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Dried, milled perennial ryegrass samples were processed using chemical and physical treatments and the extracted cellulose products were analysed for yield, crystallinity by X-ray Diffraction (XRD) and for purity using Thermogravimetric Analysis (TGA), Pyrolysis-Gas Chromatography/Mass Spectrometry (Py-GC/MS) and Fourier Transform Infrared (FTIR) spectroscopy. Extraction protocols examined the use of chemical chelation, acid and alkaline hydrolysis, along with physical degradation methods. Highest product yields were obtained using single step chemical protocols followed by physical processing, however, these products had low crystallinity and higher amorphous fraction content. Multistep chemical processing to completely remove hemicellulose and lignin with an alkali refluxing step, delivered lower yielding cellulose products of greater crystallinity and purity. In combination, the four instrumental techniques highlighted removal of amorphous fractions, providing rapid, accurate compositional data on the extracted cellulose products.

Dried, milled perennial ryegrass samples were processed using chemical and physical treatments and the extracted cellulose products were analysed for yield, crystallinity by X-ray Diffraction (XRD) and for purity using Thermogravimetric Analysis (TGA), Pyrolysis-Gas Chromatography/Mass Spectrometry (Py-GC/MS) and Fourier Transform Infrared (FTIR) spectroscopy. Extraction protocols examined the use of chemical chelation, acid and alkaline hydrolysis, along with physical degradation methods. Highest product yields were obtained using single step chemical protocols followed by physical processing, however, these products had low crystallinity and higher amorphous fraction content. Multistep chemical processing to completely remove hemicellulose and lignin with an alkali refluxing step, delivered lower yielding cellulose products of greater crystallinity and purity. In combination, the four instrumental techniques highlighted removal of amorphous fractions, providing rapid, accurate compositional data on the extracted cellulose products.

In this study, levulinic acid (LA) was produced from rice straw biomass in co-solvent biphasic reactor system consisting of hydrochloric acid and dichloromethane organic solvent. The modified protocol achieved a 15% wt LA yield through the synergistic effect of acid and acidic products (auto-catalysis) and the designed system allowed facile recovery of LA to the organic phase. Further purification of the resulting extractant was achieved through traditional column chromatography, which yielded a high purity LA product while recovering ∼85% wt. Upon charcoal treatment of the resultant fraction generated an industrial grade target molecule of ∼99% purity with ∼95% wt recovery. The system allows the solvent to be easily recovered, in excess of 90%, which was shown to be able to be recycled up to 5 runs without significant loss of final product concentrations. Overall, this system points to a method to significantly reduce manufacturing cost during large-scale LA preparation.

In this study, levulinic acid (LA) was produced from rice straw biomass in co-solvent biphasic reactor system consisting of hydrochloric acid and dichloromethane organic solvent. The modified protocol achieved a 15% wt LA yield through the synergistic effect of acid and acidic products (auto-catalysis) and the designed system allowed facile recovery of LA to the organic phase. Further purification of the resulting extractant was achieved through traditional column chromatography, which yielded a high purity LA product while recovering ∼85% wt. Upon charcoal treatment of the resultant fraction generated an industrial grade target molecule of ∼99% purity with ∼95% wt recovery. The system allows the solvent to be easily recovered, in excess of 90%, which was shown to be able to be recycled up to 5 runs without significant loss of final product concentrations. Overall, this system points to a method to significantly reduce manufacturing cost during large-scale LA preparation.

Microalgae are promising sources of valuable compounds: carotenoids, polyunsaturated fatty acids, lipids, etc. To overcome the feasibility challenge due to low yield and attain commercial potential, researchers merge technologies to enhance algal bioprocess. In this context, nanomaterials are attractive for enhancing microalgal bioprocessing, from cultivation to downstream extraction. Nanomaterials enhance biomass and product yields (mainly lipid and carotenoids) through improved nutrient uptake and stress tolerance during cultivation. They also provide mechanistic insights from recent studies. They also revolutionize harvesting via nano-induced sedimentation, flocculation, and flotation. Downstream processing benefits from nanomaterials, improving extraction and purification. Special attention is given to cost-effective extraction, showcasing nanomaterial integration, and providing a comparative account. The review also profiles nanomaterial types, including metallic nanoparticles, magnetic nanomaterials, carbon-based nanomaterials, silica nanoparticles, polymers, and functionalized nanomaterials. Challenges and future trends are discussed, emphasizing nanomaterials' role in advancing sustainable and efficient microalgal bioprocessing, unlocking their potential for bio-based industries.

