Assessment of NNARX structure as a global model for self-refilling steam distillation essential oil extraction system

Rahiman, Mohd Hezri Fazalul; Taib, Mohd Nasir; Salleh, Y.M.

doi:10.1109/itsim.2008.4632020

Cited by 10 publications

(9 citation statements)

References 12 publications

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“…Nonlinear ARX model contains of a parallel combination of nonlinear and linear blocks [6]. A NLARX model is the extension of linear ARX model.…”

Section: A Structure Of Nonlinear Arx Model (Nlarx)mentioning

confidence: 99%

“…The data was organized so that the branches relate the items. Based on nodes, where each node consists of two or more links [6].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, noise as a part of neural network is normally gives a small impact on its computational capacity. Not limited to that, neural network is able to cope with the corruption of incoming signal [6].…”

Section: Introductionmentioning

confidence: 99%

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Modeling induction-based steam distillation system by using nonlinear auto-regressive with exogenous input (NLARX) structure

Ismail

Ramli

Rahiman

2015

2015 IEEE 6th Control and System Graduate Research Colloquium (ICSGRC)

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This paper presents the performance of NonLinear Auto Regressive with Exogenous input (NLARX) model structure that is applied in modeling of induction based steam distillation system. The input is PseudoRandom Binary Sequence (PRBS) and the output is temperature. The input-output data was split into two equal set for model estimation and model validation. All the data are transferred to MATLAB R2013a software for analysis. Wavelet Network, Sigmoid Network, Tree partition Network and Feedforward Neural Network are the nonlinearity estimators used to build the NLARX model structure and their performances have been compared. The validation of estimated model will be based on best fit (R²), final prediction error (FPE), loss function, auto-correlation function (ACF) and cross correlation function (CCF). The result showed that NLARX with Feedforward neural network is the most suitable estimator among others due to it yields the highest percent of best fit (R²), lowest final prediction and loss function, and all the coefficients are within the confidence limit for CCF test.

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“…Nonlinear ARX model contains of a parallel combination of nonlinear and linear blocks [6]. A NLARX model is the extension of linear ARX model.…”

Section: A Structure Of Nonlinear Arx Model (Nlarx)mentioning

confidence: 99%

“…The data was organized so that the branches relate the items. Based on nodes, where each node consists of two or more links [6].…”

Section: Introductionmentioning

confidence: 99%

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Modeling induction-based steam distillation system by using nonlinear auto-regressive with exogenous input (NLARX) structure

Ismail

Ramli

Rahiman

2015

2015 IEEE 6th Control and System Graduate Research Colloquium (ICSGRC)

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“…Consequently, the difference of water temperature flow-in and inside the column can be reduced and thus minimized column temperature disturbance. Figure 2(b) shows a modified steam distillation process [7].…”

Section: The Laboratory Scale Steam Distillation Columnmentioning

confidence: 99%

Recurrent adaptive neuro-fuzzy inference system for steam temperature estimation in distillation of essential oil extraction process

Kasuan

Ismail

Taib

et al. 2011

2011 IEEE 7th International Colloquium on Signal Processing and Its Applications

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In this paper, recurrent adaptive neuro-fuzzy inference system (RANFIS) structure has been proposed to solve approximation problem in identifying a global model of steam temperature of packed distillation column in steam distillation essential oil extraction process. The input-output data is acquired from field experimentation via MATLAB Real-time Workshop (RTW) integrated to the plant. The derived RANFIS model is optimized in order to get the optimum ANFIS structure that includes the optimal number of membership function, fuzzy rules, data selection, epoch which gives low computation time and root means squared error (RMSE). Several experiments were carried out using both pseudo random binary sequence (PRBS) and noise as perturbation signals. Performance comparison of RANFIS with ARX model shows that RANFIS identification gives an excellent global modeling method with RMSE of 0.1778 and consumed less computation or training time.

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“…21,22 Even though the sigmoid network is robust in mapping, it requires a substantial number of parameter values to model a nonlinear system, making it less efficient. 17,23 Finally, the tree partition network, also known as the binary tree partition, is a set of piecewise linear functions that can be viewed as an approximation to a nonlinear amplifier. 22,24 A binary tree partition is a nonlinear two-dimensional data structure organized so that the algorithm branches relate the process inputs to outputs based on the number of nodes.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear System Identification for Aqueous PVA Degradation in a Continuous UV/H₂O₂ Tubular Photoreactor

Lin

Dhib

Mehrvar

2020

Ind. Eng. Chem. Res.

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In this study, the performance of three black-box identification techniques using linear autoregressive with exogenous input (ARX), nonlinear ARX (NARX), and Hammerstein–Wiener (HW) algorithm to model the dynamics of UV/H2O2 continuous tubular photochemical reactor for the treatment of poly(vinyl alcohol) (PVA) based on experimental data is investigated. In addition, the inherent nonlinearity of the reaction process is assessed. The reactor dynamics in the NARX model is estimated by wavelet, sigmoid, and tree partition networks along with the assessment of the performance of each model. Although a sigmoid network describes the nature of chemical processes better, the results show that tree partition network-based NARX is the most suitable estimator for the studied process as represented by its highest quality of fit (91.59% for training data set and 88.17% for validation of data set), lowest loss function (mean-squared error, MSE) (0.0004279), model realizability, open-loop stability, model whiteness, and model independence.

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Assessment of NNARX structure as a global model for self-refilling steam distillation essential oil extraction system

Cited by 10 publications

References 12 publications

Modeling induction-based steam distillation system by using nonlinear auto-regressive with exogenous input (NLARX) structure

Modeling induction-based steam distillation system by using nonlinear auto-regressive with exogenous input (NLARX) structure

Recurrent adaptive neuro-fuzzy inference system for steam temperature estimation in distillation of essential oil extraction process

Nonlinear System Identification for Aqueous PVA Degradation in a Continuous UV/H₂O₂ Tubular Photoreactor

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Assessment of NNARX structure as a global model for self-refilling steam distillation essential oil extraction system

Cited by 10 publications

References 12 publications

Modeling induction-based steam distillation system by using nonlinear auto-regressive with exogenous input (NLARX) structure

Modeling induction-based steam distillation system by using nonlinear auto-regressive with exogenous input (NLARX) structure

Recurrent adaptive neuro-fuzzy inference system for steam temperature estimation in distillation of essential oil extraction process

Nonlinear System Identification for Aqueous PVA Degradation in a Continuous UV/H2O2 Tubular Photoreactor

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Nonlinear System Identification for Aqueous PVA Degradation in a Continuous UV/H₂O₂ Tubular Photoreactor