Core of Chemical Reaction Engineering: One ... - ACS Publications

2202

Ind. Eng. Chem. Res. 1996,34, 2202-2225

Core of Chemical Reaction Engineering: One Industrial View &it V. Sapre* and James R. Katzer Mobil Research & Development Company, P.O. Box 1026, Princeton, New Jersey 08066

Chemical reaction engineering (CRE) is expanding in scope and breadth through the application of the core CRE principles to new problems in emerging technologies, through the application of new techniques to the more effective solution of traditional problems, and through the integration of the widely varied CRE activities into broad, powerful systems descriptions. Catalysis, chemical kinetics, transport phenomena, applied mathematics, and the modeling, design, and optimization of chemical reactors are the core and the intellectual basis of CRE. The CRE discipline will contribute significant, tangible improvements to the emerging technologies, such as biotechnology, microelectronics, and advanced materials; will further advance existing technologies in petroleum refining, petrochemicals, chemicals, and pharmaceuticals; will contribute to protecting the environment; and above all will provide new systematic knowledge and generic tools. New analytical instrumentation is providing more quantitative information on complex reaction and product mixtures, on catalyst structures, and on catalytic reaction mechanisms. Major advances in computing speed and marked changes in computer architecture, e.g., massively parallel processing, are providing new opportunities for advancement of catalyst, reactor, and process technologies and for rapid quantification and advancement in the emerging technologies, which will extend from the microscale molecular level to macroscale integration into processes and total systems. To meet the challenges of the future, strengthening core components of CRE through interdisciplinary teaming with experts in other fields is essential. This effective teaming should be enhanced by powerful global computer networks, could reduce fragmentation of the CRE profession, and provides a mechanism for enhanced development of its core disciplines. Table 1. Table of Contents

Introduction Chemical engineering, the broadest of all engineering disciplines, has as its basis the application of scientific (chemistry,physics, biology, and applied mathematics) and engineering (heat, mass, and momentum transfer) principles to the solution of problems of practical, industrial, and societal importance. Chemical reaction engineering (CRE) is the unique area of chemical engineering that integrates fundamentals of reaction and kinetic phenomena including catalysis with transport processes in the important areas of reactor design, scale-up, and commercial operation and is thereby the basis for chemical engineering as a separate discipline. Virtually all of the fuels, chemicals, and materials used by modern society are produced through chemical transformations. CRE plays an essential role in these industrial chemical transformations. In the modern petroleum refinery more than 90% of the molecules in today’s transportation fuels have passed through a reactor and over at least one catalyst. The total value of the fuels and chemicals requiring catalysts for their production is about $900 billiodyear, which represents about 20% of the U.S.Gross National Product. The scope of CRE extends from the microscale of the molecular level at one end of the spectrum to the macroscale of complete system integration at the other end of the spectrum, addressing issues such as overall system optimization,including the market, societal, and environmental needs. To meet future challenges at the microscale end of the spectrum we have to improve reaction specificity, produce more active, stable, selective catalysts, develop more efficient reactors, and modify processes t o produce purer products and minimize unwanted byproducts. At the macroscale end of the spectrum the challenges are to improve description of ever larger, complex subsystems and to integrate these into full-system models that allow optimization 0888-5885l95l2634-2202$09.00l0

Introduction Fundamental Chemical Reaction Catalyst Structure and Chemical Reaction Intraparticle Transport Phenomena Advanced Reaction Kinetics Homogeneous Kinetics Heterogeneous Kinetics Advanced Reactor Modeling Process, Plant, and System Optimization Process Design Plant-Wide Real-Time Closed-Loop Optimization Advanced Control Safety Emerging Technologies Advances in Computer Technology Conclusion

of overall system efficiency and effectiveness. The main factors driving these technological innovations are the rapidly advancing enabling technologies of enhanced computing technology and improved analytical instrumentation, increased global competition, heightened environmental concerns, and expanded societal needs. It is a premise of this paper that strengthening the core of CRE and focusing it at the frontiers are key to the competitivenessof our traditional process industries, to providing the basis for innovative developments in the emerging technologies, and to the long term strength of chemical engineering. Frontiers in CRE are the boundaries between what we know, understand, and can quantify and what we cannot yet adequately quantify. One set of frontiers is in the traditional areas of CRE, including chemical reactor design, transport phenomena, chemical kinetics, and catalysis as applied to the process industries. Particularly in this area, the frontier advances as problems are adequately solved. New environmental concerns and the highly competitive global business 0 1995 American Chemical Society

Ind. Eng. Chem. Res., Vol. 34, No. 7, 1995 2203

Technology Chain Levels

Plmt

Ewnomlw M.*M Condllione

Syatem

Yulll P1.W

*

.

Pint Model

opnmbuon \

Figure 1. Expanding sphere of chemical reaction engineering (CRE), includes mature and emerging technologies.

A Yesn

nme Scale

Day5 Ylnutws

m Sec Nano Sec

Sire Scale

Figure 2. Integration of CRE activities from micro to macro on different time and size scales.

environment are opening new opportunities for CRE in the traditional process industries. The other set of frontiers of CRE involves the application of its core principles to the solution of problems in the emerging technology areas. The emerging areas include advanced materials, microelectronics, biochemical, biomedical, pharmaceutical, and environmental technologies. CRE has always played a key role in bringing new technologies t o commercial fruition. A unique strength of CRE is the structure it brings to the analysis of physical situations. It provides a way of dealing with very complex systems, which are difficult to describe accurately, by extracting the essential elements and modeling them rigorously. This philosophy of applying well-understood physicochemical principles to new systems typically brings enhanced understanding and technological advance. These new frontiers represent a unique opportunity and critical stimulus for expanding the intellectual basis of CRE and therefore for its continuing renewal. In this paper we summarize frontier work in the traditional areas, highlight the future challenges, and briefly discuss the emerging technologies. The expanding sphere of CRE is illustrated in Figure l. The pictorial depiction of CRE activities on various scales is illustrated in Figure 2, which shows different time and size scales over which critical events occur (Cocke, 1987). Chemical reaction engineers have traditionally worked at the mesoscale, the middle portion of the diagram, utilizing phenomenological and continuum models. Many new frontiers are becoming progressively more micro in scale and more fundamental in nature; whereas other new frontiers are becoming more macro or more global in scale. Traditionally physicists and chemists have focused on the microscale

Symm

OpWmlmlon

Figure 3. Integrated technology chain from micro to macro. Road map for the paper.

phenomena, the lower left-hand corner of Figure 2. S i m c a n t progress is being made in each scientific area shown in Figure 2. A key role of CRE is to transfer information across the interfaces of the boxes, developing a cohesive linkage such that these subsystems can ultimately be tied together as a whole or a continuum. The whole system can then be more globally optimized. This calls for effective multidisciplinary teaming between materials scientists, surface scientists, physicists, chemists, mathematicians, chemical engineers, and others, dependent upon the system. With the advances in computer technology, applied mathematics, and analytical instrumentation, we foresee emerging a new philosophy in technology development. Acquiring detailed information on various scales at early stages of development is becoming easier. This allows more rapid development of broad, realistic models. It is becoming beneficial to develop initially the most complete models for each scale of the problem and t o progress them through the natural cycle of technology development, continually integrating critical information at the interfaces of the various scales depicted in Figure 2. A major challenge is to develop methodologies for doing the seamless integration effectively. Accomplishing this requires exploiting the enormous capabilities of modern analytical and computing tools in combination with advancing science and engineering capabilities. Figure 3 illustrates how the scale-ordered activities of Figure 2 are tied together in a technology chain. The left side of Figure 3 gives the generic fundamental areas representative of the scale-related components. The right-hand boxes highlight the frontier areas and how they are linked together. As the technology chain matures, there are advances in the frontier areas at all levels, and there is closer integration of fundamentals from the atomidmolecular level through to the global system optimization level. The discussion of frontiers in each area that follows is structured according to Figure 3, which is essentially a road map for the paper. In addition, Table 1 provides a Table of Contents for the readers to follow the flow of information. We will draw examples from the petroleum industry, highlighting both thermal and catalytic cracking and hydroprocessing technologies. Some examples of transport phenomena and kinetics are taken from a recent publication by Sapre and Katzer (1993). However, in the present discussion we focus on issues at all levels from the micro- to the macroscale end of the spectrum

2204 Ind. Eng. Chem. Res., Vol. 34,No. 7, 1995

and significantly expand the scope of the previous publication.

0

Fundamental Chemical Reaction Let us first consider fundamental chemical reaction at the molecular level. Simulation of small-molecule gas-phase bimolecular reactions where energy flow and redistribution are critical to the reaction progress can be made by ab initio quantum chemical, quantum, and semiclassical dynamical and by trajectory simulation and animation studies (Hase, 1994). The results can then be compared with direct experimental observation of bimolecular reaction dynamics made by real-time laser femtochemistry (Zewail and Bernstein, 1988; Zewail, 19941,which directly observes the motions of the atoms involved in the reaction. Much data are available on bimolecular nucleophilic substitutions of the type: X- RY X R Y-, where X and Y are halogen atoms (e.g., C1- CH3Br ClCH3 Br-1. Quantum chemical calculations (Shaik et al., 1992;Wolf et al., 1981;Shaik, et al., 1988)and experimental studies (Dodd and Brauman, 1986;Caldwell et al., 1984)show that the reaction path involves double potential wells separated by a central energy barrier. The double wells arise from the X--RY and XR-Yion-molecule complexes. Crossing the central barrier typically represents the rate-limiting step, consistent with transition state theory. Theoretical and experimental studies both indicate that in complex formation the relative translational energy is stored in the three low-frequency intermolecular modes and these complexes have relatively long lifetimes. The rate-limiting step of crossing the central barrier involves the difficult process of energy transfer from the intermolecular modes of the complex to the intramolecular modes forming the transition state complex which then crosses the central barrier. For molecules composed of a few atoms, theory and experiment are in good agreement. Extension of this work to more complex systems and to systems in the liquid phase is desirable. In the liquid phase the reactions appear to occur by a direct substitution mechanism (Gertner et al., 1991) presumably because of facilitated energy redistribution. It is now also feasible to accurately predict potential energy curves of elementary reaction steps in catalytic reactions of small molecules using ab initio quantum chemical calculations. A benchmark study in this field is that of the hydrogenation of ethylene on transition metal complexes reported by Koga et al. (1987).Figure 4 shows the computed potential energy as a function of the elementary reaction steps in the overall ethylene hydrogenation reaction catalyzed by Wilkinson's catalyst, RhCl(PR&:

-+

+

+

H,

+ CH,=CH,

+

-

CH3-CH3

--.c

The initial reaction step involves oxidative addition of H2 to the Rh catalyst, followed by ethylene coordination. Olefin insertion and trans t o cis isomerization of the ethyl hydride intermediate are the rate-determining steps. A net energy barrier of 20 kcdmol is computed for the rate-determining step. The isomerization and the reductive elimination of ethane, regenerating the Rh catalyst, are relatively fast. The computed potential energy curve is reasonably smooth, no barriers are too high, and no intermediate formed is too stable. This illustrates an ideal catalyst system. The net change in the potential energy based on the ab initio quantum

. -

t

Oxidative Olefin Olefin Isomerization addition coordination insertion c2H6

,L

I

Reductive elimination

I

H2

CI

Oxidative addition

Reductive elimination , L

p

y2iRl

H-CH~L HIsomerization

t5 \

CH2-CH2

I.

