Energy efficiency evaluation of wastewater treatment plants (WWTPs) based on data envelopment analysis

Yang, Junwen; Chen, Bin

doi:10.1016/j.apenergy.2021.116680

Cited by 66 publications

(24 citation statements)

References 20 publications

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“…Above 100,000 m 3 /d, there is a minimal decrease in UEC, which is a consideration for the design of WWTPs. The overall trend is consistent with the finding of previous studies. , What is more, this finding also follows the scale economy of WWTPs …”

Section: Resultssupporting

confidence: 92%

“…The overall trend is consistent with the finding of previous studies. 33,34 What is more, this finding also follows the scale economy of WWTPs. 46 The construction scale of WWTPs in China can be divided into five categories [according to the Ministry of Construction of China (2001).…”

Section: Model Demonstration and Prediction Of Energysupporting

confidence: 63%

“…30 However, RF is rarely used to analyze and predict energy consumption of WWTPs. 31,32 Data envelopment analysis 33,34 and multiple linear regression 35 are commonly used methods to analyze the energy efficiency of WWTPs. The main principle of data envelopment analysis is using the method of linear programming, and multiple linear regression is a kind of generalized linear model.…”

Section: Introductionmentioning

confidence: 99%

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Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

2021

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Wastewater treatment plants (WWTPs) can account for up to 1% of a country’s energy consumption. Meanwhile, WWTPs have high energy-saving potential. To achieve this, it is necessary to establish appropriate energy consumption models for WWTPs. Several recent models have been developed using logarithmic, exponential, or linear functions. However, the behavior of WWTPs is non-linear and difficult to fit with simple functions, particularly for non-numerical variables. Thus, traditional modeling methods cannot effectively describe the relationship between water and energy in WWTPs. Therefore, a machine learning method was adopted in this study to investigate the energy consumption in WWTPs; a novel energy consumption model with a non-numerical variable (discharge standard) for WWTPs was developed using the random forest algorithm. The model can also predict the energy consumption of WWTPs after upgrading discharge standards. We found that the unit electricity consumption of WWTPs exhibited an average increase of 17% after the effluent discharge standard was increased from class I B to class I A (as per China’s classification). The correlation coefficient of the model was 0.702. Thus, the developed model can provide a better understanding of energy efficiency in WWTPs.

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Section: Resultssupporting

confidence: 92%

Section: Model Demonstration and Prediction Of Energysupporting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

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Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

2021

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“…Industrialization has brought many advantages, such as improved medical care, productivity, time conservation, and better living standards. However, it has also resulted in negative environmental consequences, such as water shortages, solid waste pollution, air pollution, and land pollution [1]. Particularly, air quality has become a serious city problem that endangers the health and quality of life of inhabitants and causes many disease-related heart and lung problems, with the World Health Organization claiming that an estimated seven million people worldwide die each year from air pollution-related causes [2].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the relationships between the properties of air quality management-which were identified through a quantitative approach-and the hotel image as well as revisit intentions were investigated. To be more specific, the purpose of the study was: (1) to identify the properties of air quality management through a qualitative approach using a literature review and focus group interviews; (2) to verify the relationship between the properties of air quality management and the hotel's cognitive and affective images; and (3) to verify the relationship between revisit intentions and the hotel's cognitive and affective images. The results of this study are expected to provide very meaningful academic implications for studies on the hotel industry, and furthermore, provide significant strategic implications for hotel practitioners.…”

Section: Introductionmentioning

confidence: 99%

Effect of Hotel Air Quality Management on Guests’ Cognitive and Affective Images and Revisit Intentions

Park

Yoo

2021

IJERPH

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Although city air pollution levels significantly affect the hotel industry, few studies have addressed the impact of air quality management on guests’ cognitive and affective image formation and revisit intentions. Therefore, this research examined the effects of hotel air quality management on the formation of guests’ cognitive and affective images and their revisit intentions. A total of 322 valid samples were obtained by surveying hotel guests who had perceived hotel air quality management activities in the past year, with SPSS 22.0 (IBM, New York, NY, USA) and AMOS 22.0 (IBM, New York, NY, USA) employed for the empirical analysis. The cognitive and affective image constructs revealed that cognitive (perceived value and perceived quality) image influenced revisit intentions but affective image did not. These results provide insights into the need for hotel managers to develop positive cognitive and emotional images through good air quality management and the need to induce customers to revisit based on these images.

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Study on influence factors and control optimizations of sewage sludge drying and incineration system for energy conservation

Lin,

Cao,

et al. 2024

J Mater Cycles Waste Manag

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Energy efficiency evaluation of wastewater treatment plants (WWTPs) based on data envelopment analysis

Cited by 66 publications

References 20 publications

Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

Effect of Hotel Air Quality Management on Guests’ Cognitive and Affective Images and Revisit Intentions

Study on influence factors and control optimizations of sewage sludge drying and incineration system for energy conservation

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