Thermodynamic Modeling and Process Simulation through PIONA

May 16, 2013 - Thermodynamic Modeling and Process Simulation through PIONA. Characterization. Glen Hay, Herbert Loria,* and Marco A. Satyro...
0 downloads 0 Views 2MB Size
Download PDF
Article pubs.acs.org/EF

Thermodynamic Modeling and Process Simulation through PIONA Characterization Glen Hay, Herbert Loria,* and Marco A. Satyro Virtual Materials Group, #222 1829 Ranchlands Blvd. NW, Calgary, Alberta T3G 2A7, Canada ABSTRACT: Individual components may not be able to represent the structure of heavy hydrocarbons because these materials are formed by several chemical species that are difficult to characterize with the current analytical techniques. Lumped component techniques can be applied to model these types of hydrocarbons; this procedure is often based on combining many pure compounds into groups with average physical properties. Nevertheless, this technique fails for separations that are chemically driven due to the lack of chemical information in the lumped component groups. A new approach of the lumped characterization technique is shown in this work. This technique consists of using constant slates of selected compounds to cover the carbon number ranges of interest for the modeling of different refinery reactors. The different combinations of these component slates allow matching the experimental distillation curve of a given feed and calculating its chemical characteristics ranging from simple properties such as molecular weight and standard density to PIONA (n-paraffin, iso-paraffin, olefin, naphtene, and aromatic) characterization data. The key advantage of this new method is the capture of the essential chemistry of the feedstock that affects property calculations while keeping a constant and consistent component list.

1. INTRODUCTION Simulation models are usually based on well-defined component slates to represent material balances and estimate thermodynamic properties at specific locations within process flow sheets. When the models involve heavier types of feeds such as naphthas, gas oils, or residues, the use of individual components to represent the exact composition of the mixture is not realistic due to the presence of potentially thousands of individual chemical species, corresponding restrictions on currently available analytical techniques, and inefficient use of computer resources for simulation. In these situations, techniques for modeling the material based on the use of components lumped into groups (also known as “cut” or “pseudocomponent”), are applied and are often based on an easily measured property such as the normal boiling point (NBP). In these situations, one pseudocomponent with a given average NBP represents a mixture of many pure compounds that happen to boil within a certain temperature range. It is important to note that this average component represents not only components boiling at different temperatures but also components that have distinct chemical characteristics such as aromatics, naphthenes, and paraffins with different individual properties such as density and viscosity. The lumped component or pseudocomponent grouping method works well in situations where volatility is the major property of interest and no chemical reactions are present. In these cases, reliable process models can be developed using relatively inexpensive lab assays and oil characterization packages carefully integrated within process simulators.1,2 A good example of this situation is the simulation of the separation of different oil products in an atmospheric or a vacuum crude column employing pseudocomponents. However, the pseudocomponent modeling technique fails whenever chemically driven separations or chemical reactors are involved in a simulation. Notable examples are refinery extractive separations based on polarity differences between aromatics and © XXXX American Chemical Society

paraffins, the aromatics affinity to polar solvents such as methyl ethyl ketone, and the development of meaningful reactor models, which is impossible when the modeling is restricted to material balances based on “black box” models.1 The cause of these complications is the lack of chemical information in the pseudocomponents. The objective of this paper is to discuss a different and new approach of the pseudocomponent characterization technique. It consists of using a constant slate of selected compounds that covers carbon number ranges of interest for the modeling of important and diverse refinery reactors such as hydrocrackers, reformers, and visbreakers based on the use of preset molecular structure groups. These groups are used as a second dimension for the component lumping to capture in an approximate manner the diverse chemical characteristics of compounds actually present in a typical oil cut. Through combinations of these different component slates, designed to model paraffinic, olefinic, aromatic, and other important chemical types typically encountered in oils, a feed’s measured distillation curve can be matched in a way similar to that of pseudocomponents developed through standard oil characterization. The key advantage of this new method is the capture of the essential chemistry of the feedstock. The new method is flexible enough to encode in the characterized compounds known chemical characteristics of the feed ranging from simple properties such as molecular weight and density to PIONA (n-paraffin, iso-paraffin, olefin, naphtene, and aromatic) characterization data.

