A Study to Target Energy Consumption in Wastewater Treatment Plant using Machine Learning Algorithms

Das, Angsuman; Kumawat, Piyush Kumar; Chaturvedi, Nitin Dutt

doi:10.1016/b978-0-323-88506-5.50233-3

Cited by 8 publications

(6 citation statements)

References 2 publications

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“…Prior studies have applied machine learning algorithms to simulate the energy use for wastewater treatment plants (Bagherzadeh et al 2021;Das, Kumawat, and Chaturvedi (Antonopoulos and Gianniou 2022;Salvino, Gomes, and Bezerra 2022). Prior studies concluded that the ANN model was useful in simulating the energy efficiency of the water distribution system (Das, Kumawat, and Chaturvedi 2021;Salvino, Gomes, and Bezerra 2022). We also found that the DNN model, an ANN model with multiple hidden layers between the input and output layers, was the most effective machine learning algorithm in predicting the energy use of the water distribution system.…”

Section: Discussionmentioning

confidence: 91%

“…We found no comparative research on predicting energy use for the whole or the conveyance system. Prior studies have applied machine learning algorithms to simulate the energy use for wastewater treatment plants (Bagherzadeh et al 2021;Das, Kumawat, and Chaturvedi (Antonopoulos and Gianniou 2022;Salvino, Gomes, and Bezerra 2022). Prior studies concluded that the ANN model was useful in simulating the energy efficiency of the water distribution system (Das, Kumawat, and Chaturvedi 2021;Salvino, Gomes, and Bezerra 2022).…”

Section: Discussionmentioning

confidence: 99%

“…The California Energy Commission, the primary energy planning agency in California, has been using regression analysis to forecast energy use in agricultural and municipal water pumping (California Energy Commission 2005). Machine learning-based models have also proven to be useful in simulating the energy use for a wastewater treatment plant (Bagherzadeh et al 2021;Das, Kumawat, and Chaturvedi 2021;Li and Tang 2021;Zhang et al 2021) and a distribution system (Salvino, Gomes, and Bezerra 2022). Various studies have applied machine learning algorithms in forecasting water-related energy demand but were limited to forecasting the energy use for a single water facility.…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project

Kondolf

Sandoval-Solís

et al. 2022

Water Resour Manage

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Energy use forecasting is crucial in balancing the electricity supply and demand to reduce the uncertainty inherent in the inter-basin water transfer project. Energy use prediction supports the reliable water-energy supply and encourages cost-effective operation by improving generation scheduling. The objectives are to develop subsequent monthly energy use predictive models for the Mokelumne River Aqueduct in California, US. Partial objectives are to (a) compare the model performance of a baseline model (multiple linear regression (MLR)) to three machine learning-based models (random forest (RF), deep neural network (DNN), support vector regression (SVR)), (b) compare the model performance of the whole system to three subsystems (conveyance, treatment, distribution), and (c) conduct sensitivity analysis. We simulate a total of 64 cases (4 algorithms (MLR, RF, DNN, SVR) x 4 systems (whole, conveyance, treatment, distribution) x 4 scenarios (different combinations of independent variables). We concluded that the three machine learning algorithms showed better model performance than the baseline model as they reflected non-linear energy use characteristics for water transfer systems. Among the three machine learning algorithms, DNN models yielded higher model performance than RF and SVR models. Subsystems performed better than the whole system as the models more closely reflected the unique energy use characteristics of the subsystems. The best case was having water supply (t), water supply (t-1), precipitation (t), temperature (t), and population (y) as independent variables. These models can help water and energy utility managers to understand energy performance better and enhance the energy efficiency of their water transfer systems.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project

Kondolf

Sandoval-Solís

et al. 2022

Water Resour Manage

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“…The input layer takes the data to be analyzed and feeds it to one or more hidden layers, that perform the categorization function, before sending it to the output layer [30]. Many neurons make up a FNN, which is also the fundamental unit of information processing [31]. Weights connect each neuron, resulting in probability-weighted correlations among source and result [32].…”

Section: Feedforward Neural Network (Fnn)mentioning

confidence: 99%

Measurement of State of Charge of Lithium-Nickel Manganese Cobalt Battery using Artificial Neural Network and NARX Algorithm

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

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The battery's SoC is a crucial variable since it reflects its performance. An accurate estimation of SoC protects the battery, prevents overcharging or discharge, and extends its life time. Since most of the traditional methods use complex equations, ANN has been implemented to reduce the complications and provide better accuracy. In this research, Li-NMC with capacity rating of 2000mAh is used for the estimation of SoC. In this paper, Feedforward Neural Network (FNN) algorithm and Nonlinear Auto-Regressive network with exogenous inputs (NARX) have been used for designing a neural network model. Here, the performance matrixes of both neural network models have been compared and analyzed with the same dataset.

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“…Das et al [37] employed and trained sophisticated ML models, including ANN, recurrent neural networks (RNN), LSTM, and GRU, using real-world data, to forecast the EC of WWTPs. Through the utilization of four ML algorithms on the provided dataset, the optimal-fitting model was determined.…”

Section: Project Performance Prediction Modelsmentioning

confidence: 99%

Machine Learning-Based Energy Consumption Estimation of Wastewater Treatment Plants in Greece

Karadimos,

Anthopoulos

2023

Energies

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Amidst a global discourse on energy resources, it is imperative to provide decision makers with a comprehensive overview of energy consumption (EC) associated with various projects, particularly wastewater treatment plants (WWTPs). Ensuring compliance with stringent effluent quality criteria in the treatment of municipal wastewater necessitates a substantial EC, representing a predominant factor contributing to the operational expenses incurred by WWTP. Machine learning (ML) techniques can contribute to the estimation of the WWTPs’ EC, which requires efficient and accurate data. This article uses data from several municipal WWTP projects in Greece, which are examined in order to produce EC estimation models. Data were first statistically analyzed, according to the context of project attributes and the context of EC, and correlation analysis identified the appropriate predictive project variables. Then, the attribute selection function in Waikato Environment for Knowledge Analysis 3.8.4 (WEKA 3.8.4) software emphasized the most effective subset of variables. The extracted variables from the combination of the correlation analysis and the WEKA attribute function were used as input neurons for the construction of neural network (NN) models, in the Fast Artificial Neural Network Tool 1.2 (FANN Tool 1.2). The optimum NN model resulted in a mean squared error (MSE) of 8.99899 × 10−5 and was based on treatment capacity, flow rate, influent load, and served population as its inputs. Notably, the research highlights the potential generalizability of these models in Greece and beyond the Greek context, offering valuable tools for stakeholders to inform decision making, allocate resources efficiently, and improve energy-efficient designs, resulting in cost savings and sustainability benefits.

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A Study to Target Energy Consumption in Wastewater Treatment Plant using Machine Learning Algorithms

Cited by 8 publications

References 2 publications

Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project

Application of Machine Learning-based Energy Use Forecasting for Inter-basin Water Transfer Project

Measurement of State of Charge of Lithium-Nickel Manganese Cobalt Battery using Artificial Neural Network and NARX Algorithm

Machine Learning-Based Energy Consumption Estimation of Wastewater Treatment Plants in Greece

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