Design Of Separation Processes Research Articles

Novel separation process design for non-phosgene dimethylhexane-1,6-dicarbamate synthesis by reacting dimethyl carbonate with 1,6-hexanediamine

Abstract Hexamethylene-1,6-diisocyanate (HDI) is one of the most significant aliphatic diisocyanates for polyurethane production. The thermal decomposition of dimethylhexane-1,6-dicarbamate (HDC) has been considered as a very attractive non-phosgene route for HDI synthesis to realize industrialization because HDC can be synthesized by reacting 1,6-hexanediamine (HDA) with a green component, dimethyl carbonate (DMC). In this study, the optimal separation process design for non-phosgene HDC synthesis is investigated. Three design strategies to separate reaction products are proposed here. In the first two strategies, the low-boiling mixture containing un-reacted DMC and byproduct methanol is almost completely withdrawn from a column top, while some amount of un-reacted DMC is deliberately designed to be drawn out from the column bottom in the third strategy. Optimal processes designed by these three strategies are further enhanced by heat integration to reduce energy consumption. Simulation results reveal that the processes designed by strategy 3 need the least total annual cost and much economic benefit is achieved by the technologies of internal and external heat integrations. The key cost saving from internal heat integration is attributed to fully take advantage of the characteristic of DMC–methanol azeotrope at high pressure, and the higher column top temperature rendering condenser duty totally utilized by column bottom.

Vapour-liquid equilibrium measurements and extractive distillation process design for separation of azeotropic mixture (dimethyl carbonate + ethanol)

Dimethyl carbonate and ethanol can form an azeotrope with the minimum boiling point. To separate the azeotrope (dimethyl carbonate + ethanol) by extractive distillation, p-xylene, butyl propionate, and isobutyl acetate were chosen as entrainers, and the isobaric vapour-liquid equilibrium (VLE) data for the binary systems of (dimethyl carbonate + p-xylene), (dimethyl carbonate + butyl propionate), (dimethyl carbonate + isobutyl acetate) and (ethanol + isobutyl acetate) were measured at 101.3 kPa using a modified Rose type recirculating still. The thermodynamic consistency of the VLE experimental data for the four binary mixtures was tested by the Herington and van Ness methods. Furthermore, the measured VLE data were correlated by the NRTL, UNIQUAC and Wilson thermodynamic models and the binary interaction parameters of the three models were regressed. Based above, an extractive distillation process with the entrainer of p-xylene was proposed and simulated to separate the azeotrope (dimethyl carbonate + ethanol).

Equilibrium and kinetic differences of XOS2-XOS7 in xylo-oligosaccharides and their effects on the design of simulated moving bed purification process

Chromatographic separation is a promising alternative for the purification of xylo-oligosaccharides (XOSs), a functional food additive in the form of an oligomeric saccharide. DOWEX MONOSPHERE™ 99/310 resin ionized with K+, which has better selectivity compared with Ca2+ and Na+, was used as the stationary phase. Breakthrough experiments showed that xylo-oligosaccharides and the two major industrial impurities, xylose and arabinose, all exhibit linear isotherms. Transport-dispersive (TD) model parameters were determined by pulse experiments carried out at various flowrates. In order to acquire model parameters for XOS7, of which the standard sample was not available, elution profiles of XOS containing XOS2 to XOS7 were compared with those of individual XOS2 to XOS6. The result shows that XOS7 has adsorption behaviors like XOS6. The difference in Henry’s constant, decreasing from 0.335 for XOS2 to 0.0155 for XOS6 and XOS7, may have significant effects on computational design of the chromatographic separation process. Experimental results for both single column and sequential simulated moving bed (SSMB) processes were used to validate the acquired TD model parameters of individual components.

Prediction of Thermodynamic Properties of Levulinic Acid via Molecular Simulation Techniques.

Second-generation biofuels are a complex mixture of organic compounds that can be further processed to hydrocarbon fuels and other valuable chemicals. One such chemical is levulinic acid (IUPAC name: 4-oxo pentanoic acid), which is a highly versatile ketoacid obtained from cellulose present in agricultural byproducts. For oxygen-containing compounds that decompose at elevated temperatures and pressures, determining the vapor–liquid equilibria data at high temperatures via the experimental route may be challenging. The molecular simulation approach is a cost-effective tool to obtain the necessary data while also allowing us to understand the microscopic origins of macroscopic observable properties. We have employed the transferable potential for phase equilibria-united atom force field to describe the interactions in this system with the parameters for a torsional interaction that are not reported in the literature (levulinic acid is a ketoacid) being determined from density functional theory calculations. We have verified our parameterization via density computations in the isothermal–isobaric ensemble and by comparing our simulation results with the corresponding data from experiments reported in the literature. We have performed grand-canonical transition-matrix Monte Carlo simulations in the temperature range from 580 to 690 K to estimate the vapor–liquid coexistence curves in the temperature–density plane and the Clapeyron plots. From this data, the critical point (TC = 755 K, ρC = 285.4 kg/m3, and PC = 30.57 bar) has been estimated, and this may be used as input to the equations of state employed in process simulation software for design of industrial separation processes including those for “biorefining”. As levulinic acid is a “ketoacid”, hydrogen bonding occurs, and the liquid phase structure has also been studied using radial distribution functions.

