Prediction and Optimization of Ethanol Concentration in Biofuel Production Using Fuzzy Neural Network

Ezzatzadegan, Leila; Morad, Norhashimah; Yusof, Rubiyah

doi:10.11113/jt.v78.7957

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Ezzatzadegan et al [98] used Fuzzy Neural Network (FNN) and PSO to predict the yield of bioethanol from corn stover. The optimum fermentation time and required temperature were 69.39 h and 34.5 • C, respectively.…”

Section: Miscellaneous (Bioethanol Bisabolene)mentioning

confidence: 99%

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

et al. 2021

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Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel’s life cycle.

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Section: Miscellaneous (Bioethanol Bisabolene)mentioning

confidence: 99%

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

et al. 2021

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“…Ezzatzadegan et al [90] used Fuzzy Neural Network (FNN) and PSO to predict the yield of bioethanol from corn stover. The optimum fermentation time and required temperature were 69.39 h and 34.5 • C, respectively.…”

Section: Miscellaneous (Bioethanol Bisabolene)mentioning

confidence: 99%

Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Ahmad¹,

Kano²,

Menezes³

et al. 2020

Preprint

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Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. The purpose of the present study is to review the state-of-the-art ML applications in the biofuels' life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.

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“…Additionally, a neuro‐fuzzy system has the capacity to generate a solid set of fuzzy IF‐THEN rules from a representative sample of the instances under study. [ 32 ] ANFIS can deliver effective outcomes when the right number of rules are utilized, together with effective parameter training. ANFIS's adaptability, speed, and robustness have encouraged its use in a variety of academic fields, including engineering, medicine, and economics.…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques for the prediction of polymerization kinetics and polymer properties

Ishola,

McKenna

2023

Can J Chem Eng

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In the current study, the ability of two data‐driven machine learning tools, the extreme learning machine (ELM) and the adaptive neuro‐fuzzy inference system (ANFIS), to predict the polymerization rate and melt flow index of linear low‐density polyethylene produced in a gas phase process was investigated. The level of interaction between the input variables (ethylene, 1‐butene, isopentane pressures, and reaction temperature) on the outputs (melt flow index and activity) was also examined. It was found that both outputs are impacted by the presence of isopentane as an induced condensing agent. Various statistical indicators, including the coefficient of correlation (R2) and root mean square error (RMSE), were used to quantitatively evaluate both developed models. The ANFIS model outperformed the developed ELM model in terms of predicting the MFI and the catalyst activity. A sensitivity analysis of the ANFIS and ELM models showed that all the input variables under investigation had a sizable impact on the responses and none of them could have been discarded. The present study showed that machine learning tools could be employed to adequately develop empirical models to predict polymerization kinetics as well as the final polymer properties.

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Prediction and Optimization of Ethanol Concentration in Biofuel Production Using Fuzzy Neural Network

Cited by 8 publications

References 19 publications

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Machine learning techniques for the prediction of polymerization kinetics and polymer properties

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