Designing Control Strategies of Aeration System in Biological WWTP

Piotrowski, Robert; Ujazdowski, Tomasz

doi:10.3390/en13143619

Cited by 16 publications

(5 citation statements)

References 15 publications

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“…Some studies have shown that COD, BOD, and NH 3 −N are correlated 44 and the energy to power blower fans are actually the main factor in electricity consumption for the removal of COD, BOD, and NH 3 −N. 45 Considering the fact that the smaller the number of highly correlated variables, the more convenient is the practical application of the model, and we assigned the importance of the overlap between variables to the high correlation variable, which led to the low importance of NH 3 −N i /NH 3 −N e . Depending on the request to the accuracy of the model, NH 3 −N i /NH 3 −N e can be neglected during practical usage, but to analyze the model more clearly and completely, we will still take NH 3 −N i /NH 3 −N e into consideration in the following discussion.…”

Section: Regressionsupporting

confidence: 64%

“…However, the removal efficiency of NH 3 –N, one of the primary pollutants in sewage, is of relatively low importance to UEC; this is because the primary function of most WWTPs in China is to remove organic matter rather than denitrification, which leads to the lower importance of NH 3 –Ni/NH 3 –N e . Some studies have shown that COD, BOD, and NH 3 –N are correlated and the energy to power blower fans are actually the main factor in electricity consumption for the removal of COD, BOD, and NH 3 –N . Considering the fact that the smaller the number of highly correlated variables, the more convenient is the practical application of the model, and we assigned the importance of the overlap between variables to the high correlation variable, which led to the low importance of NH 3 –N i /NH 3 –N e .…”

Section: Resultsmentioning

confidence: 99%

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Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

2021

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Wastewater treatment plants (WWTPs) can account for up to 1% of a country’s energy consumption. Meanwhile, WWTPs have high energy-saving potential. To achieve this, it is necessary to establish appropriate energy consumption models for WWTPs. Several recent models have been developed using logarithmic, exponential, or linear functions. However, the behavior of WWTPs is non-linear and difficult to fit with simple functions, particularly for non-numerical variables. Thus, traditional modeling methods cannot effectively describe the relationship between water and energy in WWTPs. Therefore, a machine learning method was adopted in this study to investigate the energy consumption in WWTPs; a novel energy consumption model with a non-numerical variable (discharge standard) for WWTPs was developed using the random forest algorithm. The model can also predict the energy consumption of WWTPs after upgrading discharge standards. We found that the unit electricity consumption of WWTPs exhibited an average increase of 17% after the effluent discharge standard was increased from class I B to class I A (as per China’s classification). The correlation coefficient of the model was 0.702. Thus, the developed model can provide a better understanding of energy efficiency in WWTPs.

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Section: Regressionsupporting

confidence: 64%

Section: Resultsmentioning

confidence: 99%

Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

2021

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“…After some discussion of the literature used in Section 2.1, explanations of the hydraulic model [68][69][70][71] (gas flow, pressure, and pipe diameter's mutual dependence) used for the given example of a gas network can be found in Section 2.2 (hydraulics for water networks are explained in [72][73][74][75][76][77] and those for ventilation systems in [78], while an analogy with resistances in electrical networks can be seen in [79,80]). Two iterative methods for the optimisation of pipe diameters are given further in Section 2.3, where the methods are described using an illustrative example.…”

Section: Methodsmentioning

confidence: 99%

“…Relation between gas flow and pressure drop used in this article by Renouard [68,69] (see also [18,70]); • Flow resistance in systems of conduits: gas [71], water [72][73][74][75][76][77] and air [78];…”

mentioning

confidence: 99%

Two Iterative Methods for Sizing Pipe Diameters in Gas Distribution Networks with Loops

Brkić

2024

Computation

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Closed-loop pipe systems allow the possibility of the flow of gas from both directions across each route, ensuring supply continuity in the event of a failure at one point, but their main shortcoming is in the necessity to model them using iterative methods. Two iterative methods of determining the optimal pipe diameter in a gas distribution network with closed loops are described in this paper, offering the advantage of maintaining the gas velocity within specified technical limits, even during peak demand. They are based on the following: (1) a modified Hardy Cross method with the correction of the diameter in each iteration and (2) the node-loop method, which provides a new diameter directly in each iteration. The calculation of the optimal pipe diameter in such gas distribution networks relies on ensuring mass continuity at nodes, following the first Kirchhoff law, and concluding when the pressure drops in all the closed paths are algebraically balanced, adhering to the second Kirchhoff law for energy equilibrium. The presented optimisation is based on principles developed by Hardy Cross in the 1930s for the moment distribution analysis of statically indeterminate structures. The results are for steady-state conditions and for the highest possible estimated demand of gas, while the distributed gas is treated as a noncompressible fluid due to the relatively small drop in pressure in a typical network of pipes. There is no unique solution; instead, an infinite number of potential outcomes exist, alongside infinite combinations of pipe diameters for a given fixed flow pattern that can satisfy the first and second Kirchhoff laws in the given topology of the particular network at hand.

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“…This also translates into the efficiency of oxygenation and electricity consumption. In this case, it is necessary to properly model and control the aeration system in the SBR reactor [59,60] In the case of rapid and intensive inflows of wastewater to the wastewater treatment plant, there was a change in the 8 h cycle to the 6 h cycle in SBR reactors, which is a compensation for the buffer capacity of the facility. However, at that time, cases of deterioration in the efficiency of total suspended solids removal from wastewater were observed.…”

Section: Assessment Of the Efficiency Of Sequencing Batch Reactorsmentioning

confidence: 99%

Impact of Uneven Flow Wastewater Distribution on the Technological Efficiency of a Sequencing Batch Reactor

Masłoń

2022

Sustainability

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Variability in the load of pollutants significantly influences the efficiency of activated sludge technology in municipal wastewater treatment plants, both in terms of flow systems and in sequencing batch reactors (SBR). Diversified inflow of wastewater to the treatment plant has a significant impact on the technological efficiency of sequencing batch reactors. Additionally, this problem is intensified in technological systems in which there is no storage tank for raw wastewater. It is assumed, however, that the flexible operation of an SBR reactor allows it to be easily adapted to a variable load of pollutants. The aim of the article is to present the effects of uneven wastewater inflow on the operation of sequencing batch reactors using the example of the wastewater treatment plant in Rabka-Zdrój (Poland). The conducted research has shown that, in wastewater treatment plants, the use of sequencing batch reactors as an independent element of biological wastewater treatment does not always ensure a high degree of pollutant removal in the event of a very uneven wastewater inflow. Therefore, the use treated wastewater equalizing tanks is recommended, which can additionally clean residual contaminants from wastewater.

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Designing Control Strategies of Aeration System in Biological WWTP

Cited by 16 publications

References 15 publications

Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

Two Iterative Methods for Sizing Pipe Diameters in Gas Distribution Networks with Loops

Impact of Uneven Flow Wastewater Distribution on the Technological Efficiency of a Sequencing Batch Reactor

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