Neuro-fuzzy Soft Sensor Estimator for Benzene Toluene Distillation Column

The distillation is widely used separation technique in oil and gas refineries. Accurate measurement of the composition of separated constituents is necessary to estimate the purity of the products. Composition measurement using online analysers causes process delay and requires large initial investment. As a solution to this problem, soft sensor estimators can be used to determine the composition of separated product. In this work soft sensor estimators are used for predicting top and bottom compositions in benzene toluene distillation column. More sensitive tray temperatures, re-boiler duty and reflux rate (measured variables) of distillation column were used to predict top and bottom composition (unmeasured). Data used for soft sensor based estimation are generated using process simulation software HYSYS. NARX based ANFIS algorithm was proposed for soft sensor modelling. In this method, most influential inputs for soft sensor modelling were selected using exhaustive search. Neural network model and ANFIS model are also compared using statistical criteria like root mean square error and correlation coefficient (R2) values. It has been shown by the results that ANFIS performs better while comparing neural network method and ANFIS with the same number of iteration.

[1]  Hari Om Gupta,et al.  ANN based estimator for distillation—inferential control , 2005 .

[2]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[3]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[4]  Taghi M. Khoshgoftaar,et al.  Identification of fuzzy models of software cost estimation , 2004, Fuzzy Sets Syst..

[5]  Jie Zhang,et al.  Distillation Control Structure Selection for Energy‐Efficient Operations , 2015 .

[6]  Vijander Singh,et al.  ANN-based estimator for distillation using Levenberg-Marquardt approach , 2007, Eng. Appl. Artif. Intell..

[7]  O. Akyilmaz,et al.  Prediction of Earth rotation parameters by fuzzy inference systems , 2004 .

[8]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[9]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  J.-S.R. Jang,et al.  Input selection for ANFIS learning , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[11]  Lúcia Valéria Ramos de Arruda,et al.  A neuro-coevolutionary genetic fuzzy system to design soft sensors , 2008, Soft Comput..

[12]  Vijander Singh,et al.  Development of soft sensor for neural network based control of distillation column. , 2013, ISA transactions.

[13]  Xiao Fan Wang,et al.  Soft sensing modeling based on support vector machine and Bayesian model selection , 2004, Comput. Chem. Eng..

[14]  Javier Fernández de Cañete,et al.  Dual composition control and soft estimation for a pilot distillation column using a neurogenetic design , 2012, Comput. Chem. Eng..

[15]  Saibal Ganguly,et al.  Nonlinear model-based control algorithm for a distillation column using software sensor. , 2005, ISA transactions.

[16]  S. M. Khazraee,et al.  Composition Estimation of Reactive Batch Distillation by Using Adaptive Neuro-Fuzzy Inference System , 2010 .

[17]  R. F. Luo,et al.  Fuzzy-neural-net-based inferential control for a high-purity distillation column , 1995 .

[18]  S. Skogestad,et al.  Composition estimator in a pilot-plant distillation column using multiple temperatures , 1991 .