Predictive model for the design of reactive micro-separations

Solvent extraction at micro-scale has known increased interest in recent years. Consequently, numerous theoretical and experimental studies focused on diagnosis, detection and recovery of either biological substrates or base metals, isotopes and rare earths have been reported so far in the last few years.Within this context, the present work reports a thorough analysis of the coupling between the hydrodynamics, mass transfer and chemical reaction kinetics in multiphase micro-systems; the predictive mathematical model describes under non-steady state, fluid-wall interaction and interaction between the fluids in contact to finally predict the solute mass transport rate between the flowing liquid phases when a fully developed profile and a flat interface are not initially assumed unlike previously reported works. This approach allows tracking the interface along the complete micro device geometry and makes the model adaptive to the specific needs of each micro fluidic application. The analysis has been developed for a model system based on the mass transfer of Cr (VI) from an aqueous feed phase in a stratified 2-layer flow system in a Y-Y shape micro device. Two different scenarios have been considered: (a) two homogeneous phases, where water is the receptor phase (b) an heterogeneous system where the solute moves from the feed solution to a receptor phase composed of Shellsol D-70 and Alamine 336 as the selective extractant. Model simulations accounting for the system reaction parameters already reported have been assessed against a set of experimental runs; the two systems under consideration provided data that satisfactorily fitted simulations with an error less than 10%, thus, validating model calculations. Thus, this rigorous and flexible model seeks to provide a useful tool for the design of micro separation processes by predicting the technical performance for numerous applications at micro scale.

Efficient prediction of water vapor adsorption capacity in porous metal–organic framework materials: ANN and ANFIS modeling

Optimum design of water vapor separation process (dehumidification) using adsorption process mostly depends on the selection of appropriate porous materials or adsorbents with the highest equilibrium storage capacity of the vapor. Equilibrium capacity is generally evaluated through cost-demanding experiments via direct measurement of the vapor isotherm. Reliable prediction of the vapor adsorption capacity in porous materials provides a robust tool to a quick screening of porous materials appropriating for dehumidification process. In this article, adsorption capacity of water vapor in metal–organic framework (MOF) materials is predicted using two robust “artificial neural network (ANN)” and “Adaptive network-based fuzzy inference system (ANFIS)” methods. The three parameters of the surface area, pore volume and pore diameters are selected as input and the water vapor adsorption capacities of MOFs were computed as the output of the models. Comparison of the obtained results and real experimental data implied the superiority of the ANFIS and ANN models to predict the water vapor adsorption capacity into MOFs with a mean squared error (MSE) of 0.005 and 0.002, respectively. This clearly indicates a great potential for the application of both ANN and ANFIS methods to rapid screen MOFs suitable for water vapor adsorption.

Single-nanoparticle tracking reveals mechanisms of membrane fouling

While membrane fouling has been studied for decades, it remains challenging to obtain direct information about the dominant mechanism of fouling in a specific scenario. Here, we employed a high-throughput particle-tracking approach, which enabled the visualization of particle transport in actual microfiltration membranes under flow conditions and provided direct evidence for distinct fouling mechanisms under different operating conditions. Our results suggest that the “stickiness” of particles can qualitatively change the dominant fouling mechanism. In particular, the evolutions of effective flux, particle velocity and pathway tortuosity were found to be systematically different under “sticking” vs “reduced-sticking” conditions in two different microfiltration membranes, composed of PVDF and PTFE, respectively. Under “sticking” conditions, fouling was rapid, and individual pathways were observed to disappear with the reduction of flux. However, the average particle velocity and the tortuosity of particle trajectories were unchanged throughout the fouling process, consistent with the complete blocking of random pathways. Conversely, under “reduced-sticking” conditions, the average particle velocity decreased and the tortuosity of particle pathways increased systematically with fouling, consistent with the gradual narrowing of pathways causing increased resistance. The comprehensive information about particle dynamics in membranes achieved with this approach will assist design and optimization of reduced-fouling separation processes as well as advance the understanding of complex mass transport.

