Synergetic effect of N/O functional groups and microstructures of activated carbon on supercapacitor performance by machine learning

Rahimi, Mohammad; Abbaspour‐Fard, Mohammad Hossein; Rohani, Abbas

doi:10.1016/j.jpowsour.2021.230968

Cited by 63 publications

(26 citation statements)

References 77 publications

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“…In this article, the authors used three performance functions to analyze the target and output accuracy. These functions include the correlation coefficient R, the root mean squared error (RMSE), and the coefficient of determination

R^{2}

, 52–55 which are defined as follows:

R = \frac{y_{o} (i)}{y_{e} (i)}

RMSE = \sqrt{\frac{\sum_{i = 1}^{n} {(y_{e} (i) - y_{o} (i))}^{2}}{n}}

R^{2} = {([], \frac{\sum_{i = 1}^{n} (y_{e} (i) - \overset{true¯}{y_{e}}) (y_{o} (i) - \overset{true¯}{y_{0}})}{\sum_{i = 1}^{n} (y_{e} (i) - \overset{true¯}{y_{e}}) \sum_{i = 1}^{n} (y_{o} (i) - \overset{true¯}{y_{0}})})}^{2}

where “ y e ” and “ y 0 ” are, respectively, the estimated and the observed output, and

\overset{true¯}{y_{e}}

and

\overset{true¯}{y_{0}}

represent their mean values.…”

Section: Back Propagation Neural Network For Desalination‐renewable E...mentioning

confidence: 99%

Design of water‐energy management based on neural networks for an autonomous solar and wind renewable energy system coupled with brackish water desalination

Zgalmi

Cherif

2022

Env Prog and Sustain Energy

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In this article, smart water-energy management based on an artificial neural network for a photovoltaic-wind system coupled with water pumping and reverse osmosis desalination unit with hydraulic storage is developed. The proposed system was designed to meet freshwater demand for remote areas of Tunisia's south. The authors focus on an artificial neural network-type management strategy, ensuring power-sharing between sources, hydraulic components, and tank storage. The main objective of the management strategy is to deliver and purify as much freshwater as possible by taking advantage of all the energy available according to climatic conditions. A parametric sensitivity algorithm is developed to find the most appropriate neural network architecture. A dynamic simulator for 1 year of operation integrates the energy management of the system is developed. The used water-energy management-based artificial neural network showed good results of power-sharing during a fast time with a correlation coefficient and a coefficient of determination between the actual and estimated values of the three motor pumps electrical power equal to 96% and 98%, respectively. Also, the root mean squared error was acceptable at 0.1476. These results have been proved over a year and can be used by other researchers with a similar system.

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R^{2}

, 52–55 which are defined as follows:

R = \frac{y_{o} (i)}{y_{e} (i)}

RMSE = \sqrt{\frac{\sum_{i = 1}^{n} {(y_{e} (i) - y_{o} (i))}^{2}}{n}}

R^{2} = {([], \frac{\sum_{i = 1}^{n} (y_{e} (i) - \overset{true¯}{y_{e}}) (y_{o} (i) - \overset{true¯}{y_{0}})}{\sum_{i = 1}^{n} (y_{e} (i) - \overset{true¯}{y_{e}}) \sum_{i = 1}^{n} (y_{o} (i) - \overset{true¯}{y_{0}})})}^{2}

where “ y e ” and “ y 0 ” are, respectively, the estimated and the observed output, and

\overset{true¯}{y_{e}}

and

\overset{true¯}{y_{0}}

represent their mean values.…”

Section: Back Propagation Neural Network For Desalination‐renewable E...mentioning

confidence: 99%

Design of water‐energy management based on neural networks for an autonomous solar and wind renewable energy system coupled with brackish water desalination

Zgalmi

Cherif

2022

Env Prog and Sustain Energy

View full text Add to dashboard Cite

show abstract

“…The application of the machine learning (ML) technique has been extremely accelerated in many fields such as bio-based energy storage. − According to the fast progress in ML technology, diverse approaches have been employed to observe system modeling to predict the performance of gas storage equipment Table exhibits the utilized ML models on several gas storage objectives (such as CO 2 , H 2 , and CH 4 ).…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO₂ Capture: Machine Learning and DFT Calculation Approaches

Rahimi

Abbaspour‐Fard

Rohani

et al. 2022

Ind. Eng. Chem. Res.

Self Cite

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The CO2 emission issue has triggered the promotion of carbon capture and storage (CCS), particularly bio-route CCS as a sustainable procedure to capture CO2 using biomass-based activated carbon (BAC). The well-known multi-nitrogen functional groups and microstructure features of N-doped BAC adsorbents can synergistically promote CO2 physisorption. Here, machine learning (ML) modeling was applied to the various physicochemical features of N-doped BAC as a challenge to figure out the unrevealed mechanism of CO2 capture. A radial basis function neural network (RBF-NN) was employed to estimate the in operando efficiency of microstructural and N-functionality groups at six conditions of pressures ranging from 0.15 to 1 bar at room and cryogenic temperatures. A diverse training algorithm was applied, in which trainbr illustrated the lowest mean absolute percent error (MAPE) of <3.5%. RBF-NN estimates the CO2 capture with an R 2 range of 0.97–0.99 of BACs as solid adsorbents. Also, the generalization assessment of RBF-NN observed errors, tolerating 0.5–6% of MAPE in 50–80% of total data sets. An alternative survey sensitivity analysis discloses the importance of multiple features such as specific surface area (SSA), micropore volume (%V mic), average pore diameter (AVD), and nitrogen content (N%), oxidized-N, and graphitic-N as nitrogen functional groups. A genetic algorithm (GA) optimized the physiochemical properties of N-doped ACs. It proposed the optimal CO2 capture with a value of 9.2 mmol g–1 at 1 bar and 273 K. The GA coupled with density functional theory (DFT) to optimize the geometries of exemplified BACs and adsorption energies with CO2 molecules.

