Characteristic and correlation analysis of influent and energy consumption of wastewater treatment plants in Taihu Basin

Zou, Luxi; Li, Huaibo; Wang, Shuo; Zheng, Kuiyang; Wang, Yan; Du, Guocheng; Ji, Li

doi:10.1007/s11783-019-1167-7

Cited by 25 publications

(4 citation statements)

References 39 publications

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“…Among influent and effluent variables, the influent NH 4 + -N, TN, and TP were identified as the most important variables in the prediction of SE e , followed by the effluent COD, TN, and TP (Figure a). Most ICE plots showed increasing trends with the increase of influent NH 4 + -N, TN, and TP, which agreed with the results of SHAP and the regular knowledge that WWTPs need higher energy for aeration to remove nitrogen in wastewater with the increase of influent NH 4 + -N and TN . Meanwhile, the ICE plots with the 0th percentile of influent COD, NH 4 + -N, TP, and SS had a decreasing trend of SE e response at the beginning of the ICE plots (Figure ).…”

Section: Resultsmentioning

confidence: 99%

“…Most ICE plots showed increasing trends with the increase of influent NH 4 + -N, TN, and TP, which agreed with the results of SHAP and the regular knowledge that WWTPs need higher energy for aeration to remove nitrogen in wastewater with the increase of influent NH 4 + -N and TN. 47 Meanwhile, the ICE plots with the 0th percentile of influent COD, NH 4 + -N, TP, and SS had a decreasing trend of SE e response at the beginning of the ICE plots (Figure 6). This phenomenon implied that there was an optimized point for treating these pollutants with the lowest SE e .…”

Section: Dependance and Response Of Se Ementioning

confidence: 99%

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Prediction and Evaluation of Indirect Carbon Emission from Electrical Consumption in Multiple Full-Scale Wastewater Treatment Plants via Automated Machine Learning-Based Analysis

Luo

et al. 2022

ACS EST Eng.

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The indirect carbon emission from electrical consumption of wastewater treatment plants (WWTPs) accounts for large proportions of their total carbon emissions, which deserves intensive attention. This work proposed an automated machine learning (AutoML)-based indirect carbon emission analysis (ACIA) approach and predicted the specific indirect carbon emission from electrical consumption (SE e ; kg CO 2 /m 3 ) successfully in nine fullscale WWTPs (W1−W9) with different treatment configurations based on the historical operational data. The stacked ensemble models generated by the AutoML accurately predicted the SE e (mean absolute error = 0.02232−0.02352, R 2 = 0.65107−0.67509). Then, the variable importance and Shapley additive explanations (SHAP) summary plots qualitatively revealed that the influent volume and the types of secondary and tertiary treatment processes were the most important variables associated with SE e prediction. The interpretation results of partial dependence and individual conditional expectation further verified quantitative relationships between input variables and SE e . Also, low energy efficiency with high indirect carbon emission of WWTPs was distinguished. Compared with traditional carbon emission analysis and prediction methods, the ACIA method could accurately evaluate and predict SE e of WWTPs with different treatment scales and processes with easily available variables and reveal qualitative and quantitative relationships inside datasets simultaneously, which is a powerful tool to benefit the "carbon neutrality" of WWTPs.

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Section: Resultsmentioning

confidence: 99%

Section: Dependance and Response Of Se Ementioning

confidence: 99%

Prediction and Evaluation of Indirect Carbon Emission from Electrical Consumption in Multiple Full-Scale Wastewater Treatment Plants via Automated Machine Learning-Based Analysis

Luo

et al. 2022

ACS EST Eng.

