Artificial intelligence techniques in electrochemical processes for water and wastewater treatment: a review

“…without needing prior information but only lying on the original experimental data (e.g., percent conversion, process temperature, etc.). In the field of chemical engineering, it has already been applied to various directions, such as wastewater treatment, − predictive control, , reaction modeling, − kinetic parameter determination, , fault diagnosis, , and so on.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Kinetic-Free Hybrid Gray-Box Model to Predict Sulfuric-Acid-Catalyzed Esterification Reaction

Wen,

Bai,

Zhang

2024

J. Phys. Chem. B

View full text Add to dashboard Cite

The sulfuric−acid-catalyzed esterification reaction of 2-butanol and propionic anhydride is a vital industrial process. In this paper, several experiments are conducted via reaction calorimetry to validate that both sulfuric and propionic acids have discernible catalytic effects on the reaction. This finding complicates the accurate description of the reaction kinetics through traditional methods. So this paper turns to a kinetic-free black-box model, Gaussian process regression (GPR) model via 24 experiments, as a more adaptable approach. Besides, the best GPR model is combined with traditional heat balance model to generate a hybrid gray-box model, which can give complete knowledge of reaction process. The hybrid gray-box model finally achieves maximum root-mean-square error (RMSE) of 0.0069 for conversion, 0.6535 for temperature, and 10.6087 for heat flow rate, underscoring its pretty good predictive ability.

show abstract

“…The WWT process mainly consists of water quality monitoring, laboratory-scale research and process design. AI models are becoming more and more popular in wastewaterrelated fields, especially in recent years (see Figure 1), and have been employed for the prediction and optimization of the WWT process [9,10]. In previous WWT-related research, AI models have shown very good prediction and optimization performances [11], and have been successfully applied to WWT process design [10,12], water quality monitoring [13,14], WWT process parameters optimization [15,16] and WWT process performance prediction [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…AI models are becoming more and more popular in wastewaterrelated fields, especially in recent years (see Figure 1), and have been employed for the prediction and optimization of the WWT process [9,10]. In previous WWT-related research, AI models have shown very good prediction and optimization performances [11], and have been successfully applied to WWT process design [10,12], water quality monitoring [13,14], WWT process parameters optimization [15,16] and WWT process performance prediction [17,18]. These pieces of research have demonstrated that an AI model, as a powerful tool, has achieved great success in the applications of WWT-related fields.…”

Section: Introductionmentioning

confidence: 99%

A Review on Applications of Artificial Intelligence in Wastewater Treatment

Wang,

Cheng,

Liu

et al. 2023

Sustainability

View full text Add to dashboard Cite

In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve practical problems, has attracted much attention and has been widely used in various areas. Owing to their strong learning and accurate prediction abilities, all sorts of AI models have also been applied in wastewater treatment (WWT) to optimize the process, predict the efficiency and evaluate the performance, so as to explore more cost-effective solutions to WWT. In this review, we summarize and analyze various AI models and their applications in WWT. Specifically, we briefly introduce the commonly used AI models and their purposes, advantages and disadvantages, and comprehensively review the inputs, outputs, objectives and major findings of particular AI applications in water quality monitoring, laboratory-scale research and process design. Although AI models have gained great success in WWT-related fields, there are some challenges and limitations that hinder the widespread applications of AI models in real WWT, such as low interpretability, poor model reproducibility and big data demand, as well as a lack of physical significance, mechanism explanation, academic transparency and fair comparison. To overcome these hurdles and successfully apply AI models in WWT, we make recommendations and discuss the future directions of AI applications.

show abstract

Artificial intelligence techniques in electrochemical processes for water and wastewater treatment: a review

Cited by 20 publications

References 119 publications

Electrochemical hydrogen production: sustainable hydrogen economy

Electrochemical hydrogen production: sustainable hydrogen economy

Machine-Learning-Based Kinetic-Free Hybrid Gray-Box Model to Predict Sulfuric-Acid-Catalyzed Esterification Reaction

A Review on Applications of Artificial Intelligence in Wastewater Treatment

Contact Info

Product

Resources

About