A New Approach for Improving Microbial Fuel Cell Performance Using Artificial Intelligence

Abdollahfard, Yaser; Sedighi, Mehdi; Ghasemi, Mostafa

doi:10.3390/su15021312

Cited by 10 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…22 By adjusting parameters such as the learning rate, number, and depth of trees, the accuracy of the model can be further improved. In a previous study, Abdollahfard et al 23 found that the RF model exhibited superior predictive power (R 2 = 0.947) in predicting COD removal in microbial fuel cells (MFCs). GBDT has shown exceptional predictive performance in predicting anaerobic digestion for methane production.…”

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.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…Abdollahfard et al [12] used microbial fuel cell (MFC) datasets to make models using three key parameters (DS, Pt and Aeration) as inputs and power density and/or chemical oxygen demand (COD) removal as outputs. Random forest regression (RG) and gradient boost regression tree (GBRT) algorithms were used to build the MFC machine learning model for the prediction of power density and COD removal.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Electrochemical Prediction and Optimization of Direct Carbon Fuel Cells Fueled with Biochar

Cherni,

Halouani

2024

Electrochem

View full text Add to dashboard Cite

At present, direct carbon fuel cells constitute an emerging energy technology that electrochemically converts solid carbon to electricity with high efficiency. The recent trend of DCFCs fueled with biochar from biomass carbonization as green fuel has reinforced the environmental benefits of DCFCs as a clean and sustainable technology. However, there remain new challenges related to some complex unknown kinetic parameters, X=(αa,αc,σg,i0,a,i0,c,ilO2,ilCO2,c,ilCO2,a,ilCO), of the electrochemical conversion of biochar in DCFCs and there is a need for intelligent techniques for prediction and optimization, refering to the available experimental data. The differential evolution (DE) algorithm, which ranked as one of the top performers in optimization competitions with competitive accuracy and convergence speed, was used here for providing the optimized values of these parameters by minimizing the root mean squared errors (RMSE). The proposed technique was then applied to DCFCs fueled by activated pure carbon (APC) using CO2 and CO/CO2 electrochemical models with RMSE around 10−2 and 10−3, respectively. Then, the CO/CO2 model was applied to a DCFC fueled with almond shell biochar (ASB), which displayed a slight increase in RMSE (of the order of 10−2) due to the complex porous structure of ASB and the content of additional chemical elements that affect the electrochemistry of the DCFC and are not considered in the model.

show abstract

“…Machine learning techniques have become increasingly prevalent for modeling the hysteresis behavior of PEAs from experimental data. Among the principal machine learning approaches for this purpose are artificial neural networks (ANNs) [ 27 , 28 , 29 ], support vector machines (SVMs) [ 30 ], random forests [ 31 ], and Gaussian processes (GPs) [ 32 ]. These machine learning techniques provide flexible and data-driven approaches to hysteresis modeling, enabling accurate predictions and enhanced understanding of the dynamic characteristics of PEAs [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Combined Control for a Piezoelectric Actuator Using a Feed-Forward Neural Network and Feedback Integral Fast Terminal Sliding Mode Control

Artetxe,

Barambones,

Calvo

et al. 2024

Micromachines

View full text Add to dashboard Cite

In recent years, there has been significant interest in incorporating micro-actuators into industrial environments; this interest is driven by advancements in fabrication methods. Piezoelectric actuators (PEAs) have emerged as vital components in various applications that require precise control and manipulation of mechanical systems. These actuators play a crucial role in the micro-positioning systems utilized in nanotechnology, microscopy, and semiconductor manufacturing; they enable extremely fine movements and adjustments and contribute to vibration control systems. More specifically, they are frequently used in precision positioning systems for optical components, mirrors, and lenses, and they enhance the accuracy of laser systems, telescopes, and image stabilization devices. Despite their numerous advantages, PEAs exhibit complex dynamics characterized by phenomena such as hysteresis, which can significantly impact accuracy and performance. The characterization of these non-linearities remains a challenge for PEA modeling. Recurrent artificial neural networks (ANNs) may simplify the modeling of the hysteresis dynamics for feed-forward compensation. To address these challenges, robust control strategies such as integral fast terminal sliding mode control (IFTSMC) have been proposed. Unlike traditional fast terminal sliding mode control methods, IFTSMC includes integral action to minimize steady-state errors, improving the tracking accuracy and disturbance rejection capabilities. However, accurate modeling of the non-linear dynamics of PEAs remains a challenge. In this study, we propose an ANN-based IFTSMC controller to address this issue and to enhance the precision and reliability of PEA positioning systems. We implement and validate the proposed controller in a real-time setup and compare its performance with that of a PID controller. The results obtained from real PEA experiments demonstrate the stability of the novel control structure, as corroborated by the theoretical analysis. Furthermore, experimental validation reveals a notable reduction in error compared to the PID controller.

show abstract

A New Approach for Improving Microbial Fuel Cell Performance Using Artificial Intelligence

Cited by 10 publications

References 34 publications

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

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

Artificial Intelligence for Electrochemical Prediction and Optimization of Direct Carbon Fuel Cells Fueled with Biochar

Combined Control for a Piezoelectric Actuator Using a Feed-Forward Neural Network and Feedback Integral Fast Terminal Sliding Mode Control

Contact Info

Product

Resources

About