Insights into the S-shaped sorption isotherms and their dimensionless forms

Isotherms are of paramount importance for the interpretation of adsorption and ion exchange mechanisms and the design of separation and catalytic processes. Although the literature on sorption isotherms is rich, most of experiments and models employed are limited to systems that obey simple equilibrium isotherms, such as linear, favorable (convex-upward) or unfavorable (concave-upward). This paper deals with the rather overlooked S-shaped isotherms and their dimensionless forms, which are of great importance for the modeling of separation processes and interpretation of equilibrium data. A review on the physical significance and applications of S-shaped isotherms in adsorption and ion exchange process is also presented.

Outer-Sphere Water Clusters Tune the Lanthanide Selectivity of Diglycolamides.

Fundamental understanding of the selective recognition and separation of f-block metal ions by chelating agents is of crucial importance for advancing sustainable energy systems. Current investigations in this area are mostly focused on the study of inner-sphere interactions between metal ions and donor groups of ligands, while the effects on the selectivity resulting from molecular interactions in the outer-sphere region have been largely overlooked. Herein, we explore the fundamental origins of the selectivity of the solvating extractant N,N,N′,N′-tetraoctyl diglycolamide (TODGA) for adjacent lanthanides in a liquid–liquid extraction system, which is of relevance to nuclear fuel reprocessing and rare-earth refining technologies. Complementary investigations integrating distribution studies, quantum mechanical calculations, and classical molecular dynamics simulations establish a relationship between coextracted water and lanthanide extraction by TODGA across the series, pointing to the importance of the hydrogen-bonding interactions between outer-sphere nitrate ions and water clusters in a nonpolar environment. Our findings have significant implications for the design of novel efficient separation systems and processes, emphasizing the importance of tuning both inner- and outer-sphere interactions to obtain total control over selectivity in the biphasic extraction of lanthanides.

Calculation of the second virial coefficients of alkali metals by modified Peng–Robinson equation

In this work, the Peng–Robinson (P–R) equation of state has been modified by proposing a new α function for calculating the second virial coefficients of alkali metals. The relationship between α0.5 and (1 − T 0.5 ) is a nonlinear function. The correlation between the second virial coefficient and P–R equation was presented by expanding the P–R equation into its Taylor series form. For P–R equation, the linear correlation between parameters C1 and C2 of α function and acentric factors $$ \omega $$ of alkali metals was proposed. The new α function and its first, second and third derivatives are continuous. The average standard deviations of compressibility factor which calculated by modified P–R equation are less than 4.3%. The second virial coefficients of alkali metals were calculated over the temperature range 600–3000 K by using the modified P–R equation. Comparison with literature data, the new equation provides more reliable and accurate second virial coefficient predictions for alkali metals than the original P–R equation. It is useful to guide and improve calculation of the second virial coefficients of other metal vapors for design and operation of separation processes in vacuum metallurgy.

Systems Design, Modeling, and Thermoeconomic Analysis of Azeotropic Distillation Processes for Organic Waste Treatment and Recovery in Nylon Plants

Nylon-6 and nylon-6,6 processes produce considerable amount of organic waste (known as light oil) consisting of n-pentanol, cyclohexanone, and cyclohexene oxide, which are difficult to separate and recover. This Article proposes six novel process designs to separate the light oil into three value-added products based on azeotropic distillation using water as an entrainer. These azeotropic distillation process designs take into account direct sequence, indirect sequence, thermal coupled column, and three types of dividing wall columns (dividing wall at the top, bottom, and middle of columns, respectively) for entrainer recovery. A conventional distillation process design for separation of the same light oil is also modeled and analyzed for comparison. High-fidelity process simulations are performed for each of the seven process designs in Aspen Plus. We further conduct exergy analyses and technoeconomic analyses to evaluate and compare the exergy efficiencies and economic performances of these seven process designs. The results indicate that the proposed azeotropic distillation process design with dividing wall at the middle of the column has the best performance in terms of both exergy efficiency and total annual cost.

Accurate lattice energies of organic molecular crystals from periodic turbomole calculations.

Accurate lattice energies of organic crystals are important i.e. for the pharmaceutical industry. Periodic DFT calculations with atom-centered Gaussian basis functions with the Turbomole program are used to calculate lattice energies for several non-covalently bound organic molecular crystals. The accuracy and convergence of results with basis set size and k-space sampling from periodic calculations is evaluated for the two reference molecules benzoic acid and naphthalene. For the X23 benchmark set of small molecular crystals accurate lattice energies are obtained using the PBE-D3 functional. In particular for hydrogen-bonded systems, a sufficiently large basis set is required. The calculated lattice energy differences between enantiopure and racemic crystal forms for a prototype set of chiral molecules are in good agreement with experimental results and allow the rationalization and computer-aided design of chiral separation processes. © 2018 Wiley Periodicals, Inc.

