Mathematical modeling and optimization of the dehydrogenation of ethylbenzene to form styrene

Clough, David E.; Ramirez, W. Fred

doi:10.1002/aic.690220621

Cited by 50 publications

(34 citation statements)

References 5 publications

(6 reference statements)

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“…It was concluded that the reactor would work better if the constraints on both the temperature and steam flow were relaxed. Clough and Ramirez (1976) studied the effect of injecting steam into a styrene reactor at a point downstream of the reactor inlet. Optimization was performed to find the best injection point considering a profit objective function defined as the difference between the market value of the products and the utility cost.…”

Section: Literature Reviewmentioning

confidence: 99%

“…profit and environmental functions. The problem was solved by an improved version of the normal boundary intersection (NBI) method (Das and Dennis, 1998) which combined the Li et al (2003) treated the same problem as addressed by Clough and Ramirez (1976), but with two objective functions. Both the adiabatic and steam-injected styrene reactors were considered, while styrene production and styrene selectivity were taken as the objective functions of the optimization problem.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimal design of an autothermal membrane reactor coupling the dehydrogenation of ethylbenzene to styrene with the hydrogenation of nitrobenzene to aniline

Abo-Ghander

Logist

Grace

et al. 2010

Chemical Engineering Science

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Abstract:Coupling the dehydrogenation of ethylbenzene to styrene with the hydrogenation of nitrobenzene to aniline in a catalytic fixed bed membrane reactor has the potential for significantly improving both processes (Abo-Ghander et al. 2008). In a continuing effort to realize this potential, an optimal design is sought for a co-current flow, catalytic membrane reactor configuration. To achieve this objective, two conflicting objective functions, namely: the yield of styrene on the dehydrogenation side and the conversion of nitrobenzene on the hydrogenation side, have been considered. The total number of the decision variables considered in the optimization problem is twelve, representing a set of operational and dimensional parameters. The problem has been solved numerically by two deterministic multi-objective optimization approaches: the normalized normal constraint method and the normal boundary intersection method. It was found that the integrating reactor can be run to produce a maximum styrene yield of 97% when production of styrene is emphasized and a maximum of 80% of nitrobenzene conversion when nitrobenzene conversion is emphasized. The resulting sets of Pareto optimal solutions obtained by both techniques have been found to be identical. In addition, * Corresponding

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Optimal design of an autothermal membrane reactor coupling the dehydrogenation of ethylbenzene to styrene with the hydrogenation of nitrobenzene to aniline

Abo-Ghander

Logist

Grace

et al. 2010

Chemical Engineering Science

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“…1) and T mix2 is temperature of the reacting stream after mixing with the rest of the superheated steam at z = L. The constraints on these temperatures (Eqs. 17 and 18) are based on the minimum temperature required for the reaction to take place and the temperature at which the catalyst starts to deactivate (Clough and Ramirez, 1976). The pressure at reactor exit, P exit should be more than a certain value, say, 1.4 bar for the exit stream to flow through the heat exchanger at reactor exit.…”

Section: Optimization Formulationmentioning

confidence: 99%

“…1) is high enough for the reaction to occur. The upper bound of T eb is set at 800K to prevent undesirable side reaction before ethylbenzne enters in to the reactor (Clough and Ramirez, 1976). The range for the inlet pressure, P in is chosen based on the industrial practice.…”

Section: Optimization Formulationmentioning

confidence: 99%

“…The results show that existing reactor operation can be improved and that the performance of the steam-injected reactor is better than that of the adiabatic reactor. Clough and Ramirez (1976) developed a dynamic model for a styrene pilot plant reactor based on the main reactions selected by Sheel and Crowe (1969), and also performed steady state optimization of both adiabatic and steam-injected reactors. Sheppard et al (1986) selected several kinetic models from the literature and calibrated rate expressions using isothermal integral data provided by catalyst manufactures.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Styrene Reactor Design for Two Objectives using a Genetic Algorithm

Rangaiah

Ray

2003

International Journal of Chemical Reactor Engineering

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Optimization of industrial styrene reactor design for two objectives using the non-dominated sorting genetic algorithm (NSGA) is studied. Both adiabatic and steam-injected reactors are considered. The two objectives are maximization of styrene production and styrene selectivity. The study shows that styrene reactor design can be optimized easily and reliably for two objectives by NSGA. It provides a range of optimal designs, from which the most suitable design can be selected based on other considerations.