/

*

Rh-CI

- 3 Olefin coordination I

L

Ip

H-Rh-CI

4 H' V L 4 L

L

4

Olefin insertion

3

Figure 4. Potential energy diagram obtained from an ab initio quantum chemical study of ethylene hydrogenation by using a model Wilkinson catalyst.

chemical calculations is about 44 kcallmol, which is within 20% of the experimentally observed value from heats of formation. Such calculations are still very time consuming even for a simple reaction system such as ethylene hydrogenation. The challenges are to apply such ab initio computational methods to more complex reactions, industrially relevant catalysts, and heterogeneous materials. Application of theoretical calculations to solid catalyst surfaces is much more complex due to the complexity introduced by transition metal d-orbitals and the large number of atoms required in a surface cluster to obtain a realistic estimate of the electronic states at the surface. Large increases in computational capabilities are required to rigorously solve these problems. Thus, true chemical design or even quantitative understanding of complex catalysts is not likely for some time in the future, although progress is being made. Ab initio calculations are being increasingly applied to zeolite catalysts, with a focus on calculating acid strength and protonation of olefins. Kramer and van Santen (1993)and Cook et al. (1993)have respectively applied ab initio and local density functional theory calculations to study proton acidity in Faujasite and ZSM-5. The studies show that substitution of an aluminum ion into the zeolite structure requires considerable structural relaxation around the acid site and significantly affects the proton affinity of neighboring protons. Similarly, zeolite structure affects proton afiinity and can be correlated with Si-0 distances. The acidic OH bond is rather covalent and shows many of the properties of a rather soft acid. Viruela-Martin et al. (1993)have studied the protonation of propylene and isobutenes by ab initio molecular orbital calculations.

Ind. Eng. Chem. Res., Vol. 34, No. 7, 1995 2205 Molecules of DBT Converted x 1O15/M2-sec (400°C)

100

60

so 40

20

10 0.8 0.6

0.4

0.2

AH Formation (kcal/mole of metal) Figure 5. Volcano plot showing the relationship between the heat of formation of metal sulfides and the dibenzothiophene hydrodesulfurization activity of various catalysts.

The protonation proceeded through a stable complex which transformed into a zeolite alkoxide having covalent characteristics and reaction energies of -15 to -17 kcal/mol. Classical carbenium ion chemistry was indicated. However, results suggest that the conformation of the transition state in the zeolite pore is more important than the zeolite chemical composition, and the flexibility of the zeolite structure also plays an important role.

Catalyst Structure and Chemical Reaction With the theoretical description of surface reactions advancing slowly, another frontier area involves interpreting and describing chemical reaction at the catalyst surface. In industrial practice, catalytic surfaces are very complex, both structurally and chemically. The example we discuss here involves industrially important hydrotreating catalysts. The total sales of hydroprocessing catalysts alone exceed $350 milliodyear worldwide. Most hydrotreating catalysts are promoted metal sulfides supported on A l 2 0 3 . Maximum dibenzothiophene hydrodesulfurization activity is achieved for metals with intermediate heats of formation of metal sulfides, i.e., at intermediate metal-sulfur bond strengths [Figure 5, Chianelli et al. (198411. Reaction intermediates must be stable enough to form in high concentration but not too stable t o hinder their decomposition or further reaction, as was observed for the ethylene hydrogenation earlier. While such surface energetic considerations do not have a priori predictive capability, they

are valuable tools in guiding catalyst synthesis and prescreening. The above considerations indicate which metals may make the most active catalyst; they say nothing about the structure of the catalyst or the nature of the active site. To understand how catalysts function we must turn to structural chemistry, and materials and surface science. Modern techniques such as Auger electron spectroscopy (AES),X-ray photoelectron spectroscopy (XPS),ultraviolet photoelectron spectroscopy (UPS), Raman spectroscopy, electron energy loss spectroscopy (EELS), extrended X-ray absorption fine structure (EXAFS),low energy electron diffraction (LEED), scanning transmission microscopy (STEM), and recently developed surface microscopies are adding to our basic understanding of catalyst surfaces. A summary of surface science techniques and the type of information they provide is included in Table 2. STEM, for example, allows us t o witness dynamic events at the atomic level. Such techniques have helped to improve our understanding of hydrotreating catalysts. For example, Figure 6 schematically summarizes the spectroscopic results that the Holder-Topsoe group developed on a working Co-Moly-AlzOa hydrotreating catalyst, at practical conditions. Using in-situ Mossbauer emission spectroscopy and extended X-ray absorption fine structure (EXAFS), they have conclusively shown that the active sites on the sulfided Co-Moly-Al203 catalyst are vacancies associated with the edge sites in the CoMo-S phase. In fact, the thiophene desulfurization rate is linearly proportional to the concentration of the CoMo-S phase. This information confirms prior speculations and provides a basis for further studies and developments. Although significant scientific progress has been made in the field of catalysis and surface chemistry, industrial catalyst development to date largely remains a trial and error exercise. In this area the challenges at the frontier are to organize, quantify, and advance the more fundamental catalytic knowledge into predictive tools and capabilities that can guide and drive development of new and improved catalysts. For example, a major challenge is to design and develop the next generation hydrotreating catalyst which is a step out in improved activity and selectivity based on scientific principles and with limited empirical work and markedly reduced cost. This type of advance is needed to meet tightening environmental regulations. For example, recent EPA regulations require reduction of S in diesel fuels to 0.05 wt %, and S reductions in gasoline appear to be on the horizon. We illustrate this by comparing the empirically observed severity required to achieve 0.05 wt % S as a function of total sulfur content of feeds for a variety of distillate boiling range materials which are blended in the diesel pool (Figure 7) (Shihet al., 1991). The reactor temperature required t o achieve 0.05 wt % S in the product is poorly correlated with the total sulfur content of the feed. Therefore, we need a knowledge of the distribution of sulfur compounds in the feed and the product. Figure 8 shows the sulfur distributions obtained with high performance chromatographic separations for a feed containing 1.9 wt % S and for products at several levels of sulfur removal. Below about 0.24 wt % S, the remaining sulfur-containing species are predominantly methyl-substituted dibenzothiophenes (Shihet al., 1991). Thus, it is not the concentration of the total sulfur that

2206 Ind. Eng. Chem. Res., Vol. 34,No. 7, 1995 Table 2. Experimental Techniques for Characterizing Catalyst Surfaces and Adsorbed Species technique acronym type of information low energy electron diffraction LEED 2D structure and registry with metal surface Auger electron spectroscopy AES elemental analysis X-ray photoelectron spectroscopy XPS elemental analysis and valence state ion scattering spectroscopy ISS elemental analysis UV photoelectron spectroscopy UPS electronic structure EELS electron energy loss spectroscopy molecular structure Raman spectroscopy molecular structure X-ray diffraction bulk crystal structure XRD transmission electron microscopy TEM crystal size, shape, morphology, and structure scanning transmission microscopy microstructure and composition STEM UV spectroscopy electronic state Mdssbauer spectroscopy ionic state nuclear magnetic resonance spectrosc molecular structure and motion NMR

f The

sours.. Topwa h clauaon (reas)

Figure 6. Kinetic model of surface structure: relationship between thiophene desulfurization activity and catalyst surface structure derived from Mossbauer spectroscopy.

.

t t -1--1--

' cn"

750

700

z

5m

-

2

c

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600'F'CiES Oil

Full-Range Gas 011 NO. 2 Fuel Oil

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0

Mlxed HDS Feed Coker Gas Oil

'

_i

1

-I

I

1

-

Figure 8. Sulfur in HDS feed and products: high performance chromatographic traces of products at various degrees of desulfurization. Table 3. Relative Reaction Rates

Q

Thiophene

23.0

Benzothiophene

@J

Dibenzothlophene

@-@

13.0 1.o

2-8 Dimethyldibenzothiophene

1.O

4-6 Dimethyldibenzothiophene

Heavy Kerosine

0.1 CH,

Benzonaphthothiophene

550

CH,

& i & 3.0 @

600°F- Gas Oil

500

.l

.5

1

5

10

Total Sulfur in Feed, Wt%

Figure 7. Effect of total sulfur: relationship between temperature required to achieve 0.05% sulfur in the product and the total sulfur in feed.

is important but the concentration of methyl-substituted dibenzothiophenes. These data corroborate earlier studies (Nag et al., 1979; Houalla et al., 1980). Table 3 summarizes relative reaction rates of several model sulfur-containing organic compounds (Sapre, 1980). At fixed operating conditions, relative to dibenzothiophene, both the lighter sulfur compounds, such as thiophene and benzothiophene, and the heavier species, such as benzonaphthothiophene, react faster. On the other hand, sterically hindered 4,6-dimethyldibenzothiophene reacts an order of magnitude slower than dibenzothiophene. Thus, we know precisely at the molecular level, the kind of sulfur species we need to convert t o meet future environmental regulations. We also know a great deal about the current generation desulfuriza-

tion catalysts. The challenge clearly is to design the next generation desulfurization catalyst to have enzymelike specific selectivity. That means we now need to use these "steric hindrances" to our benefit. Intraparticle Transport Phenomena. Developing solid materials that can withstand the rigors of industrial scale operations and simultaneously exhibit enzymelike specificity has always been a goal of catalysis research and development. Over 30 years ago, Weisz and Frilette (1962)showed for the first time that zeolites (microporouscrystalline materials) exhibit "shape-selective" catalyst behavior and thus had enzyme-like specificity. However, because zeolites are fine powders, they must be bound into larger particles or pellets to make them applicable to commercial reactors, as is the case with most other catalytic materials. The particle dimensions were initially chosen to facilitate the proper flow and distribution of reactants to effectively contact the catalyst particles at acceptable reactor pressure drop. The particles also had to be strong enough to

o”

Ind. Eng. Chem. Res., Vol. 34, No. 7, 1995 2207 500

loo

--

10

-

2M)

5

31

u

!

