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Ind. Eng. Chem. Res. 1997, 36, 2910-2914
Reactor Engineering: Science, Technology, and Art Wei-Kang Yuan* East China University of Science and Technology, Shanghai 200237, China
Mooson Kwauk Institute of Chemical Metallurgy, Academia Sinica, Beijing 100080, China
As we are approaching the threshold of the 21st century, the reaction engineer can contribute more to the profession if he plays the triple role of an engineering scientist, an engineering technologist, and an engineering artist: he analyzes, models, and computes as a scientist, he uses and devises new processes and new equipment as a technologist, and he has to master, above all, the art of knowledge synthesis and redevelopment including that of his own creation and what was foreign to him. Throughout the long time prior to the first European Symposium on Chemical Reaction Engineering (ESCRE, 1957) (Rietema, 1957), which initiated a field of study leading to the present-day reactor engineering, many chemical reactors were built in the chemical and allied industries. Engineers developed, scaled up, and designed chemical reactors, though they did not know sufficiently the real happenings in these reactors. What they did depended greatly on their knowledge and experience in the unit operations, in addition to some odd scraps of information regarding reactors. Reactors were scaled up mainly through multiscale tests, and reactor design was basically empirical, aided by such simple calculations as were necessary to estimate reactor volume, coolant temperature, heat transfer area, etc. The then reactor engineer was not acquainted with such phenomena as multiple steady states, micromixing effect, and so on. But they were ingenious enough to take advantage of related technologies to develop efficient catalysts and skillful enough to integrate their knowledge to create such epoch-making processes as fluid catalytic cracking (FCC) as early as in the 1950s. However, in the framework of today’s reactor engineering, their understanding of reactors was perceptual and meagre. Compared to the unit operations, development of reactor engineering has depended to a far greater extent on computers because the combination of chemical reaction with the transport processes, more often than not, creates nonlinear problems that are harder to solve (Seider et al., 1991). This might explain why chemical reaction engineering has grown dramatically since the 1950ssside by side with computers and computation techniques. In this sense, a novel area has been opened to chemical engineers through the amalgamation of concepts, theories, and methodologies in solving problems for the design, scale-up, and operation of chemical reactors. It appears, therefore, that reactor engineering (RE) can be resolved into its three major component parts: science, with its fundamental concepts, theories, and methodologies, technology, not only of its own but as a result of interaction with and incorporation of related technologies, and art, consisting of the skill and ability of applying and integrating the available theories, methods, and experiences to create innovations. It is not intended to make this paper highly comprehensive but merely to discuss through some examples the * To whom all correspondence should be addressed. S0888-5885(96)00440-X CCC: $14.00
essence and methodology of reactor engineering from a generalized viewpoint. Creating a Scientific Basis for Reactor Engineering As a discipline, reactor engineering has its own scientific basis. During its early stage of development, a number of basic problems in reactor engineering drew most researchers’ attention: coupling of chemical reaction with heat, mass, and momentum transfer; backmixing and residence time distribution; micromixing; parametric sensitivity and multiplicity. With progress made in these areas, many conceptual conclusions were obtained to bring maturity to reactor engineering. Some new subjects have been provoked, e.g., unsteady-state operations of reactors (e.g., forced inlet reactant concentration oscillation (Bailey, 1977) and the methodology for treating complex reaction systems (Wei and Kuo, 1969). Modeling of these processes is quite different from that used in the early days for steady-state and simple reactions. People noticed with interest an expanding world in reactor engineering. They started to sense that certain problems of generalized implications were of particular significance, such as nonlinearity (Villermaux, 1993) as one of the most popular characteristics existing in almost all reaction systems due to the generally strong temperature effect of most chemical reactions. Modeling has therefore been regarded as the major method for handling this characteristic and has become the core of reactor engineering, particularly because modeling has been successfully applied to the above mentioned areas. Multiphase flow is probably another example which has drawn wide attention (Krishna and Sie, 1994). Generalization of reactor engineering problems was very similar to what happened in the 1950s when transport phenomena were regarded as to identify a more generalized area derived from the unit operations. People then focused more and more on the methodology dealing with the nonlinear properties of reactors. Several methods have been created for this problem and have been proven quite effective. Online estimation, a prerequisite of online optimization, is one of them (Windes et al., 1989). If a reactor engineer follows the catalytic reaction engineering textbooks, very possibly he will first determine the reaction kinetics (intrinsic or observed) to set up a kinetic model for a reaction system under development. The next step is to establish a reactor model to study the transport behaviors © 1997 American Chemical Society
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Figure 1. p-step ahead prediction scheme (i ) 1, 2, ..., p).