2. PIONA CHARACTERIZATION 2.1. PIONA Group Components. The new pseudocomponent characterization technique is based on molecular structures Received: February 19, 2013 Revised: May 7, 2013

A

dx.doi.org/10.1021/ef400286m | Energy Fuels XXXX, XXX, XXX−XXX

Energy & Fuels

Article

Figure 1. PIONA carbon number versus trended liquid density from experimental information.4

Table 1. Pure Component Placement in PIONA Groups

a

carbon number

n-paraffin

iso-paraffin

olefin

naphthene

aromatic

1 2 3

methane ethane propane

NCa NC NC

NC ethylene propylene

NC NC NC

4 5 6

n-butane PCb PC

iso-butane PC PC

1-butene, 2-butene, isobutene PC PC

NC acetylene methylacetylene propadiene 1,3-butadiene, methylcyclopropane, cyclobutane and cyclobutene PC PC

NC NC PC

NC = No component for structure group carbon number. bPC = PIONA type pseudo-component defined by carbon number.

pseudocomponent groups for the initial carbon numbers in each chemical structure group. Since many times this detailed information is available within light end information supplied through the oil characterization, it is suggested that pure component information be included whenever possible. Table 1 lists some of the common PIONA group pure components that should be considered for inclusion as actual components before pseudocomponent generalizations are made. A significant advantage of including defined components in the analysis is the effective removal of compounds that are not present in the liquid phase at standard conditions thus eliminating or removing the distortion caused by inclusion of light ends in the calculation of liquid feedstock properties. 2.2. PIONA’s Dehydrated Aromatic Group. The PIONA structure group classification was found to introduce unacceptable property estimation error in studies when modeling feeds with average carbon number higher than 10, where larger aromatic content was encountered. Further investigation showed that a single aromatic structure group was not enough to differentiate multiparaffin-branched aromatic components against those more reacted compounds that were stripped of straight carbon branches. This finding was further reinforced by the need of additional hydrogen donors to balance product yields from reactive thermal cracking units, that can only be provided by the transition of hydrated to dehydrated aromatics groups in the feed stocks. A similar behavior is present in heavier naphthene components with more cyclic ringed structures and less branched chains; however, these components could already be represented by a mixed fraction of the naphthene and hydrated aromatic groups. Thus, a new chemical type was defined as “dehydrated aromatic”. The usefulness of this new chemical type is illustrated

of component groups chosen from the PIONA classification. We note that this is a significant simplification since many compounds found are multifunctional in nature, but as it will be shown in this work, this is an effective way to model the chemical complexity of typical oils. These molecular structural groups are based on having a certain carbon number; each PIONA group for a given carbon number tends to have differing thermodynamic properties and, therefore, provide the basis for the calculation of physical properties when developing a process model. For example, Figure 1 shows potential PIONA molecular structures with varying carbon numbers, with special emphasis for groups with six carbon atoms, and trends of liquid density at 298 K. Other properties of interest for process simulation, such as density, viscosity, or heat capacity, can be trended versus carbon number by developing empirical trends using good quality pure component databases.3−5 It is important to note that this new method is based on physical properties for the liquid phase alone, which are then used to estimate vapor phase properties using typical estimation methods used for gas and oil.6,7 Some error will unavoidably be introduced at this level of component creation, as only the n-paraffin group has a unique structure for all carbon numbers. For example, 1-butene, 2-butene, and iso-butene are all olefin type structures with a carbon number equal to four with somewhat different physical properties. This method assumes an average value for the physical properties of the components of any chemical type within the same carbon number. For example, the densities of C4 olefins are represented by a single average value. Note that a reduction of errors in the off-gas and vent material balance estimation can be realized in models using this approach by employing pure components instead of PIONA B

dx.doi.org/10.1021/ef400286m | Energy Fuels XXXX, XXX, XXX−XXX

Energy & Fuels

Article

Figure 2. Aromatic and dehydrated aromatic groups vs experimentally trended viscosity.4

