Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends

Gülüm, Mert; Onay, Funda Kutlu; Bilgin, Atilla

doi:10.2478/rtuect-2018-0012

Cited by 21 publications

(9 citation statements)

References 33 publications

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“…As shown in Equation ( 5), the Model is as follows (Sutthichaimethee, 2016aSutthichaimethee, 2016bGulum et al, 2018:…”

Section: Estimation Of Parameters In the Var Modelmentioning

confidence: 99%

A Forecasting Model on Carrying Capacity for Government's Controlling Measure Under Environmental Law in Thailand: Adapting Non-Recursive Autoregression Based on the Var-X Model

Sutthichaimethee

Ariyasajjakorn²

2020

IJEEP

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This research aimed to analyze the relationship of causal factors and forecast CO 2 emissions for a 15 years period from 2020 to 2034 by applying a non-recursive autoregression vector autoregression with an exogeneous model (Non-Recursive Var-X model). The Non-Recursive Var-X model has been made available for use in long-term forecasting (2020-2034), particularly in regards to the implementation of the 'Industry 4.0' policy of the Thai government. The study found that the results of the Thai government's efforts or 'governmental power' (GP) will likely lead to levels of CO 2 emissions that exceed the country's carrying capacity as determined under its national strategic plan. The findings of this study show that CO 2 emissions are expected to have a growth rate of 27.23 percent (2020-2034), reaching 95.88 Mt CO 2 Eq by 2034. The Non-Recursive Var-X model provides a mean absolute percentage error (MAPE) of 1.12% and a root mean square error (RMSE) of 1.25%. In this research, the Non-Recursive Var-X model was used and CO 2 emissions were forecasted to rise continuously over the established period. This rise exceeds the carrying capacity of Thailand according to the criteria set by the Thai government.

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“…As shown in Equation ( 5), the Model is as follows (Sutthichaimethee, 2016aSutthichaimethee, 2016bGulum et al, 2018:…”

Section: Estimation Of Parameters In the Var Modelmentioning

confidence: 99%

A Forecasting Model on Carrying Capacity for Government's Controlling Measure Under Environmental Law in Thailand: Adapting Non-Recursive Autoregression Based on the Var-X Model

Sutthichaimethee

Ariyasajjakorn²

2020

IJEEP

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“…The average mean absolute deviation percent (MADP) values in the predicted values of the target output were 0.84 in response to 1 % uncertainty in each input variable of the models. Gulum et al [28] used Regression and ANN models to predict viscosity and density of ternary blends consisting of biodiesel, diesel, and vegetable oil. Exponential and rational models previously suggested [92][93][94][95][96] were compared with regression models and ANN approach.…”

Section: Biodieselmentioning

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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“…The use of vegetable oils for the production of biodiesel has so far been evaluated by Italian consortium (CONOE) as the most effective and profitable; in fact, at the current moment, biodiesel and vegetable oils are at the forefront of the substitutes to petroleum-based diesel fuel in the transportation sector [15]. Biodiesel is drawing attention as a renewable and clean alternative to fossil diesel fuel because of numerous advantages such as excellent lubricity property, reduced toxicity and exhaust emissions, higher biodegradability [16].…”

Section: Regeneration and Recycling Of Exhaust Vegetable Oilsmentioning

confidence: 99%

The Role of Environmental Evaluation within Circular Economy: An Application of Life Cycle Assessment (LCA) Method in the Detergents Sector

Lucchetti¹,

Paolotti²,

Rocchi³

et al. 2019

Environmental and Climate Technologies

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The present work aims to analyse the environmental advantages of a production process that applies circular economy. The study examines a product that is generated through the use of a certain percentage of recovered secondary materials, thus helping to avoid impacts related to the disposal of these materials and preserving the ecosystems from indiscriminate excessive natural resources extraction. The product analysed is an ecological detergent (“Ri-Detersivo” – Re-Detergent), produced by the company Tea Natura, mainly composed of regenerated vegetable oils coming from food industry. The methodology used in this paper is Life Cycle Assessment (LCA). A partial LCA will be carried out here, arriving at the saponification phase, and comparing the environmental impacts deriving from the Re-Detergent production process with those of a traditional soap, comparable to that studied in terms of function. The analysis of the case study found that the use of regenerated vegetable oils for the production of soap allows to significantly reduce the environmental impacts compared to the use of coconut oil imported from third countries.

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Evaluation of Predictive Capabilities of Regression Models and Artificial Neural Networks for Density and Viscosity Measurements of Different Biodiesel-Diesel-Vegetable Oil Ternary Blends

Cited by 21 publications

References 33 publications

A Forecasting Model on Carrying Capacity for Government's Controlling Measure Under Environmental Law in Thailand: Adapting Non-Recursive Autoregression Based on the Var-X Model

A Forecasting Model on Carrying Capacity for Government's Controlling Measure Under Environmental Law in Thailand: Adapting Non-Recursive Autoregression Based on the Var-X Model

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

The Role of Environmental Evaluation within Circular Economy: An Application of Life Cycle Assessment (LCA) Method in the Detergents Sector

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