Machine learning approaches to modeling and optimization of biodiesel production systems: State of art and future outlook

Ishola, Niyi B.; Epelle, Emmanuel I.; Betiku, Eriola

doi:10.1016/j.ecmx.2024.100669

Energy Conversion and Management: X

2024

DOI: 10.1016/j.ecmx.2024.100669

|View full text |Cite

Machine learning approaches to modeling and optimization of biodiesel production systems: State of art and future outlook

Niyi B. Ishola,

Emmanuel I. Epelle,

Eriola Betiku

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 249 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Carbon Capture and Storage Optimization with Machine Learning using an ANN model

Kotov,

Sravanthi,

Logabiraman

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

The purpose of this study is to evaluate the accuracy of predictions regarding the work capacity of CO2 and the selectivity of MOF, using machine learning methodologies in relation to CO2/N2. A dataset was used that includes numerous characteristics of MOFs for the development of a neural network model. The factors that determined the operational capacity of CO2 and the CO2/N2 selectivity included pore size, surface area, chemical composition, among others. The model demonstrated its work capacity by evaluating the selectivity of CO2; the mean absolute errors for the CO2/N2 selectivity were 25 and 0.8 mmol/g, respectively. The correlation Analysis showed a fairly negative correlation (-0.014) between the operational capacity of CO2 and its chemical makeup and a very positive correlation ( 0.029) between the surface area and amount of pore size. Thus, the gas absorbability is not top-dependent exclusively; pore size and surface area of a material contribute to the capacity as well. More research should be carried out to evaluate a machine learning capability on predicting the nature of different Flow Object Models (MOFs) with an aim of increasing efficiency, precision and dependability of the models.

show abstract

Carbon Capture and Storage Optimization with Machine Learning using an ANN model

Kotov,

Sravanthi,

Logabiraman

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

Ionic surfactants critical micelle concentration modelling in water/organic solvent mixtures using random forest and support vector machine algorithms

Soria-López,

García-Martí,

Mejuto

2024

Tenside Surfactants Detergents

View full text Add to dashboard Cite

The physicochemical property of surfactants that is widely used to study their behavior is the critical micellar concentration (CMC). The value of this property is specific to each surfactant as it depends on a number of external factors and the chemical composition of the surfactant. This research focused on using two new machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), to predict the logarithmic CMC value of 10 ionic surfactants. The same database from the previous study (a total of 258 experimental cases) was used with the same input variables – those defining the mixture of the organic solvent-water: T, molecular weight, molar fraction and log P; and the chemical composition of the surfactant: number of atoms of each element of the surfactant – to develop the predictive models. The best RF and SVM models were then compared with the best ANN model developed in the previous study. According to the results, the normalized models were those that presented the lowest RMSE values in the validation phase. Finally, the two approaches proposed in this research are suitable tools, together with the ANN, for the prediction of CMC and as possible alternative methods to replace expensive experimental laboratory measurements.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Machine learning approaches to modeling and optimization of biodiesel production systems: State of art and future outlook

Cited by 2 publications

References 249 publications

Carbon Capture and Storage Optimization with Machine Learning using an ANN model

Carbon Capture and Storage Optimization with Machine Learning using an ANN model

Ionic surfactants critical micelle concentration modelling in water/organic solvent mixtures using random forest and support vector machine algorithms

Contact Info

Product

Resources

About