Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review

Shi, Zhenyu; Chow, Christopher W.K.; Fabris, Rolando; Liu, Jixue; Jin, Bo

doi:10.3390/s22082987

Cited by 49 publications

(38 citation statements)

References 67 publications

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“…is research is di erent from the existing water quality anomaly monitoring [1][2][3][4][5][6][7] in that anomaly detection is realized through the ship's intelligent platform.…”

Section: Resultsmentioning

confidence: 99%

“…In particular, when a linear activation function is used, the under-complete autoencoder performs a linear transformation. It is equivalent to linear dimensionality reduction Direct measurement [5] approaches, such as principal component analysis (PCA) and singular value decomposition (SVD) [18]. Denote weights as W and bias as b, the encoder can be expressed as z W E X + b E , and the decoder as z W D X + b D .…”

Section: Autoencodermentioning

confidence: 99%

“…In recent years, more and more automated methods for real-time monitoring of water quality have been studied. Anomaly detection of di erent types of water quality has been studied [1][2][3][4][5][6][7], some of the main types of research literature are listed in Table 1, and the corresponding main methods are also listed in the table.…”

Section: Introductionmentioning

confidence: 99%

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A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment

Zhou

Liu

et al. 2022

Advances in Materials Science and Engineering

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Great changes have been brought about by the coastal environment when the economy develops rapidly. Coastal environmental monitoring is the basis and technical guarantee for coastal environmental protection supervision and management. It is one of the important tasks to detect and timely discover coastal seawater anomalies. Usually, a single sensor cannot determine whether the coastal environment or ship operation is an anomaly. Recently, an unmanned surface vehicle for coastal environment monitoring was developed, and stacked autoencoders are used for seawater anomaly detection using multisensor data fusion methods. The multisensor data of pH, conductivity, and ammonia nitrogen are employed to judge the anomaly of seawater. The mean, standard deviation, mean square root, and normalized power spectrum features of multisensor data are extracted, and a stacked autoencoder is employed to fuse these features for anomaly detection. The proposed method is feasible and effective for anomaly detection of coastal water quality and ship operation. Compared with other commonly used methods, the proposed method has a higher recall, precision, and F1 score performance.

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“…is research is di erent from the existing water quality anomaly monitoring [1][2][3][4][5][6][7] in that anomaly detection is realized through the ship's intelligent platform.…”

Section: Resultsmentioning

confidence: 99%

Section: Autoencodermentioning

confidence: 99%

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A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment

Zhou

Liu

et al. 2022

Advances in Materials Science and Engineering

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“…the stability of the system was greatly improved at all UV-Vis wavelengths after the digital filter was implemented, especially at the wavelength of the visible light range. The average coefficient of variation of the measurements in all wavelengths (200~700 nm) was reduced from 9.57% to 1.74%,which reached the average accuracy of the common online UV-Vis instruments for water-quality monitoring [30]. The maximum 𝐶 , which indicated the worst case of the measurement, was reduced from 59.38% to 9.79%.…”

Section: Noise Evaluation For the Spectrum Of Xenon Flash Lightmentioning

confidence: 71%

A Design of Real-Time Data Acquisition and Processing System for Nanosecond Ultraviolet-Visible Absorption Spectrum Detection

