Optimization of a Computer Simulated Styrene Plant by Surface Response and Environmental Impact Evaluation
-
Ricardo Vieira Goncalves
,
Gustavo Teixeira
Abstract
Computer Aided Design of Chemical Process is now a well established eld in the design of new process as well as in the optimization, revamp and retrot of existing ones. The use of powerful process simulators available today allows the process engineer to simulate even an entire process, but the majority of process simulators have only classical unit operations. So, if the process has a non-classical unit operation it needs to be simulated using a suitable computer language and further to be linked to the simulator. In this paper we addressed the problem of including a new unit operation in a process simulator and how to use the virtual plant to optimize and to evaluate the environmental impact of a chemical process. We used the free chemical process simulator COCO to simulate two styrene process production plant. The firrst one uses as a reactor a conventional PFR that is available in the simulator. The second plant uses a membrane reactor that was simulated using the software Scilab that was embedded in COCO simulator by using the CAPE-OPEN protocol. Then, we used both virtual plants to develop meta-models of the processes by using experimental design and surface responses. These empirical models were used after to optimize the plants and the results shown that it is possible to increase the styrene productivity up to 27.32 kmol/h using a PFR reactor and up to 30.56 kmol/h using a membrane reactor. Finally, we calculated the Potential of Environmental Impact (PEI) for each process using the WAR algorithm and we shown that both processes have PEI very similar. Therefore, the route that uses membrane reactor has an advantage over the route that uses PFR reactor since it allows to obtain higher styrene productivities.
References
[1] Rosemond Z, Chou S, Wilson J, Schwartz M, Tomei-Torres F, Ingerman L, et al. Toxicological profile for styrene. Atlanta, GA: Department of Health and Human Services, Agency for Toxic Substances and Disease Registry (Ed.), 2010.Search in Google Scholar
[2] Babu B, Chakole PG, Mubeen JS. Multi-objective differential evolution (mode) for optimization of adiabatic styrene reactor. Chem Eng Sci. 2005;60:4822–37.10.1016/j.ces.2005.02.073Search in Google Scholar
[3] Sanchez AP, Sanchez EJ, Silva RM. Simulation of the styrene production process via catalytic dehydrogenation of ethylbenzene using chemcad process simulator. Revista Ternura. 2017;21:15–31.Search in Google Scholar
[4] Wenner Ralph, Dybdal Ernest. Catalytic dehydrogenation of ethlybenzene. Chemical Engineering Progress. 1948 4;275–286.Search in Google Scholar
[5] Sheppard Charles M, Maier Edward E, Caram Hugo S. Ethylbenzene dehydrogenation reactor model. Industrial & Engineering Chemistry Process Design and Development. 1986 1;25(1):207–210.10.1021/i200032a033.Search in Google Scholar
[6] Edraki Z, Farsi M, Rahimpour M. Modeling and optimal control of conversion section of styrene plant to overcome effect of catalyst deactivation on production capacity. Chem Eng Res Des. 2018;37:350–9.10.1016/j.cherd.2018.07.025Search in Google Scholar
[7] Abdalla BK, Elnashaie SSEH, Alkhowaiter S, Elshishini SS. Intrinsic kinetics and industrial reactors modelling for the dehydrogenation of ethylbenzene to styrene on promoted iron oxide catalysts. Applied Catalysis A: General. 1994 6;113(1):89–102.10.1016/0926-860X(94)80243-2.Search in Google Scholar
[8] Elnashaie Said SEH, Abdalla Babiker K, Hughes Ronald. Simulation of the industrial fixed bed catalytic reactor for the dehydrogenation of ethylbenzene to styrene: heterogeneous dusty gas model. Industrial & Engineering Chemistry Research. 1993 11;32(11):2537–2541.10.1021/ie00023a016.Search in Google Scholar
[9] Savoretti AA, Borio DO, Bucalá V, Porras JA. Non-adiabatic radial-flow reactor for styrene production. Chemical Engineering Science. 1999 1;54(2):205–213.10.1016/S0009-2509(98)00195-X.Search in Google Scholar
[10] Sheel J, Crowe C. Simulation and optimization of an existing ethylbenzene dehydrogenation reactor. Can J Chem Eng. 1969;47:183–7.10.1002/cjce.5450470215Search in Google Scholar
