基于云模型的全尺寸污水处理厂水-能关联特征分析

Shan-Shan Yang; Xin-Lei Yu; Chen-Hao Cui; Jie Ding; Lei He; Wei Dai; Han-Jun Sun; Shun-Wen Bai; Yu Tao; Ji-Wei Pang; Nan-Qi Ren

doi:10.1016/j.eng.2022.02.011

PDF(2510 KB)

工程（英文） ›› 2024, Vol. 36 ›› Issue (5) : 63-75. DOI: 10.1016/j.eng.2022.02.011

研究论文

Article

基于云模型的全尺寸污水处理厂水-能关联特征分析

Shan-Shan Yang ^a ,
Xin-Lei Yu ^a ,
Chen-Hao Cui ^a ,
Jie Ding ^a ,
Lei He ^a ,
Wei Dai ^a ,
Han-Jun Sun ^a ,
Shun-Wen Bai ^a ,
Yu Tao ^b^,^c^,^* ,
Ji-Wei Pang ^d^,^* ,
Nan-Qi Ren ^a

作者信息 +

Cloud-Model-Based Feature Engineering to Analyze the Energy-Water Nexus of a Full-Scale Wastewater Treatment Plant

Shan-Shan Yang ^a ,
Xin-Lei Yu ^a ,
Chen-Hao Cui ^a ,
Jie Ding ^a ,
Lei He ^a ,
Wei Dai ^a ,
Han-Jun Sun ^a ,
Shun-Wen Bai ^a ,
Yu Tao ^b^,^c^,^* ,
Ji-Wei Pang ^d^,^* ,
Nan-Qi Ren ^a

Author information +

History +

Abstract

Wastewater treatment plants (WWTPs) are important and energy-intensive municipal infrastructures. High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutrality. However, water-energy nexus analysis and models for WWTPs have rarely been reported to date. In this study, a cloud-model-based energy consumption analysis (CMECA) of a WWTP was conducted to explore the relationship between influent and energy consumption by clustering its influent’s parameters. The principal component analysis (PCA) and K-means clustering were applied to classify the influent condition using water quality and volume data. The energy consumption of the WWTP is divided into five standard evaluation levels, and its cloud digital characteristics (CDCs) were extracted according to bilateral constraints and golden ratio methods. Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity. The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption, represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption. The local WWTP has high energy consumption with 0.3613 kW·h·m⁻³ despite low influent concentration and volumes, across four consumption levels from low (I) to relatively high (IV), showing an unsatisfactory operation and management level. The average oxygenation capacity, internal reflux ratio, and external reflux ratio during high energy efficiency days recognized by further clustering were obtained (0.2924-0.3703 kg O₂·m⁻³, 1.9576-2.4787, and 0.6603-0.8361, respectively), which could be used as a guide for the days with low energy efficiency. Consequently, this study offers a water-energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.

导出引用

Shan-Shan Yang, Xin-Lei Yu, Chen-Hao Cui. 基于云模型的全尺寸污水处理厂水-能关联特征分析. Engineering. 2024, 36(5): 63-75 https://doi.org/10.1016/j.eng.2022.02.011

