The optimization of biodiesel production from waste cooking oil catalyzed by ostrich-eggshell derived CaO through various machine learning approaches

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F22%3APU146164" target="_blank" >RIV/00216305:26510/22:PU146164 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2772783122000322" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2772783122000322</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cles.2022.100033" target="_blank" >10.1016/j.cles.2022.100033</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The optimization of biodiesel production from waste cooking oil catalyzed by ostrich-eggshell derived CaO through various machine learning approaches

Popis výsledku v původním jazyce

The continuous increase in demand for fossil-based fuel has led to the requirement for an alternative source that must be renewable. Biodiesel is gaining global acceptance as a renewable source of energy. This research focuses on the optimization of the transesterification of waste cooking oil under the CaO-based catalyst derived from a solid ostrich eggshell by different types of machine learning approaches. The objective of the current study is to evaluate and compare the prediction results as well as the simulating efficiency of the biodiesel production yield using heterogeneous catalysts by various machine learning (ML) techniques: type 1 fuzzy logic system (T1FLS), response surface methodology (RSM), adaptive neuro-fuzzy inference system (ANFIS), and type 2 fuzzy inference logic system (T2FLS). The influence of the independent variables, methanol-oil molar ratio (M:O), temperature, catalyst concentration, and reaction time on the production yield was investigated. Among all the input parameters, the reaction temperature is the most influential one based on the aforesaid techniques. The validity of the proposed models has been verified with the help of statistical analysis and multiple linear regression. The values of the determination coefficient (2) of type 2 fuzzy logic systems are 99.1% whereas 2 of type 1 fuzzy logic systems, response surface methodology, and adaptive neuro-fuzzy inference systems are 95.3%, 93.3%, and 83.2% respectively. All models give close predicted values. However, the type 2 fuzzy logic models were more accurate compared to other models. This proves that it is more capable of handling a wide range of dynamic processes in the chemical industry

Název v anglickém jazyce

The optimization of biodiesel production from waste cooking oil catalyzed by ostrich-eggshell derived CaO through various machine learning approaches

Popis výsledku anglicky

The continuous increase in demand for fossil-based fuel has led to the requirement for an alternative source that must be renewable. Biodiesel is gaining global acceptance as a renewable source of energy. This research focuses on the optimization of the transesterification of waste cooking oil under the CaO-based catalyst derived from a solid ostrich eggshell by different types of machine learning approaches. The objective of the current study is to evaluate and compare the prediction results as well as the simulating efficiency of the biodiesel production yield using heterogeneous catalysts by various machine learning (ML) techniques: type 1 fuzzy logic system (T1FLS), response surface methodology (RSM), adaptive neuro-fuzzy inference system (ANFIS), and type 2 fuzzy inference logic system (T2FLS). The influence of the independent variables, methanol-oil molar ratio (M:O), temperature, catalyst concentration, and reaction time on the production yield was investigated. Among all the input parameters, the reaction temperature is the most influential one based on the aforesaid techniques. The validity of the proposed models has been verified with the help of statistical analysis and multiple linear regression. The values of the determination coefficient (2) of type 2 fuzzy logic systems are 99.1% whereas 2 of type 1 fuzzy logic systems, response surface methodology, and adaptive neuro-fuzzy inference systems are 95.3%, 93.3%, and 83.2% respectively. All models give close predicted values. However, the type 2 fuzzy logic models were more accurate compared to other models. This proves that it is more capable of handling a wide range of dynamic processes in the chemical industry

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

21101 - Food and beverages

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Cleaner Energy Systems

ISSN

2772-7831

e-ISSN

—

Svazek periodika

3

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

19

Strana od-do

1-19

Kód UT WoS článku

—

EID výsledku v databázi Scopus

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