in the reactor for determining the parameters involved. Computer simulation will then be carried out to complete the reactor design and to find out the optimized conditions. These procedures are traditional and well-known, but obviously certain problems need further investigation, for instance: (1) uncertainty of the kinetic determination (with an isothermal or a gradientless reactor) and kinetic model; (2) oversimplification of the reactor model; (3) uncertainty of parameter determination and/ or estimation; (4) time variation of model parameters in the process, e.g., deactivation, or disintegration of the catalyst. Model-based a priori optimization is actually not reliable. A methodology which needs discussion is the on-line optimization strategy through which the model is refined by online parameter estimation in order to minimize its deviation from the real process. The state estimation technique can also be incorporated to account for the inaccuracy of the model and the noises in the measurements. For simple, straightforward and effective implementation of the online optimization, it has to be as follows: (1) For easy realization in industry, the fewer sampling points the better. (2) The model should be refined to adapt it to oversimplification of the model and uncertainty of model parameters. (3) Noises in the measurements should be properly handled. Since the models, basically mechanistic, are inevitably oversimplified and operating data have to be used to refine the parameters, one would rather use a case-independent model structure, e.g., neural network, to approximate the underlying functional relationship between the inputs and the outputs of a real process. This idea is advantageous in that no deep understanding of the process is needed, and therefore, the methodology can be easily transplanted. With recent developments in measurement techniques, profuse dynamic data are generally available from operating reactors. These data must be simply handled and compressed. For this purpose, neural networks can be combined with Karhunen-Loeve (KL) expansion (Tan et al., 1994), or proper orthogonal decomposition (POD), in modeling, for instance, a fixedbed reactor with oxidation of benzene as a working system (Zhou et al., 1996). Here POD is used as a preprocessor to compress the original data most effectively, while reserving as much information in the data as possible. The size of the networks can thus be greatly reduced to make on-line optimization possible. Figure 1 shows the prediction scheme of the KL-NN model. Referring to this figure, suppose two-step-ahead temperatures of a wall-cooled fixed-bed reactor are to be predicted, i.e., p ) 2. First, the average temperature Tmean(x) and eigenfunction φ1(x) are determined by the K-L expansion using the last L measurements, and
Figure 2. Five-step-ahead prediction of bed temperatures.