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Mathematical Modeling

Bockhorn

2000

Ullmann's Encyclopedia of Industrial Chemistry

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The article contains sections titled: 1. Introduction 1.1. Terminology 1.2. Application Areas of Mathematical Modeling in Industrial Chemistry and Chemical Engineering 1.3. Limitations of Mathematical Models 2. Construction and Classification of Mathematical Models 2.1. Construction of Mathematical Models 2.2. Classification of Mathematical Models 3. Empirical Models 3.1. Linear Empirical Models 3.1.1. Linear Empirical Models with One Variable 3.1.1.1. Parameter Estimation (Linear Regression) 3.1.1.2. Assessment of Estimated Parameter Values 3.1.1.3. Sensitivity Analysis 3.1.1.4. Example of a Linear Model 3.1.1.5. Concluding Remarks 3.1.2. Linear Empirical Models with Several Variables 3.1.2.1. Parameter Estimation 3.1.2.2. Assessment of Estimated Parameter Values 3.1.2.3. Concluding Remarks 3.2. Nonlinear Empirical Models 3.2.1. Parameter Estimation (Nonlinear Regression) 3.2.1.1. Transformation of the Model into Linear Form 3.2.1.2. Direct Search Methods 3.2.1.3. Gradient Methods 3.2.2. Assessment of Estimated Parameter Values 3.2.3. Example of a Nonlinear Model 3.2.4. Concluding Remarks 3.3. Further Calculation Methods and Model Types 3.3.1. Further Methods of Parameter Estimation 3.3.2. Other Types of Models 3.3.2.1. Models with Constraints on the Parameters 3.3.2.2. Models Based on Differential Equations 4. Models Based on Transport Equations for Probability Density Functions 4.1. Terminology 4.1.1. One‐Dimensional Distribution and Probability Density Functions 4.1.2. Multidimensional Distribution and Probability Density Functions 4.1.3. Conditional Probability Density Functions 4.2. Transport Equations for Single‐Point Probability Density Functions 4.2.1. General form of the Transport Equations for Probability Density Functions 4.2.2. Limitations of Single‐Point Probability Density Functions 4.3. Examples of Calculating Probability Density Functions 4.3.1. Solutions for Deterministic Systems 4.3.1.1. Age (Residence Time) Distributions in Chemical Reactors 4.3.1.2. Size Distribution in Continuously Operating Crystallizers 4.3.2. Solutions for Statistical Systems 4.3.2.1. Closure of the Transport Equation for Probability Density Functions 4.3.2.2. Solution Methods of the Transport Equation for Probability Density Functions 4.3.2.3. Example: Combustion of Propane in a Turbulent Diffusion Flame 5. Models Based on Physicochemical Principles (Transport Phenomena) 5.1. Application of the Principle of Conservation of Momentum 5.1.1. Laminar Tube Flow 5.1.2. Turbulent, Nonreactive Free Jets 5.1.2.1. Models for Reynolds Stresses 5.1.2.2. Solution Method of the Resulting System of Partial Differential Equations 5.1.2.3. Example: Turbulent Flow of Nitrogen in Air 5.2. Applications of the Principle of Conservation of Enthalpy 5.2.1. Heat Conduction 5.2.2. Heat Transfer 5.2.2.1. Exact Solution for a Boundary Layer Problem 5.2.2.2. General Principles of Modeling Heat Transfer 5.3. Applications of the Law of Conservation of Mass 5.3.1. Mass Transfer without Chemical Reaction 5.3.1.1. Exact Solution for a Boundary Layer Problem 5.3.1.2. General Principles for Modeling Mass Transfer 5.3.1.3. Examples of Simultaneous Mass and Heat Transfer: Dynamic Models 5.3.1.4. Examples of Simultaneous Mass and Heat Transfer: Static Models 5.3.2. Mass Transfer with Chemical Reactions 5.3.3. Chemical Reactions in the Homogeneous Phase 5.3.3.1. Isothermal Reactors with Frictionless Flow, Constant Density, and Reactions Without Volume Changes 5.3.3.1.1. Stability of Isothermal Reactors 5.3.3.1.2. Sensitivity Analysis 5.3.3.2. Nonisothermal Reactors 5.3.3.2.1. Heterogeneous Catalytic Reactions 5.3.3.2.2. Stability Analysis of Nonisothermal Reactors 5.3.3.2.3. Use on Statistical Processes

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Mathematical modeling and optimization of the dehydrogenation of ethylbenzene to form styrene

Cited by 50 publications

References 5 publications

Optimal design of an autothermal membrane reactor coupling the dehydrogenation of ethylbenzene to styrene with the hydrogenation of nitrobenzene to aniline

Optimal design of an autothermal membrane reactor coupling the dehydrogenation of ethylbenzene to styrene with the hydrogenation of nitrobenzene to aniline

Optimization of Styrene Reactor Design for Two Objectives using a Genetic Algorithm

Mathematical Modeling

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