scale-up of the esterification of biomass-derived products.

Many problems in science and engineering can be posed as multiobjective optimization problems where several objectives must be met simultaneously. Commonly such multiobjective optimization problems are reduced to a single optimization problem by merging all the involved objective functions by using rather subjective weighting functions. This solution procedure can produce suboptimal solutions, and it is not a systematic method since the choice of the weighting functions is up to the designer. Process control is one of the engineering fields where multiobjective optimization control problems frequently emerge because such problems normally involve conflicting objective functions such as economical profit and environmental concerns. Because the optimal value of the conflicting objective functions cannot be simultaneously achieved one has to compute a trade-off solution that best suits, in a given sense, all the objective functions. Moreover, additional requirements, beyond upset rejection and set-point tracking, such as the determination of optimal operating conditions should also be handled by dynamic real time optimal control approaches. In this work we propose a novel multiobjective optimization and control approach able to get target points while simultaneously computing optimal operating conditions. The proposed approach can be applied to nonlinear dynamic systems and does not require the specification of arbitrary weighting functions to handle conflicting multiobjective optimization problems. Several case studies using chemical reactors of varying nonlinear behavior are deployed to illustrate the practical application of the proposed dynamic real time optimization approach.

A multiobjective dynamic optimization problem using conflicting performance objectives in polymerization systems is formulated. We use the dynamic one-dimensional mathematical model of a methyl-methacrylate cell cast reactor featuring monomer conversion and molecular weight distribution as the conflicting objectives. The aim is to compute the whole set of trade-off solutions comparing the performance of three well known procedures for addressing the solution of multiobjective optimization (MO) problems: normal boundary intersection, weighted sum, and epsilon-constraint. Using the air temperature profile as the manipulated variable, we demonstrate the dynamic optimal solutions obtained using the best trade-off solution from each one of the MO techniques.

During the normal operation of plastic sheet polymerization reactors, large monomer conversion and average molecular weight should be achieved. However, improving any one of these two operating objectives would result in worsening the remaining one, and vise versa. Because of physical constraints, it would not be possible to meet both operating objectives in similar extent. Therefore, trade-off solutions must be obtained from which the designer can pick up the one that best suits her/his design objectives. In this work we compute the whole set of trade-off solutions by solving a set of multiobjective optimization problems. In this work we select as the best trade-off solution, the point on the trade-off curve closest to the utopia region. We demonstrate that dynamic optimal warming temperature profiles improve the control on the weight-average molecular weight and monomer conversion simultaneously, and lead to a polymer sheet with better homogeneity characteristics, when trade-off solutions based on multiobjective optimization are used.