[11] Clough D, Ramirez F. Mathematical modeling and optimization of the dehydrogenation of ethyl benzene to form styrene. Am Inst Chem Engineering J. 1976;22:1097–105.10.1002/aic.690220621Search in Google Scholar
[12] Armor J. Challenges in membrane catalysis. CHEMTECH, 1992:557.10.1016/0958-2118(92)90128-USearch in Google Scholar
[13] Tgarguifa A, Abderafi S, Bounahmidi T. Energy efficiency improvement of a bioethanol distillery, by replacing a rectifying column with a pervaporation unit. Renew Energy. 2018;122:239–50.10.1016/j.renene.2018.01.112Search in Google Scholar
[14] Tolksdorf G, Esche E, Wazny G, Repke J-U. Customized code generation based on user specifications for simulation and optimization. Comput Chem Eng. 2019;121:670–84.10.1016/j.compchemeng.2018.12.006Search in Google Scholar
[15] Salpingidou C, Vlahostergios Z, Misirlis D, Donnerhack S, Goulas A, Yakinthos K. Thermodynamic analysis of recuperative gas turbines and aero engines. Appl Therm Eng. 2017;124:250–60.10.1016/j.applthermaleng.2017.05.169Search in Google Scholar
[16] Tgarguifa A, Abderafi S, Bounahmidi T. Energetic optimization of moroccan distillery using simulation and response surface methodology. Renew Sustainable Energy Rev. 2017;75:415–25.10.1016/j.rser.2016.11.006Search in Google Scholar
[17] Andreadou C, Martinopoulos G. Cape-open simulation of waste-to-energy technologies for urban cities. Int J Sustainable Energy. 2016;37:96–104.10.1080/14786451.2016.1177053Search in Google Scholar
[18] Young DM, Cabezas H. Designing sustainable processes with simulation: the waste reduction (war) algorithm. Comput Chem Eng. 1999;23:1477–91.10.1016/S0098-1354(99)00306-3Search in Google Scholar
[19] Elnashaie S, Elshishini S. Modeling, simulation and optimization of industrial fixed bed catalytic reactors. London: Cordon and Breach Science Publisher, 1994.Search in Google Scholar
[20] Silva L, Murata V, Hori C, Assis A. Hydrogen production from methane steam reforming: parametric and gradient based optimization of a pd-based membrane reactor. Optim Eng. 2010;11:441–58.10.1007/s11081-010-9106-2Search in Google Scholar
[21] Gujarathi AM, Babu B. Multi-objective optimization of industrial styrene reactor: Adiabatic and pseudo-isothermal operation. Chem Eng Sci. 2010;65:2009–26.10.1016/j.ces.2009.11.041Search in Google Scholar
© 2019 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Research Articles
- Evaluating the Performance of Newly Integrated Model in Nonlinear Chemical Process Against Missing Measurements
- Human Immunoglobulin G Adsorption in Epoxy Chitosan/Alginate Adsorbents: Evaluation of Isotherms by Artificial Neural Networks
- Effect of Anode Gas Diffussion Layer Thickness and Porosity on the Performance of Passive Direct Methanol Fuel Cell
- A Multiobjective Robust Approach for the Design of Natural Gas Transmission Pipelines
- Steam Reforming of Acetic Acid: Response Surface Modelling and Study of Factor Interactions
- Optimization of a Computer Simulated Styrene Plant by Surface Response and Environmental Impact Evaluation
- A State Estimation Method Based on Integration of Linear and Extended Kalman Filters
- CFD Modeling to Predict Mass Transfer in Multicomponent Mixtures
Articles in the same Issue
- Research Articles
- Evaluating the Performance of Newly Integrated Model in Nonlinear Chemical Process Against Missing Measurements
- Human Immunoglobulin G Adsorption in Epoxy Chitosan/Alginate Adsorbents: Evaluation of Isotherms by Artificial Neural Networks
- Effect of Anode Gas Diffussion Layer Thickness and Porosity on the Performance of Passive Direct Methanol Fuel Cell
- A Multiobjective Robust Approach for the Design of Natural Gas Transmission Pipelines
- Steam Reforming of Acetic Acid: Response Surface Modelling and Study of Factor Interactions
- Optimization of a Computer Simulated Styrene Plant by Surface Response and Environmental Impact Evaluation
- A State Estimation Method Based on Integration of Linear and Extended Kalman Filters
- CFD Modeling to Predict Mass Transfer in Multicomponent Mixtures
