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논문 기본 정보

자료유형
학술저널
저자정보
Lau Kia Li (Universiti Putra Malaysia) Siti Nurul Ain Md. Jamil (Universiti Putra Malaysia) Luqman Chuah Abdullah (Universiti Putra Malaysia) Nik Nor Liyana Nik Ibrahim (Universiti Putra Malaysia) Adeyi Abel Adekanmi (Universiti Putra Malaysia) Mohsen Nourouzi (Islamic Azad University of Esfahan)
저널정보
대한환경공학회 Environmental Engineering Research Environmental Engineering Research 제25권 제6호
발행연도
2020.12
수록면
830 - 840 (11page)

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This research reports application of artificial neural network (ANN) in investigation and optimisation of boron adsorption capacity in aqueous solution using amidoxime-modified poly(acrylonitrile-co-acrylic acid) (AO-modified poly(AN-co-AA)). Both feed-forward and recurrent ANN have been utilized to predict the adsorption potential of synthesised polymer. Three operational parameters, which are adsorbent dosage, initial pH and initial boron concentration during adsorption process were designed to study their effects on the removal capacity. The ANN was trained from experimental data and serviced to optimize, develop and create various prediction models in the process of boron adsorption by AO-modified poly(AN-co-AA). Among several models, radial basis function (RBF) with orthogonal least square (OLS) algorithm displayed good prediction on boron adsorption capacity with mean square error (MSE) and coefficient of determination (R²) at 0.000209 and 0.9985, respectively. With desirable the MSE and R² values, ANN worked as a promising prediction tool that was able to generate good estimate. The simulated maximum adsorption capacity of the synthesized polymer is 15.23 ± 1.05 mg boron/g adsorbent. Besides, from the results of ANN, the AO-modified poly(AN-co-AA) was proven to be a potential adsorbent for the removal of boron in wastewater treatment.

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ABSTRACT
1. Introduction
2. Methodology
3. Results and Discussions
4. Conclusions
References

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UCI(KEPA) : I410-ECN-0101-2020-539-000545791