• Wan Sieng Yeo Chemical and Energy Engineering Department, Faculty of Engineering and Science, Curtin University Malaysia, CDT 250, 98009 Miri, Sarawak, Malaysia




Wastewater treatment, heptachlor removal, artificial intelligence models, response surface methodology, Fe/Cu nanoparticles


Water is a basic and essential resource in the human body. Every structure in the body including cells, tissues and organs needs water to work properly. Hence, without food, humans can last up to several weeks, but just a few days without water. Meanwhile, before water is consumed by a human’s body, harmful impurities such as heptachlor which is a highly toxic organochlorine compound in the water must be removed. To remove heptachlor from the wastewater, the adsorption using the bimetallic iron/cupper (Fe/Cu) nanoparticles can be a solution. However, the effectiveness of the elimination of heptachlor using the Fe/Cu nanoparticles could be affected by environmental factors including pH, adsorbent dosage, contact time, initial adsorbate concentration, and stirring rate. Response surface methodology (RSM) is widely used to correlate these factors with the heptachlor removal efficiency to achieve performance optimisation. However, the artificial intelligence models may perform better than RSM to optimise the heptachlor removal process. Therefore, this study aims to compare the performance of different artificial intelligence models with RSM for heptachlor removal using Fe/Cu nanoparticles. These different artificial intelligence models include principal component regression (PCR), artificial neural network (ANN), locally weighted kernel partial least square regression (LW-KPLSR), partial least square regression (PLSR), and least-square support vector regression (LSSVR). Based on the obtained results, the LW-KPLSR model performed better than other artificial intelligence models and RSM. Its root means square error, and mean absolute error are around 159% to 3,297% lower than other models and RSM. Moreover, its coefficient of determination which is so-called R2 is the highest among others. These results denote that LW-KPLSR is more convincing as compared to RSM to predict optimum performance of heptachlor removal.

Author Biography

Wan Sieng Yeo, Chemical and Energy Engineering Department, Faculty of Engineering and Science, Curtin University Malaysia, CDT 250, 98009 Miri, Sarawak, Malaysia

Curtin Malaysia Research Institute, Curtin University, Malaysia


Prüss-Ustün, A., et al., 2019. Burden of disease from inadequate water, sanitation and hygiene for selected adverse health outcomes: an updated analysis with a focus on low-and middle-income countries. International Journal of Hygiene and Environmental Health 222(5): 765-777. https://doi.org/10.1016/j.ijheh.2019.05.004

Xu, X., et al., 2020. Projecting China's future water footprint under the shared socio-economic pathways. Journal of Environmental Management 260: 110102.DOI: https://doi.org/10.1016/j.jenvman.2020.110102

Xagoraraki, I. and E. O’Brien, 2020. Wastewater-based epidemiology for early detection of viral outbreaks. Women In Water Quality, 75-97. Springer.. https://doi.org/10.1007/978-3-030-17819-2_5

Topal, T. and C. Onac, 2020. Determination of heavy metals and pesticides in different types of fish samples collected from four different locations of aegean and marmara sea. Journal of Food Quality 2020: 1-12. https://doi.org/10.1155/2020/8101532

Authority, E. F. S., 2007. Opinion of the Scientific Panel on contaminants in the food chain [CONTAM] related heptachlor as an undesirable substance in animal feed. European Food Safety Authority (EFSA) Journal 5(6): 478. https://doi.org/10.2903/j.efsa.2007.478

Leong, K. H., et al., 2007. Contamination levels of selected organochlorine and organophosphate pesticides in the Selangor River, Malaysia between 2002 and 2003. Chemosphere 66(6): 1153-1159. https://doi.org/10.1016/j.chemosphere.2006.06.009

Cortada, C., et al., 2009. Determination of organochlorine pesticides in complex matrices by single-drop microextraction coupled to gas chromatography–mass spectrometry. Analytica Chimica Acta 638(1): 29-35. https://doi.org/10.1016/j.aca.2009.01.062

Bhuvaneswari, R., et al., 2021. Chemisorption of Heptachlor and Mirex molecules on beta arsenene nanotubes–A first-principles analysis. Applied Surface Science. 537: 147835. https://doi.org/10.1016/j.apsusc.2020.147835

Zhang, G., et al., 2014. Distribution and bioaccumulation of organochlorine pesticides (OCPs) in food web of Nansi Lake, China. Environmental Monitoring and Assessment 186(4): 2039-2051. https://doi.org/10.1007/s10661-013-3516-5

Mahmoud, A. S., et al., 2020. Isotherm and kinetic studies for heptachlor removal from aqueous solution using Fe/Cu nanoparticles, artificial intelligence, and regression analysis. Separation Science and Technology, 55(4): 684-696. https://doi.org/10.1080/01496395.2019.1574832

Chan, M., et al., 2021. Oxidation of ammonia using immobilised FeCu for water treatment. Separation and Purification Technology 254: 117612. https://doi.org/10.1016/j.seppur.2020.117612

Mahmoud, A. S., et al., 2021. A prototype of textile wastewater treatment using coagulation and adsorption by Fe/Cu nanoparticles: Techno-economic and scaling-up studies. Nanomaterials and Technologies for Environmental Applications 11: 1-21. https://doi.org/10.1177/18479804211041181

Mahmoud, M. and A. S. Mahmoud, 2021. Wastewater treatment using nano bimetallic iron/copper, adsorption isotherm, kinetic studies, and artificial intelligence neural networks. Emergent Materials 4(5): 1455-1463. https://doi.org/10.1007/s42247-021-00253-y