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“…With the environmental degradation caused by the massive consumption of fossil fuels, it is urgent to find a new electric energy storage (EES) system to replace the traditional fossil fuels. [ 1 ] Among various EES system (lithium/sodium‐ion batteries, [ 2 ] fuel cells, [ 3 ] solar cells, [ 4 ] and supercapacitors (SCs), [ 5 ] etc. ), SCs, as a compelling green energy device, have attracted considerable attention due to their long cycle life, low cost, fast charge/discharge efficiency, and high power density.…”

Section: Introductionmentioning

confidence: 99%

“…With the environmental degradation caused by the massive consumption of fossil fuels, it is urgent to find a new electric energy storage (EES) system to replace the traditional fossil fuels. [1] Among various EES system (lithium/sodium-ion batteries, [2] fuel cells, [3] solar cells, [4] and supercapacitors (SCs), [5] improve the total specific capacitance of SCs. [21] In recent years, the heteroatom-doped carbon spheres as electrode materials of SCs have attracted extensive attention.…”

Section: Introductionmentioning

confidence: 99%

Highly Graphitized Porous Carbon Microspheres Derived from Copolymer of Glucose and Melamine for Advanced Electrodes

Lin

Song

et al. 2022

Part & Part Syst Charact

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etc.), SCs, as a compelling green energy device, have attracted considerable attention due to their long cycle life, low cost, fast charge/discharge efficiency, and high power density. [6] SCs can be divided into electrical double layer capacitors (EDLCs) and pseudocapacitance capacitors based on different charges storage mechanisms. [7] EDLCs are based on electrostatic adsorption of electrolyte ions at the double layer interface formed between electrolyte and electrode to store charge, [8] whereas the pseudocapacitance capacitors store energy through Faradaic redox reactions of metal compounds (e.g., MnO 2 , MoS 2 , Ni(OH) 2 , etc.), [9] conducting polymers (polypyrrole, polyaniline, and polythiophene), or heteroatoms (N, O, B, S, etc.). [10] However, SCs have been criticized for its low energy density at larger power density and poor capacitance under high current density in practical applications, which limit further applications. [11] Therefore, it is important to explore new functional electrode materials with ideal performances for nextgeneration EES. [12] As key components of SCs, the electrode active materials determine the total electrochemical performance for SCs. [13] Carbon materials, including activated carbons, carbon nanotubes, graphene, and carbon spheres, are considered to be ideal electrode materials due to their environmental friendliness, low cost, availability, and open porous structure. [14] As an ideal EDLCs electrode materials, [15] the hierarchical microand mesopores carbon spheres possess the following advantages: i) The three dimensional (3D) spheres architecture has larger specific surface area (S BET ) and higher electrical conductivity. [16] ii) The well-dispersed porous carbon spheres facilitated electrolyte infiltration and accelerated ions transport in the electrolyte. [17] iii) The outstanding structure stability ensures that the electrode material will not be structurally damaged at high current density. [18] Although pure carbon materials could obtain favorable performance stability and structural durability in electrochemical work, it cannot satisfy the further improvement of capacitance. [19] Therefore, the doping of heteroatoms in the carbonaceous materials introduces pseudocapacitance besides EDLCs, [20] which can further effectively The N/O co-doped hierarchical porous graphitized carbon microspheres (N/O-PCs) are successfully prepared by condensation polymerization of 5-hydroxymethylfurfural and melamine, and subsequent KOH activation strategy. The obtained carbon materials have a high specific surface area (1685.58 m 2 g -1 ), rich micro-and mesopores, and appropriate pore volume (0.828 cm 3 g -1 ). The degree of graphitization for N/O-PCs is improved because the ratio of I G and I D increases from 0.88 to 1.03. The layer spacing of the lattice plane (002) for the N/O-PCs is enlarged, facilitating the transmission of electrolyte ions. The high graphitization degree and enlarged layer spacing bring excellent electrical conductivity and wettability for N/O-PCs as...

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Synergetic effect of N/O functional groups and microstructures of activated carbon on supercapacitor performance by machine learning

Cited by 63 publications

References 77 publications

Design of water‐energy management based on neural networks for an autonomous solar and wind renewable energy system coupled with brackish water desalination

Design of water‐energy management based on neural networks for an autonomous solar and wind renewable energy system coupled with brackish water desalination

Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO₂ Capture: Machine Learning and DFT Calculation Approaches

Highly Graphitized Porous Carbon Microspheres Derived from Copolymer of Glucose and Melamine for Advanced Electrodes

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Synergetic effect of N/O functional groups and microstructures of activated carbon on supercapacitor performance by machine learning

Cited by 63 publications

References 77 publications

Design of water‐energy management based on neural networks for an autonomous solar and wind renewable energy system coupled with brackish water desalination

Design of water‐energy management based on neural networks for an autonomous solar and wind renewable energy system coupled with brackish water desalination

Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO2 Capture: Machine Learning and DFT Calculation Approaches

Highly Graphitized Porous Carbon Microspheres Derived from Copolymer of Glucose and Melamine for Advanced Electrodes

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Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO₂ Capture: Machine Learning and DFT Calculation Approaches