A-B A+B

-

28

p = 0.032

8 = 0.1

-

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c

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0

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i

8c

0.100

0

0.075

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p.1.0

-g

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0.050

B withstand the rigors of commercial operation without excessively crushing or attriting. A natural consequence of developing pelletized catalyst particles is that the transport rates of the reactants and products t o and from the active sites can become significant relative t o the rates of the reaction at the active site in the pores of the reactive solids. The coupling of kinetics with transport phenomena has been a traditional focus of CRE. The effectiveness factor and the Thiele parameter of diffusion within catalyst particles represent a way of quantifying the balance between their reactive and diffusive properties. The particle effectiveness factors show complex behavior for many different forms of kinetics under both isothermal and nonisothermal conditions. The generalized relationships between effectiveness factors and the Thiele modulus have been worked out for industrially important complex reaction kinetics with multicomponent diffusion. Several theoretical studies also point out reaction conditions for which diffusional gradients are beneficial (Morbidelli et al., 1982; Harold and Luss, 1987). As an illustrative example, here we will consider some interesting features observed commercially for autocatalytic reactions of industrial relevance reported recently by Sapre (1991). Figure 9 shows calculated effectiveness factors as a function of the Thiele modulus for an isothermal quadratic autocatalytic reaction. As shown over a certain parameter range, there is a significant enhancement in apparent catalyst activity. The effectiveness factor initially increases with increasing Thiele modulus, contrary to the expected effect of decreasing effectiveness factors for an isothermal catalyst particle. This behavior is based on the physical reasoning that reaction rate of the primary reaction is increased due to the increased concentration of rateenhancing autocatalytic species inside the particle because of intraparticle mass transport limitations. This behavior for autocatalytic reactions suggests that larger catalyst particles would lead to higher catalyst effectiveness factors for the conversion of the primary reactant and is opposite t o the normal direction of reducing particle size t o minimize mass transport limitations. Thus, larger particles would allow higher space velocity operation for a flow reactor, reducing the amount and cost of catalyst. One could either increase the capacity of a given reactor by increasing catalyst size or design a smaller reactor for the fixed capacity. The lower pressure drop with larger particles is a further practical benefit. In the petroleum industry, methanol conversion to low molecular weight hydrocarbons over zeolite catalysts is autocatalytic (Chen and Reagan, 1979; Ono et al., 1979).

0.025

0.00

0.01

0.02

0.03

0.04

0.05

Dimensionless Concentration, X

Figure 10. Post-transient two-dimensional projection of the strange attractor for two parallel cubic autocatalytic reactions.

In nature, many biochemical, biological, and photochemical reactions are autocatalytic and exhibit periodic and chaotic behavior. Celebrated models such as Brusseletor and Oregonator describe these systems (Nicholis and Prigogine, 1977). More recent developments in bifurcation analysis (Balakothia and Luss, 1986) allow evaluation of such complex systems in an easy, elegant way. Bifurcation analysis of quadratic and cubic autocatalytic reactions in catalyst particles is summarized by Hu and Sapre (1990). Interaction of diffusion and autocatalytic chemical kinetics can lead to oscillatory behavior (Scott, 1987). More recent results on periodic unstable behavior for two isothermal cubic autocatalytic reactions is summarized in Figure 10 (Lynch, 1992). Over certain parameter space this reaction system exhibits chaos. Although such complex behavior is uncommon and is avoided in industrial practice, such fundamental knowledge is critical to ensure good performance of the industrial systems. Self-sustained oscillations of the temperature of the reacting system have been observed for commercial oxo reactors Weeschhouwer et al., 1992). For example, Figure 11shows measured temperature oscillations for an open-loop commercial oxo reactor and compares them to simulated results. The industrial data show that the amplitude of a limit cycle decreases as the coolant temperature is increased, as predicted by theory. Theory points out that operating conditions near the Hopf bifurcation boundary should be avoided. Above, we considered unusual aspects of intraparticle transport phenomena at the macroscopic level, which can all be predicted using classical principles. Transport phenomena and reaction a t the more molecular or microscopic level as in zeolite catalysts is equally important and interesting but less easily predicted. Weisz and Frilette (1960) were the first to demonstrate molecular shape selective catalysis. They demonstrated that Ca zeolite A, having an effective pore diameter of 4-5 A, selectively cracked straight-chain n-paraffins that could enter the pore structures and did not crack i s o p a r f f i s that were excluded based on molecular size. Weisz and co-workers (1962) also demonstrated product selectivity by showing that of the multiple products that could be formed, only those product molecules that could exit from the pores based on their size appeared among the products.


4

+

product gas

Out

cooling in

Figure 12. Structured materials for catalysis: relationship between pore diameter of molecular sieves and catalyst structure.

T

feed + gas

Regular

Gas-lift loop-type oxo reactor; industrial scale (6m3).

1 10 atm

1o

am

Gases

1o-2

-~ Liquids

A

E

s

1m

P

lo-*

ia

10-l0 l70

lo-’* 1 0

1

Measured ao

zoo

100

1000

1

10

Pore Diameter/Angstromr

Figure 13. Relationship between diffusivity and size of aperture (pore): the regions of regular, Knudsen, and configurational diffusion.

I T‘+c

110

i a

m

10

I----------

TC

Simulated Figure 11. Measured and simulated temperature oscillations of the reacting system in a commercial oxo reactor.

“he discovery of ZSM-5 (Argauer and Landolt, 1972) having a pore diameter (5-6 A) intermediate in size between that of zeolite A (4-5 A) and that of zeolite X(Y) (8-9 A effective pore diameter) greatly increased the potential for molecular shape selective catalysis. Since then, tremendous progress has been made in the understanding and application of molecular shape selective catalysis. Some of the emerging catalytic materials

include significantly larger pore size structured materials such as VPI-5 and MCM-41; relative pore diameters of various catalytic materials are summarized in Figure 12 (Tabak and Katzer, 1992). Large-pore catalytic materials could have significantly improved residue upgrading capabilities. In addition to molecular size exclusion catalytic phenomena in zeolites, the interaction of diffusion rates and catalytic reaction is extremely important in determining their catalytic behavior. Furthermore, the diffusional rates for molecules as they approach the size of the zeolite pores is very complicated. Weisz (1973) termed this region “configurational”diffusion. Its relation to diffusion in gases, Knudson diffusion in larger pores, and diffusion in liquids is shown in Figure 13. Figure 14 illustrates the strong dependence on unidirectional diffusion rate that six carbon paraffins and methylbenzenes exhibit in ZSM-5 a t elevated temperatures as a function of their critical molecular diameter (Haag and Chen, 1987). This extreme dependence of diffusion rate on molecular size and shape indicates the complexity of “configurational”diffusion. An anomalous “cage effect” for the diffusion of nparaffins as a function of carbon number in potassium zeolite T was observed by Gorring (1973) (Figure 15).


----

C12H48

I

C23H48

($ Aromatics 315'C

0 1

0 Allphatice SOO'C

2 3 4 5

6 7 8

9 10 ll 12 13 14

Carbon Number

Figure 16. Carbon number distribution of cracked products over erionite at 340 "C. 0

2

4

6

8

10

Critical Diameter of Molecule, A

Figure 14. Diffusion coefficients in HZSMd for hexane isomers a t 500 "C and aromatics at 315 "C.

-

%e .-E

E

8

6

3

a

6

1

10-l~

0 1 2 3 4 5 6 7 8 9 1 0 1 1 l 2 1 3 1 4 Carbon number of *alkane

Figure 15. Diffusion coefficients of n-alkanes in potassium T zeolite at 300 "C.

Rather than decreasing with carbon number, diffisivities exhibit a local minimum at CSfollowed by a 2-orderof-magnitude increase to a maximum a t C12. This appears to be due to a close fit between molecular length and cage dimension. Recently, Nitsche and Wei (1992) presented a theoretical analysis of the window effect based on analogy of the configurational diffusion process with an "equivalent" one-dimensional Brownian motion of a rod through a periodic sequence of potential barriers. Numerical calculations are in reasonable agreement with Gorring's experimental results. nAlkanes longer than CS are too large t o fit entirely within the potential wells formed by the erionite cages and therefore experience smaller energy barriers to diffusion and diffise more rapidly.

When n-Czz and n-C23 paraffins are cracked over erionite, which has a pore structure similar to zeolite T, the product distribution has two distinct maxima a t about Cq and Cl2 and a distinct minimum a t c8 (Figure 16) (Chen et al., 1969). This distribution is drastically different from any produced by other acid cracking catalysts and illustrates the strong interplay between reaction and diffision; CS cannot diffuse out rapidly enough and undergoes further cracking. Much progress has been made in the area of molecular dynamics simulation of sorbate molecules in zeolites, which can provide quantitative analysis of configurational diffusivities and other transport and reaction parameters. For example, good quantitative agreement between the theoretical molecular dynamics estimates and NMR pulsed field gradient measurements for methane, xenon, and propane diffusion in low-Al ZSM -5 have been reported (June et al., 1990, 1991). The challenges in computational dynamics are to extend these principles to industrially relevant and significantly more complex systems. Previously, in many cases NMR measurements and adsorption or desorption measurements of hydrocarbon diffusion in zeolites differed by orders of magnitude. Now theoretical calculations are confirming the rates as measured by NMR techniques, which also agree with rates measured by reaction experiments reported by Haag and Chen (1987). Another recent example involves NMR measurements and molecular dynamics modeling of reactions occurring over ZSM-5 in the methanol t o gasoline (MTG)reaction. The first direct evidence of both specific reaction pathways and of diffusion-related shape selectivity has recently been reported. In this case, the use of 13C magic-angle-spinning NMR shows promise to identify the mechanism of formation of the first carbon-carbon bond and the nature of reaction intermediates in the catalytic conversion of methanol to hydrocarbons over ZSM-5 zeolite catalyst (Anderson and Kinwald, 1988). Several organic species were identified in the adsorbed phase that were not present in measurable quantities in the bulk, product phase. This direct observation of product shape selectivity and the ability t o distinguish unequivocally between mobile and adsorbed species should provide a better understanding of catalytic processes in the intracrystalline space of zeolites and assist the development of improved shape selective catalysts.

