Modeling and optimization of a wastewater pumping system with data-mining methods

Zhang, Zijun; Kusiak, Andrew; Zeng, Yaohui; Wei, Xiupeng

doi:10.1016/j.apenergy.2015.11.061

Cited by 77 publications

(33 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the revised literature, the present paper produces the following original contributions. Applies a model-free and data-driven control approach based in RL, in contrast to the use of meta-heuristics [31,32,33] or fuzzy logic control [4]. The con-6 trol philosophy is focused on operating the tank with a variable set-point wastewater level, instead of controlling the frequency increase/decrease rate like in [4].…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Data-driven predictive energy optimization in a wastewater pumping station

et al. 2019

View full text Add to dashboard Cite

Urban wastewater sector is being pushed to optimize processes in order to reduce energy consumption without compromising its quality standards. Energy costs can represent a significant share of the global operational costs (between 50% and 60%) in an intensive energy consumer. Pumping is the largest consumer of electrical energy in a wastewater treatment plant. Thus, the optimal control of pump units can help the utilities to decrease operational costs. This work describes an innovative predictive control policy for wastewater variable-frequency pumps that minimize electrical energy consumption, considering uncertainty forecasts for wastewater intake rate and information collected by sensors accessible through the Supervisory Control and Data Acquisition system. The proposed control method combines statistical learning (regression and predictive models) and deep reinforcement learning (Proximal Policy Optimization). The following main original contributions are produced: i) model-free and data-driven predictive control;ii) control philosophy focused on operating the tank with a variable wastewater setpoint level; iii) use of supervised learning to generate synthetic data for pre-training the reinforcement learning policy, without the need to physically interact with the system.The results for a real case-study during 90 days show a 16.7% decrease in electrical energy consumption while still achieving a 97% reduction in the number of alarms (tank level above 7.2 meters) when compared with the current operating scenario (operating with a fixed set-point level). The numerical analysis showed that the proposed data-driven method is able to explore the trade-off between number of alarms and consumption minimization, offering different options to decision-makers.

show abstract

Section: Related Work and Contributionsmentioning

confidence: 99%

Data-driven predictive energy optimization in a wastewater pumping station

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Till now, such advanced technology was successfully been tested in different solutions. For example, in control pumping stations, the data mining integrated with an artificial immuno-neural network was verified [56,57] and the neural network-particle swarm optimization was used to maximize the methane production in an anaerobic digester [58]. Moreover, soft-sensors, e.g., BOD, were also developed using advanced data mining models to avoid aeration absence in bioreactors [59].…”

Section: Advances In the Supervisory Control System Supported By Modementioning

confidence: 99%

Advanced Supervisory Control System Implemented at Full-Scale WWTP—A Case Study of Optimization and Energy Balance Improvement

Drewnowski

2019

Water

View full text Add to dashboard Cite

In modern and cost-effective Wastewater Treatment Plants (WWTPs), processes such as aeration, chemical feeds and sludge pumping are usually controlled by an operating system integrated with online sensors. The proper verification of these data-driven measurements and the control of different unit operations at the same time has a strong influence on better understanding and accurately optimizing the biochemical processes at WWTP—especially energy-intensive biological parts (e.g., the nitrification zone/aeration system and denitrification zone/internal recirculation). In this study, by integrating a new powerful PreviSys with data driven from the Supervisory Control and Data Acquisition (SCADA) software and advanced algorithms such as Model Predictive Control (MPC) by using the WEST computer platform, it was possible to conduct different operation strategies for optimizing and improving the energy balance at a full-scale “Klimzowiec” WWTP located in Chorzow (Southern Poland). Moreover, the novel concept of double-checking online data-driven measurements (from installed DO, NO3, NH4 sensors, etc.) by mathematical modelling and computer simulation predictions was applied in order to check the data uncertainty and develop a support operator system (SOS)—an additional tool for the widely-used in-operation and control of modern and cost-effective WWTPs. The results showed that by using sophisticated PreviSys technology, a better understanding and accurate optimization of biochemical processes, as well as more sustainable WWTP operation, can be achieved.

show abstract

“…Numerical experiments showed possibilities for significant reduction of energy costs. Zhang et al [16,17] discussed data-mining methods applied to a wastewater pumping system for its optimization. Based on measurements collected in a real system, they proposed an energy optimization model for four configurations, allowing for 6-14% of energy savings.…”

Section: Introductionmentioning

confidence: 99%

Genetic optimization and experimental verification of complex parallel pumping station with centrifugal pumps

Olszewski

2016

Applied Energy

View full text Add to dashboard Cite

Modeling and optimization of a wastewater pumping system with data-mining methods

Cited by 77 publications

References 30 publications

Data-driven predictive energy optimization in a wastewater pumping station

Data-driven predictive energy optimization in a wastewater pumping station

Advanced Supervisory Control System Implemented at Full-Scale WWTP—A Case Study of Optimization and Energy Balance Improvement

Genetic optimization and experimental verification of complex parallel pumping station with centrifugal pumps

Contact Info

Product

Resources

About