Application of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) Techniques for the Modelling and Optimization of COD Adsorption Process

Hifsa Khurshid; Muhammad Raza Ul Mustafa; Yeek Chia Ho

doi:10.1007/978-981-16-2183-3_49

Application of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) Techniques for the Modelling and Optimization of COD Adsorption Process

Hifsa Khurshid^*, Muhammad Raza Ul Mustafa, Yeek Chia Ho

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Artificial neural network (ANN) and adaptive neuro fuzzy (ANFIS) modelling techniques have been applied in this study to model and optimize the chemical oxygen demand (COD) adsorptive removal in produced water. The models were well trained and showed minimum error values for predicted data when compared to experimental data. The error values were 0.4035 and 0.2886 for sum of squared error (SSE), 0.1628 and 0.0832 for mean square error (MSE) and 0.13 and 0.23% for average relative error (ARE) using ANN and ANFIS, respectively. Error analysis and coefficient of determination (R²) of the models determined that ANFIS was better than ANN for the prediction of COD adsorption on the biochar. Also, ANFIS required minimum run time as compared to ANN. Both artificial intelligence (AI) based techniques well predicted the optimized values of adsorption process, when compared with the experimental values. It is concluded that the use of AI techniques can inevitably pave the way in the water treatment sector using adsorption for improved efficiency and process automation.

Original language	English
Title of host publication	International Conference on Artificial Intelligence for Smart Community - AISC 2020
Editors	Rosdiazli Ibrahim, Ramani Kannan, Nursyarizal Mohd Nor, K. Porkumaran, S. Prabakar
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	525-537
Number of pages	13
ISBN (Print)	9789811621826
DOIs	https://doi.org/10.1007/978-981-16-2183-3_49
State	Published - 2022
Externally published	Yes
Event	1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 - Virtual, Online Duration: 17 Dec 2020 → 18 Dec 2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	758
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	1st International Conference on Artificial Intelligence for Smart Community, AISC 2020
City	Virtual, Online
Period	17/12/20 → 18/12/20

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Adsorption
Artificial intelligence
Automation
Green technology
Optimization
Water treatment

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-16-2183-3_49

Cite this

Khurshid, H., Mustafa, M. R. U., & Ho, Y. C. (2022). Application of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) Techniques for the Modelling and Optimization of COD Adsorption Process. In R. Ibrahim, R. Kannan, N. Mohd Nor, K. Porkumaran, & S. Prabakar (Eds.), International Conference on Artificial Intelligence for Smart Community - AISC 2020 (pp. 525-537). (Lecture Notes in Electrical Engineering; Vol. 758). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-2183-3_49

Khurshid, Hifsa ; Mustafa, Muhammad Raza Ul ; Ho, Yeek Chia. / Application of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) Techniques for the Modelling and Optimization of COD Adsorption Process. International Conference on Artificial Intelligence for Smart Community - AISC 2020. editor / Rosdiazli Ibrahim ; Ramani Kannan ; Nursyarizal Mohd Nor ; K. Porkumaran ; S. Prabakar. Springer Science and Business Media Deutschland GmbH, 2022. pp. 525-537 (Lecture Notes in Electrical Engineering).

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title = "Application of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) Techniques for the Modelling and Optimization of COD Adsorption Process",

abstract = "Artificial neural network (ANN) and adaptive neuro fuzzy (ANFIS) modelling techniques have been applied in this study to model and optimize the chemical oxygen demand (COD) adsorptive removal in produced water. The models were well trained and showed minimum error values for predicted data when compared to experimental data. The error values were 0.4035 and 0.2886 for sum of squared error (SSE), 0.1628 and 0.0832 for mean square error (MSE) and 0.13 and 0.23\% for average relative error (ARE) using ANN and ANFIS, respectively. Error analysis and coefficient of determination (R2) of the models determined that ANFIS was better than ANN for the prediction of COD adsorption on the biochar. Also, ANFIS required minimum run time as compared to ANN. Both artificial intelligence (AI) based techniques well predicted the optimized values of adsorption process, when compared with the experimental values. It is concluded that the use of AI techniques can inevitably pave the way in the water treatment sector using adsorption for improved efficiency and process automation.",

keywords = "Adsorption, Artificial intelligence, Automation, Green technology, Optimization, Water treatment",

author = "Hifsa Khurshid and Mustafa, \{Muhammad Raza Ul\} and Ho, \{Yeek Chia\}",

