Dissolved Oxygen Control in Activated Sludge Process Using a Neural Network-Based Adaptive PID Algorithm

Du, Xianjun; Wang, Junlu; Jegatheesan, Veeriah; Shi, Guohua

doi:10.3390/app8020261

Cited by 87 publications

(40 citation statements)

References 27 publications

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“…On the other hand, DO was significantly higher during the June-August sampling periods than the March-May periods in Kakoba (t = 6.214, p = 0.003). The much higher DO in effluents than the EPA and NEMA standard might have resulted from the dissolution of atmospheric oxygen into the effluents during the step-aeration activated sludge treatment process [35]. The higher DO during the June-August sampling periods (dry season) than the March-May periods (wet season) in Kakoba could be associated with increased in wash of organic wastes into the treatment plant during the wet season compared to the dry season.…”

Section: Dissolved Oxygen (Do)mentioning

confidence: 98%

The Physico-Chemical Quality of Effluents of Selected Sewage Treatment Plants Draining into River Rwizi, Mbarara Municipality, Uganda

Atwebembeire¹,

Andama²,

Yatuha³

et al. 2019

JWARP

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As population increases in urban areas, the domestic and industrial activities increase resulting in an increase in the volumes of wastewater and anthropogenic pollution, hence posing a threat to public health and environment. This study assessed the physical chemical quality of two main sewage plant effluents discharging into River Rwizi. Effluent water samples were analyzed for dissolved oxygen, colour, turbidity, total suspended solid, total iron, phosphates, alkalinity, magnesium, calcium carbonate, temperature, pH, ammonium, electrical conductivity, chloride and nitrates. Parameters were analyzed following standard methods of APHA (1985). The values obtained were compared with EPA (2001), NWSC (2015) and NEMA (1999) standards for waste water. Results showed that the mean values most of the parameters tested were higher than the recommended EPA, NWSC and NEMA standards. The mean colour was 1627.67˚C and 1414.33˚C in Kakoba and Taso sewage effluents respectively compared to EPA (20-150), NWSC (500) and NEMA (300) standards (p > 0.05). The mean alkalinity was 1390.17 mg/l and 1308.33 mg/l for Kakoba and Taso respectively compared with EPA (400) and NWSC (800) standards. DO had a mean concentration of 68.27 mg/l and 63.03 mg/l in Taso and Kakoba respectively compared to EPA and NEMA standard of 5 mg/l. Mg was 243.29 mg/l and 246.49 mg/l in Kakoba and Taso sewage effluents respectively compared to NEMA standard for waste water of 100 mg/l (p > 0.05). The mean pH was 8. 26 and 8.16 in Taso and Kakoba sewage effluents respectively compared to NWSC and NEMA standard of 6.0 -8.0. Phosphate mean concentration levels were 32.2 mg/l and 27.11 mg/l for Taso and Kakoba respectively compared to standards of EPA (0.5 -0.7 mg/l) and NEMA Journal of Water Resource and Protection sewage effluents respectively compared to NWSC maximum permissible limit of 5 mg/l. The mean NH 4 concentration was 385.33 mg/l (Kakoba) and 50.0 mg/l (Taso) compared to the EPA guideline range (0.2 -4 mg/l). Chloride (Cl) had a mean of 833.33 mg/l in Kakoba compared to EPA (250 mg/l), NWSC and NEMA (500 mg/l) standards. Therefore the study recommends for effective treatment of waste effluents from Kakoba and Taso sewage treatment plants before recycling in order to avoid pollution of river Rwizi.

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Section: Dissolved Oxygen (Do)mentioning

confidence: 98%

The Physico-Chemical Quality of Effluents of Selected Sewage Treatment Plants Draining into River Rwizi, Mbarara Municipality, Uganda