Microalgae are promising sources of valuable compounds: carotenoids, polyunsaturated fatty acids, lipids, etc. To overcome the feasibility challenge due to low yield and attain commercial potential, researchers merge technologies to enhance algal bioprocess. In this context, nanomaterials are attractive for enhancing microalgal bioprocessing, from cultivation to downstream extraction. Nanomaterials enhance biomass and product yields (mainly lipid and carotenoids) through improved nutrient uptake and stress tolerance during cultivation. They also provide mechanistic insights from recent studies. They also revolutionize harvesting via nano-induced sedimentation, flocculation, and flotation. Downstream processing benefits from nanomaterials, improving extraction and purification. Special attention is given to cost-effective extraction, showcasing nanomaterial integration, and providing a comparative account. The review also profiles nanomaterial types, including metallic nanoparticles, magnetic nanomaterials, carbon-based nanomaterials, silica nanoparticles, polymers, and functionalized nanomaterials. Challenges and future trends are discussed, emphasizing nanomaterials' role in advancing sustainable and efficient microalgal bioprocessing, unlocking their potential for bio-based industries.

Algae production process is a key cost center in production of biofuels/bioproducts from microalgae. Decline in the growth of algae in outdoor ponds during non-optimal conditions is one of the hurdles for achieving consistently high algal production rates. An optimal controller can be used to overcome this limitation and provide reliable growth in outdoor conditions. A model predictive controller (MPC) was developed to optimize the algal growth, predicted by flux balance analysis, under natural disturbances, embedding within the cost function, the economic and environmental constraints associated with the process. The model, developed in MATLAB, was validated on a 30-L continuous algal culture under light, temperature and a combination of light and temperature disturbances. The MPC proved effective in minimization of a decrease in growth under these natural disturbances. The growth rates with MPC were observed to be 79-116% higher as compared to the non-MPC growth.

Algae production process is a key cost center in production of biofuels/bioproducts from microalgae. Decline in the growth of algae in outdoor ponds during non-optimal conditions is one of the hurdles for achieving consistently high algal production rates. An optimal controller can be used to overcome this limitation and provide reliable growth in outdoor conditions. A model predictive controller (MPC) was developed to optimize the algal growth, predicted by flux balance analysis, under natural disturbances, embedding within the cost function, the economic and environmental constraints associated with the process. The model, developed in MATLAB, was validated on a 30-L continuous algal culture under light, temperature and a combination of light and temperature disturbances. The MPC proved effective in minimization of a decrease in growth under these natural disturbances. The growth rates with MPC were observed to be 79-116% higher as compared to the non-MPC growth.

The possibility of using anaerobic digestate effluent (ADE) to replace freshwater and nutrients for bioethanol production was explored. The ethanol concentration yielded from ADE and post-centrifuged ADE supernatant was 79.60±1.75 g/L and 78.33±1.66 g/L, respectively, with a 24% dry mass (DM) of soft wheat. Ethanol production was enhanced in ADE by as much as 18% in comparison to the production in freshwater (66.61±0.28 g/L, p<0.01). Without yeast nutrients, ADE fermentation yielded an ethanol concentration of 81.10±2.87 g/L, which was significantly higher than that in freshwater fermentation (59.67±1.79 g/L). Analysis showed that ADE contained rich nitrogen, proteins and minerals. After one-step distillation, the ethanol concentration attained was 700.05±46.20 g/L in ADE as compared to 622.79±32.22 g/L in freshwater (p<0.05).