Quantitative Analysis of Polymetallic Ions in Industrial Wastewater Based on Ultraviolet-Visible Spectroscopy

Fengbo Zhou, Ammar Oad, Hongqiu Zhu and Changgeng Li
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Fengbo Zhou: School of Information Engineering, Shaoyang University, Shaoyang 422000, China
Ammar Oad: School of Information Engineering, Shaoyang University, Shaoyang 422000, China
Hongqiu Zhu: School of Automation, Central South University, Changsha 410083, China
Changgeng Li: School of Physics and Electronics, Central South University, Changsha 410083, China

Sustainability, 2021, vol. 13, issue 14, 1-10

Abstract: In order to detect and control the concentration of polymetallic ions in industrial wastewater in real time, a spectrophotometric method combining wavelet transform (WT) and partial least squares regression (PLSR) is proposed for the simultaneous determination of zinc, cobalt and nickel in industrial wastewater by ultraviolet-visible spectrometry, without a separation step. WT was found to be suitable for spectral preprocessing, which effectively eliminated the noise, enhanced spectral feature information, improved the linearity of the detected ions and increased the number of selectable modeling wavelengths. PLSR was used to study the simultaneous detection of zinc, cobalt and nickel. The linear detection ranges were 10–100 mg/L for zinc, 0.6–6.0 mg/L for nickel and 0.3–3.0 mg/L for cobalt. The average relative deviation for zinc, nickel and cobalt was 2.85%, 3.05% and 2.24%, respectively. The results indicated that the WT–PLSR method is suitable for the online detection of polymetallic ions by ultraviolet-visible spectroscopy in zinc industrial wastewater.

Keywords: industrial wastewater; wavelet transform; partial least squares regression; ultraviolet-visible spectroscopy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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