Thermodynamic methods based on conductor-like screening models (COSMO) originated from the use of solvation thermodynamics and computational quantum mechanics. These methods rely on sigma profiles specific to each molecule. A sigma profile is the probability distribution of a molecular surface segment having a specific charge density. Two COSMO-based thermodynamic models are COSMO-RS (realistic solvation) developed by Klamt and his colleagues, and COSMO-SAC (segment activity coefficient) published by Lin and Sandler. Quantum mechanical calculations for generating the sigma profiles represent the most time-consuming and computationally expensive aspect of using COSMO-based methods. A growing number of scientists and engineers are interested in the COSMO-based thermodynamic models but are intimidated by the complexity of performing quantum mechanical calculations. This paper presents the first free, web-based sigma profile database of 1432 compounds. We describe the procedure for sigma profile generation, and we have validated our database by comparing COSMO-based predictions of activity coefficients, normal boiling point and solubility with experimental data and thermodynamic property database. We discuss improvements which include using supplemental geometry optimization software packages to provide good initial guesses for molecular conformations as a precursor to the COSMO calculation. Finally, this paper provides a FORTRAN program and a procedure to generate additional sigma profiles, as well as a FORTRAN program to generate binary phase-equilibrium predictions using the COSMO-SAC model. Our sigma profile database will facilitate predictions of thermodynamic properties and phase behaviors from COSMO-based thermodynamic models.

Thermodynamic methods based on COSMO (COnductor-like Screening MOdels), such as COSMO-RS (Real Solvent) and COSMO-SAC (Segment Activity Coefficient), represent significant and recent developments of solvation thermodynamics and computational quantum mechanics. These are a priori prediction methods based on molecular structures and a few parameters that are fixed for all of the compounds. They require no experimental data and rely on sigma profiles specific to each molecule as their only input. A sigma profile is the probability distribution of a molecular surface segment having a specific charge density. Generating sigma profiles by quantum mechanical calculations represents the most time-consuming and computationally expensive aspect of using COSMO-based methods. This article presents a free, web-based VT-2006 Solute Sigma Profile Database for large, pharmaceutical-related solutes, to supplement the published VT-2005 Sigma Profile Database for solvents and small molecules (www.design.che.vt.edu). Together, these databases contain sigma profiles for 1645 unique compounds, enabling the users to predict binary and multicomponent vapor−liquid equilibrium (VLE) and solid−liquid equilibrium (SLE), as well as other thermodynamic properties. We validate the VT-2006 Solute Sigma Profile Database by solid solubility predictions in pure solvents for 2434 literature solubility values, which include 194 solutes, 160 solvents, and 1356 solute−solvent pairs. We also compare solubility predictions for mixed solvents to literature values for 39 systems. By comparison with experimental data, we find a root-mean-squared error (RMSE) of 0.7419 between experimental and predicted solute mole fractions (x sol) on a log10 (x sol) scale for solubilities in pure solvents. This article also presents examples investigating the effects of conformational isomerism on solubility predictions of small, medium-sized, and large drug molecules. To provide better understanding of accuracy, we compare a priori COSMO-SAC solubility predictions, which use molecule-specific sigma profiles, to those by the non-random two-liquid segment activity coefficient (NRTL-SAC) model, which uses regressed molecule-specific parameters, for 17 solutes and 258 experimental solubility values. We find that NRTL-SAC, which contains regressed parameters based on experimental data, is a more accurate method for predicting SLE behavior than the COSMO-SAC model for many of the systems studied. Finally, this article presents a set of guidelines for applying the COSMO-SAC model for solubility predictions for new drug molecules when no experimental data are available.

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.

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.

Polyolefins are one of the most widely used commodity polymers with applications in films, packaging, and the automotive industry. The modeling of polymerization processes producing polyolefins, including high-density polyethylene (HDPE), polypropylene (PP), and linear low-density polyethylene (LLDPE) using Ziegler−Natta catalysts with multiple active sites, is a complex and challenging task. Most of the studies on polyolefin process modeling over the years do not consider all of the commercially important production targets when quantifying the relevant polymerization reaction kinetic parameters based on measurable plant data. Most of the published articles also do not make efficient use of simulation tools, particularly sensitivity analysis, design specifications, and data fit, that are available in commercial modeling software for polymerization processes, such as Aspen Polymers. This paper presents an effective methodology to estimate kinetic parameters that have the most significant impacts on specific production targets, and to develop the kinetics using all commercially important production targets validated over polyolefin processes producing HDPE, PP, and LLDPE using Ziegler−Natta catalysts. We demonstrate how to estimate kinetic parameters to fit production targets in a computer-aided stepby-step procedure. The percent errors between our model predictions and plant data are equivalent to or smaller than those in reported modeling studies for polyolefin processes. We report our insights and experiences from training practicing engineers to successfully apply our methodology to several dozen commercial HDPE, PP, and LLPDE processes for sustainable design, operation, and optimization at two of the world's largest petrochemical companies in the Asia-Pacific region over the past two decades. Finally, we present supplements of detailed modeling examples and an Excel modeling spreadsheet for commercial polyolefin processes.

This paper describes the development of an ASPEN PLUS simulation model for a commercial NO x abatement system involving both absorption and selective catalytic reduction (SCR). The model helps identify operator guidelines and retrofit options needed to enable the commercial system to operate efficiently during surges in NO x -laden fumes without incurring costly fines. The resulting model applies a reactive-distillation module with a practical reaction set for NO x absorption and implements a kinetic model for SCR. The simulation results agree well with both design specifications and literature data and provide practical insights for optimum operation and economical retrofits of the commercial system.

We model an entire poly(ethylene terephthalate) (PET) solid-state polymerization (SSP) process with precrystallizers, crystallizers, SSP reactors, and dryers using a unified cell approach. This approach builds complex unit-operation models using individual cells, or continuous-stirred-tank reactor (CSTR) models. Each cell considers the essential physical properties, phase equilibrium, polymerization kinetics, mass transfer through the polymer and into the carrier gas, and crystallization kinetics. Our model development involves fundamental chemical engineering principles and advanced software tools, such as Polymers Plus and Aspen Custom Modeler. We analyze both reaction and diffusion in each cell, considering two options:  (1) to model diffusion using a second-order partial differential equation (PDE) or a simplified two-film theory and (2) to include or ignore crystallization kinetics. Performing best is the model with a PDE for diffusion and with equations describing crystallization and its effect on reaction and diffusion. We validate the model using commercial plant data of intrinsic viscosity, degree of crystallinity, and acetaldehyde concentration. We are unable to find a suitable set of parameters for other simpler models to represent accurately the plant data in its entirety. After validating our SSP model, we apply it to study the sensitivity of our SSP process on temperature and pellet geometry. For pellets with cross-sectional dimensions of 3.2 mm × 3.2 mm, we vary the length from 1.5 to 3.5 mm and predict the intrinsic viscosity. The model suggests only a mild dependence of intrinsic viscosity on the pellet length. In contrast, our model suggests that, for an increase in temperature of 12 °C, intrinsic viscosity rises by almost 0.1 dL/g. Last, we use our validated model to predict the process changes required to produce a high-viscosity product (1 dL/g). Our model suggests that, with appropriate increases in both reactor temperature and residence time, we can make this product.