2210 Ind. Eng. Chem. Res., Vol. 34,No. 7, 1995 100

80

' -

60

P)

40

20 n

1

1,2,3

1,2,4

1,3S

Trimethylbenzenes

"

1,2,3,5

1,2,4,5

1,2,3,4

Tetramethylbenzenes

Figure 17. (Polymethy1)benzene reaction selectivity of ZSMd in methanol to gasoline reaction. Table 4. Minimum van der Waals Interaction Energy for Aromatic Hydrocarbon in ZSM-6 min van der Waals enerw, kcal/mol

hydrocarbon ~

~~

~~~

o-xylene m-xylene p-xylene

~

16 -3 -13 Trimethylbenzenes

1,2,3-TrMeB 1,3,5-TrMeB 1,2,4-TrMeB Tetramethylbenzenes 1,2,3,4-TrMeB 1,2,3,5-TrMeB 1,2,4,5-TrMeB

56 45 16 54 450 21

Molecular dynamics calculations of the van der Waals interaction energy between methyl-substituted aromatics and the ZSM-5 pore structure provides further insight into factors controlling MTG product molecular shape selectivity. The results are summarized in Table 4 (Tabak and Bhore, 1991). p-Xylene exhibits a low, shallow energy minimum, versus position in ZSMd pores, and diffuses about a thousand times faster than either o- or m-xylene. Of the trimethyl aromatics, 1,2,4trimethylbenzene has the lowest energy, should diffuse more rapidly than the other isomers, and is the predominant product, exceeding equilibrium. The other trimethylbenzenes, which are thermodynamically favored but are more sterically hindered, appear a t only very low levels (Figure 17). Similarly for the tetramethylbenzenes, which represent about 4% of the product, only 1,2,4,5-tetramethylbenzeneappears in significant quantities and in strong preference to the equilibriumpreferred 1,2,3,5-tetramethylbenzene (Figure 17). Here the calculated energy minima suggest that the 1,2,4,5species should be favored and also that the 1,2,3,5species may be severely sterically hindered from forming in the pores suggesting a transition state selectivity as

proposed for other reactions (Csicsery, 1971;Chen and Garwood, 1978). Predictive techniques are just beginning to bear fruit. The knowledge gained from such studies, combined with information on the structure of the catalyst surface, on elementary reaction steps, and on macroscopic reaction kinetics, should lead t o critical understanding of both the steric and chemical requirements of active sites essential for improved activity and selectivity. The net result is that catalyst development will become less empirical and more predictive and, perhaps, will allow us to reach the elusive goal of developing solid catalysts with enzyme-like specificity. The power of CRE principles combined with modern surface science techniques to develop a step-out catalyst is illustrated by the development of selective catalytic reduction (SCR) catalysts. Specifically, reaction engineering modeling showed that the optimum balance between surface catalytic activity and diffisivity is provided by a bimodal pore structure with a substantial component of macropores (Beekman and Hegedus, 1991). Such pore structures are not physically stable for conventional vanadia on titania SCR catalysts. Although silica can be engineered to satisfy the calculated pore structure requirement, the intrinsic activity of vanadia dispersed on silica is poorer than vanadia dispersed on titania. This superior performance of vanadia on titania can be explained by Raman spectroscopy, which shows a higher proportion of noncrystalline vanadia with titania. Temperature-programmed reduction and ls0P6Oisotopic exchange studies revealed significantly more labile oxygen with this highly dispersed, noncrystalline species of vanadia (Bell, 1990). Since NO reduction by N H 3 proceeds by a redox mechanism, more facile oxygen correlates with higher catalyst activity. Therefore, an ideal catalyst would have a silica support t o achieve the optimum physical characteristic of porosity and pore size distribution and a surface which is coated first with titania to provide the desired chemical properties to achieve highly-dispersed noncrystalline vanadia. The optimized vanadidtitanid silica catalyst shows 50% higher NO conversion activity than the currently available conventional commercial catalyst. This higher activity is expected to reduce SCR reactor volume by about 33%,with associated reductions in operating and capital costs, and increase time between required catalyst changes (Beekman and Hegedus, 1991).

Advanced Reaction Kinetics The next frontier area considered is advanced kinetics. Kinetics of homogeneous reactions such as combustion and thermal cracking have undergone a continuous and remarkable advancement in the last two decades. The completeness and complexity of the current kinetics models for steam cracking, for example, are in pace with the advancement in analytical and computational capabilities. We will first consider homogeneous, thermal (steam) cracking kinetics before addressing recent advancements in the field of kinetics of heterogeneous catalytic reactions involving complex mixtures, such as petroleum fractions. Homogeneous Kinetics. Steam cracking kinetics evolved through the following stages: (1)empirical modeling, (2)apparent molecular reaction kinetics, and (3)mechanistic fundamental free-radical kinetics. With the empirical methods, pilot plant steam cracking data were regressed to develop empirical or semi-

Ind. Eng. Chem. Res., Vol. 34, No. 7, 1995 2211 theoretical scale-up rules to translate laboratory results to commercial applications. Although such models were useful over a narrow range of operation and were often used for control purposes, they were unreliable outside the range of data from which they were derived and were particularly poor in predicting the effects of changing feedstocks. As the technology advanced, effort shifted to apparent molecular or stoichiometric modeling. Only the global chemical reactions were considered. This technique is adequate to represent the pyrolysis of ethane, propane, butanes, and their mixtures. However, the modeling of liquid feedstocks, such as naphthas and gas oils, was intractable with this approach. This difficulty was partially overcome with lumped kinetic models. Although global molecular lumped models suggest a fundamental approach, such a conclusion is misleading because apparent frequency factors and activation energies are not true constants, but are functions of reactant concentration and operating variables (Van Damme et al., 1981). In 1970, Benson proposed that "There is now sufficient understanding and both kinetic and thermochemical data available to describe the behavior of the most complex pyrolysis reaction in terms of a finite number of elementary-step reactions. This clearly suggests that hydrocarbon pyrolysis was ready for quantitative modeling." In the early 1970s, Ranzi and co-workers and others recognized the shortcomings of the global molecular models and undertook developing fundamental mechanistic models. As a result, Benson's prediction is now a commercial reality. Pyrolysis models are the most fundamental kinetic models that have been developed t o date and are used routinely in the commercial operation of steam pyrolysis furnaces. An excellent review of this field is given by Ranzi et al. (1975), Dente et al. (1983, 19851, and Froment (1992). A commercial simulation package called SPYRO includes a comprehensive mechanistic kinetic model (Dente et al., 19791, and there are several other competitive commercial products which match the complexity and rigor of SPYRO. Mechanistic models allow significant extrapolation beyond the original data base in terms of feed composition and operating conditions. After characterization of the reactions and their associated intrinsic kinetic parameters, the proper use of structural and mechanistic analogies allows a priori modeling of the conversion of previously untested hydrocarbons and feed mixtures. The main pyrolysis reaction classes are summarized in Table 5. Most reactions involve radicals, but some purely molecular reactions also play a significant role. Figure 18 represents the radical reaction scheme for the thermal cracking of 3-methylpentane, one of the many components of naphtha (Clymans and Froment, 1984). Such reaction networks can be generated automatically using rule-based systems reflecting the structure of the hydrocarbon reactant and fundamental radical chemistry. Classification of elementary reactions allows the use of group contribution methods and the analogies for reactions of the same class, which significantly reduce the number of independent rate parameters that need to be obtained from experimental data. The importance of group contribution methods in thermodynamic calculations is well-known. The use of such concepts in chemical kinetics has advanced rapidly in recent years. The group contribution approach drastically reduces the number of independent

Figure 18. Mechanism of thermal cracking of 3-methylheptane. Table 5. Summary of Thermal Cracking Reactions 1.chain-initiation reactions unimolecular R'+ R (example: CzH6 2CH3') R-R bimolecular (RH in an unsaturated hydrocarbon) RH + R H R'+ R H (example: CzH6 + CzH4 2CzH5') 2. hydrogen-abstraction (metathetical) reactions R'+ R'H RH + R'' (example: CH3 + CzH6 CHI + CZH5') 3. radical-decompositionreactions R RH + R (example: n-C3H7' C2H4 + CH3.) 4. radical-addition reactions to unsaturated molecules R' + R H R (example: CH3' + CzH4 n-C3H7) 5. chain-termination reactions by recombination of radicals R' + R R-R (example: CH3' + C2H5' C3Hs) by disproportionation of radicals R'+ R RH + R"H (example: C2H3' + C2H5' 2CzH4) 6. purely molecular reactions RH + R H R H + R H "four-center" concerted reactions (example: CZH6 C2H4 + Hz) "six-center" concerted reactions(examp1es: pentene-1 CzH4 + C3H6; hexadienes 1-3 hexadienes 2-4 isomerization) Diels- Alder type dissociations (example: cyclohexene CzH4 + C4H6) 7. radical-isomerization reactions R R (example: CH~CH~CHZCHZ.CH~CHZCHCH~)

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

parameters. The accuracy of these mechanistic kinetic models t o predict ethylene yields for various feedstocks (ethane to gas oil) under different operating conditions is excellent as summarized in Figure 19. Properly applied kinetic models can predict ethylene and propylene yields in commercial furnaces within f0.5%, which is the same accuracy level as the on-line gas chromatographic measurements.

2212 Ind. Eng. Chem. Res., Vol. 34, No. 7,1995

451

Ethylene Welds

T

40 -

I

TVDeS 01Reactions -Cracking iromrrlzmtion

.-Cyclization

I

t Lam Lumplng

.Dohydropnatlon

35 -

30

-

Reactant

Reectlon

Product

*

Figure 21. Summary of new trends in catalytic reaction kinetics. A Gases 0 Naphthas

15

20

25

30

35

Predicted (%)

40

45

Figure 19. Scatter diagram for predicted versus observed ethylene yields for various feedstocks. 3-Lump

10-Lump (Link)

1960

1970

Figure 20. Historical perspective on commercial FCC kinetic models. On the left: three-lump model. On the right: 10-lump model.

For a state-of-the-art steam cracking kinetic model, the number of molecular components considered for naphtha cracking is more than 200, the number of radicals is about 40, and the number of elementary reaction steps is of the order of 10 000. However, the total number of independent rate parameters is only of the order of 100. The same approach extended to gas oil cracking involves even more components and corresponding reactions, but again the number of independent parameters is not very large. Heterogeneous Kinetics. Advances in catalytic kinetic modeling are leveraging on the concepts described before for homogeneous pyrolysis reactions. The added complexity of the interaction with the heterogeneous catalyst surface and the multicomponent feedstocks characteristic of the petroleum industry make this problem much more difficult. However, rapid advances in analytical techniques, in mathematical methods, and in computing capabilities are changing this rapidly, as discussed below. More fundamentals of reaction chemistry are now being integrated with the kinetics of heterogeneous catalytic processes. Figure 20 gives a historical perspective of industrially relevant kinetic models for catalytic cracking. On the left is a vintage 1960 3-lump model. The kinetic parameters in this model had no true chemical significance and were dependent upon

