ANN-based estimator for distillation using Levenberg-Marquardt approach

Authors:
Vijander Singh;Indra Gupta;H. O. Gupta
Affiliations:
Electrical Engineering Department, Indian Institute of Technology Roorkee, Roorkee, Uttaranchal 247667, India;Electrical Engineering Department, Indian Institute of Technology Roorkee, Roorkee, Uttaranchal 247667, India;Electrical Engineering Department, Indian Institute of Technology Roorkee, Roorkee, Uttaranchal 247667, India
Venue:
Engineering Applications of Artificial Intelligence
Year:
2007

Citing 5
Cited 6

Neural network design

Neural network design
Brief Analysis of dual-rate inferential control systems

Automatica (Journal of IFAC)
Advanced neural-network training algorithm with reduced complexity based on Jacobian deficiency

IEEE Transactions on Neural Networks
Neighborhood based Levenberg-Marquardt algorithm for neural network training

IEEE Transactions on Neural Networks
Advanced search algorithms for information-theoretic learning with kernel-based estimators

IEEE Transactions on Neural Networks

Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence

Expert Systems with Applications: An International Journal
Intelligent control of a constant turning force system with fixed metal removal rate

Applied Soft Computing
Indirect adaptive structure for multivariable neural identification and control of a pilot distillation plant

Applied Soft Computing
Fracture properties of cement and alkali activated fly ash based concrete with application to segmental tunnel lining

Advances in Engineering Software
Gauss-Newton filtering incorporating Levenberg-Marquardt methods for tracking

Digital Signal Processing
Composition Prediction of a Debutanizer Column using Equation Based Artificial Neural Network Model

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In modern chemical industries the purity of the distillate is the main objective and time to estimate the distillate composition is also the constraint. In the present paper, the Levenberg-Marquardt (LM) approach is proposed for predictive inferential control of distillation process. The developed estimator using LM approach predicts the composition of distillate using column pressure, reboiler duty, and reflux flow along with the temperature profile of the distillation column as inputs. In complex chemical industries where the output depends on many parameters, Steepest Descent Back Propagation (SDBP) algorithm does not work properly for estimating the composition of distillate, which results in saturated outputs and differs from the desired results. To overcome such type of situation, LM approach is used in developed estimator. The estimated results are compared with the simulation results and it is observed that the results obtained from LM approach are significantly improved than the results obtained from SDBP algorithm. To enhance the accuracy of the estimated results, the pressure, reflux flow and heat input with temperature profile of the column are used as input to train the neural network.