Microalgal biorefineries: Advancement in machine learning tools for sustainable biofuel production and value-added products recovery.

The microalgae can be converted into biofuels, biochemicals, and bioactive compounds in a biorefinery. Recently, designing and executing more viable and sustainable biofuel production from microalgal biomass is one of the vital challenges in the development of biorefinery. Scalable cultivation of microalgae is mandatory for commercializing and industrializing the biorefinery. The intrinsic complication in cultivation of microalgae is the physiological and operational factors that renders challenging impact to enable a smooth and profitable operation. However, this aim can only be successful via a simulation prospect. Machine learning tools provides advanced approaches for evaluating, predicting, and controlling uncertainties in microalgal biorefinery for sustainable biofuel production. The present review provides a critical evaluation of the most progressing machine learning tools that validate a potential to be employed in microalgal biorefinery. These tools are highly potential for their extensive evaluation on microalgal screening and classification. However, the application of these tools for optimization of microalgal biomass cultivation in industries in order to increase the biomass production, is still in its initial stages. Integrated hybrid machine learning tools can aid the industries to function efficiently with least resources. Some of the challenges, and perspectives of machine learning tools are discussed. Besides, future prospects are also emphasized. Though, most of the research reports on machine learning tools are not appropriate to gather generalized information, standard protocols and strategies must be developed to design generalized machine learning tools. On a whole, this review offers a perspective information about digitalized microalgal exploitation in a microalgal biorefinery.

Rice husk-derived silicon nanostructured anode to enhance power generation in microbial fuel cell treating distillery wastewater.

The present study aims to utilize rice husk as a source of silica to prepare rice husk derived silicon nanoparticles (RH-Si) and demonstrate its ability as an anode modifier in a two-chambered H-shaped microbial fuel cell (MFC). The silicon nanoparticles synthesized by magnesiothermal reduction process were spherical in shape and ranged in size from 15 to 60 nm. The anode modified with silicon nanoparticles of 0.50 mg cm-2 recorded the maximum power and current density of 190.5 mW m-2 and 1.5 A m-2 corresponding to 7.6-fold and 3-fold increase as compared to the control . The modified anode also recorded a COD removal and coulombic efficiency of 74% and 49%, respectively in MFC operated with combined distillery and domestic wastewater at a HRT and OLR of 72 h and 59.2 gCOD L-1 d-1, respectively. The results evidence that RH derived silicon NPs are good anode modifiers and effective in enhancing bioelectricity generation and COD removal in MFCs.

Prospecting Ammoniphilus sp. JF isolated from agricultural fields for butachlor degradation.

Butachlor is a chloroacetamide herbicide used worldwide for controlling weeds in plants of rice, corn, soybean and other crops. In this study, indigenous bacterial species Ammoniphilus sp. JF was isolated from the agricultural fields of Punjab and identified using 16S ribosomal RNA analysis. The bacteria utilized butachlor as the sole carbon source and showed complete degradation (100 mg/L) within 24 h of incubation. Two intermediate products, namely 1,2-benzenedicarboxylic acid, bis(2-methylpropyl) ester and 2,4-bis(1,1-dimethylethyl)-phenol were observed at the end of butachlor degradation. To the best of author's knowledge, biodegradation of butachlor by indigenous Ammoniphilus sp. JF from the agricultural fields of Punjab has not been reported so far.