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We present a new mass-transfer model for simulating industrial nylon-6 polymerization trains. In this model, both diffusion and boiling (bubble nucleation) contribute to mass transfer. With this model, we are able to simulate widely differing production technologies using identical mass-transfer parameters, along with identical models for fundamentals such as phase equilibrium, physical properties, and polymerization kinetics. To illustrate, we simulate the direct-melt process and the bubble-gas kettle process. The direct-melt process builds up the polymer molecular weight and removes nearly all residual caprolactam monomer by employing, under vacuum, a wiped-wall evaporator and a rotating-disk finisher. The bubble-gas kettle process, on the other hand, injects inert gas bubbles through the melt at nearly atmospheric pressure to build up the polymer molecular weight but does not significantly reduce the caprolactam level because the diffusion coefficient is so low. We validate our process models using commercial train performance data at different production rates, including the first known validation of a dynamic rate-change simulation for industrial polycondensation trains. Model predictions quantitatively agree with product quality data such as formic acid viscosity (FAV), polymer end-group concentration, and water extractables. The prediction errors for the direct-melt process are 2.81%, −3.13%, and −3.06% for FAV, water extractables, and amine end groups, respectively. For the bubble-gas kettle process, the prediction errors are −17.2%, −17.0%, and −7.49% for extrusion FAV, washed-and-dried FAV, and water extractables, respectively. These errors are much lower than the ca. −50% errors obtained by existing advanced models for devolatilization.

A series of pyridomorphinans derived from naloxone, oxymorphone, and hydromorphone (7a−k) were synthesized and evaluated for binding affinity at the opioid δ, μ, and κ receptors in brain membranes using radioligand binding assays and for functional activity in vitro using [³⁵S]GTP-γ-S binding assays in brain tissues and bioassays using guinea pig ileum (GPI) and mouse vas deferens (MVD) smooth muscle preparations. The pyridine ring unsubstituted pyridomorphinans possessing the oxymorphone and hydromorphone framework displayed nearly equal binding affinity at the μ and δ receptors. Their affinities at the κ site were nearly 10-fold less than their binding affinities at the μ and δ sites. Introduction of aryl substituents at the 5‘-position on the pyridine ring improved the binding affinity at the δ site while decreasing the binding affinity at the μ site. Nearly all of the ligands possessing an N-methyl group at the17-position with or without a hydroxyl group at the 14-position of the morphinan moiety displayed agonist activity at the μ receptor with varying potencies and efficacies. In the [³⁵S]GTP-γ-S binding assays, most of these pyridomorphinans were devoid of any significant agonist activity at the δ and κ receptors but displayed moderate to potent antagonist activity at the δ receptors. In antinociceptive evaluations using the warm-water tail-withdrawal assay in mice, the pyridomorphinans produced analgesic effects with varying potencies and efficacies when administered by the intracerebroventricular route. Among the ligands studied, the hydromorphone-derived 4-chlorophenylpyridomorphinan 7h was identified as a ligand possessing a promising profile of mixed μ agonist/δ antagonist activity in vitro and in vivo. In a repeated administration paradigm in which the standard μ agonist morphine produces significant tolerance, repeated administration of the μ agonist/δ antagonist ligand 7h produced no tolerance. These results indicate that appropriate molecular manipulations of the morphinan templates could provide ligands with mixed μ agonist/δ antagonist profiles and such ligands may have the potential of emerging as novel analgesic drugs devoid of tolerance, dependence, and related side effects.

This paper describes the development of a comprehensive model for the continuous gas-phase synthesis of polypropylene using stirred-bed reactors. The model considers the important issues of physical property and thermodynamic model selections, polymer properties, catalyst characterization, and reactor residence time, in addition to the traditional Ziegler−Natta polymerization kinetics. Model development involves fundamental chemical engineering principles and advanced software tools, Polymers Plus and Aspen Dynamics. We characterize a Ziegler−Natta catalyst by assuming the existence of multiple catalyst site types. The model contains a single set of kinetic and thermodynamic parameters that accurately predicts the polymer production rate, molecular weight, polydispersity index, and composition for both homopolymer and impact copolymer product grades from a large-scale commercial process. We demonstrate the application of our dynamic model and process control by comparing grade-transition strategies.

This paper presents methodologies to quantify the relationships among the molecular weight distribution (MWD), steady-shear non-Newtonian viscosity (i.e., flow curve), and melt index (MI) of three linear low-density polyethylenes manufactured using the same technology. With the aid of computer-aided process simulation tools (such as POLYMERS PLUS), polymer producers can predict accurately the MWD from manufacturing conditions. Our methodologies help the polymer producers to extend their simulation model to predict flow curves and MI from the MWD. To do this, this paper employs (1) a modified Carreau-Yasuda (CY) model or Bersted's partition model to relate the MWDs and flow curves, (2) Bremner and Rudin's model to relate the weight-average molecular weight (MWW) and MI, and (3) Rohlfing and Janzen's model to relate the flow curve and MI. We show that the Carreau-Yasuda, Bersted, and Bremner and Rudin models work very well for correlating our data, predicting flow curves that average 3−7% error and MI values that average 2% error. In addition, for the case in which we lack MI data, we use the CY or Bersted model predictions of the flow curve to generate MI values through Rohlfing and Janzen's model. These predictions are very good, averaging only 0.5−3% error. We also show how to use the CY or Bersted model and Rohlfing and Janzen's melt-indexer model to estimate closely the low shear rate region of the flow curve using MWD/MI data alone. This case corresponds to one in which we lack flow curve data. Last, we provide practical guidelines for polymer manufacturers who want to predict the flow curve and MI using the MWD.

This paper presents a thermodynamically consistent model for the phase equilibrium of water/caprolactam/nylon-6 mixtures, based on the POLYNRTL (polymer nonrandom two-liquid) activity-coefficient model. Our model predicts phase equilibrium for any binary or ternary mixture containing water, ε-caprolactam, and nylon-6 at industrially relevant temperatures and pressures with an average error of 1%. This paper also demonstrates its application to simulate a melt train and a bubble-gas kettle train for industrial production of nylon-6. Prior literature makes simplifying assumptions about liquid-phase (molten polymer) activities of water and ε-caprolactam; these assumptions are shown to be unrealistic.

We present the development of both steady-state and dynamic models for a slurry HDPE process using fundamental chemical engineering principles and advanced software tools, Polymers Plus and Aspen Dynamics. The discussion includes thermodynamic properties, phase equilibrium, reaction kinetics, polymer properties, and other modeling issues. We characterize a Ziegler−Natta catalyst by assuming the existence of multiple catalyst site types and deconvoluting data from gel permeation chromatography to determine the most probable chain-length distributions and relative amounts of polymer produced at each site type. We validate the model using plant data from two large-scale commercial slurry HDPE processes. Significantly, the model contains a single set of kinetic and thermodynamic parameters that accurately predicts the polymer production rate, molecular weight, polydispersity index, and composition for several product grades. We illustrate the utility of the dynamic model by simulating a grade change. Finally, we propose a process retrofit that permits an increase in the HDPE production rate of up to 20% while maintaining the product quality.