in Figure 2 where differences in viscosity at 100 °F are clearly shown between an aromatic and a dehydrated aromatic with identical carbon numbers. From this point onward, whenever PIONA is mentioned, the “A” makes reference to aromatic and dehydrated aromatics chemical groups. From a molecular structure configuration, these dehydrated aromatics replace branched contributions to a base aromatic ring with additional dehydrated aromatic rings. 2.3. Light to Heavy Oil Feed Characterization. When using conventional oil characterization, newly defined pseudocomponents are defined for each new feedstock of interest.1 Reductions in property estimation accuracy arise in these models when more than one feed is introduced in the simulation model and potentially blended. Different molecular structures within a single NBP range can create feeds with identical or similar distillation curves but with significantly different physical properties such as density and viscosity. A common solution is the introduction of a new pseudocomponent slate for each feed. Although easy to do, this solution is cumbersome, resulting in simulations with hundreds of pseudocomponents that eventually degrade the execution speed of large simulations to unacceptable values. For example, the optimization of feed stock blending can include well over half a dozen different available feeds. The PIONA molecular structure pseudocomponent approach is based on the minimization of the error on any available laboratory data by combining different amounts of PIONA components representing a defined carbon number that will be used to represent the desired feed. The range of carbon numbers required for the representation of the feedstock is based on the carefully selected initial boiling point (IBP) based on the pure component placement previously discussed and the estimated final boiling point (FBP) from the distillation curve data. Typical data used in this new method are distillation curves, bulk properties such as density or viscosity, and other chemistry specific information such as hydrogen to carbon ratio. There are usually multiple mathematical solutions to the minimization problem and equivalent component slates can be generated. In order to define a “best” characterization that is physically meaningful, general molecular structure trends7 (Figure 3) and carbon number distributions (Figures 1 and 2) can be used to help shape the eventual solution. Mathematically, the model seeks the best combination of PIONA component compositions based on the minimization of the error between calculated and specified physical and chemical properties. The distribution of physical properties as

Figure 3. Adapted hydrocarbon structure trends vs TBP.7

a function of carbon number for different chemical groups is shown in eq 1. p ⃗ = fi (C⃗)

(1)

where p⃗ is the physical property vector, for example, the NBP vector; f is a specific distribution for a certain physical property vector; C⃗ is the carbon number vector; and i is the specific chemical group of interest, i = P, I, O, N, A, A(dehy). The compositions of PIONA species are determined using statistical distributions such as the γ-probability density function (pdf) commonly used for hydrocarbon characterization,8 as shown in eq 2. pdf(x ⃗ ; α , β) =

x ⃗ α⃗ − 1e−x ⃗ / β α⃗

β ⃗ Γ(α⃗)

⃗

(2)

where x⃗ is the composition vector of PIONA species, and α⃗ and β⃗ are the shape and rate parameters of the gamma distribution function (Γ). The statistical distribution parameters α⃗ and β⃗ are independent variables used in the data matching process used by the new method. Note that these statistical distribution parameters are also vectors since different distributions exist for different chemical types. The γ-distribution is a more general model for describing molar distribution as compared to single carbon number methods that assume that C7+ mole fractions decrease exponentially with respect to the molecular weight.8 The use of these exponential models to determine PIONA species composition would lead to similar distributions for feeds with similar molecular weight, for this reason the continuous three-parameter γ-probability function was chosen to describe the molar distribution of PIONA species in this work. C

dx.doi.org/10.1021/ef400286m | Energy Fuels XXXX, XXX, XXX−XXX

Energy & Fuels

Article

Table 2. Naphtha Feedstock Characterization Property Comparisons specific gravity ASTM-IBP [°C] 10% [°C] 50% [°C] 90% [°C] FBP [°C] n-paraffin [wt %] iso-Paraffin [wt %] olefin [wt %] naphthene [wt %] aromatic [wt %] molecular weight H/C molar ratio viscosity 100 °F [Pa s] viscosity 210 °F [Pa s]

