Dark fermentative biohydrogen production by Acinetobacter junii-AH4 utilizing various industry wastewaters

Hydrogen (H₂) is one of the most promising renewable energy sources, anaerobic bacterial H₂ fermentation is considered as one of the most environmentally sustainable alternatives to meet the potential fossil fuel demand. Bio-H₂ is the cleanest and most effective source of energy provided by the dark fermentation utilizing organic substrates and different wastewaters. In this study, the bio-H₂ production was achieved by using the bacteria Acinetobacter junii-AH4. Further, optimization was carried out at different pH (5.0–8.0) in the presence of wastewaters as substrates (Rice mill wastewater (RMWW), Food wastewater (FWW) and Sugar wastewater (SWW). In this way, the optimized experiments excelled with the maximum cumulative H₂ production of 566.44 ± 3.5 mL/L (100% FWW at pH 7.5) in the presence of Acinetobacter junii-AH4. To achieve this, a bioreactor (3 L) was employed for the effective production of H₂ and Acinetobacter junii-AH4 has shown the highest cumulative H₂ of 613.2 ± 3.0 mL/L, HPR of 8.5 ± 0.4 mL/L/h, HY of 1.8 ± 0.09 mol H₂/mol glucose. Altogether, the present study showed a COD removal efficiency of 79.9 ± 3.5% by utilizing 100% food wastewater at pH 7.5. The modeled data established a batch fermentation system for sustainable H₂ production. This study has aided to achieve an ecofriendly approach using specific wastewaters for the production of bio-H₂.     

Biohythane production from organic waste: Recent advancements,technical bottlenecks and prospects

The availability of fossil fuels is a major factor that determines the economy of a country. However, possible exhaustion of fossil fuel deposits as well as increased pollution, and other adverse effects on the environment has prompted us to search for alternative fuels. This resulted in the development of hythane, a blend of hydrogen with methane, at concentrations of 10%–30%. The breakdown of organic substrates using sequential dark fermentation (DF) and anaerobic digestion (AD) leads to biohythane production. The quality and quantity of biohythane can be improved by altering the following aspects: selection, development, and/or genetic engineering of suitable microbial consortium; the use of cheap, appropriate substrates; improved design of bioreactors; and the implementation of two-stage fermentation system. This review focusses on the mechanism of biohythane production and the different aspects involved in increasing both its production rate and quality. A comparative study has also been done to demonstrate the superiority of biohythane over other biofuels.     

Biohydrogen production using horizontal and vertical continuous stirred tank reactor- a numerical optimization

This paper illustrates the method to predict the production of biohydrogen and biogas from the horizontal and vertical continuous mixed tank reactor using a numerical approach. Utilization of computational fluid dynamics on the estimation of bioreactor performance is very crucial owing to the uncertainty in the numerical results. Since there has been little work on CFD to determine the influence of hydraulic retention time, impeller speeds, vortex growth, and pH on biohydrogen yield rate the effort had been made. A series of simulations are done with optimal boundary conditions to ensure maximum hydrogen and biogas production rate. Two types of reactors HCSTR and VCSTR are operated at different impeller speed from 40 rpm to 120 rpm. Further, the rate of HRT and organic loading are varied. As the rpm of the impeller increase the rate of hydrogen and biomass production increases not later than 80 rpm. Meanwhile, the optimal range of HRT and pH are 4–8 h and 6.0–8.0. Running the impeller at optimized rate with derived conditions leads to high hydrogen and biogas production of 6 LH₂/Ld and 30 L/d. The obtained results are validated with the experimental findings to compare the uncertainty formed due to the numerical simulations. The optimum boundary condition obtained from the study is expected to provide the essential knowledge in establishing the full-scaled reactors.     

Green Synthesis of Zinc Oxide Nanoparticles (ZnO NPs) for Effective Degradation of Dye,Polyethylene and Antibacterial Performance in Waste Water Treatment

At present, there is a vital need for river water purification by developing new approaches to eliminate bacterial biofilms, textile dyes, and Low-Density Polyethylene (LDPE) plastics that pose severe threats to human and environmental health. The current work put forward the construction of an eco-friendly green strategy to synthesize zinc oxide nanoparticles (ZnO NPs) using areca nut (Areca catechu) extract and their application to tackle the challenges in water purification. Prepared biogenic NPs were characterized by X-ray diffraction analysis (XRD), Fourier Transform Infra-Red (FT-IR), Energy Diffraction Spectroscopy (EDS), Scanning Electronic Microscopy (SEM), Transmission Electron Microscopy (TEM) analysis, confirmed the spherical shape in 20 nm and UV–vis spectroscopy. The characteristic absorption band exhibited at 326 nm confirmed the formation of ZnO NPs using UV–vis spectroscopy. Among all the tested bacterial pathogens, the E. coli at 50 µg/mL concentration showed the highest inhibition of biofilm activity, followed by the highest growth curve, cellular leakage, and potassium ion efflux. The ZnO NPs observed with photo-degradation of Rhodamine-B (Rh-B), Methylene Blue (MB), and Nigrosine dyes under sunlight irradiation at different time intervals. Finally, the photocatalytic activity of LDPE-ZnO NPs nanocomposite film showed the highest degradation under solar light irradiation were confirmed through photo-induced weight loss, SEM, FTIR, and MALDI-TOF analysis. This study demonstrates ZnO NPs exhibit efficacy against biofilm formation, degradation of photocatalytic textile dyes, and low-density LDPE film under solar light irradiation, which can be a step forward in water purification.

