Neural networks and differential evolution algorithm applied for modelling the depollution process of some gaseous streams

The depollution of some gaseous streams containing n-hexane is studied by adsorption in a fixed bed column, under dynamic conditions, using granular activated carbon and two types of non-functionalized hypercross-linked polymeric resins. In order to model the process, a new neuro-evolutionary approach is proposed. It is a combination of a modified differential evolution (DE) with neural networks (NNs) and two local search algorithms, the global and local optimizers, working together to determine the optimal NN model. The main elements that characterize the applied variant of DE consist in using an opposition-based learning initialization, a simple self-adaptive procedure for the control parameters, and a modified mutation principle based on the fitness function as a criterion for reorganization. The results obtained prove that the proposed algorithm is able to determine a good model of the considered process, its performance being better than those of an available phenomenological model.

[1]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[2]  Yuanzhen Wang,et al.  Differential Evolution using Uniform-Quasi-Opposition for Initializing the Population , 2010 .

[3]  Yurii Sh. Matros,et al.  Theory and application of unsteady catalytic detoxication of effluent gases from sulfur dioxide, nitrogen oxides and organic compounds , 1988 .

[4]  Efrn Mezura-Montes,et al.  Constraint-Handling in Evolutionary Optimization , 2009 .

[5]  Ville Tirronen,et al.  Recent advances in differential evolution: a survey and experimental analysis , 2010, Artificial Intelligence Review.

[6]  M. Macoveanu,et al.  Comparative study of the adsorption-desorption cycles of hexane over hypercrosslinked polymeric adsorbents and activated carbon , 2010 .

[7]  Panagiotis Angelidis,et al.  Artificial Neural Networks for Improved Predictions in Flow Eestimation , 2012 .

[8]  Silvia Curteanu,et al.  Multi-objective optimization of a stacked neural network using an evolutionary hyper-heuristic , 2012, Appl. Soft Comput..

[9]  Xin Yao,et al.  Ensemble Learning Using Multi-Objective Evolutionary Algorithms , 2006, J. Math. Model. Algorithms.

[10]  M. Macoveanu,et al.  Adsorption of n-hexane vapors onto non-functionalized hypercrosslinked polymers (hypersol-macronettm) and activated carbon: equilibrium studies , 2009 .

[11]  J. Silvestre-Albero,et al.  High saturation capacity of activated carbons prepared from mesophase pitch in the removal of volatile organic compounds , 2010 .

[12]  Jin Luo,et al.  Nanoparticle-structured sensing array materials and pattern recognition for VOC detection , 2005 .

[13]  D. Cazorla-Amorós,et al.  Competitive adsorption of a benzene-toluene mixture on activated carbons at low concentration , 2006 .

[14]  Silvia Curteanu,et al.  Freeze-drying modeling and monitoring using a new neuro-evolutive technique , 2012 .

[15]  Jaime Álvarez-Gallegos,et al.  Simple recurrent neural network: A neural network structure for control systems , 1998, Neurocomputing.

[16]  Hernando Pinzón,et al.  Differential diagnosis of hemorrhagic fevers using ARTMAP and an Artificial Immune System , 2013 .

[17]  Rainer Storn,et al.  Differential Evolution Research – Trends and Open Questions , 2008 .

[18]  Krzysztof Patan,et al.  Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes , 2008 .

[19]  Silvia Curteanu,et al.  Modeling of oxygen mass transfer in the presence of oxygen-vectors using neural networks developed by differential evolution algorithm , 2011, Eng. Appl. Artif. Intell..

[20]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[21]  Silvia Curteanu,et al.  Optimization methodology based on neural networks and self-adaptive differential evolution algorithm applied to an aerobic fermentation process , 2013, Appl. Soft Comput..

[22]  J. Nelson,et al.  Application of gas adsorption kinetics. I. A theoretical model for respirator cartridge service life. , 1984, American Industrial Hygiene Association journal.

[23]  Mimi Haryani Hassim,et al.  Artificial neural networks: applications in chemical engineering , 2013 .

[24]  Bidyadhar Subudhi,et al.  An improved differential evolution trained neural network scheme for nonlinear system identification , 2009, Int. J. Autom. Comput..

[25]  Naim Dahnoun,et al.  Studies in Computational Intelligence , 2013 .

[26]  Vitaliy Feoktistov.  Differential Evolution: In Search of Solutions , 2006 .

[27]  Faisal Khan,et al.  Removal of Volatile Organic Compounds from polluted air , 2000 .

[28]  Hyochoong Bang,et al.  Adaptive nonlinear control using input normalized neural networks , 2008 .

[29]  Teresa Bernarda Ludermir,et al.  Optimization of Neural Networks Weights and Architecture: A Multimodal Methodology , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[30]  Radu-Emil Precup,et al.  Lorenz System Stabilization Using Fuzzy Controllers , 2007, Int. J. Comput. Commun. Control.

[31]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[32]  Silvia Curteanu,et al.  Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks , 2011 .

[33]  Silvia Curteanu,et al.  Neuro-evolutionary modelling of the electrodeposition stage of a polymer-supported ultrafiltration-electrodeposition process for the recovery of heavy metals , 2013, Environ. Model. Softw..

[34]  N. Verma,et al.  Removal of volatile organic compounds by cryogenic condensation followed by adsorption , 2002 .

[35]  Janez Brest,et al.  Constrained Real-Parameter Optimization with ε -Self-Adaptive Differential Evolution , 2009 .

[36]  Ajith Abraham,et al.  Differential Evolution with Laplace mutation operator , 2009, 2009 IEEE Congress on Evolutionary Computation.

[37]  Krzysztof Patan.  Approximation Abilities of Locally Recurrent Networks , 2008 .

[38]  W. Shim,et al.  Adsorption equilibrium and column dynamics of VOCs on MCM-48 depending on pelletizing pressure , 2006 .

[39]  B. Hameed,et al.  Fixed-bed adsorption of reactive azo dye onto granular activated carbon prepared from waste. , 2010, Journal of hazardous materials.

[40]  Carlo Giupponi,et al.  Environmental decision support systems: Current issues, methods and tools , 2007, Environ. Model. Softw..

[41]  Anthony Guiseppi-Elie,et al.  On pattern recognition dependency of desorption heat, activation energy, and temperature of polymer-based VOC sensors for the electronic NOSE , 2005 .

[42]  Uday K. Chakraborty,et al.  Advances in Differential Evolution , 2010 .

[43]  Landong Li,et al.  Dynamic adsorption of volatile organic compounds on organofunctionalized SBA-15 materials , 2009 .

[44]  A. K. Srivastava,et al.  Detection of volatile organic compounds (VOCs) using SnO2 gas-sensor array and artificial neural network , 2003 .

[45]  Mustafa Odabasi,et al.  Quantitative analysis of volatile organic compounds (VOCs) in atmospheric particles , 2005 .

[46]  S. Curteanu,et al.  A neuro-evolutive technique applied for predicting the liquid crystalline property of some organic compounds , 2012 .

[47]  Dario Floreano,et al.  Neuroevolution: from architectures to learning , 2008, Evol. Intell..

[48]  Ki-Joong Kim,et al.  Adsorption–desorption characteristics of VOCs over impregnated activated carbons , 2006 .