reported10 I

model2 I

reported10 II

model2 II

reported10 III

model2 III

0.6790 38 52 71 113 141 44.4 39.7 0.0 12.2 3.7 85.5 2.255 N/A N/A

0.6811 37 44 70 103 145 44.7 40.0 0.0 12.7 2.6 88.0 2.236 0.000303 0.000183

0.7056 46 61 88 138 170 37.0 34.2 0.0 19.7 9.1 93.0 2.177 N/A N/A

0.7070 42 52 80 139 162 39.0 36.1 0.0 18.5 6.4 95.7 2.140 0.000389 0.000227

0.7180 45 68 110 158 195 35.4 35.2 0.0 20.8 8.6 98.3 2.177 N/A N/A

0.7168 41 56 107 150 192 36.5 36.3 0.0 21.6 5.6 101.7 2.127 0.000462 0.000262

It is significant to note that with the new characterization method the three feedstocks can be blended without the need to add new pseudocomponents, thus, considerably reducing the computer load when performing large flow sheet calculations. In addition, if the characterization included material stream information before and after a chemical reactor, a single consistent component slate could have been created, thus making the construction of simulation models that include recycles much simpler. 3.2. General Nonreactive Applications. Some simplifications can also be made on the molecular structure groups selected for the characterization depending on the type of process under study. For example, some nonreactive refinery models can be adequately modeled using lumped paraffin (containing iso, normal paraffins and olefins), naphthene, and aromatic groups (PNA). In any of these setups hydrogen will also naturally balance since the carbon to hydrogen count in each component is available. This allows hydrogen balances around units or the full process to be completed without extra effort and simplifies the computation of hydrogen make up. Mixtures modeled using the PIONA distribution can be processed using standard unit operation models such as distillation towers and separators where new consistent distributions of compounds and physical properties will automatically be generated. Chemical extraction using polar solvents can now be simulated since interaction parameters between solvent and different chemical types such as paraffins, naphthenes, and aromatics can be defined and rational models of liquid−liquid extractors can be constructed. As an example, activity coefficient interaction parameters can be correlated using defined compounds such as sulfolane with paraffins, naphthenes, and aromatics and the further generalized based on molecular weight. These can then be assigned to binary pairs of sulfolane/paraffin, sulfolane/ naphthene, and sulfolane/aromatic, and complex extraction units can be simulated using the same thermodynamic model with varying components characterized using this new method. When mixtures of hydrocarbon material being considered in the model becomes heavier than light naphtha it is usually more practical to start lumping larger ranges of carbon numbers together. Similar to lumping ranges of NBP, a lumped carbon number group can contain hydrocarbons, once again for each extended PIONA group, between, for example, C25 and C30. In this situation, the thermodynamic properties for this group would be between C25 and C30 for whatever molecular structure

To illustrate this method, assume that the TBP (true boiling point) distillation curve describing the feed is available (either from direct measurement or through interconversion methods). If a group of f functions, such as those described in eq 1, define the NBPs for the PIONA chemical groups and if their composition vector, x,⃗ comes from the γ-distribution function, then a TBP curve can be obtained from the calculated NBPs and compositions for the PIONA chemical groups defining a feed. As a consequence, a least-squares optimization can now be constructed where the error between the calculated and specified TBPs can be constructed, as shown in eq 3. OF(α⃗ , β ⃗) =

− TBPiexp)2 ∑ (TBPCalc i i=1

(3)

The parameters α⃗ and β⃗ can be determined using any number of well-established nonlinear optimization methods such as Nelder−Mead.9 Note that eq 3 can be extended to cover any specific or combinations of physical and chemical properties, with adjustable weights assigned to each physical property being matched, thus providing for extreme flexibility when matching experimental data.

3. NONREACTIVE MODELING 3.1. Nonreactive Modeling Example. Three naphtha feed stocks from an ethylene thermal cracking furnace10 are reproduced in Table 2, including their characterization data together with results obtained using this method implemented in the VMGSim process simulator.2 Due to the error minimization, using the Nelder-Mead method,9 it can be seen that a balance is made between matching feed inputs such as the under-predicted aromatic species and hydrogen to carbon ratio of the three naphthas characterized. In addition, dehydrated aromatic species were not used to characterize these lighter feeds since dehydrated aromatics at lower carbon numbers are usually less naturally occurring and more typically a result of being exposed to higher thermal conditions. Figure 4 shows the carbon number and amounts of PIONAbased component slate for each of the different feedstocks using a single component slate. Two additional rows in Table 2 show the estimated viscosity of each naphtha feed at 100 and 210 °F, since they were not available from the original work. Note the increased viscosities of the heavier, more aromatic, feed stock as a direct result from the new method. D

dx.doi.org/10.1021/ef400286m | Energy Fuels XXXX, XXX, XXX−XXX

Energy & Fuels

Article

Figure 4. Simulated naphtha feedstock PIONA component distributions.2

Figure 5. Example PIONA component slate reactive pathways.