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序

[1]	E.I. Metcalf. Wastewater engineering: treatment and reuse. ( 4th ed.), McGraw-Hill, New York ( 2003)
[2]	Z. Zhao, Z. Wang, J. Yuan, J. Ma, Z. He, Y. Xu, et al.. Development of a novel feedforward neural network model based on controllable parameters for predicting effluent total nitrogen. Engineering, 7 (2) ( 2021), pp. 195-202
[3]	W.A. Tarpeh, X. Chen.Making wastewater obsolete: selective separations to enable circular water treatment. Environ Sci Ecotechnol, 5 ( 2021), p. 100078
[4]	J. Zhang, Y. Shao, H. Wang, G. Liu, L. Qi, X. Xu, et al.. Current operation state of wastewater treatment plants in urban China. Environ Res, 195 ( 2021), p. 110843
[5]	G. Mannina, T.F. Rebouças, A. Cosenza, M. Sànchez-Marrè, K. Gibert.Decision support systems (DSS) for wastewater treatment plants—a review of the state of the art. Bioresour Technol, 290 ( 2019), p. 121814
[6]	A. Soares.Wastewater treatment in 2050: challenges ahead and future vision in a European context. Environ Sci Ecotechnol, 2 ( 2020), p. 100030
[7]	A.K. Plappally, V.J.H. Lienhard. Energy requirements for water production, treatment, end use, reclamation, and disposal. Renew Sustain Energy Rev, 16 (7) ( 2012), pp. 4818-4848
[8]	G. Sabia, L. Petta, F. Avolio, E. Caporossi.Energy saving in wastewater treatment plants: a methodology based on common key performance indicators for the evaluation of plant energy performance, classification and benchmarking. Energy Convers Manage, 220 ( 2020), p. 113067
[9]	P. Foladori, M. Vaccari, F. Vitali. Energy audit in small wastewater treatment plants: methodology, energy consumption indicators, and lessons learned. Water Sci Technol, 72 (6) ( 2015), pp. 1007-1015
[10]	S. Reifsnyder, F. Cecconi, D. Rosso.Dynamic load shifting for the abatement of GHG emissions, power demand, energy use, and costs in metropolitan hybrid wastewater treatment systems. Water Res, 200 ( 2021), p. 117224
[11]	Z. Xie. China’s historical evolution of environmental protection along with the forty years’ reform and opening-up.Environ Sci Ecotechnol, 1 ( 2020), p. 100001
[12]	X. Chu, L. Luo, X. Wang, W. Zhang. Analysis on current energy consumption of wastewater treatment plants in China. China Water Wastewater, 34 (07) ( 2018), pp. 70-74 Chinese
[13]	Y. He, Y. Zhu, J. Chen, M. Huang, P. Wang, G. Wang, et al.. Assessment of energy consumption of municipal wastewater treatment plants in China. J Clean Prod, 228 ( 2019), pp. 399-404
[14]	L. Castellet-Viciano, V. Hernández-Chover, F. Hernández-Sancho. Modelling the energy costs of the wastewater treatment process: the influence of the aging factor. Sci Total Environ, 625 ( 2018), pp. 363-372
[15]	D.W. Gao, R. An, Y. Tao, J. Li, X.X. Li, N.Q. Ren. Simultaneous methane production and wastewater reuse by a membrane-based process: evaluation with raw domestic wastewater. J Hazard Mater, 186 (1) ( 2011), pp. 383-389
[16]	Z.L. Li, K. Sun, F. Chen, X.Q. Lin, C. Huang, Z. Yao, et al.. Efficient treatment of alizarin yellow R contained wastewater in an electrostimulated anaerobic-oxic integrated system. Environ Res, 185 ( 2020), p. 109403
[17]	K.H. Chen, H.C. Wang, J.L. Han, W.Z. Liu, H.Y. Cheng, B. Liang, et al.. The application of footprints for assessing the sustainability of wastewater treatment plants: a review. J Clean Prod, 277 ( 2020), p. 124053
[18]	T. Sangeetha, Z. Guo, W. Liu, L. Gao, L. Wang, M. Cui, et al.. Energy recovery evaluation in an up flow microbial electrolysis coupled anaerobic digestion (ME-AD) reactor: role of electrode positions and hydraulic retention times. Appl Energy, 206 ( 2017), pp. 1214-1224
[19]	B. Wang, W. Liu, Y. Zhang, A. Wang.Intermittent electro field regulated mutualistic interspecies electron transfer away from the electrodes for bioenergy recovery from wastewater. Water Res, 185 ( 2020), p. 116238