coefficient a1(k), by the last measurement; a1(k + 1) is generated by the network with inputs a1(k) and the operating conditions U(k), i.e., the inlet benzene concentration, the wall temperature, and the gas flow rate. It is assumed that the eigenfunction φ1(x) and the average temperature in the time domain (k - L + 1, k) are the same as those in (k - L + 2, k + 1). Then the temperature T(x,k+1) is predicted from the average temperature, the eigenfunction, and the coefficient generated by the network. With T(x,k+1) available by prediction, the K-L expansion is updated and a new K-L coefficient is available. Thus, a two-step prediction is obtained. A p-step-ahead prediction can be obtained by repeating these procedures p times. Figure 2 shows a five-step-ahead prediction (18 s step length) as compared with experimental data. Curves numbered from 1 to 5 correspond to the thermocouples positioned at 0.2, 0.3, 0.4, 0.5, and 0.6 m, respectively, from the top of the reactor, at a wall temperature of 350 °C, a gas flow rate of 25 L(STP)/min, and an inlet pseudorandom binary signal (PRBS) of benzene concentration. Although there are limitations for the mechanistic models as mentioned above, it was not implied that modeling is to be negated. On the contrary, modeling is by all means extremely important for reactor engineers both in theoretical studies and in the application of theories as well. In addition to reactor modeling, it is also very important for reactor engineers to master different methods, to invent new methods, and to create an all-round scientific basis for reactor engineering. Thus, the science part of the reactor engineer consists of his perception in seeing the controlling factors of a chemical reactor problem, his ability in formulating mathematically the problem in terms of these factors (now quantified), his skill in solving the mathematical formulation, and his acumen in interpreting the solutions in relation to physical reality, particularly in discovering the new and innovating the existent as a result of such quantitative analyses. Although theories derived from such quantified analysis are themselves rigorous, and modeling does play a significant role in theoretical studies, it is by no means infallible and all-encompassing. We have yet to confess that after having done so much research, we are still seldom able to design a reactor, even the conventional fixed-bed reactor, completely from modeling using only
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kinetic and transport data. What is often needed is to find through experiments the so-called “windows” which show the proper operating conditions (Schmidt et al., 1994). But compared to the early days of chemical reaction engineering, much more has been learned, quantitatively, regarding reactor behavior by applying existing theories and methods. Benefiting from Related Technologies for Reactor Engineering Technologies related to reactor engineering include mechanical (e.g., mixers, fluid distributors, etc.), material (e.g., corrosion resistance, membrane, etc.), structural (e.g., bed grids, heat exchangers, etc.), and instrumental (instruments, controllers, computers, etc.), and so on. Some of these technologies may appear to the theorists as tricks or techniques. Analytically they might not appear sophisticated, yet they might be equally ingenious and highly effective in practice and contribute as much, if not less, to improvement of reactor performance. These technologies are developed along professional expertise and intellectual standards other than those for solving PDEs. Catalyst, for example, is a problem of great concern to reactor engineers. Reactor engineers need catalysts with the following desirable characteristics: (1) high activity; (2) high product selectivity; (3) toxic resistant and long operating time; (4) attrition resistant and mechanically strong; (5) low cost and easy to prepare. Since the past decade, in pursuit of a rational basis for making catalysts, people have kept talking about catalyst design. A well-known aspect of catalyst design is the spatial distribution of active species within a catalyst pellet. A conventional policy is to uniformly load the catalyst support pellet with catalytically active species, in order to maximize catalytic activity per unit reactor volume. If the effectiveness factor is significantly low, however, the active species can be saved by leaving the inner core of the pellet free without losing much of its overall activity. This applies also to the case for a higher desired product selectivity, or when the reaction is consecutive and the desired product is an intermediate compound. Such a catalyst is called the “egg-shell” type. Nonuniform activity distribution possesses some particular advantage when an irreversible poisoning reaction occurs, especially when it is diffusion limited; that is, poisoning appears essentially in the outer shell of the pellet. In such a case, the bare support can be relied upon to absorb the poison in the outer shell, and the active species needs to be impregnated only in an inner layer or in the core of the pellet. We thus have the “egg-white”- and “egg-yolk”-type catalysts. Understandably, the major consideration to determine the suitable activity distribution of the active species comes from the classical simultaneous reaction-diffusion concepts. More and more attention has been paid to combining reaction and separation into a single step, so that the reaction product is continuously removed to facilitate the reaction to proceed, thus circumventing stalling the reaction in view of chemical equilibrium due to product accumulation, e.g., reactive distillation columns and membrane reactors. Another interesting example is a modified zeolite which is capable of promoting p-xylene selectivities to values far higher than that for equilibrium when alkylating toluene with methane. When equilibrium distribution of xylene isomers occurs at the active sites, p-xylene molecules tend to leave much
