AIMC Topic: Biological Oxygen Demand Analysis
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Convolutional neural network-multi-kernel radial basis function neural network-salp swarm algorithm: a new machine learning model for predicting effluent quality parameters.
Environmental science and pollution research international
Aug 23, 2023
A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitoring and control of WWTPs, researchers develop different models and systems. This study introduces a ...
Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm.
Environmental science and pollution research international
Aug 2, 2023
Biochemical oxygen demand (BOD) is one of the most important parameters used for water quality assessment. Alternative methods are essential for accurately prediction of this parameter because the traditional method in predicting the BOD is time-cons...
Application of an artificial neural network for the improvement of agricultural drainage water quality using a submerged biofilter.
Environmental science and pollution research international
Sep 25, 2020
Artificial neural network (ANN) mathematical models, such as the radial basis function neural network (RBFNN), have been used successfully in different environmental engineering applications to provide a reasonable match between the measured and pred...
Assessing the biochemical oxygen demand using neural networks and ensemble tree approaches in South Korea.
Journal of environmental management
Jun 5, 2020
The biochemical oxygen demand (BOD), one of widely utilized variables for water quality assessment, is metric for the ecological division in rivers. Since the traditional approach to predict BOD is time-consuming and inaccurate due to inconstancies i...
BP-ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System.
International journal of environmental research and public health
Jul 10, 2019
Electro-oxidation is an effective approach for the removal of 2-chlorophenol from wastewater. The modeling of the electrochemical process plays an important role in improving the efficiency of electrochemical treatment and increasing our understandin...
An influent responsive control strategy with machine learning: Q-learning based optimization method for a biological phosphorus removal system.
Chemosphere
Jun 14, 2019
Biological phosphorus removal (BPR) is an economical and sustainable processes for the removal of phosphorus (P) from wastewater, achieved by recirculating activated sludge through anaerobic and aerobic (An/Ae) processes. However, few studies have sy...
Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods.
Environmental monitoring and assessment
May 19, 2019
Rivers, as the most prominent component of water resources, have a key role to play in increasing the life expectancy of living creatures. The essential characteristics of water pollutants can be described by water quality indices (WQIs). Hence, a fe...
Sequential treatment of paper and pulp industrial wastewater: Prediction of water quality parameters by Mamdani Fuzzy Logic model and phytotoxicity assessment.
Chemosphere
Apr 5, 2019
Recycling of industrial wastewater meeting quality standards for agricultural and industrial demands is a viable option. In this study, paper and pulp industrial wastewater were treated with three biological treatments viz. aerobic, anaerobic and seq...
The key role of inoculated sludge in fast start-up of sequencing batch reactor for the domestication of aerobic granular sludge.
Journal of environmental sciences (China)
Sep 7, 2018
Two types of inoculated sludges, granular sludge that had been stored at -20°C and activated sludge, were investigated for the domestication of aerobic granular sludges (AGSs) in sequencing batch reactors (SBRs). The results showed that using the sto...
Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques.
Environmental science and pollution research international
May 16, 2018
Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. Th...
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