Hybrid Intelligent Approach for Predicting Product Compositions of a Distillation Column

Compositions measurement is a vitally critical issue for the modelling and control of distillation process. The product compositions of distillation columns are traditionally measured using indirect techniques via inferring tray compositions from its temperature or by using an online analyser. These techniques were reported as inefficient and relatively slow methods. In this paper, an alternative procedure is presented to predict the compositions of a binary distillation column. Particle swarm optimisation based artificial neural network PSO-ANN is trained by different algorithms and tested by new unseen data to check the generality of the proposed method. Particle swarm optimization is utilised, here, to choose the optimal topology of the network. The simulation results have indicated a reasonable accuracy of prediction with a minimal error between the predicted and simulated data of the column.

[1]  A. Vijayakumari,et al.  Decoupled control of grid connected inverter with dynamic online grid impedance measurements for micro grid applications , 2015 .

[2]  Mohamed Azlan Hussain,et al.  Review of the applications of neural networks in chemical process control - simulation and online implementation , 1999, Artif. Intell. Eng..

[3]  A. Abraham.  Hybrid Intelligent Systems: Evolving Intelligence in Hierarchical Layers , 2005 .

[4]  Robert X. Gao,et al.  Online product quality monitoring through in-process measurement , 2014 .

[5]  Ana Maria Frattini Fileti,et al.  A self tuning controller for multicomponent batch distillation with soft sensor inference based on a neural network , 1999 .

[6]  Lídio Mauro Lima de Campos,et al.  A Comparative Analysis of Methodologies for Automatic Design of Artificial Neural Networks from the Beginnings until Today , 2013, 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence.

[7]  P. Drew,et al.  Artificial neural networks. , 2000, Surgery.

[8]  Amir F. Atiya,et al.  Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances , 2011, IEEE Transactions on Neural Networks.

[9]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[10]  Krzysztof Jemielniak,et al.  Advanced monitoring of machining operations , 2010 .

[11]  S. Skogestad,et al.  Estimation of distillation compositions from multiple temperature measurements using partial-least-squares regression , 1991 .

[12]  J. Havel,et al.  Artificial neural networks in medical diagnosis , 2013 .

[13]  Nazario D. Ramirez-Beltran,et al.  Application of neural networks to chemical process control , 1999 .

[14]  Wuqiang Yang,et al.  Online measurement and control of solids moisture in fluidised bed dryers , 2009 .

[15]  Riccardo Poli,et al.  Analysis of the publications on the applications of particle swarm optimisation , 2008 .

[16]  Bahaa I. Kazem,et al.  Prediction of Friction Stir Welding Characteristic Using Neural Network , 2008 .

[17]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[18]  W. Luyben.  Distillation Design and Control Using Aspen Simulation , 2006 .

[19]  Gérard Dreyfus,et al.  Neural networks - methodology and applications , 2005 .

[20]  Cecil L. Smith.  Distillation Control: An Engineering Perspective , 2012 .

[21]  W. L. Luyben.  Feedback Control of Distillation Columns by Double Differential Temperature Control , 1969 .

[22]  Minyou Chen,et al.  Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks. , 2003, Clinical cancer research : an official journal of the American Association for Cancer Research.

[23]  Richard W. Baker,et al.  'Membrane Separation Systems : Recent Developments and Future Directions , 1991 .

[24]  Asish Bandyopadhyay,et al.  Application of artificial neural network for the prediction of laser cladding process characteristics at Taguchi-based optimized condition , 2014 .

[25]  Luigi Fortuna,et al.  Soft sensors for product quality monitoring in debutanizer distillation columns , 2005 .

[26]  Hare Krishna Mohanta,et al.  Online monitoring and control of particle size in the grinding process using least square support vector regression and resilient back propagation neural network. , 2015, ISA transactions.

[27]  Narendra G. Bawane,et al.  Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery , 2015, Appl. Soft Comput..

[28]  Michael Nikolaou,et al.  Identification test design for multivariable model-based control: An industrial perspective , 2014 .

[29]  David Greiner,et al.  The Role of Artificial Neural Networks in Evolutionary Optimisation: A Review , 2015 .