inaccuracies of bulk property estimation within heavy component mixtures. Due to interest in off-gas or vented vapors, keeping specific carbon number resolution for the smaller carbon numbers is suggested. As hydrocarbon mixtures become heavier, such as vacuum residues and bitumen, there is a tendency for heteroatom content

group it represents. Errors similar to NBP lumped methods are introduced into this PIONA approach when component lumping become so large as to imply the definition of fractions that encompass too many components to be meaningful from a chemistry point of view. These errors ultimately produce oversimplified separation of groups in distillation units and larger E

dx.doi.org/10.1021/ef400286m | Energy Fuels XXXX, XXX, XXX−XXX

Energy & Fuels

Article

With the carbon number and molecular structure type component slate, the feeds and products of reactive units can be tracked. Further, this component slate sets up the stage for the representation of meaningful reactive pathways for components undergoing different types of reactions. Figure 5 shows an example of the types of pathways combining the different extended PIONA structures. These pathways are not complete and include more alternatives such as iso-paraffin components dehydrating to olefin type structures or undergoing cyclization to ringed components. Note that some of these kinetic pathways would also be almost negligible such as cracking of the dehydrated aromatic group. 4.2. Reactive Modeling Example. Component allocation in a constant slate before and after reactions occurring within a cracking furnace unit can be demonstrated with characterized gas oil. The properties used to initialize the component slate distribution for this gas oil, similar to the earlier exercise with naphtha feeds in Section 3.1, were gathered from Zimmermann and Walzl.11 Their values against the simulation model results are provided in Table 3. Figure 6 shows the component allocation of the feed and products, and one can get the sense of what dominant reactions happen in the reactor. Due to the high temperature and lack of hydrogen, the thermal cracking combined with dehydration of normal and iso-paraffin feed components lead to the creation of lower carbon number olefins. Figure 7 shows the ASTM D86 distillation curves of gas oil feed and product, the feed curve shows both experimental and calculated data whereas the product curve is only presented with the calculated data since the experimental one was not reported by Zimmermann and Walzl.11 For modeling of reactive units, as in gas oil furnace coils, the property estimation through the volume of the reactor can be completed rigorously at each point. This allows energy and momentum balances to be calculated more accurately, thus allowing pressure drop, heat transfer, and other key variables to be profiled. Heat of reaction effects combined with kinetics are also tracked more rigorously, since the component carbon number and structure can be used to determine more accurately

Table 3. Gas Oil Feedstock Characterization Property Comparison specific gravity IBP [°C] 10% [°C] 50% [°C] 90% [°C] FBP [°C] PION [wt %] aromatics [wt %] H/C mass ratio viscosity 100 °F [Pa s] viscosity 210 °F [Pa s]

reported feed11 (dry basis)

model feed2 (dry basis)

0.8174 170 203 248 316 352 92.830 7.170 0.1582 N/A N/A

0.8072 166 188 252 331 349 91.851 8.149 0.1649 0.005910 0.001939

such as nitrogen, oxygen, sulfur, or metals to occur in aromatic molecules and altered property trends will result. Although not covered in this paper, the addition of other dehydrated aromatic content groups is now under study. In these situations, a heteroatom dominated content group would be defined and mixed fractions of dehydrated aromatics and this group can be used to represent heteroatom components at different carbon number ranges. If elemental chemical analysis or combustion analysis is available for different fractions of feedstock it is conceivable that an automated procedure can be developed and easily integrated to the method proposed here.

4. REACTIVE MODELING 4.1. Reactive Modeling Applications. In the case of recycle points within a simulation flow sheet, the handling of reactive units within a process can become difficult if the product yield material needs to be characterized with new components versus the feed materials. The change on running conditions of the reactive unit or reactivity mechanisms, such as catalyst, would lead to altered component lists and once again calculations cannot easily be completed in the model on any assumed constant component slate.

