Application of artificial intelligence-based methods in bioelectrochemical systems: Recent progress and future perspectives

Li, Chunyan; Guo, Dongchao; Dang, Yan; Sun, Dezhi; Li, Pengsong

doi:10.1016/j.jenvman.2023.118502

Cited by 8 publications

(3 citation statements)

References 62 publications

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“…These algorithms included logistic regression multiclass (GLMNET), random forest (RF), the scalable tree boosting system (XGBOOST), the neural network (NNET), k-nearest neighbor (KNN), and the support vector machine with radial kernel (SVM). The NNET method demonstrated the highest accuracy (93 ± 6%), allowing for the distinction between various feed substrates [110]. Similarly, a study by Leropoulos et al [111] applied automation by utilizing a robotic platform known as EcoBot.…”

Section: Integrated Strategies For Enhancing Eetmentioning

confidence: 99%

“…The outcomes derived from these illustrative instances demonstrate that artificial intelligence has the potential to enhance and evaluate the performance of MFCs. Furthermore, genetic programming (GP) is an additional technique that makes it possible to optimize the AI model's structure by minimizing errors through the use of algorithms like cross-validation and the Kennard-Stone algorithm [110].…”

Section: Integrated Strategies For Enhancing Eetmentioning

confidence: 99%

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Strategies for Enhancing Extracellular Electron Transfer in Environmental Biotechnology: A Review

Hazzan,

Zhao,

Xiao

2023

Applied Sciences

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Extracellular electron transfer (EET) is a biological mechanism that plays a crucial role in various bioelectrochemical systems (BESs) and has substantial implications for renewable energy production. By utilizing the metabolic capacities of exoelectrogens, BESs offer a viable and environmentally friendly approach to electricity generation and chemical production; however, the diminished effectiveness of EET remains a hindrance to their optimal application in practical contexts. This paper examines the various strategies that have the potential to be employed to enhance the efficiency of EET systems and explores the potential for the integration of BESs technology with contemporary technologies, resulting in the development of an enhanced and sustainable system. It also examines how quorum sensing, electrode modifications, electron shuttles, and mediators can aid in improving EET performance. Many technological innovations, such as additive manufacturing, the science of nanotechnology, the technique of genetic engineering, computational intelligence, and other combinations of technologies that can be used to augment the efficacy of BESs are also discussed. Our findings will help readers understand how BESs, though an evolving technology, can play an important role in addressing our environmental concerns. Technical constraints are identified, and future directions in the field of EET are suggested.

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Section: Integrated Strategies For Enhancing Eetmentioning

confidence: 99%

Section: Integrated Strategies For Enhancing Eetmentioning

confidence: 99%

Strategies for Enhancing Extracellular Electron Transfer in Environmental Biotechnology: A Review

Hazzan,

Zhao,

Xiao

2023

Applied Sciences

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“…In recent years, machine learning (ML) has revolutionized data analysis across various scientific fields by offering unparalleled predictive capabilities. By detecting intricate patterns in multivariate data, ML has emerged as a transformative computational approach for predictive modeling in complex biochemical systems. − In particular, tree-based models, like random forest (RF), gradient-boosted decision tree (GBDT), and extreme gradient boosting (XGBoost), have shown success in handling nonlinear relationships in biological systems and modeling complex biochemical processes. , The RF model is able to effectively handle nonlinear relationships, making it robust to outliers. Increasing the number and depth of trees can improve model accuracy by aiding in parameter selection .…”

Section: Introductionmentioning

confidence: 99%

Enhancing Predictions of Acetate and Ethanol Production from Microbial Electrosynthesis Using Optimized Machine Learning Models

Li,

et al. 2024

ACS Sustainable Chem. Eng.

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Microbial electrosynthesis (MES) offers a promising pathway for CO 2 -based sustainable chemical production. However, the accurate prediction of product yields, notably acetate and ethanol concentrations, has been challenging. Here, we employed machine learning (ML) algorithms, including random forest, gradient-boosted decision trees, and eXtreme gradient boosting (XGBoost), to address this challenge. The models were trained on experimental data gathered by varying cathode material, pH, applied potential, temperature, and inorganic carbon (IC) concentrations and exhibited proficiency in predicting acetate and ethanol concentrations. After hyperparameter optimization, XGBoost demonstrated the highest accuracy in predicting both acetate (R 2 = 0.877) and ethanol (R 2 = 0.647) concentrations. By adopting a two-stage modeling approach where predicted concentrations of acetate and total organic carbon (TOC) feed into ethanol concentration predictions, we further enhanced XGBoost's performance in predicting ethanol concentrations. The resulting twostage XGBoost model showcased R 2 values of 0.998 for training and 0.727 for testing in ethanol predictions. Feature importance assessments revealed that features such as current, pH, and IC were paramount, with the two-stage XGBoost model highlighting the importance of IC, TOC, and pH in predicting ethanol concentrations. In contrast, traditionally significant features like applied potential and temperature exhibited diminished influence. This study not only demonstrates the promising ability of ML, especially XGBoost, to advance MES optimization and uncover insights into factors influencing MES but also offers the potential for enhancing MES performance through timely operational adjustments in the future. Therefore, these findings are crucial for refining and optimizing sustainable chemical production.

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Emerging frontiers in microbial fuel cell technology for sustainable energy generation

Pandey,

Andola

et al. 2024

J Solid State Electrochem

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Application of artificial intelligence-based methods in bioelectrochemical systems: Recent progress and future perspectives

Cited by 8 publications

References 62 publications

Strategies for Enhancing Extracellular Electron Transfer in Environmental Biotechnology: A Review

Strategies for Enhancing Extracellular Electron Transfer in Environmental Biotechnology: A Review

Enhancing Predictions of Acetate and Ethanol Production from Microbial Electrosynthesis Using Optimized Machine Learning Models

Emerging frontiers in microbial fuel cell technology for sustainable energy generation

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