Quantitative structure property relationship schemes for estimation of autoignition temperatures of organic compounds

Dashti, Amir; Jokar, Mojtaba; Amirkhani, Farid; Mohammadi, Amir H.

doi:10.1016/j.molliq.2019.111797

Cited by 14 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy of the results in this work will significantly improve due to better estimation of parameters for nonlinear problems performed by the hybrid model. Previous studies suggest that the Gaussian type has been applied as membership functions (MFs) 37, 39–43, 62–67.…”

Section: Resultsmentioning

confidence: 99%

Modeling of Catalytic CO₂ Methanation Using Smart Computational Schemes

et al. 2021

Self Cite

View full text Add to dashboard Cite

An analyzing tool as a sustainable method to combine CO2 capture and production of CH4 by utilizing CO2 as a feedstock is proposed. The impact of incorporating metallic promoters such as Fe, La, Ce, and Co to an Al2O3‐supported catalyst containing Ni as the first metal in the CO2 methanation was modeled. Smart models were employed to analyze the CO2 conversion and CH4 selectivity in CH4 production from CO2. The genetic programming (GP) model provides a mathematical framework for the estimation of CO2 conversion and CH4 selectivity. The model inputs are catalyst surface area, temperature, H2/CO2 ratio, gas‐hourly space velocity, and catalyst pore volume and diameter. The results confirm that the GP model estimates the CO2 conversion and CH4 selectivity very well.

show abstract

Section: Resultsmentioning

confidence: 99%

Modeling of Catalytic CO₂ Methanation Using Smart Computational Schemes

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In ANFIS system configuration, the relation between inputs and output is defined by applying of if-then fuzzy rules, i.e., Takagi and Sugeno's type. In this case, the algorithm is described as follows [26]:…”

Section: Adaptive Network-based Fuzzy Inferencementioning

confidence: 99%

Estimation of CO₂ Absorption by a Hybrid Aqueous Solution of Amino Acid Salt with Amine

Amirkhani,

Dashti,

Abedsoltan

et al. 2023

Chem Eng & Technol

Self Cite

View full text Add to dashboard Cite

Four developed machine learning algorithms are proposed to prognosticate the CO2 solubility in amino acid salt solutions, blended with amine solutions as additives, in broad ranges of temperature and pressure. From literature 375 experimental data points for CO2 solubility were collected. The results from the applied algorithms indicated that the CO2 solubility is estimated acceptably close to the experimental values. In the best case, the developed network estimates CO2 solubility in the stated solutions with an average relative deviation of 6.53 % and a correlation coefficient of 0.9892.

show abstract

“…The ANFIS combines the principles of the ANN and fuzzy logic to overcome the shortcomings of each model individually . The ANFIS consists of anodes in a five-layer network to build an inference system for predicting/estimating parameters in non-linear systems. , The fuzzy logic helps us to construct this inference through the training step . The ANFIS utilizes membership functions (MFs) to define each node output O i j , in which i is the number of nodes in layer j .…”

Section: Model Developmentmentioning

confidence: 99%

Evaluation of CO₂ Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

et al. 2021

Self Cite

View full text Add to dashboard Cite

Amino acid salt (AAs) aqueous solutions have recently exhibited a great potential in CO 2 absorption from various gas mixtures. In this work, four hybrid machine learning methods were developed to evaluate 626 CO 2 and AAs equilibrium data for different aqueous solutions of AAs (potassium sarcosinate, potassium l -asparaginate, potassium l -glutaminate, sodium l -phenylalanine, sodium glycinate, and potassium lysinate) gathered from reliable references. The models are the hybrids of the least squares support vector machine and coupled simulated annealing optimization algorithm, radial basis function neural network (RBF-NN), particle swarm optimization–adaptive neuro-fuzzy inference system, and hybrid adaptive neuro-fuzzy inference system. The inputs of the models are the CO 2 partial pressure, temperature, mass concentration in the aqueous solution, molecular weight of AAs, hydrogen bond donor count, hydrogen bond acceptor count, rotatable bond count, heavy atom count, and complexity, and the CO 2 loading capacity of AAs aqueous solution is considered as the output of the models. The accuracies of the models’ results were verified through graphical and statistical analyses. RBF-NN performance is promising and surpassed that of other models in estimating the CO 2 loading capacities of AAs aqueous solutions.

show abstract

Quantitative structure property relationship schemes for estimation of autoignition temperatures of organic compounds

Cited by 14 publications

References 39 publications

Modeling of Catalytic CO₂ Methanation Using Smart Computational Schemes

Modeling of Catalytic CO₂ Methanation Using Smart Computational Schemes

Estimation of CO₂ Absorption by a Hybrid Aqueous Solution of Amino Acid Salt with Amine

Evaluation of CO₂ Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

Contact Info

Product

Resources

About

Quantitative structure property relationship schemes for estimation of autoignition temperatures of organic compounds

Cited by 14 publications

References 39 publications

Modeling of Catalytic CO2 Methanation Using Smart Computational Schemes

Modeling of Catalytic CO2 Methanation Using Smart Computational Schemes

Estimation of CO2 Absorption by a Hybrid Aqueous Solution of Amino Acid Salt with Amine

Evaluation of CO2 Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

Contact Info

Product

Resources

About

Modeling of Catalytic CO₂ Methanation Using Smart Computational Schemes

Modeling of Catalytic CO₂ Methanation Using Smart Computational Schemes

Estimation of CO₂ Absorption by a Hybrid Aqueous Solution of Amino Acid Salt with Amine

Evaluation of CO₂ Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods