Comparison of three ionic liquids pretreatment of Arundo donax L. For enhanced photo-fermentative hydrogen production

Zhou, C.; Jiang, Danping; Zhang, Tian; Lei, Tingzhou; Zhang, Huan; Yang, Jiabin; Shui, Xuenan; Li, Fang; Zhang, Yang; Zhang, Quanguo

doi:10.1016/j.biortech.2021.126088

Cited by 22 publications

(5 citation statements)

References 27 publications

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“…Table 1 also displays the influence of different DES pretreatment durations (3–15 h) on corncob composition with a fixed temperature of 50°C. With increasing pretreatment duration, lignin removal of corncob increased from 53.95% to 82.83% ( p < 0.05), confirming that the pretreatment duration was positively correlated with the dissolution of polymer molecules (lignin) [ 39 ]. The anions and ChCl cations of the DES system formed hydrogen bond complexes with hydrogen and oxygen atoms in the hydroxyl group, which promote the ionization of the phenolic group of lignin and lead to the dissolution of lignin in aqueous solution [ 19 ].…”

Section: Resultsmentioning

confidence: 90%

“…Moreover, biohydrogen production from various sources was compared, and the results are shown in Table 3 . Hydrogen yield from DES-pretreated corncob was much higher than that of giant reed pretreated by ionic liquids [ 39 ], sugarcane bagasse pretreated by hydrochloric acid [ 50 ], wheat straw pretreated by hydrothermal alkali [ 49 ], and corncob pretreated by acid steam-explosion [ 44 , 46–48 ]. The hydrogen yields of corn stalk pretreated by 2% NaOH [ 5 ] and corncob pretreated thermochemically by 2% H 2 SO 4 [ 25 ] were similar to the yields obtained in the present study.…”

Section: Resultsmentioning

confidence: 99%

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Effect of deep eutectic solvent pretreatment on biohydrogen production from corncob: pretreatment temperature and duration

Zhou,

Li,

et al. 2023

Bioengineered

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Deep eutectic solvent pretreatment with different temperatures and durations was applied to corncob to increase hydrogen yield via photo-fermentation. The correlation of composition, enzymatic hydrolysis, and hydrogen production in pretreated corncobs, as well as energy conversion was evaluated. Deep eutectic solvent pretreatment effectively dissolved lignin, retained cellulose, and enhanced both enzymatic hydrolysis and hydrogen production. The maximum cumulative hydrogen yield obtained under a pretreatment condition of 50°C and 12 h was 677.45 mL; this was 2.72 times higher than that of untreated corncob, and the corresponding lignin removal and enzymatic reduction of sugar concentration were 79.15% and 49.83 g/L, respectively; the highest energy conversion efficiency was 12.08%. The hydrogen production delay period was shortened, and the maximum shortening time was 18.9 h. Moreover, the cellulose content in pretreated corncob was positively correlated with both reducing sugar concentration and hydrogen yield and had the strongest influence on hydrogen production.

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Section: Resultsmentioning

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Effect of deep eutectic solvent pretreatment on biohydrogen production from corncob: pretreatment temperature and duration

Zhou,

Li,

et al. 2023

Bioengineered

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“…These advantages make ILs promising materials in carbohydrate science and technology. [70][71][72][73][74][75][76][77] ILs engage in complex interactions involving Coulomb forces, hydrogen bonds, and van der Waals forces. 78 These facilitate interactions with polysaccharides, resulting in successful solubilization.…”

Section: Ionic Liquids (Ils)mentioning

confidence: 99%

Solvent effects on carbohydrate transformation: insights into chemical pathway modulation

Sun,

Tong,

et al. 2024

Green Chem.

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“…Apart from reaction conditions, or solid loading, the biomass pretreatment e ciency can vary depending on the nature of ILs as well as biomass properties (Z. Chen et al, 2022;Chourasia et al, 2021). An exhaustive exploration of such a large variable space for the identi cation of in uential factors and their optimal setting through laboratory experiments is extremely tedious, if not impossible.…”

Section: Introductionmentioning

confidence: 99%

“…b ntree -number of learning cycles, nleaf -minimum number of observations per leaf in regression tree, nsplit -maximum number of split, navr -number of variables randomly selected at each node. Selected predictors 1-13 refer to (1) molecular weight, (2) Density, (3) viscosity, (4) conductivity, (5) melting point of IL; (6) particle size, (7) cellulose, (8) hemicellulose, (9) lignin,(10) other extractive contents in biomass;(11) temperature, (12) Time, (13) solid to liquid ration in pretreatment step.…”

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confidence: 99%

Machine learning models for the prediction on efficacy of ionic liquid-aided biomass pretreatment

Mahanty,

Gharami,

Haldar

2024

Preprint

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The influence of ionic liquids (ILs) characteristics, lignocellulosic biomass (LCB) properties, and process conditions on LCB pretreatment is not well understood. In this study, a total of 129 experimental data on cellulose, hemicellulose, lignin, and solid recovery from IL-based LCB pretreatment were compiled from literature to develop machine learning models. Following data imputation, bilayer artificial neural network (ANN) and random forest (RF) regression were developed to model the dataset. The full-featured ANN following Bayesian hyperparameter (HP) optimization though offered excellent fit on training (R2:0.936–0.994), cross-validation (R2CV) performance remained marginally poor, i.e., between 0.547 and 0.761. The fitness of HP-optimized RF models varied between 0.824–0.939 for regression, and between 0.383–0.831 in cross-validation. Temperature and pretreatment time had been the most important predictors, except for hemicellulose recovery. Bayesian predictor selection combined with HPO improved the R2CV boundary for ANN (0.555–0.825), as well as for RF models (0.474–0.824). As the predictive performance of the models varied depending on the target response, the use of a larger homogeneous dataset may be warranted.

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Comparison of three ionic liquids pretreatment of Arundo donax L. For enhanced photo-fermentative hydrogen production

Cited by 22 publications

References 27 publications

Effect of deep eutectic solvent pretreatment on biohydrogen production from corncob: pretreatment temperature and duration

Effect of deep eutectic solvent pretreatment on biohydrogen production from corncob: pretreatment temperature and duration

Solvent effects on carbohydrate transformation: insights into chemical pathway modulation

Machine learning models for the prediction on efficacy of ionic liquid-aided biomass pretreatment

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