GAMS supported optimization and predictability study of a multi-objective adsorption process with conflicting regions of optimal operating conditions

Abstract In process systems engineering, it is critical to design an effective and optimized process in a short period with minimum experimental trials. However, improvement of some process variables may deteriorate some other criteria due to conflicting regions of factor interests for optimal solution in multi-objective optimization (MOO) processes. Here, the global optimization of an adsorption case study with conflicting optimal solutions based on multi-objective Response Surface Methodology (RSM) design is facilitated with the implementation of BARON solver based on General Algebraic Modeling System (GAMS) with identical factor variables, levels, and model equations. RSM suggested fifteen different optimum settings of which the validation is quite expensive and onerous, whereas GAMS suggested a single optimum setting which makes it more economically viable especially for large scale systems. In addition, the GAMS-based optimization provided more accurate and reliable results when experimentally validated as compared to the RSM-based solution.

[1]  M. Z. Alam,et al.  Statistical optimization of adsorption processes for removal of 2,4-dichlorophenol by activated carbon derived from oil palm empty fruit bunches. , 2007, Journal of environmental sciences.

[2]  I. Rezić.  Prediction of the surface tension of surfactant mixtures for detergent formulation using Design Expert software , 2011 .

[3]  M. Amosa,et al.  Electrostatic Biosorption of COD, Mn and H2S on EFB-Based Activated Carbon Produced through Steam Pyrolysis: An Analysis Based on Surface Chemistry, Equilibria and Kinetics , 2016 .

[4]  M. Bahram,et al.  Central composite design for the optimization of removal of the azo dye, methyl orange, from waste water using fenton reaction , 2012 .

[5]  M. Amosa.  Sorption of water alkalinity and hardness from high-strength wastewater on bifunctional activated carbon: process optimization, kinetics and equilibrium studies , 2016, Environmental technology.

[6]  L. K. Tartibu,et al.  Multi-objective optimization of the stack of a thermoacoustic engine using GAMS , 2015, Appl. Soft Comput..

[7]  T. Majozi,et al.  Optimization of Integrated Water and Multiregenerator Membrane Systems , 2016 .

[8]  Richard D. Noble,et al.  Principles of Chemical Separations with Environmental Applications , 2004 .

[9]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[10]  I. Grossmann,et al.  Optimal Design of Distributed Wastewater Treatment Networks , 1998 .

[11]  Nilay Shah,et al.  Optimization of Water Network Synthesis for Single-Site and Continuous Processes: Milestones, Challenges, and Future Directions , 2014 .

[12]  R. Tan,et al.  A Superstructure Optimization Approach for Membrane Separation-Based Water Regeneration Network Synthesis with Detailed Nonlinear Mechanistic Reverse Osmosis Model , 2011 .

[13]  Thokozani Majozi,et al.  Effective Synthesis and Optimization Framework for Integrated Water and Membrane Networks: A Focus on Reverse Osmosis Membranes , 2015 .

[14]  Gurmit Singh,et al.  Mitochondria and Cancer , 2013, BioMed research international.

[15]  M. Amosa,et al.  A Two-Step Optimization and Statistical Analysis of COD Reduction from Biotreated POME Using Empty Fruit Bunch-Based Activated Carbon Produced from Pyrolysis , 2015, Water Quality, Exposure and Health.

[17]  Abdullah Al Mamun,et al.  Application of response surface methodology (RSM) for optimization of color removal from POME by granular activated carbon , 2015, International Journal of Environmental Science and Technology.

[18]  L. K. Tartibu,et al.  Lexicographic multi-objective optimization of thermoacoustic refrigerator’s stack , 2015 .

[19]  Wan Mohd Ashri Wan Daud,et al.  A Comparison of Central Composite Design and Taguchi Method for Optimizing Fenton Process , 2014, TheScientificWorldJournal.

[20]  Thokozani Majozi,et al.  Synthesis and optimisation of an integrated water and membrane network framework with multiple electrodialysis regenerators , 2016, Comput. Chem. Eng..

[21]  Optimization of Manganese Reduction in Biotreated POME onto 3A Molecular Sieve and Clinoptilolite Zeolites , 2016, Water environment research : a research publication of the Water Environment Federation.

[22]  V. Parcha,et al.  Design Expert Supported Mathematical Optimization and Predictability Study of Buccoadhesive Pharmaceutical Wafers of Loratadine , 2013, BioMed research international.

[23]  G. Derringer,et al.  Simultaneous Optimization of Several Response Variables , 1980 .

[24]  M. Amosa.  Process optimization of Mn and H2S removals from POME using an enhanced empty fruit bunch (EFB)-based adsorbent produced by pyrolysis , 2015 .

[25]  I. Tan,et al.  Optimization of basic dye removal by oil palm fibre-based activated carbon using response surface methodology. , 2008, Journal of hazardous materials.

[26]  Nikolaos V. Sahinidis,et al.  A polyhedral branch-and-cut approach to global optimization , 2005, Math. Program..