et al. 2022

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Ultraviolet-visible absorption spectroscopy is widely used to monitor water quality, and rapid optical signal detection is a key technology in the process of spectrum measurement. In this paper, an ultrafast spectrophotometer system that can achieve spectrum data acquisition in a single flash of the xenon lamp (within 200 ns) is introduced, and a real-time denoising method for the spectrum is implemented on a field programmable gate array (FPGA) to work cooperatively with the nanosecond spectrum acquisition system, in order to guarantee the quality of the spectrum signals without losing running speed. The hardware of the data acquisition and processing system are constructed on a Xilinx Spartan 6 FPGA chip and its peripheral circuit, including an analog to digital converter and a complementary metal-oxide-semiconductor transistor (CMOS) sensor’s diver circuit. An oversampling method that is suitable for the CMOS sensor’s output is proposed, which works on the CMOS sensor’s dark current noise and readout noise. Another moving-average filter method is designed adaptively, which works on the low-frequency component to filter out the residual spectrum noise of the spectrum signal. The implementation of the filter on the FPGA has been optimized by using a pipelined structure and dual high-speed random-access memory (RAM). As a result, the CMOS linear image sensor successfully captured the spectrum of xenon flash light at the readout clock frequency of 500 kHz and the processing manipulation to the full UV-Vis spectrum data was accomplished at a sub-microsecond speed performance. After the digital filter and oversampling technology were implemented, the coefficient of variation of the measurements reduced from 9.57% to 1.74%, while the signal noise ratio (SNR) of the absorption spectrum increased nine times, compared to the raw data of the CMOS sensor’s output. The tests towards different analyte samples were conducted, and the system shows good performance on distinguishing different concentrations of different analyte solutions on both ultra-violet and visible spectrum bands. The present work showcases the potential of the CMOS sensor’s technique for the fast detection of contaminated water containing nitrate and organic compounds.

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“…These sensors enable in-situ measurements during operation and real-time adjustments of the treatment process. However, the detection limit in waters with a complex composition and the difficulty of detecting the UV-Vis spectra of some pollutants in the water, such as suspended solids, dissolved inorganic substances and pathogenic microorganisms remain challenging [80]. Monitoring tools that interact continuously and automatically are not yet established.…”

Section: Wastewater Treatment and Monitoringmentioning

confidence: 99%

Possibilities of Real Time Monitoring of Micropollutants in Wastewater Using Laser-Induced Raman & Fluorescence Spectroscopy (LIRFS) and Artificial Intelligence (AI)

Post

Heyden

Reinartz

et al. 2022

Sensors

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The entire water cycle is contaminated with largely undetected micropollutants, thus jeopardizing wastewater treatment. Currently, monitoring methods that are used by wastewater treatment plants (WWTP) are not able to detect these micropollutants, causing negative effects on aquatic ecosystems and human health. In our case study, we took collective samples around different treatment stages (aeration tank, membrane bioreactor, ozonation) of a WWTP and analyzed them via Deep-UV laser-induced Raman and fluorescence spectroscopy (LIRFS) in combination with a CNN-based AI support. This process allowed us to perform the spectra recognition of selected micropollutants and thus analyze their reliability. The results indicated that the combination of sensitive fluorescence measurements with very specific Raman measurements, supplemented with an artificial intelligence, lead to a high information gain for utilizing it as a monitoring purpose. Laser-induced Raman spectroscopy reaches detections limits of alert pharmaceuticals (carbamazepine, naproxen, tryptophan) in the range of a few µg/L; naproxen is detectable down to 1 × 10−4 mg/g. Furthermore, the monitoring of nitrate after biological treatment using Raman measurements and AI support showed a reliable assignment rate of over 95%. Applying the fluorescence technique seems to be a promising method in observing DOC changes in wastewater, leading to a correlation coefficient of R2 = 0.74 for all samples throughout the purification processes. The results also showed the influence of different extraction points in a cleaning stage; therefore, it would not be sensible to investigate them separately. Nevertheless, the interpretation suffers when many substances interact with one another and influence their optical behavior. In conclusion, the results that are presented in our paper elucidate the use of LIRFS in combination with AI support for online monitoring.

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Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review

Cited by 49 publications

References 67 publications

A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment

A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment

A Design of Real-Time Data Acquisition and Processing System for Nanosecond Ultraviolet-Visible Absorption Spectrum Detection

Possibilities of Real Time Monitoring of Micropollutants in Wastewater Using Laser-Induced Raman & Fluorescence Spectroscopy (LIRFS) and Artificial Intelligence (AI)

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