scholarly journals Study on the Choice of Wastewater Treatment Process Based on the Emergy Theory

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1648
Author(s):  
Cui Wang ◽  
Changyi Liu ◽  
Xiaoxiao Si ◽  
Cuixia Zhang ◽  
Fan Liu ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Yield Ratio ◽  
Environmental Load ◽  
Wastewater Treatment Process ◽  
Development Index ◽  
Anaerobic Baffled Reactor ◽  
Treatment Processes ◽  
Load Rate ◽  
Emergy Theory ◽  
Sustainable Wastewater Treatment

With the increase in industrialization and urbanization, water pollution has become increasingly serious, and wastewater treatment has become a common step in preventing this. For a greater understanding of the sustainability of different wastewater treatment systems, two processes, Anaerobic Baffled Reactor + Anaerobic-Anoxic-Oxic and Anaerobic Baffled Reactor + Cyclic Activated Sludge System, were selected, and their sustainability was evaluated based on three indicators, namely emergy yield ratio, environmental load rate, and emergy sustainability development index, according to emergy theory. The results show that the emergy yield ratio and environmental load rate of the ABR + CASS process were lower than those of the ABR + A2/O process, and the emergy sustainability development index of the ABR + CASS process was higher than that of the ABR + A2/O process, showing better sustainability. The research methods and findings of this study play an important role for decision makers in selecting sustainable wastewater treatment processes.

scholarly journals Trends and progress in Microalgae-based wastewater treatment technologies: A review

2021 ◽  
Vol 308 ◽  
pp. 01014
Author(s):  
Yujia Wan ◽  
Ning Yan ◽  
Jiaqi Zhao ◽  
Hegang Zhi ◽  
Wenmin Wang
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
High Energy ◽  
Impact Factors ◽  
Innovative Technology ◽  
Transformative Change ◽  
Wastewater Treatment Process ◽  
Treatment Processes ◽  
Treatment Technologies ◽  
Sustainable Development Goal 6

A transformative change is underway in wastewater treatment as the world aims at meeting Sustainable Development Goal 6 in 2030, and the conventional wastewater treatment processes have high energy consumption and greenhouse emissions. Microalgae-based wastewater treatment process has emerged as an innovative technology that can reach the demand for lowering energy consumption, mitigating climate change, and recycling resources. This review provides an overview of the basic theories of microalgae-based wastewater treatment processes, microalgae species commonly used, impact factors of microalgae cultivation, the conventional and hybrid microalgae-based wastewater treatment systems. Moreover, suggestions are proposed for further research and development.

Occurrence of nonylphenol, nonylphenol ethoxylate surfactants and nonylphenol carboxylic acids in wastewater in Japan

2006 ◽  
Vol 53 (11) ◽  
pp. 27-33 ◽  
Author(s):  
K. Komori ◽  
Y. Okayasu ◽  
M. Yasojima ◽  
Y. Suzuki ◽  
H. Tanaka
Keyword(s):  
Wastewater Treatment ◽  
Degradation Rate ◽  
Treatment Process ◽  
Field Survey ◽  
Wastewater Treatment Plants ◽  
Anaerobic Conditions ◽  
Wastewater Treatment Process ◽  
Nonylphenol Ethoxylate ◽  
Related Substances ◽  
Treatment Processes

Nonylphenol (NP) is known to be a byproduct of nonylphenol ethoxylates (NPnEO) which are used as detergents in industry. It is important that not only NP but also NPnEO and their related substances are analysed when behaviour of NP in the wastewater treatment process is surveyed. NPnEO are biodegraded to shorter ethoxylate (EO) chain NPnEO or nonylphenol carboxylates (NPnEC) under aerobic conditions, and then biodegraded to NP under anaerobic conditions. NP is one of the suspected endocrine disruptors (ED). Moreover, shorter EO chain NPnEO has greater toxicity than longer EO chain NPnEO. We conducted a field survey of NP and its related substances in 20 wastewater treatment plants (WWTP). The concentrations (median) of NP and its related substances in the WWTPs' influent ranged from 0.1 to 8.3 μg/L, showing NP concentration as the same level as those previously reported. The reduction of the long EO chain NPnEO in the WWTPs was almost complete, while the removal efficiency for the short EO chain NPnEO was less significant than the long EO chain NPnEO, suggesting that the degradation rate of the short EO chain NPnEO was lower than that of the long EO chain NPnEO in the wastewater treatment processes.