faster since their kinetic diameter is approximately 0.3 Å smaller than those of the other two isomers (Chen et al., 1989). With control of the pore size of zeolite, the effective diffusivity can be made 3 orders of magnitude higher than those of the other two isomers. The key technique is how to prepare the controlled-pore-size zeolite pellets. There are other types of combined reaction-separation technologies, such as the incorporation of a solid adsorbent, packed together with a catalyst in a fixedbed reactor, to reduce a designated product in a reversible reaction so as to enhance conversion. This technology has been tested for the dehydrogenation of methylcyclohexane to toluene over a Pt-Al2O3 catalyst at temperatures as low as 450 K. Experiments have been carried out to scan the high temperature (400700 K) adsorption properties of methylcyclohexane, toluene, and hydrogen on various adsorbents (Alpay and Chatsiriwech, 1994). A clay-based adsorbent was found to be particularly suitable for this case. Clearly, to develop a process like this, we need a low-temperature catalyst and a high-temperature adsorbent, possessing appropriate reaction and adsorption properties, respectively. Another combined reaction-separation technology consists of the use of membranes to remove one of the reaction products in order to induce a reaction to proceed or to raise its selectivity beyond equilibrium. Membranes can also be used for staging reactant feeds to reactors in order to optimize the reactant concentration profiles for higher product yields (Veldsink et al., 1992). Another technique of interest to the reactor engineer is the control of catalyst strength versus its surface-tovolume ratio (S/V) and of bed pressure drop versus S/V. To make a catalyst more attrition-resistant, often a special technique needs to be developed. For instance, vanadium phosphate catalyst has been found to be too weak to withstand the mechanical forces in a fluidized bed. Spray-drying vanadium phosphate solution together with an added silica hydrogel allows the silica to migrate to the outer region of the pellets, thus encapsulating the active core in a porous yet strong silica shell (Contractor and Bergema, 1987), which retains sufficient free openings for reactant and product molecules to diffuse in and out. In the field of solids processing in fluidized-bed reactors, the bubbling species of fluidization was in vogue for many years: the Winkler coal gasifier followed by the bubbling fluid-bed coal combustor, the bubbling fluid-bed pyrite roaster, etc. Improved modes of fluidsolids processing have resulted in the adoption of the circulating fluid-bed combustor, although pyrite roasting seems to have continued its conventional practice, apparently unaware of advancement in the technology of fluidization. Recent emphasis on bubbleless fluidization (Kwauk, 1992) has identified a new class of alternate species of fluidization other than bubbling, promising better fluid-solids contacting, lower pressure drop, and smaller equipment. Furthermore, studies based on multi-scale-energy-minimization modeling (Li and Kwauk, 1994) has differentiated the bubbling-type and the non-bubbling-type particle-fluid two-phase flow systems and uncovered a continuous range of transition states, forecasting future trends of transplanting, in practice, the advantages of the one to the other (Kwauk et al., 1996). Such in-depth investigations provide the
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reactor engineer with sufficient derivative technologies to improve his work. The reactor engineer needs to be constantly aware of what is new in neighboring technologies which might be of use and to be interested in learning enough of the ABC’s of these technologies to be conversant with their experts, in order to extend the frontiers of his own profession and realize innovation and improvement in his own work. Compared to decades ago, the current rapidly changing environment is throwing out more new problems that challenge reactor engineers (Wei, 1990). Mastering the Art of Knowledge Synthesis for Reactor Engineering There are indeed many ways to synthesize or to integrate (and to apply) existing concepts, theories, methods, and technologies so as to design novel reactors or reaction systems, just as a painter integrates colors and shapes or a composer combines notes and tempos. Colors and notes are all well-known, but the paintings and musical compositions synthesized therefrom are all unique by themselves. Let us