A Novel Check-List Strategy to Evaluate the Potential of Operational Improvements in Wastewater Treatment Plants

Zejda, Vojtěch; Máša, Vítězslav; Václavková, Šárka; Skryja, Pavel

doi:10.3390/en13195005

Cited by 3 publications

(2 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of the water industry, water demand is expected to double by 2035 [2]; therefore, large amounts of additional energy are expected to be consumed for water supply and wastewater treatment, unless the energy efficiency of plants is increased. Wastewater treatment plants (WWTP) have been found to have considerable potential for reducing energy consumption and costs [3]- [5], and several strategies for the energyefficient operation of WWTPs are being introduced [6]. The main energy-intensive tasks in a WWTP include pumping and aeration processes.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Smart Joint Control Scheme for On/Off Pumping Systems in Wastewater Treatment Plants

et al. 2021

View full text Add to dashboard Cite

In this paper, we propose a deep reinforcement learning (DRL) based predictive control scheme for reducing the energy consumption and energy cost of pumping systems in wastewater treatment plants (WWTP), in which the pumps are operated in a binary mode, using on/off signals. As global energy consumption increases, the efficient operation of energy-intensive facilities has also become important. A WWTP in Busan, Republic of Korea is used as the target of this study. This WWTP is a large energyconsuming facility, and the pumping station accounts for a significant portion of the energy consumption of the WWTP. The framework of the proposed scheme consists of a deep neural network (DNN) model for forecasting wastewater inflow and a DRL agent for controlling the on/off signals of the pumping system, where proximal policy optimization (PPO) and deep Q-neural network (DQN) are employed as the DRL agents. To implement smart control with DRL, a reward function is designed to consider the energy consumption amount and electricity price information. In particular, new features and penalty factors for pump switching, which are essential for preventing pump wear, are also considered. The performance of our designed DRL agents is compared with those of WWTP experts and conventional approaches such as scheduling method and model predictive control (MPC), in which integer linear programming (ILP) optimization is employed. Results show that the designed agents outperform the other approaches in terms of compliance with operating rules and reducing energy costs.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Smart Joint Control Scheme for On/Off Pumping Systems in Wastewater Treatment Plants

et al. 2021

View full text Add to dashboard Cite

show abstract

“…One of the industrial sectors where this tendency is observed corresponds to the Wastewater Treatment Plants (WWTPs), which are characterised by running very complex processes where individual operations and actions can change the whole operation of the plant [ 7 ]. For that reason, a huge number of control loops are required in order to ensure that each individual operation is correctly performed.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning in Wastewater Treatment Plant Control Design: From Conventional to Long Short-Term Memory-Based Controllers

Pisa

Morell

Vilanova

et al. 2021

Sensors

View full text Add to dashboard Cite

In the last decade, industrial environments have been experiencing a change in their control processes. It is more frequent that control strategies adopt Artificial Neural Networks (ANNs) to support control operations, or even as the main control structure. Thus, control structures can be directly obtained from input and output measurements without requiring a huge knowledge of the processes under control. However, ANNs have to be designed, implemented, and trained, which can become complex and time-demanding processes. This can be alleviated by means of Transfer Learning (TL) methodologies, where the knowledge obtained from a unique ANN is transferred to the remaining nets reducing the ANN design time. From the control viewpoint, the first ANN can be easily obtained and then transferred to the remaining control loops. In this manuscript, the application of TL methodologies to design and implement the control loops of a Wastewater Treatment Plant (WWTP) is analysed. Results show that the adoption of this TL-based methodology allows the development of new control loops without requiring a huge knowledge of the processes under control. Besides, a wide improvement in terms of the control performance with respect to conventional control structures is also obtained. For instance, results have shown that less oscillations in the tracking of desired set-points are produced by achieving improvements in the Integrated Absolute Error and Integrated Square Error which go from 40.17% to 94.29% and from 34.27% to 99.71%, respectively.

show abstract

Evaluation of airlift pump performance for vertical conveying of coal particles

Enany¹,

Shevchenko²,

Drebenshtedt³

2023

Int J Coal Sci Technol

View full text Add to dashboard Cite

One of the crucial aspects of reducing air consumption when conveying particles with an airlift pump is to know the factors that affect the process of particle motion at an initial velocity of zero. To determine the influence of submergence ratio and physical properties of particles (such as size, shape, and mass) on the onset of vertical particle motion, the airlift pump was taken as the research object, and spherical glass together with irregular shaped coal were used as experimental test particles. The results show that unlike the water-solid environment, the start of particle motion in the water-air mixture does not always occur at a certain value of superficial water velocity and this value also increases with increasing submergence level. Among the parameters considered, the role of submergence ratio is much more effective than the dimensions and the shape of the particle, because by increasing submergence from 0.3 to 0.8, it is possible to reduce air consumption by up to 8 times. Based on this study the corresponding theoretical model derived by Fujimoto et al. is optimized, wherein the overall agreement between the modified theory and present experimental data is particularly good. Contrary to Fujimoto, the minimum superficial water velocity for lifting solids in the air-water mixture is not always smaller than water ambient which indicates on optimum submergence ratio higher than 0.7. Finally, a new criterion was introduced to describe the moment of onset of the particle motion as a function of the superficial fluid velocity ratio for each submergence value.

show abstract

A Novel Check-List Strategy to Evaluate the Potential of Operational Improvements in Wastewater Treatment Plants

Cited by 3 publications

References 66 publications

Deep Reinforcement Learning-Based Smart Joint Control Scheme for On/Off Pumping Systems in Wastewater Treatment Plants

Deep Reinforcement Learning-Based Smart Joint Control Scheme for On/Off Pumping Systems in Wastewater Treatment Plants

Transfer Learning in Wastewater Treatment Plant Control Design: From Conventional to Long Short-Term Memory-Based Controllers

Evaluation of airlift pump performance for vertical conveying of coal particles

Contact Info

Product

Resources

About