feedstock. However, for a fixed bed, it was very useful in interpreting reactor performance, in comparing catalysts, and in translating between reactor types. This model underwent a significant refinement over the next decade. The 10-lump model developed by Mobil in the early 1970s was a significant milestone (Jacobs et al., 1976). This model is called lumped invariant kinetics (LINK), as the rate constants for the lumps are invariant with respect to the origin of the feed. The choice of lumps and reaction pathways was clearly a major breakthrough of that time. Each lump in this model, however, contains a large number of molecular types and therefore is a significant simplification. The recent trend in kinetic modeling is clearly toward delumping and development of mechanistic models. This new trend is schematically presented in Figure 21. The top line represents the class of lumped models discussed above, such as the catalytic cracking models of Figure 20. As occurred in thermal pryolysis, modern kinetic modeling of catalytic reactions today requires and involves much more molecular detail on feedstock composition as illustrated by the bottom of Figure 21. Modern analytical chemistry now provides the ability to characterize feedstocks at an almost molecular level of detail (Sullivan et al., 1989; Green and DiSanzo, 1991). Molecular level feedstock characterization is now possible due to advancement in analytical techniques such as high-performanceliquid chromatography(WLC), gas chromatography (GO, mass spectrometry (MS), field ionization mass spectrometry (FIMS), high-resolution nuclear magnetic resonance (NMR), etc. Details of the compositional analysis of an oil sample using HPLC/FIMS are summarized in Figure 22 (Boduszynski, 1988). Thus, a wealth of analytical information is now available about the composition of reactants and of products. Such detailed feedstock analysis allows integration of a deeper understanding of the chemical reactions into the reaction networks and reaction kinetics. Reaction networks for each characterized and very narrow molecular lump can be developed. Individual reaction types such as cracking, isomerization, etc. can be accurately modeled. Furthermore, for each reaction type, elementary reaction steps and single event kinetics can be incorporated. For example, in catalytic cracking, carbenium ion theory, stability and reactivity of primary, secondary,and tertiary carbenium ions, P-scission rules, etc. are applied at the molecular level. In this way, a high level of detail can now be incorporated at the reaction step of Figure 21, even for very complex feedstocks. Because of the almost molecular level of reactant information and the extent of detailed chemistry incorporated in the reaction step, the resulting product distribution now has a molecular level detail. This


-

(HPLC FIMS) Molar Mass 0

600 1200

m

c u t lo----

-e

Cl

HPLC

c

0 354

Polars (41.8 wt

Hexaaromatlcs andlor Azarenes (22.5 W%)

oaromilia (4.9 MX) i l i u (3.2 WX) 1lCS ( 4 9 MU)

15s (6.8 M%)

ado

9M)

idoo

iioo

iiw

ijoo

Molar Mass, MIZ

Pl;ltlirOm.tbs (8.0 WX)

*All wt% Figurer are on the Cut 10 Basis

Figure 22. Detailed compositional breakdown of a narrow cut of a Kern river gas oil using HPLCDIMS analysis.

capability allows accurate assessment of product composition and accurate prediction of product properties. Lumped models cannot provide this kind of product information. In the past and to a substantial extent yet today, fuels that are complex mixtures of hydrocarbons are sold based on their physical properties (density, boiling range, octane, etc.). However, this is changing rapidly. Fuels are being viewed more as mixtures having constrained compositional ranges, driven by emission and other regulations. The technological capabilities represented by this advanced kinetics modeling will be needed to help meet tomorrow’s challenges. A new paradigm is being established: it simply states “More is Less” (Sapre and Krambeck, 1991). Referring to Figure 21 though, it is easy to visualize that we can create less information out of more but not vice versa. In the past, reaction pathways were identified by doing experiments with model compounds representing initial feed components and identified reaction intermediates. The relative rate data for the hydrodesulfurization of the thiophenic sulfur homologous series represented in Table 3 is reminiscent of this approach. Although not shown in Table 3, the detailed reaction pathways were identified for each molecule. Such fundamental information on model compound studies is valuable to develop the d e s . In addition, integration of this information with fundamental reaction chemistry allows accurate description of reaction pathways. The reaction kinetics of related, similar molecules are represented by a simple principle: the nature and rate of reaction that a particular molecule undergoes is a function of its molecular structure. Molecules can be represented as a collection of structural increments, and group contribution methods can now be applied to reaction kinetics. A new method, called structure oriented lumping (SOL), which employs this principle for describing the chemical reactions of complex mixtures, the resultant product composition, and the properties of complex hydrocarbon mixtures has been developed as described by Quann and Jaffe (1992). Complex reaction networks for each component are derived from rule-based systems. One such SOL-based reaction network is illustrated in Figure 23. The SOL approach provides a foundation for molecular-based modeling of all refinery processes. The group contribution methods allow description of complex reaction mixtures by a few parameters, and this

in essence justifies the new paradigm “More is Less”. The concept of the relation between molecular structure and reactivity is illustrated in Figure 24 (Klein et al., 1990). It shows a good correlation of the experimentally measured rate constant for the hydrogenation of the ring of aromatic compounds with the n-electron density on that specific ring. Thus, two parameters, the slope of the line and intercept, allow estimation of hydrogenation rates of compounds in a homologous series. The power of this methodology is really in the implied predictive ability. For example, the rate of hydrogenation of a molecule not studied experimentally can be determined by performing molecular orbital calculations t o determine the x-electron density and then utilizing the linear relationship of Figure 24. The key element in the new approach is correlating kinetic parameters as a function of substructures or functional groups. Klein and co-workers have successfully applied the group contribution technique with the Monte Carlo simulation technique to develop a general mathematical framework for describing reaction chemistry and catalyst interactions for hydrocarbon conversion systems (Neurock et al., 1993). Work by Allen and co-workers represents the stateof-the-art in published catalytic cracking kinetics following the new functional group kinetics approach. Their recent results for cracking a gas oil over an amorphous catalyst are summarized in Figure 25, which shows the carbon number distributions as a function of reaction time. Time equal to zero corresponds to the carbon number distribution in the feed. For each carbon number, approximately 20-35 molecular structures are included. As the reaction proceeds, hundreds of molecular species are produced. Conversion of heavy hydrocarbons to light products with increasing reaction times is depicted in Figure 25. This approach accurately predicts product compositions and properties such as gasoline octanes for amorphous catalysts (Liguras and Allen, 1989, 1990). In addition, Allen and co-workers have established relationships between amorphous catalyst properties and reaction rate parameters (Allen et al., 1993). A challenge in this area is extending this approach so that changes in the catalyst can be correlated to zeolite content of catalysts and to heavier feedstocks such as resids. With zeolites, shape selectivity will play an important role in modifymg the product selectivity.


@f + L Figure 23. A typical aromatic saturation network developed using Mobil's structure oriented lumping (SOL) framework.

(Functional Group Kinetics) 250 7

0.99

1.oo

1.Ol

IL

1.02

- Electron Density

1.03

1

1.04

Figure 24. Linear free-energy relationship for the hydrogenation of aromatic compounds.

A structure-based model for the diffusivityof hydrocarbons in zeolites coupled with a reaction kinetics model involving transition state selectivities will be required. Developing effective structural models for the effect of size and transition state selectivitiesfor simple reactions in zeolites is in itself a challenging problem. Advanced Reactor Modeling The next frontier area considered is that of advanced reactor modeling. Fundamental rigorous modeling of microscale events coupled with precise experiments both on the microscale and on the macroscale is resulting in a significant advancement in the modeling, design, and

Carbon Number Source: Liguras & Allen (1989) Figure 25. Carbon number distributions of product as a function

of reaction time (s) for catalytic cracking of gas oil.

operation of industrial-scale chemical reactors. For example, significant progress has been made during the last decade in our understanding of design, scale-up, and operation of trickle-bed reactors. Trickle-bed reactors are used in most hydroprocessing applications. They are packed with catalyst particles, with gas and liquid reactants flowing concurrently downward through the reactor. In the petroleum industry alone, more than 20 million bbVday of hydrocarbon streams are processed through such reactors. In the past, trickle-bed reactors have been notorious for flow maldistribution and poor gas-liquid-solid contacting efficiencies. Commercial

Ind. Eng. Chem. Res., Vol. 34,No. 7, 1995 2215 a) Low Flux Liquid Streamlines 1.6

1.2

Vertical Position, 0.6 m

Figure 26. Teaming between CRE and hydrodynamics leads to improved trickle-bed reactor performance. Macroscale phenomena have microscale roots

units with as low as 50% contacting efficiency relative to small-scale pilot units were not uncommon just a few years ago. A safety factor of 2 for scale-up was quite commonly used in design calculations. Recently a major advance in this field came about through an effective interdisciplinary teaming between hydrodynamicists and chemical reaction engineers (Figure 26). Approaches to modeling the hydrodynamics in trickle beds can be divided into two categories: microscopic approaches which examine the flows at the singleparticle and pore levels, e.g., Melli and Scriven (1991) and macroscopic (volume-averaged) approaches that capture the gross flow characteristics over a few catalyst particles, e.g., Saez and Carbonell (1985). A microscopic approach is essential to capture the multiplicity of hydrodynamic states and quantitatively describe hysterisis phenomena observed in the trickling flow regime. Typically, all other industrially relevant flow characteristics can be described either through a microscopic or a macroscopic approach. In the industrial practice though, accurate macroscopic hydrodynamic description of fluid flow can allow improved reactor design, scaleup, and operating strategies for commercial reactors. Carbonell and Sundaresan extended the concepts of relative permeability, successfully used in the creeping flow description of multiphases in petroleum reservoir modeling, to the high velocity inertial flow regime of trickle beds. Reservoir simulation includes the macroscopic models of flow in porous media that are generally written in Darcy’s law form describing the flow velocity for each phase as proportional to pressure gradient. Effects of phase interaction are included in the relative permeability coefficients which scale the flow velocity relative to that calculated for each phase flowing individually. The relative permeabilities of the gas and liquid phases are functions of the saturation of the liquid phase, the properties of the porous medium, and the liquid properties. These functions are found from an analysis of experimental data. The resulting constitutive relationships accurately predict pressure drop and liquid holdup. In addition, Grosser et al. (1988)pointed out that this constitutive formalism can quantitatively explain the trickling to pulsing transition. Dankworth and Sundaresan (1989) have extended this analysis to countercurrent gas-liquid flow in packed columns and demonstrated that the flooding point corresponds to the loss of existence of uniform states. The microscopic hydrodynamic description of twophase flow in packed beds by Melli and Scriven (1991) builds on the macroscopic description discussed above. They describe the hydrodynamics in a network of passages as a statistical combination of four local flow

0

0.3

0.6

0.9

1.2

Horizontal Position, m

b) High Flux

Liquid Streamlines 1.6 1.2

Vertical Position, 0.6 m

0

0.3

0.6

0.9

1.2

Horizontal Position, m

Figure 27. Liquid streamlines in a trickle-bed reactor with a restricted outlet. -, model predictions; - - -,experimental results.

regimes in the individual passages. The flow regimes in the passages are the outcome of local competition of the two phases for the void space. The microscale constitutive equations and the allowability and accessibility rules governing changes in local regimes predict the macroscale time evolution of flow regimes, the transition from trickling to pulsing, and phenomena such as hysterisis. The theory demonstrates that the macroscale flow regimes are rooted in the microscale flow mechanisms. Modeling the macroscale phenomena, such as pulsing, in terms of microscale mechanisms and microscale and macroscale experiments clearly delineates the interactions of different forces (gravity, inertia, viscocity, and capillary forces) that are active in the voids of tricklebed reactors. These hydrodynamic descriptions in a volume-averaged macroscale model are quite adequate to describe the trickle-bed performance in cold-flow units and laboratory reactors and to develop efficient scaleup strategies. The simulation results of one such model agree with experimental data on flow distribution, liquid spreading, and phase segregation in a large-scale twodimensional trickle bed with air-water flow over a wide range of operating conditions spanning both the trickling and pusling regimes, Figure 27 (Anderson and Sapre, 1991). Once available, a reliable hydrodynamic description can be integrated with the heat and mass transfer effects to develop a multidimensional reactor simulation model to study scale-up effects. Such computational tools are helping to improve the performance of existing equipment and develop efficient new designs. The amount of detail that one can identify through large-


