Microwave-assisted chemical pre-treatment of waste sorghum leaves: Process optimization and development of an intelligent model for determination of volatile compound fractions

Cereal Waste Valorization through Conventional and Current Extraction Techniques-An Up-to-Date Overview

Nowadays, in the European Union more than 100 million tons of food are wasted, meanwhile, millions of people are starving. Food waste represents a serious and ever-growing issue which has gained researchers’ attention due to its economic, environmental, social, and ethical implications. The Sustainable Development Goal has as its main objective the reduction of food waste through several approaches such as the re-use of agro-industrial by-products and their exploitation through complete valorization of their bioactive compounds. The extraction of the bioactive compounds through conventional methods has been used for a long time, whilst the increasing demand and evolution for using more sustainable extraction techniques has led to the development of new, ecologically friendly, and high-efficiency technologies. Enzymatic and ultrasound-assisted extractions, microwave-assisted extraction, membrane fractionation, and pressure-based extraction techniques (supercritical fluid extraction, subcritical water extraction, and steam explosion) are the main debated green technologies in the present paper. This review aims to provide a critical and comprehensive overview of the well-known conventional extraction methods and the advanced novel treatments and extraction techniques applied to release the bioactive compounds from cereal waste and by-products.

Pretreatment of lignocellulosic biomass: A review on recent advances

Depleting fossil reserves and growing energy needs have raised the demand for an alternative and clean energy source. The use of ubiquitously available lignocellulosic biomass for developing economic and eco-friendly large scale biorefinery applications has provided the much-needed impetus in this regard. The pretreatment process is a vital step for biomass transformation into added value products such as sugars, biofuels, etc. Different pretreatment approaches are employed to overcome the recalcitrance of lignocellulosic biomass and expedite its disintegration into individual components- cellulose, hemicellulose, and lignin. The conventional pretreatment methods lack sustainability and practicability for industrial scale up. The review encompasses the recent advances in selective physical and chemical pretreatment approaches such as milling, extrusion, microwave, ammonia fibre explosion, eutectic solvents etc. The study will allow a deeper understanding of these pretreatment processes and increase their scope as sustainable technologies for developing modern biorefineries.

Impact of nanoparticle inclusion on bioethanol production process kinetic and inhibitor profile

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NiO nanoparticle (NP) inclusion enhanced bioethanol production up to 59.96 %.

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Band energy gap impact NP catalytic performance in bioethanol production.

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NiO nanoparticle biocatalyst improved bioethanol productivity by 145 %.

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Modified Gompertz model was used to describe ethanol production with NP inclusion.

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Metallic NiO nanoparticles significantly reduced acetic acid concentration by 110 %.

This study examines the effects of nanoparticle inclusion in instantaneous saccharification and fermentation (NIISF) of waste potato peels. The effect of nanoparticle inclusion on the fermentation process was investigated at different stages which were: pre-treatment, liquefaction, saccharification and fermentation. Inclusion of NiO NPs at the pre-treatment stage gave a 1.60-fold increase and 2.10-fold reduction in bioethanol and acetic acid concentration respectively. Kinetic data on the bioethanol production fit the modified Gompertz model (R ² > 0.98). The lowest production lag time (t _L) of 1.56 h, and highest potential bioethanol concentration (P _m) of 32 g/L were achieved with NiO NPs inclusion at different process stages; the liquefaction stage and the pre-treatment phase, respectively. Elevated bioethanol yield, coupled with substantial reduction in process inhibitors in the NIISF processes, demonstrated the significance of point of nanobiocatalysts inclusion for the scale-up development of bioethanol production from potato peels.

Optimized activated charcoal detoxification of acid-pretreated lignocellulosic substrate and assessment for bioethanol production

This study optimized an activated charcoal (AC) detoxification method for the reduction of three different fermentation inhibitor compounds, while minimising the reducing sugar loss from acid-pretreated sorghum leaf (SL) wastes. Process optimization demonstrated a 98%, 88% and 37% removal efficiency for furfural, 5-hydroxymethylfurfural (HMF) and acetic acid respectively, with a 7% reducing sugar loss. Subsequently, the logistic and modified Gompertz models were used to comparatively evaluate the kinetics of Saccharomyces cerevisiae growth and ethanol production using the non-detoxified (NDF) and optimized detoxified (ODF) substrate. Yeast cell growth and bioethanol kinetic coefficients revealed that the ODF process was more effective than the NDF system. The experimental data generated from this study revealed that a suitable, cost-effective AC detoxification enhanced cell growth and bioethanol production efficiency. These findings pave the way for biomass pretreatment, detoxification and bioethanol process development using lignocellulosic wastes.

Development of artificial neural network tools for predicting sugar yields from inorganic salt-based pretreatment of lignocellulosic biomass

This study developed two Artificial Neural Network (ANN) tools for predicting sugar yields from inorganic salt-based pretreatment of lignocellulosic biomass. Pretreatment data from 90 experimental runs with 8 different input conditions were used to develop a microwave-based and a steam-based model. Both models exhibited high coefficients of determination (R²) of 0.97. Knowledge extraction revealed reducing sugar yields from the steam- and microwave-based models exhibited high sensitivity to both salt and alkali concentration. These models may be employed as initial screening tools in lignocellulosic bioprocesses, thereby potentially enhancing the economic and productivity of lignocellulosic-based bioprocesses.

Optimization of a novel sequential alkalic and metal salt pretreatment for enhanced delignification and enzymatic saccharification of corn cobs

This study presents a sequential sodium phosphate dodecahydrate (Na₃PO₄·12H₂O) and zinc chloride (ZnCl₂) pretreatment to enhance delignification and enzymatic saccharification of corn cobs. The effects of process parameters of Na₃PO₄·12H₂O concentration (5-15%), ZnCl₂ concentration (1-5%) and solid to liquid ratio (5-15%) on reducing sugar yield from corn cobs were investigated. The sequential pretreatment model was developed and optimized with a high coefficient of determination value (0.94). Maximum reducing sugar yield of 1.10±0.01g/g was obtained with 14.02% Na₃PO₄·12H₂O, 3.65% ZnCl₂ and 5% solid to liquid ratio. Scanning electron microscopy (SEM) and Fourier Transform Infrared analysis (FTIR) showed major lignocellulosic structural changes after the optimized sequential pretreatment with 63.61% delignification. In addition, a 10-fold increase in the sugar yield was observed compared to previous reports on the same substrate. This sequential pretreatment strategy was efficient for enhancing enzymatic saccharification of corn cobs.

Kinetics of Bioethanol Production from Waste Sorghum Leaves Using Saccharomyces cerevisiae BY4743

Kinetic models for bioethanol production from waste sorghum leaves by Saccharomyces cerevisiae BY4743 are presented. Fermentation processes were carried out at varied initial glucose concentrations (12.5–30.0 g/L). Experimental data on cell growth and substrate utilisation fit the Monod kinetic model with a coefficient of determination (R2) of 0.95. A maximum specific growth rate (μmax) and Monod constant (KS) of 0.176 h−1 and 10.11 g/L, respectively, were obtained. The bioethanol production data fit the modified Gompertz model with an R2 value of 0.98. A maximum bioethanol production rate (rp,m) of 0.52 g/L/h, maximum potential bioethanol concentration (Pm) of 17.15 g/L, and a bioethanol production lag time (tL) of 6.31 h were observed. The obtained Monod and modified Gompertz coefficients indicated that waste sorghum leaves can serve as an efficient substrate for bioethanol production. These models with high accuracy are suitable for the scale-up development of bioethanol production from lignocellulosic feedstocks such as sorghum leaves.