     

A modified ant-colony optimisation algorithm to minimise the completion time variance of jobs in flowshops

In this work, the flowshop scheduling problem is considered with the objective of minimising the completion-time variance (CTV) of jobs, and an Ant Colony Optimisation (ACO) algorithm is presented. Two implementations of the Modified Ant Colony Optimisation algorithm (MACO-I and MACO-II) are proposed to solve the permutation flowshop scheduling problem. The proposed ant-colony-algorithm implementations have been tested on 90 benchmark flowshop scheduling problems. The solutions yielded by the proposed MACO implementations are compared with various algorithms and with the best CTV of jobs reported in the literature. The proposed MACO implementations are found to perform very well in minimising the chosen performance measure.     

Insights into evolutionary trends in molecular biology tools in microbial screening for biohydrogen production through dark fermentation

Access to clean energy is vital to combat global warming and climate change, and nothing but hydrogen could better deliver it with ease to secure future energy needs. Biohydrogen could be produced in different routes including photolysis, water-gas shift reaction, dark, photo-fermentation and combination of both. Dark fermentative hydrogen production (DFHP) is efficient in comparison with photo-fermentation and utilizing organic waste ensures land usage and water for agriculture. Several microbes are involved in the process of biohydrogen production via dark fermentation and characterizing them at molecular level unveils holistic approach and understanding. Limited resources were available in terms of molecular tools for microbial characterization and this paper attempts to review the evolution of advanced molecular techniques including their merits and demerits. Understanding the composition of micro-flora is important in DFHP and could be classified as pure, co-cultures, enriched mixed cultures and mixed microbiota. These cultures act as seed sources for batch and continuous fermentations that help in understanding the efficiency of these methods. The schematics and systematic assessment of the various molecular tools (cloning, PCR-DGGE, FISH, NGS, CE-SSCP) for quantification, identification, detection and characterization of the microbial cell activity have been elaborated. Lastly, a comparative tabulation recapitulates the merits and drawbacks of each technique discussed. This provides valued information for choosing the right kind of microbial and molecular assessment tool for future characterization. Such analysis aids in suitable identification and characterization of microflora as potential biocatalysts for biohydrogen production through dark fermentation.     

Generating non-permutation schedules in flowline-based manufacturing sytems with sequence-dependent setup times of jobs: a heuristic approach

A flowline-based manufacturing system is a manufacturing environment where machines are arranged in accordance with the order of processing of jobs, with all jobs having an identical and unidirectional flow pattern through the machines; however, some or all jobs may have missing operations on some machines. In several practical situations the setup times of jobs are separable, significant and sequence-dependent. The problem of scheduling in such a flowline-based manufacturing system is considered with the focus on the development of non-permutation schedules. The deficiency of using the existing set of recursive equations in developing the timetable for permutation schedules is first highlighted, and a correct and modified set of recursive equations to take account of the missing operations properly is formulated. A simple heuristic procedure to derive non-permutation schedules from a given permutation sequence is proposed subsequently. Through extensive computational experimentation, it is shown that the proposed heuristic procedure yields solutions of good quality.     

Pretreatment of second and third generation feedstock for enhanced biohythane production: Challenges,recent trends and perspectives

In the context of biofuel production and achieving sustainable bioeconomy, the use of lignocellulosic and algae biomass in anaerobic fermentation processes yields biohythane that has a typical composition of 10–15% H₂, 50–55% CH₄ and 30–40% CO₂. Using organic biomass-based substrates has been shown to minimize environmental impacts due to the versatile production of high-value products under normal operating conditions that are practically achievable. However, the biohythane yield depends on different factors such as the biomass type, the organic loading rate, soluble metabolic products formed, the type of fermentation (single/dual stage) and the pretreatment strategy adopted for the biomass. Different pretreatment strategies based on physical, chemical and biological processes have been proposed in the literature. In this review, improvements in biohythane yield as a result of these pretreatment strategies, the need/effect of inoculum enrichment, the effects of pH, temperature, trace element addition and organic loading rate has been reviewed. Finally, the major developments of improving biohythane yield due to the addition of co-substrates and the current trends are discussed.     

Reliability-based design optimization with progressive surrogate models

Reliability-based design optimization (RBDO) has traditionally been solved as a nested (bilevel) optimization problem, which is a computationally expensive approach. Unilevel and decoupled approaches for solving the RBDO problem have also been suggested in the past to improve the computational efficiency. However, these approaches also require a large number of response evaluations during optimization. To alleviate the computational burden, surrogate models have been used for reliability evaluation. These approaches involve construction of surrogate models for the reliability computation at each point visited by the optimizer in the design variable space. In this article, a novel approach to solving the RBDO problem is proposed based on a progressive sensitivity surrogate model. The sensitivity surrogate models are built in the design variable space outside the optimization loop using the kriging method or the moving least squares (MLS) method based on sample points generated from low-discrepancy sampling (LDS) to estimate the most probable point of failure (MPP). During the iterative deterministic optimization, the MPP is estimated from the surrogate model for each design point visited by the optimizer. The surrogate sensitivity model is also progressively updated for each new iteration of deterministic optimization by adding new points and their responses. Four example problems are presented showing the relative merits of the kriging and MLS approaches and the overall accuracy and improved efficiency of the proposed approach.