about 50%) and a reasonably well performing reactor (contacting efficiency greater than 90%). Close to 100% contacting efficiencies are achieved in industrial practice today with modern reactor design. In reactor specification problems, fluid mechanics plays a dominant role and reaction kinetics themselves are of secondary importance. The critical aspects of reaction kinetics are the heat effects, vaporization, and molar change. Computationally, it is not yet feasible to couple the complex fluid mechanics problem with the complete description of advanced kinetics. Even the largest supercomputer available today cannot solve this problem nor will more powerful models be able to in the near future. Therefore, simplified kinetics and heat release models must be integrated with detailed fluid mechanics. The advanced kinetics models described above can be simplified using concepts of continuous reaction mixture theory. Frontier research in this field is focusing on going from a complex discrete distribution to a continuous one which enables one to reduce the number of parameters by orders of magnitude. The future challenges in the description of advanced reactors are to extend the concepts developed for trickle beds to other reactor types. Another major reactor type that requires much further quantification is the fluidbed chemical reactor which is of tremendous industrial importance as indicated in Table 6. Fluid-bed chemical reactors have evolved relatively rapidly in terms of their scale and design configurations, but to date this evolution has been highly empirical in nature. Fluid catalytic cracking (FCC) will be used to illustrate the relevant reactor design issues. The general fluid-bed concepts were developed relatively early, but the correlations quantitatively describing the various rate processes and other operational phenomena have developed slowly. Fluid-bed reactors are currently designed by scaling from prior experience since they cannot be adequately modeled and thus cannot be described quantitatively. As with trickle-bed reactors, hydrodynamic models are critically needed as a basis for reactor model development and thus reactor design and optimization. We need to emulate our successes in the trickle-bed reactor hydrodynamic modeling and achieve similar results for fluid-bed reactors. It is encouraging to see that the theory developed to determine the trickling to pulsing transition is now being extended to explore the structural similarity between seemingly different systems, such as the transition from bubbling to slugging in gassolid fluid-bed reactors. Perhaps these constitutive

0-

5 Above

01.90 1.80 1.70 1.60 1.50 01.40 1.30

1.90

- 1.80 - 1.70 - 1.60 -- 1.50 ,E - 1.40 a - 1.30 L - 1.20 0

11.20 0Below

1.10 1.00

6

10

-

I

I

15-

20

-

25

-

30

-5 -4

-3 -2 -1

0

1

2

3

4

5

Radial Coordinate Figure 28. Two-dimensional temperature map predicted by a rigorous trickle-bed reactor model.

scale computations is illustrated in Figure 28 (Anderson et al., 1990). Figure 28 summarizes the two-dimensional axisymmetric temperature profile for a typical reactor simulation. The reactor bottom configuration induces uneven gas-liquid distribution throughout the catalytic bed. The exothermic reaction further degrades the reactor performance. Contrary to an ideal reactor design, this reactor has to be operated at a higher average reactor temperature to overcome the effect of flow maldistribution. Furthermore, due to a tendency to form a local hot spot with this design, the reactor operation could approach a runaway limit. The use of simulation results, combined with laboratory and industrial scale experiments, has resulted in significant improvement of commercial trickle-bed design and operation. The degree of flow maldistribution can be assessed using a thermal tracer technique (Sapre and Anderson, 1989). Typical spatial flow distribution results using this technique are summarized in Figure 29 for a poorly performing reactor (contacting efficiency

(6) Good Flow Distribution Contacting Efficiency > 90%

(A) Poor Flow Distribution Contacting Efficiency-=50%

Thermal Residence Time

Thermal Residence Time

J -

loo 80

6040

-

.

Thermowell2

.,

Outlet Inlet

" 0

0.17

0.33

0.50

0.67

0.83

1.0

1

I

0.2

0.4

I

0.6

1

I

0.8

1.o

Normalized Distance Normalized Distance Figure 29. Relative thermal residence times measured in commercial trickle-bed reactors. (A) Poor flow distribution; (B)good flow distribution.

Ind. Eng. Chem. Res., Vol. 34, No. 7, 1995 2217 Table 6. Examples of Applications of Fluid-Bed Reactors urocess

status

fluid catalytic cracking (1942) phthalic anhydride (1945) Fischer-Tropsch synthesis (1955) chlorinated hydrocarbons and chlorine (early 1950s) acrylonitrile (1960) polyethylene (high density, 1968) (low density, 1977) circulating fluid-bed boilers (1979)

10 x lo6 bbl/day, 2350 units 0.3 x lo9 lb/year (United States) 19 units large number of units > 6 x lo9 lb/year, 250 units 250 units

Predicted Catalyst Flow Pattern

Oxygen Breakthrough, %

> 100 units 1.0

Unlior

Bubbllng

Inlet

Outlet 0.0001 0.001

Minimum Buoyancy

0.01 x Minimum Bubbling

t

0.1 x Terminal Velocity

x Blowout Velocity

1.0

10

x

100

Supcrtlolel Gar Veloclty (mh)

Feed Nitrogen Converted toNO,,%

20,

Figure 31. Hydrodynamic flow regimes for different parts of modern FCC reactor and regenerator units.

Before

0

20

40

80

Afterburning, (Flue Gas T Dense Bed T), 'F ~

Figure 30. Predicted catalyst flow patterns and oxygen breakthrough in cross-flow FCC dense-bed regenerators. Improvements in NOz emissions as a function of afterburning after the hardware modifications were implemented based on model predictions.

relationships will offer the basis for developing future hydrodynamic models to describe phase segregation, clustering, radial and axial density gradients, and unusual stability phenomena in industrial-scale equipment. With an adequate hydrodynamic model, we can then integrate the heat and mass transfer, the energy balance, and the chemical kinetics t o achieve the full predictive capability. Commercial data could then be used to validate the model's predictive capability for scale-up, design, and optimization purposes. Coupling of a hydrodynamic model with coke combustion kinetics has been successfully used to improve the efficiency of combustion of FCC dense-bed regenerators (Sapre et al., 1990). The predicted catalyst flow pattern and oxygen breakthrough above the dense bed for a cross-flow design are summarized in Figure 30. This figure also shows that NO, emissions can be reduced by up t o 50% with optimized air grid design using this simulation methodology. As shown in Figure 31, today's high-efficiency riser fluidized-bed FCC units contain all major fluidization regimes (Squires et al., 1985). This provides major challenges to integrate fluid mechanics with CRE to develop descriptive models for the whole system, or even for limited portions thereof. For example, let us consider only the riser portion. Much progress has recently been made in the description of riser hydrodynamics (Sinclair and Jackson, 1989; Ding and Gidaspow, 1990; Tsuo and Gidaspow, 1990). The occurrence of marked

segregation of particles over the riser cross section and downward flux of solids near the wall are well-known in FCC units. Possible causes of this phenomenon have been qualitatively explained by a hydrodynamic model developed by Sinclair and Jackson (1989). In this continuum model, volume-averaged constitutive relationships are developed for each phase, gas and solid, and the interaction between phases is included at the level of mean velocities for each phase. In addition, interaction between the mean solids velocity and the fluctuating solids velocity is accounted for by the concept of granular flow temperature. This development is analogous to the kinetic theory of dense gases. In this theory, the usual thermal temperature is replaced by a granular flow temperature for which a differential equation is derived using the method of the kinetic theory of dense gases. Thus, the solids viscosity and the solids stress are a function of this granular temperature. This relationship is based on the fact that the interaction of the fluctuating part of the particle motion with the mean particle motion generates stresses in the particle assembly. In addition to phase segregation and radial catalyst distribution, this model can capture the existence of steady-state multiplicity wherein different pressure gradients can be obtained for the same gas and solids fluxes. This phenomena has been known to occur experimentally. Pita and Sunderesan (1991) used the SinclairJackson model to understand certain scale-up characteristics of an FCC riser. "he computed results matched cold-flow data very well for a certain set of parameters. The comparison of model predictions to experimental data is summarized in Figure 32. Radial density profiles and downward solids flux near the wall are predicted reasonably well. However, this model manifests an unsatisfactory degree of sensitivity to the inelasticity of the particle-particle collisions and the damping of particle-phase fluctuating motion by the gas. For example, if the coefficient of restitution is changed


0.4

1

.c. 0

?k

&

0,3/

II

I

Table 7. FCC Yield@

conversion, wt % gasoline yield, wt % coke yield, wt % a

Radial Distance (cm)

-0.414 0

1

3

'

6

u

' ' I ]

I

9

1

2

1

5

Radial Distance (cm)

Figure 32. Comparison between observed and predicted radial solids distribution in FCC riser cold-flow unit. W, solids downflow near the wall.

from 1.000 to 0.999 in the above simulations, the agreement between the model and data becomes unsatisfactory. This clearly suggests that some critical aspects of the physics are missed by the theory. In this model, the motion in the particle phase is treated as laminar, since it recognizes only the local mean velocity and the random fluctuations at the level of individual particles. For conditions typical of riser flows the Reynolds number for the particle phase, based on duct diameter, is very high (-lo5). It also suggests that inclusion of gas- and solid-phase turbulence may improve the performance of this model. Such a model is not available at the present time. For turbulent models of this nature, a large number of speculative closure relations become necessary, and additional research in this area is needed. A part of the difficulty with this approach may also be related to developing continuum models for discrete systems. Perhaps a microscopic view of starting from very dilute systems containing only a few particles and then building more dense systems with proper scale averaging may help. Also, the other extreme of shear flow of a dense solid phase with minimal gas flow is a tractable problem. Developing a cohesive linkage that can span the entire spectrum of gas and solids flow is the ultimate challenge. For predicting the FCC performance, the fully developed flow relationships may not be sufficient, since the entrance region and development of flow patterns at the bottom of the riser are just as critical, Even after 50 years of commercial FCC operation, our understanding of mixing of hot catalyst and oil feed, subsequent vaporization, rapid reactions, and catalyst deactivation

bench scale

pilot plant

74 55 4.9

70 51 4.9

commercial unit 66 46 4.9

All data at constant riser top temperature.

is rudimentary. Industrial know-how and design methodologies are empirical, and significant potential exists to rejuvenate this so-called mature technology by shedding light on largely unexplained basics. Additional research and modeling in this area is clearly needed. The potential for performance improvement in commercial FCC units can be gauged by comparing process performance data on the same catalyst and vacuum gas oil feed at constant coke yield in three different scale units summarized in Table 7 (Martin et al., 1992). The conversion and gasoline yield are lower by more than 8 w t % in the commercial unit compared to an ideal benchscale unit. The results for the pilot plant are intermediate, probably because the flow maldistribution and catalyst-oil contacting are not as poor as the commercial unit. The authors developed a simple radial dispersion model which accounted for the core-annulus catalyst distribution structure and integrated it with a 3-lump kinetic model to explain these observed results, Poorer gasoline selectivity in the commercial units was explained by lower conversion due to poor gas-solid contacting and thermal overcracking of gasoline in the annulus which contains very little catalyst. However, such simplistic models give very little insight into how to improve the process performance. Considering the total FCC capacity in the U.S. alone, a mere 1%shift in product selectivityto gasoline would allow a reduction in oil imports by more than 8 million bbl/year of oil. A complete riser reactor model that integrates reaction kinetics, transport processes, and hydrodynamics would be a valuable tool for scale-up and optimization of reactor performance and for developing novel riser designs. To understand the impact of reactor design and operating variables, however, we would need a t least two-dimensional models for which the resulting nonlinear coupled differential equations can be solved efficiently only on supercomputers (Anderson and Sapre, 1988). With the addition of a full kinetic description of complex feedstocks and heat effects, the solution will require markedly more computing power. The solution of hydrodynamic models typically involves solving linear equations with very large sparse matrices. Developing efficient stable algorithms to solve such problems on advanced supercomputers is yet another challenge. Although raw computational power is needed to obtain answers quickly, mathematical models that describe the underlying physical and chemical processes are still the keystone. Analytical techniques to quantitatively measure the hydrodynamic performance of multiphase reactors have improved significantly in the last few years. Radiation tracer techniques have matured to provide reliable flow information for both the laboratory and industrial-scale equipment. The work on bubble column reactors using a noninvasive computer automated radioacive particle tracking (CARPT) technique developed at the Washington University by Professor Dudukovic and his coworkers is an excellent example of recent accomplishments in this difficult area. With the CARP" technique, the motion of a single neutrally buoyant radioactive