examine a typical example of combining traditional autothermal reactors (cf. Westerterp, 1992). The classical method of utilizing the heat released in an exothermic reaction is to heat up the incoming reactant gas by the outgoing hot product stream through a heat exchanger. For a reversible reaction, some appropriate temperature profile should be maintained, at the same time, in order to guarantee high conversion. The shortages of the classical method are a large heat transfer surface area is needed and, more seriously, the pressure drop through the reacting and heat exchanging system is high. For reversible reactions, such as SO2 conversion in making sulfuric acid, a multistage reactor with interstage heat exchangers needs an even higher pressure drop. In the 1980s Matros suggested a reversing flow SO2 reactor (Matros, 1985), using the thermal capacity of the catalyst bed for heat regeneration, which dispenses with the need of a heat exchanger and thus consumes less metal and energy. The reversing flow SO2 reactor is somewhat analogous to the intermittent water-gas generator of yore and presents a synthesis and modification of some conventional methods. Matros’ contribution was an ingenious combination of unsteady-state operation with adiabatic chemical reaction to create a preferred moving temperature profile and, as such, constitutes a noteworthy technological breakthrough. A large amount of research then followed to extend the application of the Matros concept. For example, by integrating with one or several heat exchangers, this reactor configuration can hopefully be used for endothermic reactions, such as ethylbenzene dehydrogenation (Haynes et al., 1992). Reactors with flow reversal can be used to treat waste gas streams containing combustible components (Nieken et al., 1994). Based on the flow reversal operation, a few reactor configurations have been derived, for instance, hot gas injection (Nieken et al., 1994), central cooling (Nieken et al., 1994), interstage quenching (Xiao and Yuan, 1994), and hot gas withdrawal (Nieken et al., 1994), so as to better control the temperature profile. The Matros scheme, original as well as derived, has naturally generated quite a number of fundamental research projects, including those on process optimization. Another example, though yet somewhat speculative, may show how a reactor engineer might use his imagi-
nation. Sometimes a reversible gas/solid reaction may be used to advantage by decoupling it into two reactions working against each other, such as in metals recovery and separation from complex ores. For instance, in chlorinating roasting of nonferrous ores, while the nonferrous metals are converted into their chlorides, iron is chlorinated at the same time. Since iron is often present in such ores at much higher concentration than the nonferrous metals, it consumes several times more chlorine than is needed for the nonferrous metals, and downstream disposal of large amounts of iron chloride also spells an abominable problem. However, it is highly possible, in thermodynamic principle, to utilize the reverse reaction to chlorination to regenerate iron oxide, thus avoiding the production of iron chloride and recovering chlorine at the same time (Academic Division of Chemistry, 1995) ca. 1000 °C
Fe2O3 + 3Cl2 y\ z 2FeCl3(g) + 5O2 ca. 700 °C To go another step further in imagination, one might even cogitate whether or not it is possible to separate metal elements as some suitable compounds in the vapor phase through chemical transport as has been conventional in ore dissolution in the molten state as in pyrometallurgy and in aqueous solution as in hydrometallurgy. The reactor engineer really needs to play with concepts, theories, methods, and technologies (Krishna and Sie, 1994), just as the artist plays with colors and forms and the composer with notes and tempos, to assert his skill and ability, not only in science or in transplanting readily available technologies but in synthesis and integration of even more basic components. Vested in him is the very attribute of the “engineering artist”, who is skillful at integrating well-known principles in the engineering disciplines together with well-known knowhows in related areas, to make his work ever more creative. Final Remarks on Science, Technology, and Art We visualize that the reactor engineer is an engineering scientist, an engineering technologist, and an engineering artist, three in one. The functional attributes of this triple personality are briefly
Science: phenomenon, concept, prediction Technology: planning, experiment, know-how Art: integration, configuration, realization The relations among these attributes are interactive rather than mutually dominative. We hope this portrayal of the reactor engineer is not too demanding, when we think that he is already at the threshold of converting the heritage of the 20th century into the fresh missions of the 21st. Literature Cited Academic Division of Chemistry, CAE. Bull. Chin. Acad. Sci. 1995, 9, 310. Alpay, E.; Chatsiriwech, D. Combined Reaction and Separation in Pressure Swing Processes. Chem. Eng. Sci. 1994, 49, 5845. Bailey, J. E., in Lapidus and Amundson, N. R. Chemical Reaction Engineering: A Review; Prentice-Hall: Englewood Cliffs, NJ, 1977.