Figure 6. Simulated gas oil feed to product PIONA component distributions.2 F

dx.doi.org/10.1021/ef400286m | Energy Fuels XXXX, XXX, XXX−XXX

Energy & Fuels

■ ■

Article

ACKNOWLEDGMENTS The authors are grateful to Virtual Materials Group, Inc. for the permission to publish this work.

Figure 7. Gas oil feed to product ASTM D86 curves.2,11

individual pseudocomponent enthalpies of formation. Even situations such as isomerization reactors that change NBP only slightly can now be simulated using meaningful chemical reaction pathways.

5. CONCLUSIONS A new characterization model based on the use of an extended PIONA and carbon number style component slates for tracking and estimation of hydrocarbon mixture thermodynamic properties was presented. It is now possible to track different molecular structures through a process simulation model that will affect property calculations while keeping a constant and consistent component list. Some inaccuracy is introduced through this approach at the characterization level in finding a combination of components to define a given mixture, which is offset by the ease of model maintenance and increased understanding of underlining chemistry of hydrocarbon feedstock. The introduction of a dehydrated aromatic group into the component list was a necessary enhancement to the original PIONA chemical types when modeling heavier hydrocarbon mixtures. This was more significant in reactive situations where the modeling of paraffin type branch removal from aromatic rings is necessary. The tracking of carbon and hydrogen counts in each component also added the benefit of naturally balancing hydrogen around any process unit. Another significant advantage of this approach is in process models where separation is driven through polarity or more rigorous reactor models are required. In the case of reactors, individual components can be tracked through basic reaction pathways and used to generate property values; this removes the need to use “black-box” reactors. Recycle points in these flow sheets are also easier to maintain when component lists are consistent between feeds and product yields of every unit.

■

REFERENCES

(1) Kaes, G. L. Refinery Process Modeling. A Practical Guide to Steady State Modeling of Petroleum Processes; Kaes Consulting: Colbert, GA, 2000. (2) Virtual Materials Group, Inc. VMGSim Process Simulator,Version 7.0; Virtual Materials Group, Inc.: Calgary, Alberta, Canada, 2012. (3) National Institute of Standards and Technology (NIST). ThermoData Engine; Standard Reference 103b, Version 7.0; NIST, Gaithersburg, MD, 2005; http://www.nist.gov. (4) Yaws, C. L. Chemical Properties Handbook: Physical, Thermodynamic, Environmental, Transport, Safety, and Health Related Properties for Organic and Inorganic Chemicals; McGraw-Hill: New York, 1999. (5) Haynes, W. M. Handbook of Chemistry and Physics, 92nd ed.; CRC: Boca Raton, FL, 2011. (6) API Technical Data Book - Petroleum Refining, 5th ed.; Daubert, T. E., Danner, R. P., Eds.; American Petroleum Institute (API): Washington, DC, 1992. (7) Riazi, M. R. Characterization and Properties of Petroleum Fractions; ASTM International: West Conshohocken, PA, 2005. (8) Whitson, C. H.; Brule, M. R. Phase Behavior; Society of Petroleum Engineers (SPE): Richardson, TX, 2000; SPE Monograph Series Vol. 20. (9) Nelder, J. A.; Mead, R. Comput. J. 1965, 1 (8), 308−313. (10) Van Damme, P. S.; Froment, G. F. Ind. Eng. Chem. Process Des. Dev. 1981, 20, 366−376. (11) Zimmermann, H.; Walzl, R. Ethylene. In Ullmann’s Encyclopedia of Industrial Chemistry, 7th ed.; B. Elvers, Eds.; Wiley-VCH: Weinheim, 2007.

AUTHOR INFORMATION

Corresponding Author

*Tel. +1 403 457 45 96. Fax +1 403 457 46 37. E-mail: herbert@ virtualmaterials.com. Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes

The authors declare no competing financial interest. G

dx.doi.org/10.1021/ef400286m | Energy Fuels XXXX, XXX, XXX−XXX