A hybrid numerical approach for multi-responses optimization of process parameters and catalyst compositions in CO2 OCM process over CaO-MnO/CeO2 catalyst

A new hybrid numerical approach, using Weighted Sum of Squared Objective Functions (WSSOF) algorithm, was developed for multi-responses optimization of carbon dioxide oxidative coupling of methane (CO2 OCM). The optimization was aimed to obtain optimal process parameters and catalyst compositions with high catalytic performances. The hybrid numerical approach combined the single-response modeling and optimization using Response Surface Methodology (RSM) and WSSOF technique of multi-responses optimization. The hybrid algorithm resulted in Pareto-optimal solutions and an additional criterion was proposed over the solutions to obtain a final unique optimal solution. The simultaneous maximum responses of C2 selectivity and yield were obtained at the corresponding optimal independent variables. The results of the multi-response optimization could be used to facilitate in recommending the suitable operating conditions and catalyst compositions for the CO2 OCM process.

[1]  Tadashi Hattori,et al.  Neural network as a tool for catalyst development , 1995 .

[2]  D. Himmelblau,et al.  Optimization of Chemical Processes , 1987 .

[3]  Liu Chang,et al.  Effects of the calcination conditions on the mechanical properties of a PCoMo/Al2O3 hydrotreating catalyst , 2002 .

[4]  Malcolm L. H. Green,et al.  Potential utilisation of Indonesia's Natuna natural gas field via methane dry reforming to synthesis gas , 2001 .

[5]  Ebrahim A. Youness.  Characterization of efficient solutions of multi-objective E-convex programming problems , 2004, Appl. Math. Comput..

[6]  Daeho Ko,et al.  Multiobjective Optimization of Cyclic Adsorption Processes , 2002 .

[7]  Huang Kai,et al.  Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm , 2003 .

[8]  Liang-Shih Fan,et al.  Neural network multi-criteria optimization image reconstruction technique (NN-MOIRT) for linear and non-linear process tomography , 2003 .

[9]  Kechang Xie,et al.  Possibility of Direct Conversion of CH4 and CO2 to High-Value Products , 2001 .

[10]  Nor Aishah Saidina Amin,et al.  Optimization of process parameters and catalyst compositions in carbon dioxide oxidative coupling of methane over CaO–MnO/CeO2 catalyst using response surface methodology , 2006 .

[11]  Anjana D. Nandasana,et al.  Dynamic Model of an Industrial Steam Reformer and Its Use for Multiobjective Optimization , 2003 .

[12]  Huang Kai,et al.  Artificial neural network-aided design of a multi-component catalyst for methane oxidative coupling , 2001 .

[13]  Ye Wang,et al.  Mn-based binary oxides as catalysts for the conversion of methane to C2 hydrocarbons with carbon dioxide as oxidant , 2001 .

[14]  V. S. Summanwar,et al.  Solution of constrained optimization problems by multi-objective genetic algorithm , 2002 .

[15]  Dezhao Chen,et al.  Optimizing operating conditions based on ANN and modified GAs , 2000 .

[16]  Richard O. Lynch,et al.  Introduction to Design and Analysis of Experiments , 1999, Technometrics.

[17]  Ye Wang,et al.  Carbon dioxide-induced selective conversion of methane to C2 hydrocarbons on CeO2 modified with CaO , 1998 .

[18]  Sanjeev S. Tambe,et al.  Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst , 2004 .

[19]  Ariane Leites Larentis,et al.  Modeling and optimization of the combined carbon dioxide reforming and partial oxidation of natural gas , 2001 .

[20]  Xiaoqun Wu,et al.  Artificial neural network aided design of catalyst for propane ammoxidation , 1997 .

[21]  Weifang Yu,et al.  Application of Multiobjective Optimization in the Design and Operation of Reactive SMB and Its Experimental Verification , 2003 .

[22]  M. L. Luyben,et al.  Analyzing the interaction of design and control—2. reactor-separator-recycle system , 1994 .

[23]  K. Kusakabe,et al.  Synthesis of ethane and ethylene from methane and carbon dioxide over praseodymium oxide catalysts , 1997 .

[24]  Anthony B. Atkinson,et al.  How to Apply Response Surface Methodology , 1993 .

[25]  A. K. Ray,et al.  Multiobjective optimization of SMB and varicol process for chiral separation , 2002 .

[26]  Tadashi Hattori,et al.  Artificial intelligence approach to catalyst design , 1991 .

[27]  Y. Nishiyama,et al.  Conversion of methane with carbon dioxide into C2 hydrocarbons over metal oxides , 1995 .

[28]  Chris T. Kiranoudis,et al.  Pareto design of fluidized bed dryers , 2000 .

[29]  Thomas F. Coleman,et al.  Optimization Toolbox User's Guide , 1998 .

[30]  Jun Zhao,et al.  Selective Conversion of Methane to C2 Hydrocarbons Using Carbon Dioxide over Mn-SrCO3 Catalysts , 2003 .

[31]  Arthur Jutan,et al.  Continuous optimization using a dynamic simplex method , 2003 .

[32]  Don Edwards,et al.  How to Apply Response Surface Methodology , 1991 .

[33]  Attilio Converti,et al.  Optimization of xylitol recovery by crystallization from synthetic solutions using response surface methodology , 2004 .

[34]  Ye Wang,et al.  Carbon dioxide as oxidant for the conversion of methane to ethane and ethylene using modified CeO2 catalysts , 1999 .

[35]  Evaristo Chalbaud Biscaia,et al.  Genetic algorithm development for multi-objective optimization of batch free-radical polymerization reactors , 2003, Comput. Chem. Eng..

[36]  S. Gummadi,et al.  Statistical analysis on some critical parameters affecting the formation of protoplasts from the mycelium of Penicillium griseofulvum , 2003 .

[37]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[38]  Ye Wang,et al.  CaO–ZnO Catalyst for Selective Conversion of Methane to C2 Hydrocarbons Using Carbon Dioxide as the Oxidant , 2000 .