RETRACTED ARTICLE: Computational evaluation of microalgae biomass conversion to biodiesel

Milić, Momir; Petković, Biljana; Selmi, Abdellatif; Petković, Dalibor; Jermsittiparsert, Kittisak; Radivojević, Aleksandar; Milovančević, Miloš; Khan, Afrasyab; Vidosavljević, Slađana; Denić, Nebojša; Kuzman, Boris

doi:10.1007/s13399-021-01314-2

Cited by 20 publications

(6 citation statements)

References 80 publications

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“…The development of science and technology [10][11][12][13][14][15] provides the possibility for disaster prevention and control. Studying the natural laws of the basin [16,17] and predicting the hydrometeorological elements of the basin are important measures to prevent natural disasters.…”

Section: Introductionmentioning

confidence: 99%

Joint Effects of the DEM Resolution and the Computational Cell Size on the Routing Methods in Hydrological Modelling

Chen

et al. 2022

Water

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Natural disasters, including droughts and floods, have caused huge losses to mankind. Hydrological modelling is an indispensable tool for obtaining a better understanding of hydrological processes. The DEM-based routing methods, which are widely used in the distributed hydrological models, are sensitive to both the DEM resolution and the computational cell size. Too little work has been devoted to the joint effects of DEM resolution and computational cell size on the routing methods. This study aims to study the joint effects of those two factors on discharge simulation performance with two representative routing methods. The selected methods are the improved aggregated network-response function routing method (I-NRF) and the Liner-reservoir-routing method (LRR). Those two routing methods are combined with two runoff generation models to simulate the discharge. The discharge simulation performance is evaluated under the cross combination of four DEM resolutions (i.e., 90 m, 250 m, 500 m, and 1000 m) and fifty-six computational cell sizes (ranging from 5 arc-min to 60 arc-min). Eleven years of hydroclimatic data from the Jianxi basin (2000–2010) and the Shizhenjie basin (1983–1993) in China are used. The results show that the effects of the DEM resolution and the computational cell size are different on the I-NRF method and the LRR method. The computational cell size has nearly no influence on the performance of the I-NRF methods, while the DEM resolution does. On the contrary, the LRR discharge simulation performance decreases with oscillating values as the computational cell size increases, but is hardly affected by the DEM resolution. Furthermore, the joint effects of the DEM resolution and the computational cell size can be ignored for both routing methods. The results of this study will help to establish the appropriate DEM resolution, computational cell size, and routing method when researchers build hydrological models to predict future disasters.

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Section: Introductionmentioning

confidence: 99%

Joint Effects of the DEM Resolution and the Computational Cell Size on the Routing Methods in Hydrological Modelling

Chen

et al. 2022

Water

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“…The system employs an integrated algorithm containing the back propagation technique and the least squares method to modulate the model parameters. The algorithm was introduced into the conversion of microalgae to biodiesel production by Momir Milićet al The training data were obtained from experimental data in the published literature (Milićet al, 2021). The method was a fundamental guide for the conversion of microalgae to biodiesel.…”

Section: Conversion From Microalgae To Energymentioning

confidence: 99%

Machine learning for microalgae detection and utilization

Ning

Zhou

2022

Front. Mar. Sci.

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Microalgae are essential parts of marine ecology, and they play a key role in species balance. Microalgae also have significant economic value. However, microalgae are too tiny, and there are many different kinds of microalgae in a single drop of seawater. It is challenging to identify microalgae species and monitor microalgae changes. Machine learning techniques have achieved massive success in object recognition and classification, and have attracted a wide range of attention. Many researchers have introduced machine learning algorithms into microalgae applications, and similarly significant effects are gained. The paper summarizes recent advances based on various machine learning algorithms in microalgae applications, such as microalgae classification, bioenergy generation from microalgae, environment purification with microalgae, and microalgae growth monitor. Finally, we prospect development of machine learning algorithms in microalgae treatment in the future.

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“…Various numerical solution techniques have been developed for physical [32] and chemical problems [33,34] that are based on different optimization techniques. Stochastic, deterministic, and hybrid optimization methods have been also implemented to solve such complex problems.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on the Uniform Energy Deposition Achievable via Optimized Plasmonic Nanoresonator Distributions

et al. 2022

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Plasmonic nanoresonators of core–shell composition and nanorod shape were optimized to tune their absorption cross-section maximum to the central wavelength of a short laser pulse. The number density distribution of randomly located nanoresonators along a laser pulse-length scaled target was numerically optimized to maximize the absorptance with the criterion of minimal absorption difference between neighboring layers illuminated by two counter-propagating laser pulses. Wide Gaussian number density distribution of core–shell nanoparticles and nanorods enabled to improve the absorptance with low standard deviation; however, the energy deposited until the overlap of the two laser pulses exhibited a considerable standard deviation. Successive adjustment resulted in narrower Gaussian number density distributions that made it possible to ensure almost uniform distribution of the deposited energy integrated until the maximal overlap of the two laser pulses. While for core–shell nanoparticles the standard deviation of absorptance could be preserved, for the nanorods it was compromised. Considering the larger and polarization independent absorption cross-section as well as the simultaneously achievable smaller standard deviation of absorptance and deposited energy distribution, the core–shell nanoparticles outperform the nanorods both in optimized and adjusted nanoresonator distributions. Exception is the standard deviation of deposited energy distribution considered for the complete layers that is smaller in the adjusted nanorod distribution. Optimization of both nanoresonator distributions has potential applications, where efficient and uniform energy deposition is crucial, including biomedical applications, phase transitions, and even fusion.

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RETRACTED ARTICLE: Computational evaluation of microalgae biomass conversion to biodiesel

Cited by 20 publications

References 80 publications

Joint Effects of the DEM Resolution and the Computational Cell Size on the Routing Methods in Hydrological Modelling

Joint Effects of the DEM Resolution and the Computational Cell Size on the Routing Methods in Hydrological Modelling

Machine learning for microalgae detection and utilization

Comparative Study on the Uniform Energy Deposition Achievable via Optimized Plasmonic Nanoresonator Distributions

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