On the unsteady forced convection in porous media subject to inlet flow disturbances-A pore-scale analysis

The importance of energy systems and their role in economics and politics is not hidden for anyone. This issue is not only important for the advanced industrialized countries, which are major energy consumers but is also essential for oil-rich countries. In addition to the nature of these fuels, which contains polluting substances, the issue of their ending up has aggravated the growing concern. Biofuels can be used in different fields for energy production like electricity production, power production, or for transportation. Various scenarios have been written about the estimated biofuels from different sources in the future energy system. The availability of biofuels for the electricity market, heating, and liquid fuels is critical. Accordingly, the need for handling, modeling, decision making, and forecasting for biofuels can be of utmost importance. Recently, machine learning (ML) and deep learning (DL) techniques have been accessible in modeling, optimizing, and handling biodiesel production, consumption, and environmental impacts. The main aim of this study is to review and evaluate ML and DL techniques and their applications in handling biofuels production, consumption, and environmental impacts, both for modeling and optimization purposes. Hybrid and ensemble ML methods, as well as DL methods, have found to provide higher performance and accuracy.

Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research

The development of social productive forces leads to the increasing consumption of fossil fuels. However, the burning of traditional fossil fuels releases huge amounts of carbon emissions into the atmosphere, resulting in drastically increased global surface temperatures, and hence, global warming and abnormal climate change. Biodiesel, which can be produced by (trans)esterification of bio-oils using solid acid catalysts, is recognized as renewable and clean energy, alternative to fossil-derived diesel, and it can meet society's requirements. This review describes the catalytic conversion of bio-derived oils into biodiesel using various sulfonic acid-functionalized heterogeneous catalytic materials that show higher catalytic efficiency and superior recyclability. Besides, various methods of biodiesel preparation and the appropriate design and preparation of robust and efficient catalytic materials for biodiesel production were provided. Finally, the mechanisms of different catalytic esterification and transesterification reactions for biodiesel synthesis, the relevant reaction kinetic models, and techno-economic analysis of biodiesel production were critically discussed in this review.

Sulfonic acid-functionalized heterogeneous catalytic materials for efficient biodiesel production: A review

On-the-fly artificial neural network for chemical kinetics in direct numerical simulations of premixed combustion

The consumption of fossil fuels has exponentially increased in recent decades, despite significant air pollution, environmental deterioration challenges, health problems, and limited resources. Biofuel can be used instead of fossil fuel due to environmental benefits and availability to produce various energy sorts like electricity, power, and heating or to sustain transportation fuels. Biodiesel production is an intricate process that requires identifying unknown nonlinear relationships between the system input and output data; therefore, accurate and swift modeling instruments like machine learning (ML) or artificial intelligence (AI) are necessary to design, handle, control, optimize, and monitor the system. Among the biodiesel production modeling methods, machine learning provides better predictions with the highest accuracy, inspired by the brain’s autolearning and self-improving capability to solve the study’s complicated questions; therefore, it is beneficial for modeling (trans) esterification processes, physicochemical properties, and monitoring biodiesel systems in real-time. Machine learning applications in the production phase include quality optimization and estimation, process conditions, and quantity. Emissions composition and temperature estimation and motor performance analysis investigate in the consumption phase. Fatty methyl acid ester stands as the output parameter, and the input parameters include oil and catalyst type, methanol-to-oil ratio, catalyst concentration, reaction time, domain, and frequency. This paper will present a review and discuss various ML technology advantages, disadvantages, and applications in biodiesel production, mainly focused on recently published articles from 2010 to 2021, to make decisions and optimize, model, control, monitor, and forecast biodiesel production.

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A Review on Machine Learning Application in Biodiesel Production Studies

Modeling and Simulation of Reactive Flows

Obtaining a reduced kinetic mechanism for methyl decanoate using layerless neural networks

Solutions for a turbulent jet diffusion flame of ethanol with NOx formation using a reduced kinetic mechanism obtained by applying ANNs

Development of an analytical–numerical solution for a steady and axisymmetric turbulent jet diffusion flame for the hydrogen based on a reduced kinetic mechanism

The computational treatment of detailed kinetic reaction mechanisms for combustion is expensive, especially in the case of biodiesel fuels. In this way, great efforts in the search of techniques for the development of reduced kinetic mechanisms have been observed. As Methyl Butanoate (MB, $$C_3H_7COOCH_3$$
 ) is an essential model frequently used to represent the ester group of reactions in saturated methyl esters of large chain, this paper proposes a reduction strategy and uses it to obtain a reduced kinetic mechanism for the MB. The reduction strategy consists in the use of artificial intelligence to define the main chain and produce a skeletal mechanism, apply the traditional hypotheses of steady-state and partial equilibrium, and justify these assumptions through an asymptotic analysis. The main advantage of the strategy employed here is to reduce the work required to solve the system of chemical equations by two orders of magnitude for MB, since the number of reactions is decreased in the same order.

F.N. Pereira

Papers

Modeling and Simulation of Reactive Flows

Obtaining a reduced kinetic mechanism for methyl decanoate using layerless neural networks

Solutions for a turbulent jet diffusion flame of ethanol with NOx formation using a reduced kinetic mechanism obtained by applying ANNs

Development of an analytical–numerical solution for a steady and axisymmetric turbulent jet diffusion flame for the hydrogen based on a reduced kinetic mechanism

Obtaining a reduced kinetic mechanism for Methyl Butanoate