Data to intelligence: The role of data-driven models in wastewater treatment

Bahramian, Majid; Dereli, Recep Kaan; Zhao, Wanqing; Giberti, Matteo; Casey, Eoin

doi:10.1016/j.eswa.2022.119453

Cited by 52 publications

(10 citation statements)

References 254 publications

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“…They enable researchers and engineers to analyze large amounts of data and identify patterns and relationships that may not be easily discernible through traditional analytical methods. [39][40][41][42][43][44][45] Using machine learning algorithms, researchers can develop accurate models that can predict the performance of catalysts in degrading organic pollutants. [46][47][48] These predictive models can then be used to optimize the selection and design of catalysts, leading to more efficient and cost-effective wastewater treatment processes.…”

Section: Introductionmentioning

confidence: 99%

Photocatalytic degradation of drugs and dyes using a maching learning approach

Anandhi,

Iyapparaja

2024

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Photocatalytic degradation of drugs and dyes using a maching learning approach

Anandhi,

Iyapparaja

2024

RSC Adv.

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“…Four kinds of prevalent machine learning algorithms, including support vector machine (SVM), decision tree (DT), random forest (RF), and gradient boosting decision tree (GBDT), were exploited for modeling, followed by an assessment of model applicability by comparing fitting accuracy. Finally, the optimal model was selected for focused analysis [44]. Among them, the SVM algorithm was employed to establish a particular optimal decision hyperplane and maximize the 2 closest classes on both sides of this plane and itself to generalize each classification [45].…”

Section: Machine Learning Modelmentioning

confidence: 99%

Insightful Analysis and Prediction of SCOD Component Variation in Low-Carbon/Nitrogen-Ratio Domestic Wastewater via Machine Learning

Zhang,

Guo,

Luo

et al. 2024

Water

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The rapid identification of the amount and characteristics of chemical oxygen demand (COD) in influent water is critical to the operation of wastewater treatment plants (WWTPs), especially for WWTPs in the face of influent water with a low carbon/nitrogen (C/N) ratio. Given that, this study carried out batch kinetic experiments for soluble chemical oxygen demand (SCOD) and nitrogen degradation for three WWTPs and established machine learning (ML) models for the accurate prediction of the variation in SCOD. The results indicate that four different kinds of components were identified via parallel factor (PARAFAC) analysis. C1 (Ex/Em = 235 nm and 275/348 nm, tryptophan-like substances/soluble microbial by-products) contributes to the majority of internal carbon sources for endogenous denitrification, whereas C4 (230 nm and 275/350 nm, tyrosine-like substances) is crucial for readily biodegradable SCOD composition according to the machine learning (ML) models. Furthermore, the gradient boosting decision tree (GBDT) algorithm achieved higher interpretability and generalizability in describing the relationship between SCOD and carbon source components, with an R2 reaching 0.772. A Shapley additive explanations (SHAP) analysis of GBDT models further validated the above result. Undoubtedly, this study provided novel insights into utilizing ML models to predict SCOD through the measurements of the excitation–emission matrix (EEM) in specific Ex and Em positions. The results could help us to identify the degradation and transformation relationship between different kinds of carbon sources and nitrogen species in the wastewater treatment process, and thus provide a novel guidance for the optimized operation of WWTPs.

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“…Data-driven modeling: Artificial intelligence techniques, especially machine learning and deep learning, are widely used in data-driven modeling of complex systems [40]. By analyzing large-scale data sets, these methods are able to identify patterns, trends, and correlations in systems to build more accurate mathematical models.…”

Section: Figure 5 Application Of Artificial Intelligence In Mathemati...mentioning

confidence: 99%

Artificial Intelligence in Mathematical Modeling of Complex Systems

Zhao

2024

EAI Endorsed Trans e-Learn

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This article introduces artificial intelligence techniques in mathematical modelling of complex systems and their applications. Mathematical modelling of complex systems is a method of studying the structure and behaviour of complex systems, aiming to understand interactions and nonlinear effects in the system. Commonly used modelling methods include system dynamics, network theory, and algebraic methods. Artificial intelligence technologies include machine learning and deep learning, which can be used for tasks such as prediction and classification, anomaly detection, optimization and decision-making. In mathematical modelling of complex systems, artificial intelligence technology can learn system patterns and laws from large amounts of data, and can be applied to image and speech recognition, time series analysis and other fields. Deep learning and machine learning are important branches of artificial intelligence. They realize the modelling and analysis of complex systems by building neural network models. Data-driven modelling is a modelling method based on actual data that, combined with traditional theoretical modelling, can better describe and predict the behaviour of complex systems. Self-control of complex systems means that the system realizes its own optimization and adjustment through adaptive control algorithms and feedback mechanisms. In summary, artificial intelligence technology has broad application prospects in mathematical modelling of complex systems and will provide new tools and methods for in-depth understanding and solving problems in complex systems.

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Data to intelligence: The role of data-driven models in wastewater treatment

Cited by 52 publications

References 254 publications

Photocatalytic degradation of drugs and dyes using a maching learning approach

Photocatalytic degradation of drugs and dyes using a maching learning approach

Insightful Analysis and Prediction of SCOD Component Variation in Low-Carbon/Nitrogen-Ratio Domestic Wastewater via Machine Learning

Artificial Intelligence in Mathematical Modeling of Complex Systems

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