Design of a single chambered microbial electrolytic cell reactor for production of biohydrogen from rice straw hydrolysate

Integrating bioprocess and metagenomics studies to enhance humic acid production from rice straw

Rice straw burning annually (millions of tons) leads to greenhouse gas emissions, and an alternative solution is producing humic acid with high added-value. This study aimed to examine the influence of a microbial consortium and other additives (chicken manure, urea, olive mill waste, zeolite, and biochar) on the composting process of rice straw and the subsequent production of humic acid. Results showed that among the fungal species, Thermoascus aurantiacus exhibited the most prominent impact in expediting maturation and improving compost quality, and Bacillus subtilis was the most abundant bacterial species based on metagenomics analysis. The highest temperature, C/N ratio reduction, and amount of humic acid production (Respectively in lab 61 °C, 54.67%, 298 g kg-1 and in pilot level 65 °C, 72.11%, 310 g kg-1) were related to treatments containing these microorganisms and other additives except urea. Consequently, T. aurantiacus and B. subtilis can be employed on an industrial scale as compost additives to further elevate quality. Functional analysis showed that the bacterial enzymes in the treatments had the highest metabolic activities, including carbohydrate and amino acid metabolism compared to the control. The maximum enzymatic activities were in the thermophilic phase in treatments which were significantly higher than that in the control. The research emphasizes the importance of identifying and incorporating enzymatically active strains that are suitable for temperature conditions, alongside the native strains in decomposing materials. This strategy significantly improves the composting process and yields high-quality humic acid during the thermophilic phase.

Sustainable hydrogen production via microalgae: Technological advancements, economic indicators, environmental aspects, challenges, and policy implications

Hydrogen has emerged as an alternative energy source to meet the increasing global energy demand, depleting fossil fuels and environmental issues resulting from fossil fuel consumption. Microalgae-based biomass is gaining attention as a potential source of hydrogen production due to its green energy carrier properties, high energy content, and carbon-free combustion. This review examines the hydrogen production process from microalgae, including the microalgae cultivation technological process for biomass production, and the three main routes of biomass-to-hydrogen production: thermochemical conversion, photo biological conversion, and electrochemical conversion. The current progress of technological options in the three main routes is presented, with the various strains of microalgae and operating conditions of the processes. Furthermore, the economic and environmental perspectives of biomass-to-hydrogen from microalgae are evaluated, and critical operational parameters are used to assess the feasibility of scaling up biohydrogen production for commercial industrial-scale applications. The key finding is the thermochemical conversion process is the most feasible process for biohydrogen production, compared to the pyrolysis process. In the photobiological and electrochemical process, pure hydrogen can be achieved, but further process development is required to enhance the production yield. In addition, the high production cost is the main challenge in biohydrogen production. The cost of biohydrogen production for direct bio photolysis it cost around $7.24 kg-1; for indirect bio photolysis it costs around $7.54 kg-1 and for fermentation, it costs around $7.61 kg-1. Therefore, comprehensive studies and efforts are required to make biohydrogen production from microalgae applications more economical in the future.

Artificial neural network and response surface methodology: a comparative analysis for optimizing rice straw pretreatment and saccharification

The present study demonstrates a comparative analysis between the artificial neural network (ANN) and response surface methodology (RSM) as optimization tools for pretreatment and enzymatic hydrolysis of lignocellulosic rice straw. The efficacy for both the processes, that is, pretreatment and enzymatic hydrolysis was evaluated using correlation coefficient (R²) & mean squared error (MSE). The values of R² obtained by ANN after training, validation, and testing were 1, 0.9005, and 0.997 for pretreatment and 0.962, 0.923, and 0.9941 for enzymatic saccharification, respectively. On the other hand, the R² values obtained with RSM were 0.9965 for cellulose recovery and 0.9994 for saccharification efficiency. Thus, ANN and RSM together successfully identify the substantial process conditions for rice straw pretreatment and enzymatic saccharification. The percentage of error for ANN and RSM were 0.009 and 0.01 for cellulose recovery and for 0.004 and 0.005 for saccharification efficiency, respectively, which showed the authority of ANN in exemplifying the non-linear behavior of the system.