[20]	Y. Gu, Y. Li, X. Li, P. Luo, H. Wang, X. Wang, et al.. Energy self-sufficient wastewater treatment plants: feasibilities and challenges. Energy Procedia, 105 ( 2017), pp. 3741-3751
[21]	X. Yang, J. Wei, G. Ye, Y. Zhao, Z. Li, G. Qiu, et al.. The correlations among wastewater internal energy, energy consumption and energy recovery/production potentials in wastewater treatment plant: an assessment of the energy balance. Sci Total Environ, 714 ( 2020), p. 136655
[22]	D. Torregrossa, G. Schutz, A. Cornelissen, F. Hernández-Sancho, J. Hansen. Energy saving in WWTP: daily benchmarking under uncertainty and data availability limitations. Environ Res, 148 ( 2016), pp. 330-337
[23]	X. Hao, X. Wang, R. Liu, S. Li, M.C.M. van Loosdrecht, H. Jiang. Environmental impacts of resource recovery from wastewater treatment plants. Water Res, 160 ( 2019), pp. 268-277
[24]	R.P. Zanardo, J.C.M. Siluk, S.F. de Souza, P.S. Schneider. Energy audit model based on a performance evaluation system. Energy, 154 ( 2018), pp. 544-552
[25]	R.X. Hao, F. Liu, H.Q. Ren, S.Y. Cheng. Study on a comprehensive evaluation method for the assessment of the operational efficiency of wastewater treatment plants. Stochastic Environ Res Risk Assess, 27 (3) ( 2013), pp. 747-756
[26]	K. Chen, H. Wang, B. Valverde-Pérez, S. Zhai, L. Vezzaro, A. Wang. Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning. Chemosphere, 279 ( 2021), p. 130498
[27]	B. Guo, W. Zang, X. Yang, X. Huang, R. Zhang, H. Wu, et al.. Improved evaluation method of the soil wind erosion intensity based on the cloud-AHP model under the stress of global climate change. Sci Total Environ, 746 ( 2020), p. 141271
[28]	S. Zhang, M. Xiang, Z. Xu, L. Wang, C. Zhang.Evaluation of water cycle health status based on a cloud model. J Clean Prod, 245 ( 2020), p. 118850
[29]	Ministry of Ecology and Environment of the People’s Republic of China. GB 18918- 2002: Discharge standard of pollutants for municipal wastewater treatment plant. Chinese standard. China Environment Publishing Group, Beijing ( 2002) Chinese
[30]	F. Wei. Methods for monitoring and analysis of water and wastewater. (4th ed.), China Environmental Science Press, Beijing ( 2002)
[31]	M. Ansari, F. Othman, A. El-Shafie. Optimized fuzzy inference system to enhance prediction accuracy for influent characteristics of a sewage treatment plant. Sci Total Environ, 722 ( 2020)
[32]	H. Zhang, H. Chen, Y. Guo, J. Wang, G. Li, L. Shen.Sensor fault detection and diagnosis for a water source heat pump air-conditioning system based on PCA and preprocessed by combined clustering. Appl Therm Eng, 160 ( 2019), p. 114098
[33]	C. Zhu, C.U. Idemudia, W. Feng.Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques. Inf Med Unlocked, 17 ( 2019), p. 100179
[34]	I.T. Jolliffe.Principal component analysis. ( 2nd ed.), Springer, New York ( 2002)
[35]	J. Wallace, P. Champagne, G. Hall. Multivariate statistical analysis of water chemistry conditions in three wastewater stabilization ponds with algae blooms and pH fluctuations. Water Res, 96 ( 2016), pp. 155-165
[36]	Y.J. Liao, H.T. Zhao, Y. Jiang, Y.K. Ma, X. Luo, X.Y. Li.An innovative method based on cloud model learning to identify high-risk pollution intervals of storm-flow on an urban catchment scale. Water Res, 165 ( 2019), p. 115007
[37]	B.J. Cardoso, E. Rodrigues, A.R. Gaspar, Á. Gomes.Energy performance factors in wastewater treatment plants: a review. J Clean Prod, 322 ( 2021), p. 129107
[38]	J. Guo, S. Gao, T.W. Li, Q. Huang, F.J. Meng.An improved cloud-theory-based method to evaluate shipborne navigation equipment's effectiveness. 2015 IEEE International Conference on Information and Automation; 2015 Aug 8-10 ; Lijiang, China, IEEE, New York ( 2015), pp. 1403-1408
