Enhanced biodiesel production from wet microalgae biomass optimized via response surface methodology and artificial neural network

Muhammad, Gul; Ngatcha, Ange Douglas Potchamyou; Lv, Yongkun; Xiong, Wenlong; El‐Badry, Yaser A.; Asmatulu, Eylem; Xu, Jingliang; Alam, Md. Asraful

doi:10.1016/j.renene.2021.11.091

Cited by 78 publications

(12 citation statements)

References 75 publications

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“…For this, the use of residual oils, the selection of inedible oilseed species with high oil yield, and the optimization of processes are fundamental studies [ 259 ]. Among the optimization techniques, the response surface methodology stands out due to its advantages, such as the determination of the independent variables’ magnitudes, the ability to model the system mathematically, as well as the time savings and cost reduction due to the smallest number of experiments necessary for the construction of the response surface [ 260 , 261 , 262 , 263 , 264 , 265 , 266 , 267 , 268 , 269 , 270 , 271 , 272 ].…”

Section: Discussion and Resultsmentioning

confidence: 99%

Biofuels and Nanocatalysts: Python Boosting Visualization of Similarities

et al. 2023

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Among the most relevant themes of modernity, using renewable resources to produce biofuels attracts several countries’ attention, constituting a vital part of the global geopolitical chessboard since humanity’s energy needs will grow faster and faster. Fortunately, advances in personal computing associated with free and open-source software production facilitate this work of prospecting and understanding complex scenarios. Thus, for the development of this work, the keywords “biofuel” and “nanocatalyst” were delivered to the Scopus database, which returned 1071 scientific articles. The titles and abstracts of these papers were saved in Research Information Systems (RIS) format and submitted to automatic analysis via the Visualization of Similarities Method implemented in VOSviewer 1.6.18 software. Then, the data extracted from the VOSviewer were processed by software written in Python, which allowed the use of the network data generated by the Visualization of Similarities Method. Thus, it was possible to establish the relationships for the pair between the nodes of all clusters classified by Link Strength Between Items or Terms (LSBI) or by year. Indeed, other associations should arouse particular interest in the readers. However, here, the option was for a numerical criterion. However, all data are freely available, and stakeholders can infer other specific connections directly. Therefore, this innovative approach allowed inferring that the most recent pairs of terms associate the need to produce biofuels from microorganisms’ oils besides cerium oxide nanoparticles to improve the performance of fuel mixtures by reducing the emission of hydrocarbons (HC) and oxides of nitrogen (NOx).

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Section: Discussion and Resultsmentioning

confidence: 99%

Biofuels and Nanocatalysts: Python Boosting Visualization of Similarities

et al. 2023

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“…The significance assessment of the model was based on the correlation coefficient R 2 and lack of fit value. [ 49,50 ] The ANOVA results of the model are presented in Table S8, where a large F ‐value ( F = 45.32) and a small p ‐value ( p < 0.0001) indicated the significance of the model. According to the pure error, the lack of fit ( p = 0.38) also suggested that the model fitted well.…”

Section: Resultsmentioning

confidence: 99%

Engineering an efficient whole‐cell catalyst for d‐allulose production from glycerol

Zhang

Zhao

et al. 2023

Biotechnology Journal

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d‐Allulose has many health‐benefiting properties and therefore sustainably applied in food, pharmaceutical, and nutrition industries. The aldol reaction‐based route is a very promising alternative to Izumoring strategy in d‐allulose production. Remarkable studies reported in the past cannot get rid of by‐product formation and costly purified enzyme usage. In the present study, we explored the glycerol assimilation by modularly assembling the d‐allulose synthetic cascade in Escherichia coli envelop. We achieved an efficient whole‐cell catalyst that produces only d‐allulose from cheap glycerol feedstock, eliminating the involvement of purified enzymes. Detailed process optimization improved the d‐allulose titer by 1500.00%. Finally, the production was validated in 3‐L scale using a 5‐L fermenter, and 5.67 g L−1 d‐allulose was produced with a molar yield of 31.43%.

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“…This statement is in agreement with other previous studies. 35,40 However, a slight decrease is observed between 480 and 540 min. These results imply that very high reaction times are not required to increase FAME yield because of factors such as solvent loss, overheating, energy expenditure, and FAME loss.…”

Section: Optimization Of the Fame Extraction And Analysis Of Thementioning

confidence: 96%

“…33,34 Muhammad et al and other studies reported a FAME yield increasing proportionally with acid catalyst dosage, but a slight depletion was also recorded at a concentration of 4000 mL per kg dry biomass. 35 Meanwhile, in another study, Duongbia et al showed this to have an advantage as to yield the best fatty acid; fatty acid production increased with the concentration rate of acid (1 M for 44.35%, over 3 M for 55.25%, and 4.5 M for 53.53% of fatty acid produced). 36 Considering the experimental results, 8 vol% DES dosage in methanol was adopted in the current experiment.…”

Section: Screening Of the Catalystmentioning

confidence: 99%