Energy consumption model for wastewater treatment process control

2013 ◽  
Vol 67 (3) ◽  
pp. 667-674 ◽  
Author(s):  
Xiaoqi Huang ◽  
Honggui Han ◽  
Junfei Qiao
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Treatment Process ◽  
Wastewater Treatment Process ◽  
Elman Neural Network ◽  
Effluent Quality ◽  
Treatment Processes ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
The Relationship

Wastewater treatment must satisfy discharge requirements under specified constraints and have minimal operating costs (OC). The operating results of wastewater treatment processes (WWTPs) have significantly focused on both the energy consumption (EC) and effluent quality (EQ). To reflect the relationship between the EC and EQ of WWTPs directly, an extended Elman neural network-based energy consumption model (EENN-ECM) was studied for WWTP control in this paper. The proposed EENN-ECM was capable of predicting EC values in the treatment process. Moreover, the self-adaptive characteristic of the EENN ensured the modeling accuracy. A performance demonstration was carried out through a comparison of the EC between the benchmark simulation model No.1 (BSM1) and the EENN-ECM. The experimental results demonstrate that this EENN-ECM is more effective to model the EC of WWTPs.

scholarly journals REDISAIN INSTALASI PENGOLAHAN AIR LIMBAH (IPAL) RUMAH SAKIT UMUM DAERAH (RSUD) TIMIKA, PAPUA

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Setiyono Setiyono ◽  
Petrus Nugro Rahardjo
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Treatment Process ◽  
Human Life ◽  
Treatment Plant ◽  
The Other ◽  
Local Hospital ◽  
Wastewater Treatment Process ◽  
Combination Technique ◽  
Treatment Processes

Hospital is playing an important role in serving people who need to get health. On the other hand its occurrence causes some problems as well. One of them is the infectious wastewaters which are potential to cause a dangerous effect for human life. A lot of hospitals in Indonesia do not have the proper wastewater treatment plant yet. The local hospital of County Timika has already had a wastewater treatment plant (WWTP), but until now the capacity of the unit can not fulfil the required level of environmental standard yet. One of the problems is the technically improper treatment processes. To solve the problems, the WWTP must be redesigned and modifief by using a combination technique of anaerobik and aerobic biofilter treatment processes.The newly proposed design process of WWTP for the local hospital in TImika has already prepared to be applied. Keywords : Medical Wastewater Treatment Process, anerobic/aerobic biofilter 

Learning Bayesian networks based diagnosis system for wastewater treatment process with sensor data

2008 ◽  
Vol 58 (12) ◽  
pp. 2381-2393 ◽  
Author(s):  
Seong-Pyo Cheon ◽  
Sungshin Kim ◽  
Jongrack Kim ◽  
Changwon Kim
Keyword(s):  
Wastewater Treatment ◽  
Bayesian Model ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Sensor Data ◽  
Biological Phosphorus Removal ◽  
Wastewater Treatment Process ◽  
Diagnosis System ◽  
Treatment Processes ◽  
On Line

Contemporary technical capabilities allow an operator to easily monitor and control several remote wastewater treatment processes simultaneously but an on-line automatic diagnostic system has not yet been installed. In this paper, an on-line diagnostic system is proposed, designed and implemented for the lab-scale five-stage step-feed Enhanced Biological Phosphorus Removal plant based upon a learning Bayesian network. In order to practically diagnose wastewater treatment processes, a lab-scale pilot plant was built and the proposed on-line diagnostic method was applied to evaluate the performance of the algorithm. In experimental results, real abnormal conditions occurred 21 times in a three month period. The suggested on-line diagnosis system made correct predictions 14 times and incorrect predictions 7 times. Moreover, a comparison of the prediction results of the Bayesian model and learning Bayesian model clearly show that learning algorithm became more effective as time passed.

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