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

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Quantitative Analysis of Polymetallic Ions in Industrial Wastewater Based on Ultraviolet-Visible Spectroscopy sustainability Article Quantitative Analysis of Polymetallic Ions in Industrial Wastewater Based on Ultraviolet-Visible Spectroscopy Fengbo Zhou 1,* , Ammar Oad 1 , Hongqiu Zhu 2 and Changgeng Li 3,* 1 School of Information Engineering, Shaoyang University, Shaoyang 422000, China; [email protected] 2 School of Automation, Central South University, Changsha 410083, China; [email protected] 3 School of Physics and Electronics, Central South University, Changsha 410083, China * Correspondence: [email protected] (F.Z.); [email protected] (C.L.) 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- Citation: Zhou, F.; Oad, A.; Zhu, H.; visible spectroscopy Li, C. Quantitative Analysis of Polymetallic Ions in Industrial Wastewater Based on Ultraviolet- Visible Spectroscopy. Sustainability 1. Introduction 2021, 13, 7907. https://doi.org/ Zinc hydrometallurgical wastewater contains a large number of heavy metal ions, 10.3390/su13147907 which are various, toxic and abundant [1]. According to statistics, the annual discharge of wastewater from the zinc hydrometallurgy industry in China is 30,549 million tons, account- Academic Editor: Alessio Siciliano ing for 65.8% of the total discharge of wastewater from the non-ferrous metal hydromet- allurgy industry [2–4]. The huge amount of wastewater discharged necessitates massive Received: 9 June 2021 energy consumption and treatment costs [5–8]. If the wastewater is not treated properly in Accepted: 13 July 2021 Published: 15 July 2021 the later stages, excessive emissions of metal ions will seriously pollute the environment and affect human health [9–12]. Therefore, it is very important to monitor the concentration Publisher’s Note: MDPI stays neutral of polymetallic ions in real time and accurately in zinc hydrometallurgical wastewater. with regard to jurisdictional claims in In current production, zinc-smelting enterprises mainly rely on manual offline analysis of published maps and institutional affil- polymetallic ion concentration in industrial wastewater, which makes the process of wastew- iations. ater discharge expensive, cumbersome and time-consuming [13–15]. Therefore, modern detection methods are urgently needed for rapid online detection of metal ion concentra- tions in industrial wastewater. In recent years, several methods, such as potentiometric titration, polarography, inductively coupled plasma mass spectrometry, ultraviolet-visible (UV-vis) absorption spectroscopy, and laser-induced breakdown spectroscopy, have been Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. proposed for the online determination of metal ions [16–20]. Among them, UV-vis spec- This article is an open access article troscopy is widely used in the field of analysis and detection because of its simplicity, distributed under the terms and accuracy, speed, versatility and low cost [21–24]. conditions of the Creative Commons As a routine quantitative analysis method, UV-vis spectrophotometry has been widely Attribution (CC BY) license (https:// applied in order to simultaneously detect metal concentration in solutions. Several spec- creativecommons.org/licenses/by/ trophotometric methods, such as simultaneous equations, difference spectrophotome- 4.0/). try, principal component regression (PCR), PLSR, dual-wavelength spectrophotometry, Sustainability 2021, 13, 7907. https://doi.org/10.3390/su13147907 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 7907 2 of 10 the area under the curve method, and derivative spectrophotometry, have been proposed for multicomponent analysis [25–27]. However, in industrial wastewater treatment, UV-vis spectral data are collected by a microfiber spectrometer. The instrument has the advan- tages of modularization, miniaturization and intellectualization, and is suitable for rapid online detection of multi-metal ions in the industrial field, but the photometric noise of the acquisition signal is relatively large [28–30]. In addition, due to their similar chemical prop- erties, the spectra of zinc (Zn), cobalt (Co) and nickel (Ni) in the zinc wastewater solution overlap seriously [31]. Therefore, it is difficult for the above spectrophotometric methods to simultaneously detect the concentration of polymetallic ions for quantitative evaluation. 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 is used to study the simultaneous detection of zinc, cobalt and nickel. The linear detection ranges are 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 root mean squared error of prediction (RMSEP) is 0.856 for Zn, 0.067 for Ni and 0.032 for Co. The results indicated that the WT–PLSR method is suitable for online detection of polymetallic ions by ultraviolet-visible spectroscopy in zinc industrial wastewater. 2. Theory 2.1. Spectral Pretreatment The acquired spectral data contains noise and background interference, and it is necessary to preprocess the spectral data to improve the accuracy of quantitative anal- ysis. The most common methods in spectral pretreatment are mainly Savitzky–Golay (SG), Fourier transform (FT) and wavelet transform (WT). The SG filtering method is a data-smoothing method of local polynomial least-squares convolution fitting, which can effectively remove noise. The filtering process is expressed as: m ∗ Yj = ∑ CiYj+i/N (1) i=−m where m is the width of the window, N is the number of sliding windows, Ci is the filter ∗ coefficient, Yj+i is the original data, and Yj is the filtered data. FT is a typical frequency domain denoising algorithm. The frequency distribution of signal and noise is different. The signal frequency band is concentrated, and the spectral coefficient is large. The noise is distributed throughout the frequency domain and has a small spectral coefficient. Therefore, noise can be filtered by a threshold setting and windowing function. DFT is defined as: N−1 −j 2p k X(k) = ∑ x(n)e N , k= 0, 1, ··· , N − 1 (2) n=0 The wavelet transform has good local characteristics of the time domain and frequency domain. The wavelet is used to decompose the noisy signal into approximate coefficient and detail coefficient, and the noise is mainly distributed in the detail coefficients. Thus, the wavelet coefficient of noise is set to zero by threshold quantization to effectively eliminate noise. The wavelet transform is expressed as follows: Z ¥ 1 ∗ t − b Wf (a, b) =< f (t), ja,b(t) >= p f (t)j ( )dt (3) a −¥ a Sustainability 2021, 13, 7907 3 of 10 where b is a translation parameter, a is a scale parameter, and j(t) denotes the basis function. 2.2. Partial Least Squares Regression The PLSR method is a commonly used chemometric method for the quantitative analysis of spectral multi-component concentration. PLSR mainly studies the regression modeling of multiple dependent variables on multiple independent variables, especially when the variables are highly linearly correlated internally, and the partial least squares regression method is more effective. In addition, the partial least squares regression solves the problem that the number of samples is fewer than the number of variables. In order to simultaneously detect the concentration of polymetallic ions in zinc wastewater solution, the specific process of PLS modeling is as follows: establish an m × n absorbance matrix A, n × l concentration matrix C, where m is the number of wavelengths, n is the number of samples, and l is the number of metal ion species. A and C can be decomposed as follows: A = TP (4) C = UV (5) where T and U are the score matrixes, P and V are the loads. A and C conform to the Beer- Lambert law and have a linear relationship, so U and T also satisfy the linear relationship: U = TB (6) where B is the correlation coefficient matrix. The concentration prediction formula
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