Predicting emulsion breakdown in the emulsion liquid membrane process: Optimization through response surface methodology and a particle swarm artificial neural network

被引:14
作者
Fetimi, Abdelhalim [1 ,9 ]
Daas, Attef [2 ]
Merouani, Slimane [3 ]
Alswieleh, Abdullah M. [4 ]
Hamachi, Mourad [1 ]
Hamdaoui, Oualid [5 ]
Kebiche-Senhadji, Ounissa [1 ]
Yadav, Krishna Kumar [6 ]
Jeon, Byong-Hun [7 ]
Benguerba, Yacine [8 ]
机构
[1] Univ Bejaia, Fac Technol, Lab Procedes Membranaires & Tech Separat & Recupe, Bejaia 06000, Algeria
[2] Univ Mohamed Cher Messaadia, Fac Sci & Technol, Lab Mat Phys & Radiat, POB1553, Souk Ahras 41000, Algeria
[3] Univ Salah Boubnider, Fac Proc Engn, Dept Chem Engn, Lab Environm Proc Engn, POB 72, Constantine 25000, Algeria
[4] King Saud Univ, Coll Sci, Dept Chem, POB 2455, Riyadh 11451, Saudi Arabia
[5] King Saud Univ, Coll Engn, Chem Engn Dept, POB 800, Riyadh 11421, Saudi Arabia
[6] Madhyanchal Profess Univ, Fac Sci & Technol, Ratibad 462044, Bhopal, India
[7] Hanyang Univ, Dept Earth Resources & Environm Engn, 222 Wangsimni Ro, Seoul 04763, South Korea
[8] Ferhat ABBAS Set 1 Univ, Fac technol, Dept Proc Engn, Setif 19000, Algeria
[9] Univ Batna 2, Fac Technol, Dept Proc Engn, Batna 05076, Algeria
关键词
Water pollution; Emulsion liquid membrane (ELM); Emulsion breakage; Artificial neural network (ANN); Particle swarm optimization (PSO); ANN-PSO algorithm; POLYMER INCLUSION MEMBRANE; AQUEOUS-SOLUTIONS; TRANSPORT; REMOVAL; CHROMIUM(VI); EXTRACTION; WATER;
D O I
10.1016/j.cep.2022.108956
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
To anticipate emulsion breakdown in the ELM process, the Box-Behnken design was used with an artificial neural network (ANN) and a metaheuristic approach, namely particle swarm optimization (PSO) and response surface methodology (RSM). Membrane stability testing began with an experimental component to collect data. The following parameters were used to estimate membrane breakdown: emulsification time (3-7 min), surfactant loadings (2-6% v/v), internal phase concentration ([Na2CO3]: 0.01-1 mg L-1), external phase to w/o emulsion volume ratio (1-11), and internal aqueous phase to membrane volume ratio (0.5 to 1.5). The PSO algorithm was used to determine the optimal ANN parameter values. The hybrid ANN-PSO model outperformed the RSM in identifying optimal ANN parameters (weights and thresholds) and accurately forecasting emulsion breaking percentages throughout the ELM process. The hybrid ANN-PSO method may be a valuable optimization tool for predicting critical data for ELM stability under various operating conditions.
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页数:8
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