[39]	H.S. Su, S.S. Wang. Optimizing available transfer capability based on chaos cloud particle swarm algorithm with gold section criteria. Int J Netw Secur Appl, 11 (4) ( 2017), pp. 59-70
[40]	C.Y. Liu, M. Feng, X.J. Dai, D.Y. Li. A new algorithm of backward cloud. J Syst Simul, 11 ( 2004), pp. 2417-2420 Chinese
[41]	G.Y. Wang, C.L. Xu, Q.H. Zhang, X.R. Wang. A multi-step backward cloud generator algorithm. J. Yao, Y. Yang, R. Słowiński, S. Greco, H. Li, S. Mitra ( 8th International Conference; Eds.), Lecture Notes in Computer Science: Rough Sets and Current Trends in Computing. 2012 Aug 17-20 ; Chengdu, China, Springer, Berlin ( 2012), pp. 313-322
[42]	K. Qin, K. Xu, Y. Yi Du, D.Y. Li.An image segmentation approach based on histogram analysis utilizing cloud model. 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery; 2010 Aug 10-12 ; Yantai, China, IEEE, New York ( 2010), pp. 524-528
[43]	S. Wang, D. Li, W. Shi, D. Li, X. Wang. Cloud model-based spatial data mining. Geogr Inform Sci, 9 (1-2) ( 2003), pp. 60-70
[44]	T. Wang, X. Wang, L. Wang, C.P. Au-Yong, A.S. Ali.Assessment of the development level of regional industrialized building based on cloud model: a case study in Guangzhou. China J Build Eng, 44 ( 2021), p. 102547
[45]	H.W. Wu, J. Zhen, J. Zhang. Urban rail transit operation safety evaluation based on an improved CRITIC method and cloud model. J Rail Transp Plan Manage, 16 ( 2020), Article 100206
[46]	Z. Zuo, H. Guo, J. Cheng, Y. Li.How to achieve new progress in ecological civilization construction?—Based on cloud model and coupling coordination degree model. Ecol Indic, 127 ( 2021), p. 107789
[47]	W.K. Härdle,L. Simar. Applied multivariate statistical analysis. ( 5th ed.), Springer, Berlin ( 2019)
[48]	I.T. Jolliffe. Discarding variables in a principal component analysis. I: artificial data. J R Stat Soc Ser C Appl Stat, 21 ( 1972), pp. 160-173
[49]	H. Dai, T. Han, T. Sun, H. Zhu, X. Wang, X. Lu. Nitrous oxide emission during denitrifying phosphorus removal process: a review on the mechanisms and influencing factors. J Environ Manage, 278 (Pt 1) ( 2021), Article 111561
[50]	W. Zhao, Y. Zhang, D. Lv, M. Wang, Y. Peng, B. Li. Advanced nitrogen and phosphorus removal in the pre-denitrification anaerobic/anoxic/aerobic nitrification sequence batch reactor (pre-A₂NSBR) treating low carbon/nitrogen (C/N) wastewater. Chem Eng J, 302 ( 2016), pp. 296-304
[51]	P.J. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math, 20 ( 1987), pp. 53-65
[52]	R. Lletí, M.C. Ortiz, L.A. Sarabia, M.S. Sánchez. Selecting variables for K-means cluster analysis by using a genetic algorithm that optimizes the silhouettes. Anal Chim Acta, 515 (1) ( 2004), pp. 87-100
[53]	X. Du, Q. Yin, K. Huang, D. Liang. Transformation between qualitative variables and quantity based on cloud models and its application. Syst Eng Electron, 4 ( 2008), pp. 772-776 Chinese
[54]	X. Nie, T. Fan, H. Dong, B. Wang. IOWA-cloud model-based study on risk assessment of operation safety of long distance water transfer project. Water Resour Hydrop Eng, 50 (2) ( 2019), pp. 151-160 Chinese
[55]	K. Niu, J. Wu, L. Qi, Q. Niu. Energy intensity of wastewater treatment plants and influencing factors in China. Sci Total Environ, 670 ( 2019), pp. 961-970
[56]	L. Zou, H. Li, S. Wang, K. Zheng, Y. Wang, G. Du, et al.. Characteristic and correlation analysis of influent and energy consumption of wastewater treatment plants in Taihu Basin. Front Environ Sci Eng, 13 (6) ( 2019), p. 83
[57]	C. Jiang, A.M. Yang, Y.P. Gan, C.L. Meng, Y.Z. Peng, S.J. Zhang, et al.. Energy consumption analysis and energy saving solutions in WWTP. China Water Wastewater, 27 (04) ( 2011), pp. 33-36 Chinese