Atwebembeire¹,

Andama²,

Yatuha³

et al. 2019

JWARP

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Section: Featured Applicationmentioning

confidence: 99%

“…Linear methods mainly include multiple regression methods (MLR), partial least squares (PLS), and principal component regression (PCR) [25]. Nonlinear methods include support vector regression (SVR) and artificial neural network (ANN) methods [26][27][28][29][30].However, the QSPR study based on various artificial intelligence algorithms also has some shortcomings, such as high computational cost [31]. Therefore, it is necessary to develop a QSPR model with high accuracy, high efficiency, and good stability.The pKa value is a key parameter of some compounds, but its determination experiments are cumbersome.…”

mentioning

confidence: 99%

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

Zhang

Liu

et al. 2018

Applied Sciences

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Featured Application:The proposed hybrid intelligent model can be applied in engineering design, material performance prediction, numerical calculation, and the prediction of physical and chemical properties.Abstract: A quantitative structure-property relationship (QSPR) model is proposed to explore the relationship between the pKa of various compounds and their structures. Through QSPR studies, the relationship between the structure and properties can be obtained. In this study, a novel chaos-enhanced accelerated particle swarm algorithm (CAPSO) is adopted to screen molecular descriptors and optimize the weights of back propagation artificial neural network (BP ANN). Then, the QSPR model based on CAPSO and BP ANN is proposed and named the CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm was a reliable method for screening molecular descriptors. The five molecular descriptors obtained by the CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed that the CAPSO BP ANN model exhibited good performance in predicting the pKa values of various compounds. The absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the high prediction accuracy. The proposed hybrid intelligent model can be applied in engineering design and the prediction of physical and chemical properties.The establishment of the QSPR model mainly involves the following steps: acquisition of experimental data, construction and optimization of the molecular structure, calculation and screening of molecular descriptors, establishment and verification of the model, etc. First of all, the variable selection is important in many fields, such as spectroscopy [7,8], QSPR [9,10], and other fields [11,12]. The selection of molecular descriptors largely determines the quality of the QSPR model [13][14][15]. The step of molecular descriptor screening aims to reflect more structural information so that there is no noise in the descriptors. Many methods have been developed to screen molecular descriptors and can be mainly divided into two categories [16][17][18]. The first category includes the common methods, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), and forward/backward/bi-directional stepwise multiple linear regression (MLR). The second includes the modern search algorithms, such as genetic algorithm (GA), simulated annealing algorithm (SA), ant colony algorithm (AC), particle swarm optimization (PSO), and other swarm intelligence algorithms [7,11,[19][20][21]. The common methods are the most simple and efficient, but their overall performances are low in complex nonlinear problems. The modern search algorithms based on the optimization strategy have obvious advantages and can search for optimal variables and deal with complex large data points. The model establishment is important in the QSPR study and commonly used QSPR model...

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“…Simulations results showed an improvement in effluent quality and reduction in energy use. A DO control system using neural network-based adaptive PID algorithm was proposed [12]. The powerful learning and adaptive ability of the radial basic function neural network were applied for adaptive adjustment of the PID parameters.…”

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confidence: 99%

Improving SBR Performance Alongside with Cost Reduction through Optimizing Biological Processes and Dissolved Oxygen Concentration Trajectory

2019

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Authors of this paper take under investigation the optimization of biological processes during the wastewater treatment in sequencing batch reactor (SBR) plant. A designed optimizing supervisory controller generates the dissolved oxygen (DO) trajectory for the lower level parts of the hierarchical control system. Proper adjustment of this element has an essential impact on the efficiency of the wastewater treatment process as well as on the costs generated by the plant, especially by the aeration system. The main goal of the presented solution is to reduce the plant energy consumption and to maintain the quality of effluent in compliance with the water-law permit. Since the optimization is nonlinear and includes variations of different types of variables, to solve the given problem the authors performed simulation tests and decided to implement a hybrid of two different optimization algorithms: artificial bee colony (ABC) and direct search algorithm (DSA). Simulation tests for the wastewater treatment plant case study are presented.

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Dissolved Oxygen Control in Activated Sludge Process Using a Neural Network-Based Adaptive PID Algorithm

Cited by 87 publications

References 27 publications

The Physico-Chemical Quality of Effluents of Selected Sewage Treatment Plants Draining into River Rwizi, Mbarara Municipality, Uganda

The Physico-Chemical Quality of Effluents of Selected Sewage Treatment Plants Draining into River Rwizi, Mbarara Municipality, Uganda

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

Improving SBR Performance Alongside with Cost Reduction through Optimizing Biological Processes and Dissolved Oxygen Concentration Trajectory

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