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Electrical energy recovery from wastewater: prediction with machine learning algorithms

  • Applications of Emerging Green Technologies for Efficient Valorization of Agro-Industrial Waste: A Roadmap Towards Sustainable Environment and Circular Economy
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A Correction to this article was published on 31 December 2022

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Abstract

Wind, solar, biomass, tidal, etc. are renewable energy sources obtained from natural sources. Among these resources, biomass can be characterized as a significant energy source. Today, the process of producing biogas from waste and turning it into electrical energy has become more popular. So, clean, sustainable, and eco-friendly energy is generated as the waste is managed and converted into electrical energy. The estimation of the electrical energy that will be produced by wastewater recovery using machine learning (ML) algorithms is vital and has not yet been investigated. Thus, this study fills this gap. In this study, it is aimed to predict the electrical energy recovery potential of the sewage sludge of Kahramanmaraş Advanced Biological Wastewater Treatment Plant (KABWWTP) (Turkey), through incineration and anaerobic digestion. For this aim, 6 distinct ML algorithms including linear regression (LR), extreme gradient boosting (XGB), Gaussian process regression (GPR), ridge regression (RR), Lasso regression (LASReg), and Bayesian ridge regression (BR) have been used. Another novelty in this study is the restricted number of input parameters. That is, the electrical energy (output parameter) is predicted using only 3 distinct input parameters (gas flow, conductivity, and TSS). With a MAPE value of 1.032, the XGB method has been determined as the most successful model. Heat mapping and correlation analyses are used to evaluate the relationship between these parameters. Performance results are presented in tables and graphs.

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Abbreviations

ADB:

Adaptive boosting algorithm

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural network

BG:

Bagging algorithm

BR:

Bayesian ridge regression

COD:

Chemical oxygen demand

DCB:

Deep cascade-forward backpropagation,

DFB:

Deep feed-forward backpropagation

DNN:

Deep neural networks

DT:

Decision Tree

FFNN:

Feed-forward neural network

GA:

Genetic algorithm

GB:

Gradient boost algorithm

GBDT:

Gradient boost decision tree

GBT:

Gradient boosting trees

GPR:

Gaussian process regression

IFFNN:

Improved feed-forward neural network

KABWWTP:

Kahramanmaraş advanced biological wastewater treatment plant

KNN:

K-Nearest neighbors

LASReg:

Lasso regression

LightGBM:

Light gradient boosting

LR:

Linear regression

ML:

Machine learning

MLCM:

Machine learning cost modeling

MSLE:

Mean squared log error

M5P:

Reconstruction of Quinlan’s M5 algorithm

NSE:

Nash–Sutcliff efficiency

QUA:

Quantile regression neural network modeling

QTA:

Qualitative trend analysis

RAE:

Relative absolute error

RF:

Random forest

RNN:

Recurrent neural network

RR:

Ridge regression

SML:

Supervised machine learning

SVM:

Support vector machine

SVR:

Support vector regression

TF:

Traditional feed-forward

TSS:

Total suspended solids

VSS:

Volatile suspended solids

WWTP:

Wastewater treatment plant

XGB:

Extreme gradient boosting

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Acknowledgements

The authors thank the General Directorate of Kahramanmaraş Water and Sewerage Administration (KASKİ) (Turkey) for their cooperation in obtaining and using these data.

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Authors and Affiliations

  1. Department of Electrical Electronics Engineering, Engineering and Architecture Faculty, Kahramanmaraş Sütçü İmam University, Kahramanmaraş, Turkey

    Alper Kerem & Ekrem Yuce

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  1. Alper Kerem
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  2. Ekrem Yuce
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Contributions

AK: idea, methodology, editing, software, supervision. EY: data gathering, investigation, software, methodology, writing—original draft preparation. All the authors read and approved the final manuscript.

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Correspondence to Alper Kerem.

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In the nomenclature, ACO: Ant colony optimization" and "PSO: Particle swarm optimisation" should be removed. Introduction section, at last paragraph the text "the 4 parameters" should be changed to "the 3 parameters". The year in Qambar and Al Khalidy reference should be 2022.

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Kerem, A., Yuce, E. Electrical energy recovery from wastewater: prediction with machine learning algorithms. Environ Sci Pollut Res 30, 125019–125032 (2023). https://doi.org/10.1007/s11356-022-24482-8

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