Designer solvent ability of alcohols in aqueous biphasic systems composed of deep eutectic solvents and potassium phosphate

Abstract Deep eutectic solvents (DES) have been proposed as phase forming components of aqueous biphasic systems (ABS). However, the DES hydrogen bonding complexes are not stable in the high concentrations of water present in this type of systems. Therefore, as previously shown, DES-based ABS should be treated as quaternary systems. This confers DES-based ABS with an extra degree of freedom for the design of separation processes since while one of the DES components acts as a phase forming component, the other could induce the modification of the ABS phase properties and, consequently, the control of the partition of various biomolecules. In this context, the designer solvent effect of the hydrogen bond donor (HBD), using four different alcohols – ethanol, n-propanol 1,2-propanediol and ethylene glycol – mixed at three different molar fractions (2:1; 1:1 and 1:2) with cholinium chloride (the hydrogen bond acceptor, HBA) in quaternary systems composed of K2HPO4 and water, was evaluated in this work. The results show that the presence of the HBD has an impact upon the liquid–liquid equilibrium, and these changes are dependent on the alcohol nature. The NRTL model was correlated to the tie-line experimental data with a low mean deviation. Moreover, several biomolecules (phenolic compounds, alkaloids, and amino acids) were use as molecular probes to evaluate the ability of alcohols to tune the partition in the studied systems. The alcohol presence changes the properties of the ABS’s phases and it is here shown that the HBD of the DES can indeed be used to modulate the partition behavior of target molecules.

Shortcut-based optimization of distillation-based processes by a novel reformulation of the feed angle method

The conceptual design of distillation-based separation processes is a well known and thoroughly investigated research subject in chemical engineering science. Nevertheless, there are only few shortcut methods available that are derived without simplifying assumptions, such as constant relative volatilities or constant molar overflow, allowing for a thermodynamically sound evaluation of the minimum energy demand for the separation of non-ideal mixtures. The current article presents a novel reformulation of the previously introduced feed angle method, which represents a geometrical shortcut method that is based on a direct calculation of characteristic pinch points. The reformulation retains the beneficial properties of the original formulation, providing high accuracy and an equation-based formulation that can be embedded in an optimization problem, while being numerically simpler and requiring significantly less case-specific modifications. The benefit of the so-called FAM+ formulation and the applicability to complex design problems are illustrated on several case studies, including the optimization of a closed-loop flowsheet and a superstructure with two reactors and several distillation columns.

Insights into the selection and design of fluid separation processes

Abstract Separations account for approximately 50% of all manufacturing costs, making the selection of the proper technology, and a potential affinity separation agent (ASA) of essential importance for process design. This selection is not straightforward. In this paper, aspects of fluid separation technologies, including shortcut calculations to estimate heat duties, are reviewed and applied to create insights in ab initio fluid separation process selection without extensive process simulations. It was found that composition and state of the feed can have major impacts on the minimal required heat duty, as well as the desired product purity. Distillation of dilute feeds is intrinsically hindered by a low internal efficiency and solvent based separations should be considered. Furthermore, the minimal heat duty for liquid-liquid extraction (LLX) with high boiling solvents is primarily feed composition dependent, while for low boiling solvents the solvent to feed ratio is important. This is also the case for the minimal duty for extractive distillation (ED) with light solvents, whereas the minimal duty of ED using high boiling solvents is independent of the composition, and generally higher than the minimal LLX duty. ED can be operated with only two columns, whereas LLX generally requires at least three, leading to higher capital costs. The feed composition dependence of the LLX minimal heat duty can result in a feed compositional break-even point when comparing LLX with ED. Using these theoretical insights in fluid separations, a series of industrial cases was reviewed and critical aspects in technology selection, and solvent selection and design such as selectivity and capacity are discussed. The results confirm the applicability of the minimal heat duty approach as a quick prediction tool for opportunities of solvent based technologies, as well as the need for including other considerations such as the number of required columns (capital costs) and the possibility to recover sensible heat.

Simulation Study of a Reactive Distillation Process for the Ethanol Production

A simulation study of the effects of design and operating parameters under which optimal-stable steady states may occur in a reactive distillation process for synthesis of ethanol has been presented. The conceptual design of the reactive separation process is based on the element concept where the reactive driving force approach is employed. To validate the proposed design, steady state simulations using ASPEN PLUS® software package were accomplished, and a sensitivity analysis was done to establish the operating conditions of the process. The analysed parameters were as follows: i) the operating pressure and ii) the reflux ratio. The results showed that a new energy-efficient design with minimal energy requirements may be considered a viable technological alternative for ethanol synthesis. Also, this reaction-separation process allows to satisfy the design and operating constrains. This is, a novel reactive distillation process capable to increase the purity of the ethanol from a dilute solution, i.e., up to 30% ethanol in water under optimal operating conditions.

