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

2021 ◽  
Author(s):  
Shike Zhang ◽  
Hongtao Wang ◽  
Arturo A. Keller
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Energy Consumption ◽  
Wastewater Treatment Plants ◽  
Energy Consumption Model ◽  
Consumption Model

scholarly journals Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2860 ◽  
Author(s):  
Jee-Heon Kim ◽  
Nam-Chul Seong ◽  
Wonchang Choi
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Prediction Accuracy ◽  
Learning Algorithm ◽  
Training Data ◽  
Machine Learning Algorithm ◽  
Air Conditioning System ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
Input Variables

This study was conducted to develop an energy consumption model of a chiller in a heating, ventilation, and air conditioning system using a machine learning algorithm based on artificial neural networks. The proposed chiller energy consumption model was evaluated for accuracy in terms of input layers that include the number of input variables, amount (proportion) of training data, and number of neurons. A standardized reference building was also modeled to generate operational data for the chiller system during extended cooling periods (warm weather months). The prediction accuracy of the chiller’s energy consumption was improved by increasing the number of input variables and adjusting the proportion of training data. By contrast, the effect of the number of neurons on the prediction accuracy was insignificant. The developed chiller model was able to predict energy consumption with 99.07% accuracy based on eight input variables, 60% training data, and 12 neurons.

Energy consumption model for wastewater treatment process control

2013 ◽  
Vol 67 (3) ◽  
pp. 667-674 ◽  
Author(s):  
Xiaoqi Huang ◽  
Honggui Han ◽  
Junfei Qiao
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Treatment Process ◽  
Wastewater Treatment Process ◽  
Elman Neural Network ◽  
Effluent Quality ◽  
Treatment Processes ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
The Relationship

Wastewater treatment must satisfy discharge requirements under specified constraints and have minimal operating costs (OC). The operating results of wastewater treatment processes (WWTPs) have significantly focused on both the energy consumption (EC) and effluent quality (EQ). To reflect the relationship between the EC and EQ of WWTPs directly, an extended Elman neural network-based energy consumption model (EENN-ECM) was studied for WWTP control in this paper. The proposed EENN-ECM was capable of predicting EC values in the treatment process. Moreover, the self-adaptive characteristic of the EENN ensured the modeling accuracy. A performance demonstration was carried out through a comparison of the EC between the benchmark simulation model No.1 (BSM1) and the EENN-ECM. The experimental results demonstrate that this EENN-ECM is more effective to model the EC of WWTPs.

scholarly journals Detection of untreated sewage discharges to watercourses using machine learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter Hammond ◽  
Michael Suttie ◽  
Vaughan T. Lewis ◽  
Ashley P. Smith ◽  
Andrew C. Singer
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Training Data ◽  
Regulatory Oversight ◽  
Untreated Wastewater ◽  
Untreated Sewage ◽  
Water Companies ◽  
Analytical Approaches ◽  
Sewage Discharges

AbstractMonitoring and regulating discharges of wastewater pollution in water bodies in England is the duty of the Environment Agency. Identification and reporting of pollution events from wastewater treatment plants is the duty of operators. Nevertheless, in 2018, over 400 sewage pollution incidents in England were reported by the public. We present novel pollution event reporting methodologies to identify likely untreated sewage spills from wastewater treatment plants. Daily effluent flow patterns at two wastewater treatment plants were supplemented by operator-reported incidents of untreated sewage discharges. Using machine learning, known spill events served as training data. The probability of correctly classifying a randomly selected pair of ‘spill’ and ‘no-spill’ effluent patterns was above 96%. Of 7160 days without operator-reported spills, 926 were classified as involving a ‘spill’. The analysis also suggests that both wastewater treatment plants made non-compliant discharges of untreated sewage between 2009 and 2020. This proof-of-principle use of machine learning to detect untreated wastewater discharges can help water companies identify malfunctioning treatment plants and inform agencies of unsatisfactory regulatory oversight. Real-time, open access flow and alarm data and analytical approaches will empower professional and citizen scientific scrutiny of the frequency and impact of untreated wastewater discharges, particularly those unreported by operators.

scholarly journals Machine Learning-Based Energy System Model for Tissue Paper Machines

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 655
Author(s):  
Huanhuan Zhang ◽  
Jigeng Li ◽  
Mengna Hong
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Gradient Boosting ◽  
Paper Machine ◽  
Steam Consumption ◽  
Tissue Paper ◽  
Paper Machines ◽  
Energy Consumption Model ◽  
Extreme Gradient Boosting ◽  
Consumption Model

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

scholarly journals Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1149
Author(s):  
Pedro Oliveira ◽  
Bruno Fernandes ◽  
Cesar Analide ◽  
Paulo Novais
Keyword(s):  
Wastewater Treatment ◽  
Energy Consumption ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Wastewater Treatment Plants ◽  
Learning Approach ◽  
Sustainable Cities ◽  
Short Term ◽  
Long Short Term Memory ◽  
Gated Recurrent Units

A major challenge of today’s society is to make large urban centres more sustainable. Improving the energy efficiency of the various infrastructures that make up cities is one aspect being considered when improving their sustainability, with Wastewater Treatment Plants (WWTPs) being one of them. Consequently, this study aims to conceive, tune, and evaluate a set of candidate deep learning models with the goal being to forecast the energy consumption of a WWTP, following a recursive multi-step approach. Three distinct types of models were experimented, in particular, Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), and uni-dimensional Convolutional Neural Networks (CNNs). Uni- and multi-variate settings were evaluated, as well as different methods for handling outliers. Promising forecasting results were obtained by CNN-based models, being this difference statistically significant when compared to LSTMs and GRUs, with the best model presenting an approximate overall error of 630 kWh when on a multi-variate setting. Finally, to overcome the problem of data scarcity in WWTPs, transfer learning processes were implemented, with promising results being achieved when using a pre-trained uni-variate CNN model, with the overall error reducing to 325 kWh.

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