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Big Data and Artificial Intelligence in Sustainable Water and Wastewater Management

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Waste and Recycling".

Deadline for manuscript submissions: closed (22 June 2022) | Viewed by 22396

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Prof. Dr. Jae Kwang (Jim) Park

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Guest Editor

Department of Civil & Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
Interests: wastewater; nutrient removal; artificial intelligence applications to environmental engineering fields; river restoration; water quality modeling; waste recycling; bioremediation
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Special Issue Information

Dear Colleagues,

We have been scratching our head on big data that are collected in water and wastewater management with the question, “now what?” Artificial intelligence (AI) provides new opportunities to use big data for better insight into optimization, control, and sustainability. Furthermore, deterministic models are limited in real-time prediction due to the inability to formulate complex processes. There have been substantial research activities in water and wastewater industries to apply AI for big data analysis. This special issue is aimed to compile research activities that occurred all over the world on water and wastewater management and to provide future research directions.

The scope of the special issue is as follows:

Water quality management in rivers, lakes, and ocean
Decision making for infrastructure investment
Water treatment plant operation and optimization
Wastewater treatment plant operation and optimization
Energy management in water and wastewater management

Prof. Dr. Jae Kwang (Jim) Park
Guest Editor

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Keywords

artificial intelligence
big data
infrastructure
machine learning
sustainability
wastewater treatment
water quality
water treatment

Published Papers (5 papers)

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Research

15 pages, 8158 KiB

Open AccessArticle

Prediction of Sludge Volume Index in a Wastewater Treatment Plant Using Recurrent Neural Network

by Praewa Wongburi and Jae K. Park

Sustainability 2022, 14(10), 6276; https://doi.org/10.3390/su14106276 - 21 May 2022

Cited by 7 | Viewed by 7149

Abstract

Sludge Volume Index (SVI) is one of the most important operational parameters in an activated sludge process. It is difficult to predict SVI because of the nonlinearity of data and variability operation conditions. With complex time-series data from Wastewater Treatment Plants (WWTPs), the Recurrent Neural Network (RNN) with an Explainable Artificial Intelligence was applied to predict SVI and interpret the prediction result. RNN architecture has been proven to efficiently handle time-series and non-uniformity data. Moreover, due to the complexity of the model, the newly Explainable Artificial Intelligence concept was used to interpret the result. Data were collected from the Nine Springs Wastewater Treatment Plant, Madison, Wisconsin, and the data were analyzed and cleaned using Python program and data analytics approaches. An RNN model predicted SVI accurately after training with historical big data collected at the Nine Spring WWTP. The Explainable Artificial Intelligence (AI) analysis was able to determine which input parameters affected higher SVI most. The prediction of SVI will benefit WWTPs to establish corrective measures to maintaining stable SVI. The SVI prediction model and Explainable Artificial Intelligence method will help the wastewater treatment sector to improve operational performance, system management, and process reliability. Full article

(This article belongs to the Special Issue Big Data and Artificial Intelligence in Sustainable Water and Wastewater Management)

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19 pages, 4038 KiB

Open AccessArticle

Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality

by Abdulaziz Alqahtani, Muhammad Izhar Shah, Ali Aldrees and Muhammad Faisal Javed

Sustainability 2022, 14(3), 1183; https://doi.org/10.3390/su14031183 - 21 Jan 2022

Cited by 28 | Viewed by 2619

Abstract

The prediction accuracies of machine learning (ML) models may not only be dependent on the input parameters and training dataset, but also on whether an ensemble or individual learning model is selected. The present study is based on the comparison of individual supervised ML models, such as gene expression programming (GEP) and artificial neural network (ANN), with that of an ensemble learning model, i.e., random forest (RF), for predicting river water salinity in terms of electrical conductivity (EC) and dissolved solids (TDS) in the Upper Indus River basin, Pakistan. The projected models were trained and tested by using a dataset of seven input parameters chosen on the basis of significant correlation. Optimization of the ensemble RF model was achieved by producing 20 sub-models in order to choose the accurate one. The goodness-of-fit of the models was assessed through well-known statistical indicators, such as the coefficient of determination (R²), mean absolute error (MAE), root mean squared error (RMSE), and Nash–Sutcliffe efficiency (NSE). The results demonstrated a strong association between inputs and modeling outputs, where R² value was found to be 0.96, 0.98, and 0.92 for the GEP, RF, and ANN models, respectively. The comparative performance of the proposed methods showed the relative superiority of the RF compared to GEP and ANN. Among the 20 RF sub-models, the most accurate model yielded the R² equal to 0.941 and 0.938, with 70 and 160 numbers of corresponding estimators. The lowest RMSE values of 1.37 and 3.1 were yielded by the ensemble RF model on training and testing data, respectively. The results of the sensitivity analysis demonstrated that HCO₃⁻ is the most effective variable followed by Cl⁻ and SO₄²⁻ for both the EC and TDS. The assessment of the models on external criteria ensured the generalized results of all the aforementioned techniques. Conclusively, the outcome of the present research indicated that the RF model with selected key parameters could be prioritized for water quality assessment and management. Full article

