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 Sensitivity analysis of a municipal wastewater treatment plant model

2019 ◽  
Vol 252 ◽  
pp. 05010
Author(s):  
Paweł Król ◽  
Alberto Gallina ◽  
Michał Lubieniecki ◽  
Tadeusz Uhl ◽  
Tadeusz Żaba
Keyword(s):  
Wastewater Treatment ◽  
Municipal Wastewater ◽  
Treatment Process ◽  
Biological Process ◽  
Treatment Plant ◽  
Numerical Procedure ◽  
Municipal Wastewater Treatment ◽  
Wastewater Treatment Process ◽  
Plant Model ◽  
The Relationship

Waste management is a crucial process to keep the environment in wholesome conditions. The environmental impact of solid waste and wastewater is reduced through construction of appropriate disposal installations. The objective of wastewater treatment in biological reactors is to control the process of biomaterial growth by aerating the sewage content. The process is complex, as depending on a plenty of parameters. In the last decades an effective numerical model, called the Activated Sludge Model (ASM), has been proposed for describing the biological process. The ASM is implemented in the Benchmark Simulation Model (BSM) that simulates the whole wastewater treatment process. The most important parameters in ASM are the kinetic and stoichiometric coefficients. The former describes rate-concentration dependence. The latter characterises the relationship between the components of chemical reactions taking place in the cleaning process. Above parameters are determined by on-site calibration and their importance is relevant during the development of numeric models. This paper aims to examine the influence of kinetic and stoichiometric parameters on the wastewater treatment process of a plant in Płaszów, Kraków. The analysis is carried out by a sample-based numerical procedure. It highlights the ASM parameters playing a major role in the treatment process. Results obtained from the analysis are important for future validation and optimisation processes.

scholarly journals A Novel Model with GA Evolving FWNN for Effluent Quality and Biogas Production Forecast in a Full-Scale Anaerobic Wastewater Treatment Process

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Zehua Huang ◽  
Renren Wu ◽  
XiaoHui Yi ◽  
Hongbin Liu ◽  
Jiannan Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Treatment Process ◽  
Biogas Production ◽  
Anaerobic Treatment ◽  
Production Rates ◽  
Wastewater Treatment Process ◽  
Effluent Quality ◽  
Anaerobic Wastewater Treatment ◽  
Novel Model

The anaerobic treatment process is a complicated multivariable system that is nonlinear and time varying. Moreover, biogas production rates are an important indicator for reflecting operational performance of the anaerobic treatment system. In this work, a novel model fuzzy wavelet neural network based on the genetic algorithm (GA-FWNN) that combines the advantages of the genetic algorithm, fuzzy logic, neural network, and wavelet transform was established for prediction of effluent quality and biogas production rates in a full-scale anaerobic wastewater treatment process. Moreover, the dataset was preprocessed via a self-adapted fuzzy c-means clustering before training the network and a hybrid algorithm for acquiring the optimal parameters of the multiscale GA-FWNN for improving the network precision. The analysis results indicate that the FWNN with the optimal algorithm had a high speed of convergence and good quality of prediction, and the FWNN model was more advantageous than the traditional intelligent coupling models (NN, WNN, and FNN) in prediction accuracy and robustness. The determination coefficients R2 of the FWNN models for predicting both the effluent quality and biogas production rates were over 0.95. The proposed model can be used for analyzing both biogas (methane) production rates and effluent quality over the operational time period, which plays an important role in saving energy and eliminating pollutant discharge in the wastewater treatment system.

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.

scholarly journals Significant energy savings by optimising membrane design in the multi-stage reverse osmosis wastewater treatment process

2018 ◽  
Vol 4 (3) ◽  
pp. 449-460 ◽  
Author(s):  
M. A. Al-Obaidi ◽  
C. Kara-Zaïtri ◽  
I. M. Mujtaba
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Treatment Process ◽  
Energy Savings ◽  
Design Parameters ◽  
Total Energy Consumption ◽  
Wastewater Treatment Process ◽  
Multi Stage ◽  
Proper Design ◽  
Membrane Design

The total energy consumption of the multi stage spiral wound RO process has continuously improved as a result of discovering the proper design parameters for each module that can save more energy besides keeping high removal of chlorophenol.

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