Integration of machine learning-based prediction for enhanced Model's generalization: Application in photocatalytic polishing of palm oil mill effluent (POME)

Kim Hoong Ng; Y. S. Gan; Chin Kui Cheng; Kun Hong Liu; Sze Teng Liong

doi:10.1016/j.envpol.2020.115500

Integration of machine learning-based prediction for enhanced Model's generalization: Application in photocatalytic polishing of palm oil mill effluent (POME)

Kim Hoong Ng
, Y. S. Gan
, Chin Kui Cheng
, Kun Hong Liu
, Sze Teng Liong

Chemical and Petroleum Engineering

Fuzhou University
Xiamen University
Feng Chia University

Research output: Contribution to journal › Article › peer-review

27 Scopus citations

Abstract

The Gaussian Process Regression (GPR) model, from machine learning algorithm, exhibited high generalization and adequacy in correlating the photocatalytic degradation of POME with its operating conditions.

Original language	British English
Article number	115500
Journal	Environmental Pollution
Volume	267
DOIs	https://doi.org/10.1016/j.envpol.2020.115500
State	Published - Dec 2020

UN SDGs

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

SDG 6 Clean Water and Sanitation

Keywords

Generalization enhancement
Machine learning predictive models
Palm oil mill effluent
Photocatalytic treatment

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10.1016/j.envpol.2020.115500

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title = "Integration of machine learning-based prediction for enhanced Model's generalization: Application in photocatalytic polishing of palm oil mill effluent (POME)",

abstract = "The Gaussian Process Regression (GPR) model, from machine learning algorithm, exhibited high generalization and adequacy in correlating the photocatalytic degradation of POME with its operating conditions.",

keywords = "Generalization enhancement, Machine learning predictive models, Palm oil mill effluent, Photocatalytic treatment",

author = "Ng, \{Kim Hoong\} and Gan, \{Y. S.\} and Cheng, \{Chin Kui\} and Liu, \{Kun Hong\} and Liong, \{Sze Teng\}",

note = "Funding Information: This work was funded by Ministry of Science and Technology (MOST) (Grant Number: MOST 109-2221-E-035-065-MY2, MOST 108-2218-E-009-054-MY2, MOST 108-2218-E-035-007- and MOST 108-2218-E-227-002-.), National Natural Science Foundation of China (No. 61772023) and National Key R\&D Program of China (No. 2019QY1803). Funding Information: This work was funded by Ministry of Science and Technology (MOST) (Grant Number: MOST 109-2221-E-035-065-MY2 , MOST 108-2218-E-009-054-MY2 , MOST 108-2218-E-035-007- and MOST 108-2218-E-227-002- .), National Natural Science Foundation of China (No. 61772023 ) and National Key R\&D Program of China (No. 2019QY1803 ). Publisher Copyright: {\textcopyright} 2020 Elsevier Ltd",

year = "2020",

month = dec,

doi = "10.1016/j.envpol.2020.115500",

language = "British English",

volume = "267",

journal = "Environmental Pollution",

issn = "0269-7491",

publisher = "Elsevier Ltd",

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T1 - Integration of machine learning-based prediction for enhanced Model's generalization

T2 - Application in photocatalytic polishing of palm oil mill effluent (POME)

AU - Ng, Kim Hoong

AU - Gan, Y. S.

AU - Cheng, Chin Kui

AU - Liu, Kun Hong

AU - Liong, Sze Teng

N1 - Funding Information: This work was funded by Ministry of Science and Technology (MOST) (Grant Number: MOST 109-2221-E-035-065-MY2, MOST 108-2218-E-009-054-MY2, MOST 108-2218-E-035-007- and MOST 108-2218-E-227-002-.), National Natural Science Foundation of China (No. 61772023) and National Key R&D Program of China (No. 2019QY1803). Funding Information: This work was funded by Ministry of Science and Technology (MOST) (Grant Number: MOST 109-2221-E-035-065-MY2 , MOST 108-2218-E-009-054-MY2 , MOST 108-2218-E-035-007- and MOST 108-2218-E-227-002- .), National Natural Science Foundation of China (No. 61772023 ) and National Key R&D Program of China (No. 2019QY1803 ). Publisher Copyright: © 2020 Elsevier Ltd

PY - 2020/12

Y1 - 2020/12

N2 - The Gaussian Process Regression (GPR) model, from machine learning algorithm, exhibited high generalization and adequacy in correlating the photocatalytic degradation of POME with its operating conditions.

AB - The Gaussian Process Regression (GPR) model, from machine learning algorithm, exhibited high generalization and adequacy in correlating the photocatalytic degradation of POME with its operating conditions.

KW - Generalization enhancement

KW - Machine learning predictive models

KW - Palm oil mill effluent

KW - Photocatalytic treatment

UR - https://www.scopus.com/pages/publications/85090744844

U2 - 10.1016/j.envpol.2020.115500

DO - 10.1016/j.envpol.2020.115500

M3 - Article

C2 - 33254722

AN - SCOPUS:85090744844

SN - 0269-7491

VL - 267

JO - Environmental Pollution

JF - Environmental Pollution

M1 - 115500

ER -

Integration of machine learning-based prediction for enhanced Model's generalization: Application in photocatalytic polishing of palm oil mill effluent (POME)

Abstract

UN SDGs

Keywords

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