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“…The BOD/TP ratio can be used to assess the feasibility of biological phosphorus removal. Generally, a BOD/TP ratio greater than 20 ensures good phosphorus removal efficiency [28]. As presented in Figure 4d, the BOD/TP ratio varies significantly from 8.9 to 529.4, with an average value of 63.9 and a medium value of 32.0.…”

Section: Ratios Of Water Quality Parametersmentioning

confidence: 91%

Analysis on Operation and Water Quality Characteristics of Centralized Wastewater Treatment Plants of Industrial Parks in Yellow River Basin, China

Wang,

Yuan,

Xue

et al. 2024

Water

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The Yellow River basin serves as an important economic belt and industrial base in China, featuring numerous industrial parks. However, alongside its economic significance, the basin struggles with significant water environmental challenges. This study analyzed the operational status, influent water quality, and energy consumption of 63 centralized wastewater treatment plants (WWTPs) from 54 major industrial parks in the Yellow River basin. The scale of these WWTPs was primarily within the range of 1 × 104~5 × 104 m3/d, with an average hydraulic loading rate of 53.8%. Aerobic treatment processes are predominant. The influent concentrations of chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP) in the WWTPs exhibited a right-skewed distribution. The BOD/COD ratio of the WWTPs fluctuated between 0.1 and 1.6, and 75% of the WWTPs showed a COD/TN ratio lower than eight. The average BOD5/TN was 2.7, and the probability of influent BOD5/TP > 20 was 84.6%. A significant linear correlation exists between the influent BOD and COD concentrations, while moderate linear relationships are also observed among NH3-N, TN and TP, emphasizing the importance of maintaining appropriate nitrogen and phosphorus levels for efficient pollutant removal. The average electricity consumption of WWTPs in the Yellow River basin in 2023 was 1.1 kWh/m3. It is important to upgrade these WWTPs and reduce their energy consumption. Further strengthening the construction of industrial wastewater collection and treatment facilities based on regional characteristics is recommended to promote the high-quality development of industrial wastewater treatment in the Yellow River basin.

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“…30 According to process-related and physical factors, 11 operational conditions included the technical process, inlet quantity and quality. Ratios characterizing the biodegradability on COD, 31 TN, 32 and TP 33 were also considered. Besides, the externalities of the region and season were selected.…”

Section: Methodsmentioning

confidence: 99%

Spatial and Temporal Modeling on Energy Consumption of Wastewater Treatment Based on Machine Learning Algorithms

Huang,

Yu,

Wang

et al. 2023

ACS EST Water

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To explore the water-energy-carbon nexus of wastewater treatment (WWT), advanced tools such as machine learning play a crucial role. Current research has primarily constructed energy efficiency models, but there exists a lack in considering comprehensive dimensions and comparing pollutant removal types. In this study, we conducted spatial and temporal modeling to predict the energy consumption (EC) of WWT via machine learning approaches. EC (kWh) was the target feature, with the input features covering operational conditions, environmental benefits, and externalities. The optimal spatial model obtained a test R 2 of 0.8224 in ridge regression, while the temporal model achieved a test R 2 of 0.7253 in random forest. Besides, the removal amount (10 3 kg) fit best with EC during the spatial modeling, while the discharge concentration (mg/L) fit best with EC during the temporal modeling. Notably, treatment volume, the removal of chemical oxygen demand, and the removal of ammonia nitrogen emerged as the most significant factors. Given this, our findings suggest optimization implications including scale economy utilization and aeration improvement. The spatial and temporal dimensions also illuminated tailored strategies on influent regulation, technology selection, and effluent standard settings for a specific region and season. Results will provide valuable guidance for existing operation and future design of WWT projects toward energy-saving and carbon neutrality.

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Characteristic and correlation analysis of influent and energy consumption of wastewater treatment plants in Taihu Basin

Cited by 25 publications

References 39 publications

Prediction and Evaluation of Indirect Carbon Emission from Electrical Consumption in Multiple Full-Scale Wastewater Treatment Plants via Automated Machine Learning-Based Analysis

Prediction and Evaluation of Indirect Carbon Emission from Electrical Consumption in Multiple Full-Scale Wastewater Treatment Plants via Automated Machine Learning-Based Analysis

Analysis on Operation and Water Quality Characteristics of Centralized Wastewater Treatment Plants of Industrial Parks in Yellow River Basin, China

Spatial and Temporal Modeling on Energy Consumption of Wastewater Treatment Based on Machine Learning Algorithms

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