Ind. Eng. Chem. Res., Vol. 34,No. 7, 1995 2219 particle is monitored by an array of detectors and analyzed by an on-line computer to map the flow field (Yang et al., 1992). Experimental hydrodynamic data will help us to improve our basic understanding, verify theories, and improve reliability of scale-up.

Primary Reactor

Catalytic Distillation

Methanol Extraction

1

MEOH

T

Methanol Recovery

c4 Raftinate

I

-

Process, Plant, and System Optimization Chemical reaction engineers have traditionally stopped at the reactor level description. However, significant gains are t o be made by extending the fundamental concepts to larger macroscale issues, such as the overall process, or entire plant scale (Figure 3). In this section we describe recent advances in process design, automation technologies such as real-time closed-loopoptimization with advanced control and safety. Process Design. New exciting process designs and operating strategies often result when reactor design and downstream physical separation operations are examined in the context of the entire process. For example, reactive distillation processes for etherification, benzene alkylation, and methyl acetate manufacturing have eliminated conventional reactors, resulting in significant cost savings. For example, Eastman Chemical's new methyl acetate process is based on the reactive distillation technology and has resulted in almost an order-of-magnitude reduction in operating costs (Spear, 1992). The previous conventional route required several reactors for the methanol-acetic acid reaction, followed by a train of up t o eight distillation columns. In the new process, the entire processing is done in a single reactive distillation unit. The reactants are fed into the column or reactor a t opposite ends, separated by the feed point for the sulfuric acid catalyst. Most reaction takes place below this point. The bottom section of the column, below the methanol feed point, acts as a methanol-water separator. The top section refines the methyl acetate which is continuously taken off as the top product. To meet new environmental regulations in the US., addition of oxygenates such as methyl tert-butyl ether (MTBE) to gasoline is necessary. Catalytic distillation processes have been developed for MTBE production and provide an alternative to traditional tubular reactors or adiabatic fixed (upflow and downflow) and expanded beds (Keefer et al., 1991). The different reactor designs evolved primarily to control the exothermic temperature rise and to ensure an all-liquidphase reaction for high selectivity. High-temperature hot spots lead to irreversible catalyst deactivation and unwanted side reactions; therefore, a flexible reactor design which can operate over a range of feed flow rates and reactant concentrations is required. In catalytic distillation, a cation exchange resin catalyst is loaded directly into the distillation tower (Figure 33). This process continuously withdraws the highest boiling components (MTBE) as the reaction proceeds, thus shifting the equilibrium toward MTBE formation and producing higher single-pass conversion and ether selectivity (up to 99%). In addition, the exothermic heat of reaction is effectively used to fractionate the product resulting in energy efficiency. This synergy for catalytic distillation results in better performance compared to conventional fwed-bed reactor technology. Furthermore, the azeotrope formation between methanol and C4's in the downstream debutanizer experienced in the conventional technology is also avoided (Voloch et al., 1986). Very high conversion levels achievable in catalytic distillation allow further recovery of copolymer

Water

Mixed

MTBE

Waler + Contaminants

m

Figure 33. Catalytic distillation process for methyl tert-butyl ether (MTBE)production.

grade l-butene in downstream equipment. This synergy between reactor and distillation column results in savings in capital as well as in operating costs. Many other catalytic distillation processes have been commercialized. In the future, reactive extraction and reactive crystallization could also become important in commercial practice. In the above example, we considered integration of the reactor with the downstream separation equipment for one process in a plant that may have several other processes that depend on each other for raw materials, intermediate streams, utilities, etc. This one process then is a subsystem of a larger system. Next we examine integration of a set of subsystems into a representation of the entire plant. The frontier areas in process engineering today are simultaneous optimization of the entire plant in real time and maintaining the plant operation at the optimum with advanced control. We will highlight the advances in this field by looking a t a commercial olefins plant and discuss plantwide real-time optimization and advanced control. Plant-Wide Real-Time Closed-Loop Optimization. Gas-fired furnaces containing parallel reactor coils are used for thermal cracking of hydrocarbons for olefins production. Worldwide production of ethylene, the primary product in about 200 plants, exceeds 120 billion lb/year. Figure 34 is a schematic representation of a typical ethylene plant. An accurate and detailed description of thermal cracking of various feedstocks in different furnaces and subsequent separation of products in downstream equipment allows their efficient integration and operation t o maximize product quality and continuous uplift within the existing constraints of an operating plant. On-line optimization and closedloop control using rigorous models of furnaces and downstream separation equipment significantly increases plant efficiency and profitability (Fatora et al., 1992). The heart of the olefins plant is the cracking furnace. The product from the furnace is rapidly quenched and then separated into polymer grade ethylene and propylene, butanes, pyrolysis gasoline, and aromatics in hot fractionation and cold fractionation sections. The unconverted ethane and propane are recycled back to the furnaces. Also, the product fuel gas is burnt in the furnaces and provides the bulk of the energy for the endothermic cracking. Typically several furnaces (6-


PYROLYSIS FURNACE

Elhano Racycle

Figure 34. Schematic process flow diagram for a typical olefins plant.

12) feed the downstream separation train. Thus, the plant operation is closely integrated through heat and material recycles. Optimum furnace operation is a strong function of economics and plant operating constraints. Rigorous plant modeling involves the accurate description of several subsystems such as cracking kinetics, heat transfer t o coils, firebox model, separation equipment, etc. Since the plants are heat integrated, a true optimum can only be achieved through simultaneous solution of an integrated plant model representing these subsystems. Each subsystem is modeled in great detail as described below. Such close integration and simultaneous optimization and advanced control strategies have the potential to improve the process performance by 3-8% (Fatora and Ayala, 1992; Nair and Canfield, 1992). The furnace kinetic models typically involve the mechanistic free-radical kinetics discussed above. Due to the existence of a high Reynolds number (-200 0001, plug flow can be assumed on the tube side and classical continuity, energy, and momentum balance equations are solved with the comprehensive kinetics. However, on the furnace side, a rigorous description of hydrodynamics, local heat fluxes, and temperature distribution is required. The modeling of the temperature distribution in the furnace firebox and local heat fluxes along the coils requires detailed solution of radiative heat transfer equations. The heat flux in the inlet and outlet zone varies markedly, e.g., the heat flux may vary from 90 kJ/(m2 s) to 50 kJ/(m2 &from front to back. The three-dimensional positioning of burners, the shadowing effect of furnace tubes, and flue gas flow patterns are all important t o determine the temperature profiles along the tubes. An additional complicating factor is furnace tube coking, which changes the tube side pressure drop and the external skin temperature over the cycle length. The tube skin temperature is an important process constraint, and it may evolve from 950 to 1050 "C during the cycle. Such detailed modeling can result in accurate prediction of furnace performance. The downstream separation equipment is also rigorously modeled. Tray by tray heat and mass balances for distillation equipment, rating models for compressors, and accurate description of refrigeration equipment allows accurate prediction of equipment performance and operating constraints. Such rigorous

representation of each subsystem and simultaneous solution of the integrated plant are key to optimization success with the olefins plants. These complex optimization problems can now be routinely solved due to major advances in math programming technology (Biegler, 1992) and use of openform simulation methodology. The simultaneous simulation and optimization approach allows an accurate description of multiple furnaces, complex heat integration, recycle flows, and hydraulic, thermodynamic, and other constraints in operating plants. The open equations approach allows efficient on-line tuning of models, data reconciliation, and optimal set point prediction for advanced control applications of large integrated plants (Gallun et al., 1992). The use of open equations perhaps represents a shift in the paradigm in the way to conceptualize and solve problems. Over the years, due to limited computational resources, methods were devised for decomposing large problems into a set of smaller problems that could be solved in series. This approach led to approximations at the boundaries of subsystems and as a result gave suboptimal design or operating performance. With the increased performance of computers today and breakthroughs in nonlinear programming technology, we can now look at the entire problem simultaneously and as a result this allows us to represent all interactions rigorously (Biegler, 1992). The open-form methods are also easy t o adapt to advanced computing architectures, such as multitasking parallel processors. For a typical ethylene process as shown in Figure 34, with several furnaces, distillation towers, flash drums, compressors, and refrigeration loops, up to 200 000 simultaneous nonlinear equations are involved in the optimization problem. Such problems with 40-50 degrees of freedom for optimization can be solved today both in on-line and in off-line mode. Commercial profitability of on-line optimization of ethylene plants is summarized in Table 8 (Lauks et al., 1992). Typically, the optimization program runs automatically a couple of times per shift. The resulting set points are downloaded to the control system t o move the plant t o better operation. With the advances in applied mathematics and improvements in cost performance of computers, it is now realistic to build fully integrated battery limit to battery

Ind. Eng. Chem. Res., Vol. 34,No. 7, 1995 2221 Table 8. Examples of the Application and Benefits of On-Line Optimization year

application

user

1983 1986 1988 1990 1990 1990 1990 1991 1991

ethylene plant ethylene plant power station refinery gas plant crude unit ethylene plant ethylene plant ethylene plant

Shell Oil Shell Oil Wilton Texaco Amaco Pointer Star Enterprise Chevron USA 6MV Deutschland Lyondell

profit

3-5% $4 milliodyear 2-6% $4 milliodyear $4 milliodyear $3 milliodyear 5-10% 1-3% 9 month payout