Rigorous design of reaction-separation processes using disjunctive programming models

A systematic and efficient method for the rigorous design of complex chemical processes is significant in the chemical industry. In this paper, a superstructure-based optimization approach for the rigorous and simultaneous design of reaction and separation processes using generalized disjunctive programming (GDP) models is presented. In the reactor network, disjunctions for conditional reactors are introduced where the balance and reaction kinetic equations are applied only if the reactor is selected. Based on the proposed reactor disjunctions, two different reactor superstructures are developed and employed. In addition, the GDP representation of distillation columns is used to model the separation network. The reliability and efficiency of the proposed optimization method are demonstrated on two case studies, i.e., one cyclohexane oxidation process and one benzene chlorination process. The flowsheet structure and process-unit operating conditions are simultaneously optimized to minimize the total annual cost of the processes.

Improvement of predictive tools for vapor-liquid equilibrium based on group contribution methods applied to lipid technology

Predictive methodologies based on group contribution methods, such as UNIFAC, play a very important role in the design, analysis and optimization of separation processes found in oils, fats and biodiesel industries. However, the UNIFAC model has well-known limitations for complex molecular structures that the first-order functional groups are unable to handle. In the particular case of fatty systems these models are not able to adequately predict the non-ideality in the liquid phase. Consequently, a new set of functional groups is proposed to represent the lipid compounds, requiring thereby, new group interaction parameters. In this work, the performance of several UNIFAC variants, the Original-UNIFAC, the Linear-UNIFAC, Modified-UNIFAC and the Dortmund-UNIFAC is compared. The same set of experimental data and the parameter estimation method developed by Perederic et al. (2017) have been used. The database of measured data comes from a special lipids database developed in-house (SPEED Lipids database at KT-consortium, DTU, Denmark). All UNIFAC models using the new lipid-based parameters show, on average, improvements compared to the same models with their published parameters. There are rather small differences between the models and no single model is the best in all cases.

Genetic programming based models for prediction of vapor-liquid equilibrium

The design, operation, and control of chemical separation processes heavily rely on the knowledge of the vapor-liquid equilibrium (VLE). Often, conducting experiments to gain an insight into the separation behavior becomes tedious and expensive. Thus, standard thermodynamic models are used in the VLE prediction. Sometimes, exclusively data-driven models are also used in VLE prediction although this method too possesses drawbacks such as a trial and error approach in specifying the data-fitting function. For overcoming these difficulties, this paper employs a machine learning (ML) formalism namely “genetic programming (GP)” possessing certain attractive features for the VLE prediction. Specifically, three case studies have been performed wherein GP-based models have been developed using experimental data, for predicting the vapor phase composition of a ternary, and a group of non–ideal binary systems. The inputs to models consists of three pure component attributes (acentric factor, critical temperature, and critical pressure), and as many intensive thermodynamic parameters (liquid phase composition, pressure, and temperature). A comparison of the VLE prediction and generalization performance of the GP-based models with the corresponding standard thermodynamic models reveals that the former class of models possess either superior or closely comparable performance vis-a-vis thermodynamic models. Noteworthy features of this study are: (i) a single GP-based model can predict VLE of a group of binary systems, and (ii) applicability of a GP-based model trained on an alcohol-acetate series data for its higher homolog. The VLE modeling approach exemplified here can be gainfully extended to other ternary and non-ideal binary systems, and for designing corresponding experiments in different pressure and temperature ranges.

Permeate Flux in Ultrafiltration Membrane: A Review

Membrane processes exist for most of the fluid separations encountered in industry. The most widely used is membrane ultrafiltration, pressure driven process which is capable of separating particles in the approximate size range of 0.001 to 0.1 μm. The design of membrane separation processes, like all other processes, requires quantitative expressions relating material properties to separation performance. The factors controlling the performance of ultrafiltration are extensively reviewed. There have been a number of seminal approaches in this field. Most have been based on the rate limiting effects of the concentration polarization of the separated particles at the membrane surface. Various rigorous, empirical and intuitive models exist, which have been critically assessed in terms of their predictive capability and applicability. The decision as to which of the membrane filtration models is the most correct in predicting permeation rates is a matter of difficulty and appears to depend on the nature of the dispersion to separated.