2914 Ind. Eng. Chem. Res., Vol. 36, No. 8, 1997 Chen, N. Y.; Garwood, W. E.; Dwyer, F. G. Shape Selective Catalysis in Industrial Applications; Marcel Dekker: New York, 1989. Contractor, R. M.; Bergema, H. E. Butane Oxidation to Maleic Anhydride over Vanadim Phosphate Catalysts. Catal. Today 1987, 1, 49. Haynes, T. N.; Geogakis, C.; Caram, H. S. The Application of Reverse Flow Reactors to Endothermic Reactions. Chem. Eng. Sci. 1992, 47, 2927. Krishina, R.; Sie, S. T. Strategies for Multiphase Reactor Selection. Chem. Eng. Sci. 1994, 49, 4029. Kwauk, M. FLUIDIZATION, Idealized and Bubbleless; Science Press: Beijing and Ellis Horwood: Chichester, 1992. Kwauk, M.; Li, J.; Liu, D. Particulate and Aggregative Fluidizations50 years in Retrospect. World Congr. Chem. Eng., 5th 1996. Li, J.; Kwauk, M. ParticleFluid Two-Phase Flow: The Energyminimization Multi Scale Method; Metallurgical Industry Press: Beijing, 1994. Matros, Y. Unsteady Processes in Catalytic Reactors; Elsevier: Amsterdam, 1985. Nieken, U.; Kolios, G.; Eigenberger, G. Control of the Ignited Steady State in Autothermal Fixed-Bed Reactors for Catalytic Combustion. Chem. Eng. Sci. 1994, 49, 5507. Rietema, K. Chemical Reaction Engineering; Pergamon: Oxford, 1957. Schmidt, L. D.; Huff, M.; Bharadwaj, S. S. Catalytic Partial Oxidation Reactions and Reactors. Chem. Eng. Sci. 1994, 49, 3981. Seider, W. D.; Brengel, D.; Widagado, S. Nonlinear Analysis in Process Design. AIChE J. 1991, 37, 1. Tan, H.; Dai, Y. C.; Yuan, W. K. A Novel Kinetic Model Discrimination Method: Eigenfunction Distribution via Forced Concentration Oscillation. Chem. Eng. Sci. 1994, 49, 5563.
Veldsink, J. W.; Van Damme, R. M. J.; Versteeg, G. F.; Van Swaaij, W. P. M. A Catalytically Active Membrane Reactors for Fast Exothermic Hetergeneously Catalyzed Reactions. Chem. Eng. Sci. 1992, 47, 2939. Villermaux, J. Future Challenges for Basic Research in Chemical Engineering. Chem. Eng. Sci. 1993, 48, 2525. Wei, J. New Horizons in Reaction Engineering. Chem. Eng. Sci. 1990, 45, 1947. Wei, J.; Kuo, J. A Lumping Analysis in Monomolecular Reaction systems. Ind. Eng. Chem. Fundam. 1969, 8, 114. Westerterp, K. Multifunctional Reactors. Chem. Eng. Sci. 1992, 47, 2195. Windes, L. C.; Schwedock, M. J.; Ray, W. H. Steady State and Dynamic Modeling of a Packed Bed Reactor for the Partial Oxidation of Methanol to Formaldehyde. Chem. Eng. Commun. 1989, 47, 1. Xiao, W. D.; Yuan, W. K. Modeling and Simulation for Adiabatic Fixed-Bed Reactors with flow reversal. Chem. Eng. Sci. 1994, 49, 3631. Zhou, X. G.; Liu, L. H.; Yuan, W. K.; Hudson, J. L. Modeling of a Fixed-Bed Reactor using the K-L Expansion and Neural Networks. Chem. Eng. Sci. 1996, 51, 2179.
Received for review July 22, 1996 Revised manuscript received December 4, 1996 Accepted December 19, 1996X IE960440S
X Abstract published in Advance ACS Abstracts, June 15, 1997.
