RSM-Based Analysis and Optimization Approach for Chemical Processes

Authors

  • Bi jue Jia School of Science, Southwest Petroleum University, Chengdu, 610500, China

DOI:

https://doi.org/10.53555/nnas.v3i4.653

Keywords:

RSM, BBD, chemical process, optimization

Abstract

This research addressed a convenient analysis and optimization approach for chemical process based on software simulation, Response Surface Methodology (RSM) and the mathematical software Matlab simultaneously. A case study of CO2 dehydration was used to showcase the capabilities of the model. In this paper, the objective of the model was to analyze the separate effects and interaction effects of factors to total energy cost of the CO2 dehydration process, and obtain explicit formulae between independent parameters and response values. With Matlab, the minimum energy onsumption was determined by optimization of parameter setting.

References

Enríquez A H, Tanco M, Kim J K. Simulation-based process design and integration for the sustainable retrofit of chemical processes. Industrial & Engineering Chemistry Research, 2011, 50(21): 12067-12079

Zhu L, Deng J, Yang Y, et al. Optimization of energy utilization for natural gas dehydration facilities based on RSM. Chemical Engineering (China), 2015, 43(2): 40-43

Karadag A, Yang X, Ozcelik B, et al. Optimization of preparation conditions for quercetin nanoemulsions using response surface methodology. Journal of agricultural and food chemistry, 2013, 61(9): 2130-2139

Chin L H, Hameed B H, Ahmad A L. Process optimization for biodiesel production from waste cooking palm oil (Elaeis guineensis) using response surface methodology. Energy & Fuels, 2009, 23(2): 1040-1044

Basha C A, Saravanathamizhan R, Manokaran P, et al. Photoelectrocatalytic oxidation of textile dye effluent: modeling using response surface methodology. Industrial & Engineering Chemistry Research, 2012, 51(7): 2846-2854

Ferreira S L C, Bruns R E, Ferreira H S, et al. Box-Behnken design: An alternative for the optimization of analytical methods. Analytica chimica acta, 2007, 597(2): 179-186

Khuri A I, Mukhopadhyay S. Response surface methodology. Wiley Interdisciplinary Reviews: Computational Statistics, 2010, 2(2): 128-149

Hasan M M F, Boukouvala F, First E L, et al. Nationwide, regional, and statewide CO2 capture, utilization, and sequestration supply chain network optimization. Industrial & Engineering Chemistry Research, 2014, 53(18): 7489-7506

Cormos C C. Evaluation of energy integration aspects for IGCC-based hydrogen and electricity co-production with carbon capture and storage. International Journal of Hydrogen Energy, 2010, 35(14): 7485-7497

Shackley S, McLachlan C, Gough C. The public perception of carbon dioxide capture and storage in the UK: results from focus groups and a survey. Climate Policy, 2004, 4(4): 377-398

Huang B, Xu S S, Gao S W, et al. Industrial test of CO2 capture in Huaneng Beijing coal-fired power station. Proceedings of the CSEE, 2009, 29(17): 14-20

Zhang Y J, Zhang W W, Wang Y, et al. Issues related to design of long-distance CO2 pipeline. Oil & Gas Storage and Transportation, 2014, 33(4): 364-368

Ormiston R M, Luce M C. Surface processing of carbon dioxide in EOR projects. Journal of Petroleum Technology, 1986, 38(08): 823-828

De Visser E, Hendriks C, Barrio M, et al. Dynamis CO2 quality recommendations. International Journal of Greenhouse Gas Control, 2008, 2(4): 478-484

Grynia E W, Carroll J J, Griffin P J. Dehydration of acid gas prior to injection. Acid Gas Injection and Related Technologies, 2010: 107-127

] Aboudheir A, McIntyre G. Industrial design and optimization of CO2 capture, dehydration, and compression facilities//87th Annual GPA Convention. 2008

Chen X, Deng D M, Wan Y F. Analysis of dehydration from CO2 by TEG. Chemical Engineering of Oil & Gas, 2014, 43(6): 585-589

Kwak D H, Yun D, Binns M, et al. Conceptual process design of CO2 recovery plants for enhanced oil recovery applications. Industrial & Engineering Chemistry Research, 2014, 53(37): 14385-14396

CArroll J, Hatcher N, Weiland R. Glycol dehydration of high-acid gas streams. GAS, 2011: 43-48

Li Q, Ji Z L, Duan X H, et al. An optimization method for energy consumption of natural gas sweetening facilities based on the HYSYS simulator and genetic algorithms. Natural Gas Industry, 2011, 31(9): 102-106

Darwish N A, Hilal N. Sensitivity analysis and faults diagnosis using artificial neural networks in natural gas TEGdehydration plants. Chemical Engineering Journal, 2008, 137(2): 189-197

Nuchitprasittichai A, Cremaschi S. Optimization of CO2 capture process with aqueous amines–a comparison of two simulation–optimization approaches. Industrial & Engineering Chemistry Research, 2013, 52(30): 10236-10243

Bucher C G, Bourgund U. A fast and efficient response surface approach for structural reliability problems. Structural safety, 1990, 7(1): 57-66

Mee R W. New Box-Behnken designs. University of Tennessee, Department of Statistics Technical Report, 2000, 4

Downloads

Published

2016-04-30

How to Cite

Jia, B. jue. (2016). RSM-Based Analysis and Optimization Approach for Chemical Processes. Journal of Advance Research in Applied Science (ISSN 2208-2352), 3(4), 33-42. https://doi.org/10.53555/nnas.v3i4.653