Ngu, J. C. Y., et al., 2023. The application of machine learning in nanoparticle treated water: A review. Curtin Global Campus Higher Degree by Research Colloquium (CGCHDRC 2022), MATEC Web of Conference. https://doi.org/10.1051/matecconf/202337701009

Yeo, W. S., et al., 2019. Adaptive soft sensor development for non-Gaussian and nonlinear processes. Industrial Engineering Chemistry Research. 58(45): 20680 20691.https://doi.org/10.1021/acs.iecr.9b03821

Malang, J., et al., 2023. A comparison study between different kernel functions in the least square support vector regression model for penicillin fermentation process. Curtin Global Campus Higher Degree by Research Colloquium (CGCHDRC 2022), Miri, Sarawak, Malaysia, MATEC Web of Conferences https://doi.org/10.1051/matecconf/202337701025

Pervez, M., et al., 2023. Prediction of the Diameter of Biodegradable Electrospun Nanofiber Membranes: An Integrated Framework of Taguchi Design and Machine Learning. Journal of Polymers the Environment. 1-17. https://doi.org/10.1007/s10924-023-02837-7

Ngu, J. C. Y. and W. S. Yeo, 2023. A comparative study of different kernel functions applied to LW-KPLS model for nonlinear processes. Biointerface Research in Applied Chemistry. 13(2): 1-16. https://doi.org/10.33263/BRIAC132.184

Yeo, W. S., et al., 2017. Development of adaptive soft sensor using locally weighted kernel partial least square model. Chemical Product and Process Modeling 12(4): 1-13. https://doi.org/10.1515/cppm-2017-0022

Kuang, B., et al., 2015. Comparison between artificial neural network and partial least squares for on-line visible and near infrared spectroscopy measurement of soil organic carbon, pH and clay content. Soil and Tillage Research 146(Part B): 243-252. https://doi.org/10.1016/j.still.2014.11.002

Abiodun, O. I., et al., 2018. State-of-the-art in artificial neural network applications: A survey. Heliyon 4(11): e00938. https://doi.org/10.1016/j.heliyon.2018.e00938

Zhang, Z., 2018. Artificial neural network. Multivariate time series analysis in climate and environmental research, 1-35. Springer. https://doi.org/10.1007/978-3-319-67340-0_1

Yeo, W. S., 2021. Prediction of Yellowness Index Using Partial Least Square Regression Model. 2021 International Conference on Green Energy, Computing and Sustainable Technology (GECOST), IEEE. https://doi.org/10.1109/GECOST52368.2021.9538723

Wold, S., et al., 2001. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58(2): 109-130. https://doi.org/10.1016/S0169-7439(01)00155-1

Vandeginste, B. M., et al., 1998. Handbook of Chemometrics and Qualimetrics. Data handling in science and technology 20B. https://doi.org/10.1016/S0922-3487(98)80045-2

Suykens, J., et al., 2002. Least squares support vector machines. Singapore, World Scientific Publishing.

Xu, S., et al., 2013. Multi-output least-squares support vector regression machines. Pattern Recognition Letters 34(9):1078-1084. https://doi.org/10.1016/j.patrec.2013.01.015

Shahmansouri, A. A., et al., 2021. Mechanical properties of GGBFS-based geopolymer concrete incorporating natural zeolite and silica fume with an optimum design using response surface method. Journal of Building Engineering 36: 102138. https://doi.org/10.1016/j.jobe.2020.102138

Montgomery, D. C., et al., 2021. Introduction to linear regression analysis, John Wiley & Sons.

Morley, S. K., et al., 2018. Measures of model performance based on the log accuracy ratio. Space Weather 16(1): 69-88. https://doi.org/10.1002/2017SW001669

Thien, T. F. and W. S. Yeo, 2021. A comparative study between PCR, PLSR, and LW-PLS on the predictive performance at different data splitting ratios. Chemical Engineering Communications. 1-18. https://doi.org/10.1080/00986445.2021.1957853

Pervez, M. N., et al., 2023. Sustainable fashion: Design of the experiment assisted machine learning for the environmental-friendly resin finishing of cotton fabric. Heliyon 9(1): e12883. https://doi.org/10.1016/j.heliyon.2023.e12883

Willmott, C. J. and K. Matsuura, 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research 30(1): 79-82. https://doi.org/10.3354/cr030079

Yeo, W. S., et al., 2020. Missing data treatment for locally weighted partial least square‐based modelling: A comparative study. Asia‐Pacific Journal of Chemical Engineering 15(2): 1-13. https://doi.org/10.1002/apj.2422

Pizarro Inostroza, M. G., et al., 2020. Software-Automatized Individual Lactation Model Fitting, Peak and Persistence and Bayesian Criteria Comparison for Milk Yield Genetic Studies in Murciano-Granadina Goats. Mathematics 8(9): 1505. https://doi.org/10.3390/math8091505

Yeo, W. S. and W. J. Lau, 2021. Predicting the whiteness index of cotton fabric with a least squares model. Cellulose 28: 8841–8854 https://doi.org/10.1007/s10570-021-04096-y

Chakravarty, S., et al., 2020. Fuzzy regression functions with a noise cluster and the impact of outliers on mainstream machine learning methods in the regression setting. Applied Soft Computing 96: 106535. https://doi.org/10.1016/j.asoc.2020.106535

Liu, Y., et al., 2012. Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes. Industrial & Engineering Chemistry Research 51(11): 4313-4327. https://doi.org/10.1021/ie201650u




How to Cite