Recent advances and sustainable development of biofuels production from lignocellulosic biomass

Saravanan, A.; Kumar, Pradeep; Jeevanantham, S.; Karishma, S.; Vo, Dai‐Viet N.

doi:10.1016/j.biortech.2021.126203

Cited by 166 publications

(51 citation statements)

References 131 publications

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“…Biofuels, the fuels derived from biomass provide solutions for three big worldwide energy problems: energy crisis, environmental pollution, and waste organization 1 . Among various biofuels, bioethanol is considered a popular oxygenated improver of gasoline due its high oxygen content (35%) and a high-research octane number (RON 109) (octane number is a measure of antiknocking property of a fuel).…”

Section: Introductionmentioning

confidence: 99%

“…Use of food crops could lead to food versus energy crisis in the future and hence finding alternative sources for production of ethanol should be a priority. So far, lignocellulosic biomass (rice straw) is considered a good alternative for bioethanol production, also termed as second-generation biofuels 1 . Production of third-generation biofuels takes place from algal feedstock i.e.…”

Section: Introductionmentioning

confidence: 99%

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Rice straw structure changes following green pretreatment with petha wastewater for economically viable bioethanol production

Kumari

Singh

2022

Sci Rep

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Energy efficient and environment friendly pretreatment processes for the production of biofuel have remained elusive and the research is further compounded by the high cost of processing lignocellulosic biomass—an essential factor for producing sustainable biofuels. In the last few decades, a number of pretreatment methods have been proposed, specifically chemical pretreatments but are either expensive or harmful to the environment. To address this urgent need, we propose a green pretreatment method that utilises the highly alkaline by-product, petha wastewater to pretreat the lignocellulosic waste rice straw (RS). The effectiveness of the pretreatment was analysed by monitoring both enhanced cellulose content and reducing sugar yield along with removal of hemicellulose and lignin. We found that PWW pretreatment yielded five times more reducing sugar than native RS with 10.12% increment in cellulose content. SEM and EDX studies further revealed that our process enhanced surface roughness and carbon content (from 32.19% increased to 41.59% and 41.66% for A and D, respectively) along with reduction in silica content (from 8.68% in RS to 4.30% and 7.72% for A and D, respectively). XRD and FTIR analyses indicate crystallinity index (CI) and alteration in lignocellulosic structure of RS, respectively. Decrease in CI was about 43.4% in A whereas only 4.5% in D as compared to native RS (CI 54.55%). Thereby we found PWW to be better substitute of an alkali for pretreatment of RS with negligible environmental impacts.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rice straw structure changes following green pretreatment with petha wastewater for economically viable bioethanol production

Kumari

Singh

2022

Sci Rep

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“…However, the complexity of the structure of lignocellulosic biomasses, such as sugarcane leaves, is a major challenge, making them highly recalcitrant to AD and ultimately resulting in low methane yield [6]. Lignin is a physical barrier to lignocellulosic biomass [13][14][15], preventing enzymes from accessing cellulose [16][17][18][19]. Therefore, a pretreatment step is required to overcome this problem.…”

Section: Introductionmentioning

confidence: 99%

Biohydrogen and Methane Production from Sugarcane Leaves Pretreated by Deep Eutectic Solvents and Enzymatic Hydrolysis by Cellulolytic Consortia

et al. 2022

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This study determined the optimal conditions for the deep eutectic solvent (DES) pretreatment of sugarcane leaves and the best fermentation mode for hydrogen and methane production from DES-pretreated sugarcane leaves. Choline chloride (ChCl):monoethanolamine (MEA) is the most effective solvent for removing lignin from sugarcane leaves. The optimum conditions were a ChCl: MEA molar ratio of 1:6, 120 °C, 3 h, and substrate-to-DES solution ratio of 1:12. Under these conditions, 86.37 ± 0.36% lignin removal and 73.98 ± 0.42% hemicellulose removal were achieved, whereas 84.13 ± 0.77% cellulose was recovered. At a substrate loading of 4 g volatile solids (VS), the simultaneous saccharification and fermentation (SSF) and separate hydrolysis and fermentation (SHF) processes yielded maximum hydrogen productions of 3187 ± 202 and 2135 ± 315 mL H2/L, respectively. In the second stage, methane was produced using the hydrogenic effluent. SSF produced 5923 ± 251 mL CH4/L, whereas SHF produced 3583 ± 128 mL CH4/L. In a one-stage methane production process, a maximum methane production of 4067 ± 320 mL CH4/L with a substrate loading of 4 g VS was achieved from the SSF process. SSF proved to be more efficient than SHF for producing hydrogen from DES-pretreated sugarcane leaves in a two-stage hydrogen and methane production process as well as a one-stage methane production process.

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“…The results showed that gas and tar are the key elements of fast pyrolysis of hemicellulose 30 . Furthermore, recent advances and sustainable development of biofuels production from lignocellulosic biomass have been reviewed 31 …”

Section: Introductionmentioning

confidence: 99%

“…30 Furthermore, recent advances and sustainable development of biofuels production from lignocellulosic biomass have been reviewed. 31 Materials' thermal characteristics are required to monitor the reaction equilibria, estimate the products' energy content, and obtain the adiabatic temperature at the gasification unit. 13,32 The biomass heat capacity is a fundamental thermophysical property required to calculate the standard molar thermodynamic functions and reaction equilibria to design a highly efficient reactor for plant biomass processing.…”

Section: Introductionmentioning

confidence: 99%

Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches

Karimi

Khosravi

Fathollahi

et al. 2022

Energy Science & Engineering

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Heat capacity is among the most well‐known thermal properties of cellulosic biomass samples. This study assembles a general machine learning model to estimate the heat capacity of the cellulosic biomass samples with different origins. Combining the uncertainty and ranking analyses over 819 artificial intelligence models from seven different categories confirmed that the least‐squares support vector regression (LSSVR) with the Gaussian kernel function is the best estimator. This model is validated using 700 laboratory heat capacities of four cellulosic biomass samples in wide temperature ranges (absolute average relative deviation = 0.32%, mean square errors = 1.88 × 10−3, and R2 = 0.999991). The data validity investigation approved that only one out of 700 experimental data is an outlier. The LSSVR model considers the effect of the cellulosic samples' crystallinity, temperature, and sulfur and ash content on their heat capacity. The overall prediction accuracy of the LSSVR is more than 62% better than the achieved accuracy using the empirical correlation.

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Recent advances and sustainable development of biofuels production from lignocellulosic biomass

Cited by 166 publications

References 131 publications

Rice straw structure changes following green pretreatment with petha wastewater for economically viable bioethanol production

Rice straw structure changes following green pretreatment with petha wastewater for economically viable bioethanol production

Biohydrogen and Methane Production from Sugarcane Leaves Pretreated by Deep Eutectic Solvents and Enzymatic Hydrolysis by Cellulolytic Consortia

Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches

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