(This article belongs to the Special Issue Big Data and Artificial Intelligence in Sustainable Water and Wastewater Management)

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10 pages, 1274 KiB

Open AccessArticle

Modeling Performance of Butterfly Valves Using Machine Learning Methods

by Alex Ekster, Vasiliy Alchakov, Ivan Meleshin and Alexandr Larionenko

Sustainability 2021, 13(24), 13545; https://doi.org/10.3390/su132413545 - 07 Dec 2021

Cited by 1 | Viewed by 2933

Abstract

Control of airflow of activated sludge systems has significant challenges due to the non-linearity of the control element (butterfly valve). To overcome this challenge, some valve manufacturers developed valves with linear characteristics. However, these valves are 10–100 times more expensive than butterfly valves. By developing models for butterfly valves installed characteristics and utilizing these models for real-time airflow control, the authors of this paper aimed to achieve the same accuracy of control using butterfly valves as achieved using valves with linear characteristics. Several approaches were tested to model the installed valve’s characteristics, such as a formal mathematical model utilizing Simscape/Matlab software, a semi-empirical model, and several machine learning methods (MLM), including regression, support vector machine, Gaussian process, decision tree, and deep learning. Several versions of the airflow-valve position models were developed using each machine learning method listed above. The one with the smallest forecast error was selected for field testing at the

55.5 \times 10^{3} m^{3} / day (12 MGD)

City of Chico activated sludge system. Field testing of the formal mathematical model, semi-empirical model, and the regularized gradient boosting machine model (the best among MLMs) showed that the regularized gradient boosting machine model (RGBMM) provided the best accuracy. The use of the RGBMMs in airflow control loops since 2019 at the City of Chico wastewater treatment plant showed that these models are robust and accurate (2.9% median error). Full article

(This article belongs to the Special Issue Big Data and Artificial Intelligence in Sustainable Water and Wastewater Management)

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16 pages, 7892 KiB

Open AccessArticle

Big Data Analytics from a Wastewater Treatment Plant

by Praewa Wongburi and Jae K. Park

Sustainability 2021, 13(22), 12383; https://doi.org/10.3390/su132212383 - 10 Nov 2021

Cited by 6 | Viewed by 5273

Abstract

Wastewater treatment plants (WWTPs) use considerable workforces and resources to meet the regulatory limits without mistakes. The advancement of information technology allowed for collecting large amounts of data from various sources using sophisticated sensors. Due to the lack of specialized tools and knowledge, operators and engineers cannot effectively extract meaningful and valuable information from large datasets. Unfortunately, the data are often stored digitally and then underutilized. Various data analytics techniques have been developed in the past few years. The methods are efficient for analyzing vast datasets. However, there is no wholly developed study in applying these techniques to assist wastewater treatment operation. Data analytics processes can immensely transform a large dataset into informative knowledge, such as hidden information, operational problems, or even a predictive model. The use of big data analytics will allow operators to have a much clear understanding of the operational status while saving the operation and maintenance costs and reducing the human resources required. Ultimately, the method can be applied to enhance the operational performance of the wastewater treatment infrastructure. Full article

(This article belongs to the Special Issue Big Data and Artificial Intelligence in Sustainable Water and Wastewater Management)

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14 pages, 3939 KiB

Open AccessArticle

A Prescriptive Intelligent System for an Industrial Wastewater Treatment Process: Analyzing pH as a First Approach

by Luis Arismendy, Carlos Cárdenas, Diego Gómez, Aymer Maturana, Ricardo Mejía and Christian G. Quintero M.

Sustainability 2021, 13(8), 4311; https://doi.org/10.3390/su13084311 - 13 Apr 2021

Cited by 7 | Viewed by 2969

Abstract

An important issue today for industries is optimizing their processes. Therefore, it is necessary to make the right decisions to carry out these activities, such as increasing the profit of businesses, improving the commercial strategies, and analyzing the industrial processes performance to produce better goods and services. This work proposes an intelligent system approach to prescribe actions and reduce the chemical oxygen demand (COD) in an equalizer tank of a wastewater treatment plant (WWTP) using machine learning models and genetic algorithms. There are three main objectives of this data-driven decision-making proposal. The first is to characterize and adapt a proper prediction model for the decision-making scheme. The second is to develop a prescriptive intelligent system based on expert’s rules and the selected prediction model’s outcomes. The last is to evaluate the system performance. As a novelty, this research proposes the use of long short-term memory (LSTM) artificial neural networks (ANN) with genetic algorithms (GA) for optimization in the WWTP area. Full article

(This article belongs to the Special Issue Big Data and Artificial Intelligence in Sustainable Water and Wastewater Management)

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