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Khurshid, H, Mustafa, MRU & Ho, YC 2022, Application of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) Techniques for the Modelling and Optimization of COD Adsorption Process. in R Ibrahim, R Kannan, N Mohd Nor, K Porkumaran & S Prabakar (eds), International Conference on Artificial Intelligence for Smart Community - AISC 2020. Lecture Notes in Electrical Engineering, vol. 758, Springer Science and Business Media Deutschland GmbH, pp. 525-537, 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020, Virtual, Online, 17/12/20. https://doi.org/10.1007/978-981-16-2183-3_49

Application of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) Techniques for the Modelling and Optimization of COD Adsorption Process. / Khurshid, Hifsa; Mustafa, Muhammad Raza Ul; Ho, Yeek Chia.
International Conference on Artificial Intelligence for Smart Community - AISC 2020. ed. / Rosdiazli Ibrahim; Ramani Kannan; Nursyarizal Mohd Nor; K. Porkumaran; S. Prabakar. Springer Science and Business Media Deutschland GmbH, 2022. p. 525-537 (Lecture Notes in Electrical Engineering; Vol. 758).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Application of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) Techniques for the Modelling and Optimization of COD Adsorption Process

AU - Khurshid, Hifsa

AU - Mustafa, Muhammad Raza Ul

AU - Ho, Yeek Chia

PY - 2022

Y1 - 2022

N2 - Artificial neural network (ANN) and adaptive neuro fuzzy (ANFIS) modelling techniques have been applied in this study to model and optimize the chemical oxygen demand (COD) adsorptive removal in produced water. The models were well trained and showed minimum error values for predicted data when compared to experimental data. The error values were 0.4035 and 0.2886 for sum of squared error (SSE), 0.1628 and 0.0832 for mean square error (MSE) and 0.13 and 0.23% for average relative error (ARE) using ANN and ANFIS, respectively. Error analysis and coefficient of determination (R2) of the models determined that ANFIS was better than ANN for the prediction of COD adsorption on the biochar. Also, ANFIS required minimum run time as compared to ANN. Both artificial intelligence (AI) based techniques well predicted the optimized values of adsorption process, when compared with the experimental values. It is concluded that the use of AI techniques can inevitably pave the way in the water treatment sector using adsorption for improved efficiency and process automation.

AB - Artificial neural network (ANN) and adaptive neuro fuzzy (ANFIS) modelling techniques have been applied in this study to model and optimize the chemical oxygen demand (COD) adsorptive removal in produced water. The models were well trained and showed minimum error values for predicted data when compared to experimental data. The error values were 0.4035 and 0.2886 for sum of squared error (SSE), 0.1628 and 0.0832 for mean square error (MSE) and 0.13 and 0.23% for average relative error (ARE) using ANN and ANFIS, respectively. Error analysis and coefficient of determination (R2) of the models determined that ANFIS was better than ANN for the prediction of COD adsorption on the biochar. Also, ANFIS required minimum run time as compared to ANN. Both artificial intelligence (AI) based techniques well predicted the optimized values of adsorption process, when compared with the experimental values. It is concluded that the use of AI techniques can inevitably pave the way in the water treatment sector using adsorption for improved efficiency and process automation.

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KW - Automation

KW - Green technology

KW - Optimization

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DO - 10.1007/978-981-16-2183-3_49

M3 - Conference contribution

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SN - 9789811621826

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A2 - Ibrahim, Rosdiazli

A2 - Kannan, Ramani

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PB - Springer Science and Business Media Deutschland GmbH

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Khurshid H, Mustafa MRU, Ho YC. Application of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) Techniques for the Modelling and Optimization of COD Adsorption Process. In Ibrahim R, Kannan R, Mohd Nor N, Porkumaran K, Prabakar S, editors, International Conference on Artificial Intelligence for Smart Community - AISC 2020. Springer Science and Business Media Deutschland GmbH. 2022. p. 525-537. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-16-2183-3_49

Application of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) Techniques for the Modelling and Optimization of COD Adsorption Process

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this