An artificial neural network estimator design for the inferential model predictive control of an industrial multi-component distillation column

Download
2003
Bahar, Almila
An inferential control methodology, that utilizes an artificial neural network (ANN) estimator for a model predictive controller, is developed for an industrial multi-component distillation column. In the column, propane and butane is separated from a mixture of propane, n-butane, i-butane, and i-pentane with a top product purity of 96% propane and a bottom product purity of 63% n- butane. Dual composition control of the column must be used in a multivariable model predictive controller for an efficient operation. Feedback control systems necessitate the knowledge of the control variables. However; in distillation columns, the on-line measurements of top and bottom product compositions are difficult. Therefore, temperature measurements must be used to predict the product compositions in an inferential way. Temperature measurement points for the inferential control of the column are selected by the help of SVD analysis and also by considering the column dynamics, using a dynamic simulation program, called modified-DAL. A moving window neural network estimator, which incorporates the system dynamics into account, is designed to find out the product compositions from temperature measurements on trays. In this study, the data for the ANN is collected using the modified-DAL instead of the real plant. Since in a normal plant operation, compositions are measured discretely by taking samples for analysis, the estimator results are further corrected in 30 minutes intervals with the composition data taken from the simulation of the column. A Multi Input Multi Output (MIMO) MPC is used with the developed ANN estimator to achieve the dual composition control of the column. The performance of the developed control system utilizing ANN estimator is tested considering set-point tracking and disturbance rejection performances for the unconstrained and constrained cases. The comparison of the responses resulting from the controller utilizing ANN estimator, with the case where direct composition values are used in the control system shows that there is not much difference between the two; IAE scores are similar and the shapes of response curves are similar.

Suggestions

Artificial neural network estimator design for the inferential model predictive control of an industrial distillation column
Bahar, A; Özgen, Canan; Leblebicioğlu, Mehmet Kemal; Halıcı, Uğur (2004-09-15)
An inferential control methodology, that utilizes an artificial neural network (ANN) estimator for a model predictive controller (MPC), is developed for an industrial multicomponent distillation column. In the control of product compositions by a feedback control system, because of the difficulty of on-line measurements of compositions, temperature measurements can be utilized. The selection of the temperature measurement points for the inferential control is done by the help of singular value decomposition...
A digital neuron realization for the random neural network model
CERKEZ, CUNEYT; AYBAY, HADİ IŞIK; Halıcı, Uğur (1997-06-12)
In this study the neuron of the random neural network (RNN) model (Gelenbe 1989) is designed using digital circuitry. In the RNN model, each neuron accumulates arriving pulses and can fire if its potential at a given instant of time is strictly positive. Firing occurs at random, the intervals between successive firing instants following an exponential distribution of constant rate. When a neuron fires, it routes the generated pulses to the appropriate output lines in accordance with the connection probabili...
An improved method for inference of piecewise linear systems by detecting jumps using derivative estimation
Selcuk, A. M.; Öktem, Hüseyin Avni (Elsevier BV, 2009-08-01)
Inference of dynamical systems using piecewise linear models is a promising active research area. Most of the investigations in this field have been stimulated by the research in functional genomics. In this article we study the inference problem in piecewise linear systems. We propose first identifying the state transitions by detecting the jumps of the derivative estimates, then finding the guard conditions of the state transitions (thresholds) from the values of the state variables at the state transitio...
A neuro-fuzzy MAR algorithm for temporal rule-based systems
Sisman, NA; Alpaslan, Ferda Nur; Akman, V (1999-08-04)
This paper introduces a new neuro-fuzzy model for constructing a knowledge base of temporal fuzzy rules obtained by the Multivariate Autoregressive (MAR) algorithm. The model described contains two main parts, one for fuzzy-rule extraction and one for the storage of extracted rules. The fuzzy rules are obtained from time series data using the MAR algorithm. Time-series analysis basically deals with tabular data. It interprets the data obtained for making inferences about future behavior of the variables. Fu...
An experimental study on Power Amplifier linearisation by artificial neural networks Yapay Sinir Aǧlari ile Güç Yükselteç Doǧrusalląstirma Amaçli Deneysel Bir Çalisma
Yesil, Soner; Kolagasioglu, Ahmet Ertugrul; Yılmaz, Ali Özgür (2018-07-05)
This paper represents an experimental study on the linearisation of Power Amplifiers especially on high output power regions by utilizing an artificial neural network structure and open-loop training method. For the same in-band output power, 9dB EVM and 6dB ACLR improvement has been observed on hardware by feeding the proposed digital predistortion signal (DPD) to the PA under test.
Citation Formats
A. Bahar, “An artificial neural network estimator design for the inferential model predictive control of an industrial multi-component distillation column,” M.S. - Master of Science, Middle East Technical University, 2003.