Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms

Özbaş, Emine Elmaslar; Aksu, Doğukan; Öngen, Atakan; Aydın, Muhammed Ali; Özcan, Hüseyin

doi:10.1016/j.ijhydene.2019.02.108

Cited by 148 publications

(27 citation statements)

References 30 publications

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“…In recent years, the ML methods have become popular as they allow researchers to improve the prediction accuracy of concrete properties [4] and are used for various engineering applications [5,6]. e ML methods have been used to increase the prediction accuracy of concrete properties [7][8][9][10][11][12][13][14][15], and the data derived from the literature sources were used.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods

Cihan

2019

Advances in Civil Engineering

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Machine learning methods have been successfully applied to many engineering disciplines. Prediction of the concrete compressive strength (fc) and slump (S) is important in terms of the desirability of concrete and its sustainability. The goals of this study were (i) to determine the most successful normalization technique for the datasets, (ii) to select the prime regression method to predict the fc and S outputs, (iii) to obtain the best subset with the ReliefF feature selection method, and (iv) to compare the regression results for the original and selected subsets. Experimental results demonstrate that the decimal scaling and min-max normalization techniques are the most successful methods for predicting the compressive strength and slump outputs, respectively. According to the evaluation metrics, such as the correlation coefficient, root mean squared error, and mean absolute error, the fuzzy logic method makes better predictions than any other regression method. Moreover, when the input variable was reduced from seven to four by the ReliefF feature selection method, the predicted accuracy was within the acceptable error rate.

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Section: Introductionmentioning

confidence: 99%

Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods

Cihan

2019

Advances in Civil Engineering

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“…The gasification process of forest biomass leads to syngas production through a series of thermal cracking reactions (Burbano et al, 2011). Forest biomass, including seeds, leaves, tree trunks, and fruit shells, could be pyrolyzed in a fixed bed gasifier for a long time at high temperatures (above 1,200°C) to produce hydrogen-rich syngas (Brachi et al, 2014;Ozbas et al, 2019), which has been highlighted as one of the most promising alternative sources of energy (Shih and Hsu, 2011). It is claimed that 1.3 Gt/yr of biomass can produce 100 Mt/yr of hydrogen (Duan et al, 2020).…”

Section: Conversion Of Forest Biomass Into Gaseous Biofuelsmentioning

confidence: 99%

An Overview on the Conversion of Forest Biomass into Bioenergy

Qian

Wang

et al. 2021

Front. Energy Res.

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Biomass plays a crucial role in mitigating the concerns associated with increasing fossil fuel combustion. Among various types of biomass, forest biomass has attracted considerable attention given its abundance and variations. In this work, an overview is presented on different pathways available to convert forest biomass into bioenergy. Direct use of forest biomass could reduce carbon dioxide emissions associated with conventional energy production systems. However, there are certain drawbacks to the direct use of forest biomass, such as low energy conversion rate and soot emissions and residues. Also, lack of continuous access to biomass is a severe concern in the long-term sustainability of direct electricity generation by forest biomass. To solve this problem, co-combustion with coal, as well as pelletizing of biomass, are recommended. The co-combustion of forest biomass and coal could reduce carbon monoxide, nitrogen oxides, and sulfide emissions of the process. Forest biomass can also be converted into various liquid and gaseous biofuels through biochemical and thermochemical processes, which are reviewed and discussed herein. Despite the favorable features of forest biomass conversion processes to bioenergy, their long-term sustainability should be more extensively scrutinized by future studies using advanced sustainability assessment tools such as life cycle assessment, exergy, etc.

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“…Training a neural network on weather and turbine data, Google's DeepMind system predicted "wind power output 36 hours ahead of actual generation ... [and] boosted the value of ... wind energy by roughly 20 percent" [22]. A study of the prediction of hydrogen production via biomass gasification is undertaken in [23] where the following four algorithms (e.g., [24,25]) are used: linear regression, K-nearest neighbors regression, support vector machine regression, and decision tree regression.…”

Section: Introductionmentioning

confidence: 99%

Proton Exchange Membrane Fuel Cell Stack Design Optimization Using an Improved Jaya Algorithm

Chakraborty

2019

Energies

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Fuel cell stack configuration optimization is known to be a problem that, in addition to presenting engineering challenges, is computationally hard. This paper presents an improved computational heuristic for solving the problem. The problem addressed in this paper is one of constrained optimization, where the goal is to seek optimal (or near-optimal) values of (i) the number of proton exchange membrane fuel cells (PEMFCs) to be connected in series to form a group, (ii) the number of such groups to be connected in parallel, and (iii) the cell area, such that the PEMFC assembly delivers the rated voltage at the rated power while the cost of building the assembly is as low as possible. Simulation results show that the proposed method outperforms four of the best-known methods in the literature. The improvement in performance afforded by the proposed algorithm is validated with statistical tests of significance.

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Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms

Cited by 148 publications

References 30 publications

Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods

Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods

An Overview on the Conversion of Forest Biomass into Bioenergy

Proton Exchange Membrane Fuel Cell Stack Design Optimization Using an Improved Jaya Algorithm

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