limit models of a large chemical plant. Payout times for such computer projects are less than one year. Such modeling activities are a clear example of integrating reaction engineering principles into a larger system description depicted in Figure 3. Frontier areas in this field continue to be robust and fast solutions of large, sparse nonlinear equations and integration of discrete events, such as intermediate tankage, etc. Some of the mathematical solution methodologies developed in this area could also substantially improve the solution to advanced kinetics and chemical reactor simulations problems. The same integration approach for large-scale refineries with many processes could require as many as a million nonlinear equations in the optimization problem. Further expansion t o include planning and scheduling activities requiring resource allocation to several plant sites and distribution of products to the market results in large-scale mixed-integer nonlinear programming problems. Frontier research is taking place in solving such large-scale computational problems. Such problems are now becoming amenable to practical solution due to unrelenting advances in computer technology. Computer technology as a single resource will continue to have an ever increasing impact on the process industries. Advanced Control. To improve profits an increasing number of chemical companies and refiners are aggressively pursuing low-cost automation strategies. Real-time steady-state rigorous on-line economic optimization is one piece of this strategy. The model-based multivariable advanced control is the other important element of the technology to implement the optimum operating targets. Dynamic input output linear models representing process interactions between the manipulated and control and constraint variables are developed using stimulus response perturbation techniques on operating plants. The predictive multivariable modelbased advanced controls allow good disturbance rejection and constraint pushing capabilities and improve the dynamic performance of operating plants. The realtime optimization systems provide the optimum steadystate set points and quality specifications t o the advanced control systems. On-line analyzers provide the necessary feedstock composition and product quality data. These are the integrated set of tools that monitor the process responses, position the control valves, and become an extension of the operator and an integral part of the process. Advanced control projects improve process profitability by 3-5%, and pay-back times are less than one year. Closed-loop real-time optimization systems further increase the profitability. This automation strategy pushes more units to their constraints and improves capacity utilization of plant assets within the operating, mechanical, environmental, and safety limits.

Safety. Operations integrity and safety are important considerations during both the design and operation of chemical plants and refineries. Safety is becoming an increasingly high-profile public issue. As chemical process technology becomes more complex and regulations become more stringent, chemical reaction engineers need a more detailed and fundamental understanding of safety. Today, safety has developed into an engineering discipline with a scientific basis which includes comprehensive theories and practices (Crow1 and Louvar, 1990). Examples of the reaction engineering contributions to the technology of safety include: hydrodynamic models simulating two-phase flow through process vessel relief systems, reactor stability analysis to prevent runaways, dispersion models representing, the spread of toxic vapor through a plant and surroundings after a release, and quantitative analysis of the ways that processes can fail and the probability of failure. Most accidents in refineries and chemical plants result in spills of highly flammable, toxic, and sometimes explosive materials. Complex physicochemical processes occur during an accidental release. Rigorous release modeling allows safe design and operating practices to protect the health and safety and the environment. Reactor stability analysis and fundamental process dynamic models allow control strategies to be designed to prevent runaways and potentially unsafe operations. Dispersion models describe the airborne transport of flammable or toxic materials away from the accident site through the plant and into the community. The maximum concentration of released material occurs at the release point. Concentrations downwind are less, due to turbulent mixing and dispersion of the substance with air. Comprehensive three-dimensional dispersion modeling plays an important role in designing safety systems to minimize the impact of potential accidents. Reaction engineering has played a significant role in the development of quantitative process risk analysis. Quantitative risk analysis provides techniques to quantify and analyze process risks and abatement alternatives during plant design and operation. Emerging Technologies CRE has made marked progress in the last 15 years and is the basis for bridging the gap between the truly molecular scale of chemical reactions and the broad systems scale of a chemical plant or a refinery, providing the basis for plant design, control, and optimization. The integration of chemistry and powerful analytical and computing tools drives this breadth and is the wellspring of CRE. CRE is also impacting emerging technologies and can be expected to help significantly increase understanding, to advance commercial development of these technologies, and t o increase their commercial value. In these areas, a greater integration of interdisciplinary skills is required and is extremely healthy for all disciplines involved. Molecular biology and medicine are spawning new technologies and new opportunities including artificial organs, highly-specific diagnostic tests, and new drugs. For example, the search for antiviral drugs involves integrated use of supercomputers, theoretical computational chemistry, X-ray crystallography, and molecular biology (Alper, 1990). Molecular-level models of virus surfaces are developed, and the binding of potential drugs to specific sites is evaluated by molecular mechanics. Although the species are different, the

2222 Ind. Eng. Chem. Res., Vol. 34, No. 7, 1995 I

0.1

I

I

1

I

I I l l 1

L 10

1

$ Figure 35. Comparison of theoretical and experimental effectiveness factors as a function of the Thiele modulus for heparin conversion in a biomedical reactor system.

techniques are similar to those of catalyst design. Although very complex, the human body is in ways analogous t o a refinery. Some of the integrated approaches applied to complex process plants can be applied to understand the interactive chemical transformations in the human body, to develop better drugs and drug treatment regimes, and to minimize side effects. Opportunities in bioengineering, include using genetically-engineered systems for the synthesis of chemicals, the biological treatment of waste, and environmental cleanup. Significant advances are likely through genetic alteration to enhance specific,desirable traits (Garg and Garg, 1990). Cells can perform certain types of chemistry better than anything else (Gottschalk, 1986). Significant challenges in bioprocess engineering include reactor design, product separation, biocatalyst development, and immobilization and stabilization of cells. Biological remediation can be cost effective for cleaning up hazardous wastes and hydrocarbon spills (Song et al., 1990; Rozich and Zitrides, 1989; St-Cyr et al., 1992). A challenge is to be able to predict and optimize contaminant destruction rates and cleanup times in large systems such as oil spills and to guarantee safe cleanup. CRE principles are important in applying medical therapies such as controlled release drug delivery systems, immobilized enzyme bioreactors to remove toxic substances from blood, and mammalian cell reactors to address organ donor problems (Langer, 1990). Use of the classical effectiveness factor-Thiele modulus plot for heparin degradation using heparinase immobilized on an agarose matrix is shown in Figure 35. Advanced materials also offer many challenges and opportunities. Similarities in making reinforced ceramic fibers, ceramic matrix composites, and composite polymers are significant. Successful design and fabrication of these materials depends on understanding the connections between microscale phenomena and macroscale behavior of the resulting material. The use of advanced polymer composites has traditionally been in the aerospace and auto industries. However, medical uses of composites in orthopedic implants, including artificial hips, is on the rise. The semiconductor and microelectronics technologies involve application of CRE, particularly in chemical vapor deposition. Development of new high-temperature superconductors continues, although the initial excitement of major breakthroughs has largely been replaced by more serious and sustained scientific in-

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CRAY Y-MPI864

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. m 1 1950

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1960

1970

1980

1990

2000

Year

Figure 36. Rapidly declining yearly computing cost: cost of the most powerful commercial machines of each era vs year.

vestigations. These materials are mainly produced via solid-solid reactions (Luss, 1990). CRE can significantly improve the conversion of these discoveries to practical materials production technologies. Unit operations such as precipitation, spray drying or roasting, and calcination which play a critical role are still empirically scaled up. Advanced materials offer many opportunities (Research Briefings, 1990). Another important “new frontier“ is the environment. Environmental problems, whether related to the formation of pollutants in combustion or their elimination by combustion, the treatment of exhaust gases for pollutant removal, or the behavior of species in the environment, are generally characterized by complex chemistry coupled with transport processes. Solutions to environmental problems require all the fundamental strengths of CRE, beginning at the microscale of the chemical reaction and extending to the macroscale of air shed modeling, e.g., the Los Angeles basin, or even global climate change modeling. As we look ahead, fundamental understanding and capabilities for applying CRE will improve on all scales, from the microscale to macroscale. The real challenge will be linking the key events, developing theories and capabilities to rationalize between scales, and effectively integrating the knowledge gained among all scales from micro to macro (Figure 2). This enhanced base should help rejuvenate mature technologies and advance the development of new technologies.

Advances in Computer Technology Advances in microprocessor performance, memory size, vectorization, and massively parallel processing are significantly enhancing technical computing capabilities each year. Figure 36 is a score card of what has been accomplished in the last 40 years. Computing costs have been halved approximately every three years, as indicated in this graph of the most powerful commercial machines of each era (Tesler, 1991). Furthermore, massively parallel computing appears to already have dramatically changed the slope of the relative cost curve.

Ind. Eng. Chem. Res., Vol. 34,No. 7, 1995 2223 For many applications, the new massively parallel architectures or even workstation clusters provide tremendous speed and cost advantages if problems can be effectively matched to parallel computing architecture. Most problems in science and engineering demonstrate this capability. Massively parallel computers have thousands or even tens of thousands of processors tied together and operating simultaneously. The four major computational methods traditionally used in studying the world from the smallest atomic scale to the largest galactic distances (multipole methods, direct matrix methods, iterative methods on discrete grids, and spectral methods) have each been successfully handled by parallel approaches. Increases in parallel processing capabilities are sure to continue aggressively for the foreseeable future. Parallel architecture should lend itself uniquely to the efficient solution of problems across many scales (Figure 2) using multigrid and adaptive multigrid and blocking (or tiling) techniques in that all scales must be solved in parallel with matching at the scale interfaces. Computer technology is rgvolutionizing the way in which we conceptualize and solve problems. We see a significant change in the philosophy of technology development due to advanced computing technology. It is now realistic to imagine that computer experiments describing fundamental phenomena will guide or even significantly replace much of laboratory and field experimentation. This should significantly reduce the time and cost to develop new technology. The growing power of global computer networks will create “electronic communities” and “virtual laboratories” that will integrate people and resources in ways never before possible and will allow the formation of the crossdisciplinary teams required to effectively solve complex problems without geographical constraints. It will ultimately change the way we do research.

Conclusion Advances in computer technology, analytical instrumentation, and the formulation of problems from the microscale to the macroscale are key elements in our future technology development and competitiveness in the globalized environment. Chemical reaction engineering, which quantitatively integrates fundamentals of chemical reactions, catalysis, and chemical kinetics with transport phenomena, reactor modeling, design, and scale-up, and process and plant optimization, will continue to play a key role in these developments. Driven by these capabilities, a shift in paradigm is already occurring away from breaking up a large problem into smaller problems and solving each of them in series t o an approach which looks a t integrating key pieces of subsystems together and solving the entire problem simultaneously. This can lead to major advances. The key professional integration needs are intellectual, scale, and interdisciplinary teaming. This will be greatly facilitated by the power of global computer networks. The future of chemical reaction engineering is very exciting and challenging.

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