Research article Special Issues

Persistence and extinction of infection in stochastic population model with horizontal and imperfect vertical disease transmissions

  • Epidemic models are used to understand the dynamics of disease transmission and explore the possible measures for preventing the spread of infection in the population. Disease transmission is intrinsically random and severely affected by environmental factors. We investigated a stochastic population model of the susceptible-infected-susceptible (SIS) type, in which infection spreads via both vertical and horizontal transmission routes. To incorporate stochasticity to the system, white multiplicative noise was taken into account in the horizontal disease transmission term. We proved that noise intensity, disease transmission, and recovery rates are potential routes for eradicating the disease. Furthermore, the parasite population reduces its fitness for some fixed noise if the relative fecundity of infected hosts and the disease transmission are low. However, if either of these is increased, it observes enhanced fitness. A simulation study illustrated the system's analytically dynamic properties and provided different insights. A case study for the imperfect vertical and horizontal infection transmission is also presented, supporting some of our observed theoretical results.

    Citation: Abhijit Majumder, Debadatta Adak, Adeline Samson, Nandadulal Bairagi. Persistence and extinction of infection in stochastic population model with horizontal and imperfect vertical disease transmissions[J]. Mathematical Biosciences and Engineering, 2025, 22(4): 846-875. doi: 10.3934/mbe.2025030

    Related Papers:

    [1] Rim BOUKHCHINA, Mohamed HAMDI, Souheil EL ALIMI . Power-to-hydrogen: A review of applications, market development, and policy landscape. AIMS Energy, 2025, 13(3): 696-731. doi: 10.3934/energy.2025025
    [2] Daido Fujita, Takahiko Miyazaki . Techno-economic analysis on the balance of plant (BOP) equipment due to switching fuel from natural gas to hydrogen in gas turbine power plants. AIMS Energy, 2024, 12(2): 464-480. doi: 10.3934/energy.2024021
    [3] Antzela Fivga, Lais Galileu Speranza, Carolina Musse Branco, Miloud Ouadi, Andreas Hornung . A review on the current state of the art for the production of advanced liquid biofuels. AIMS Energy, 2019, 7(1): 46-76. doi: 10.3934/energy.2019.1.46
    [4] Edith Martinez-Guerra, Veera Gnaneswar Gude . Energy aspects of microalgal biodiesel production. AIMS Energy, 2016, 4(2): 347-362. doi: 10.3934/energy.2016.2.347
    [5] Gbadebo Oladosu . An economic evaluation of alternative biofuel deployment scenarios in the USA. AIMS Energy, 2017, 5(3): 374-396. doi: 10.3934/energy.2017.3.374
    [6] Michael E. Salassi, Lawrence L. Falconer, Tyler B. Mark, Michael A. Deliberto, Brian M. Hilbun, Todd L. Cooper . Economic Potential for Energy Cane Production as a Cellulosic Biofuel Feedstock in the Southeastern United States. AIMS Energy, 2015, 3(1): 25-40. doi: 10.3934/energy.2015.1.25
    [7] Pallav Purohit, Subash Dhar . Lignocellulosic biofuels in India: current perspectives, potential issues and future prospects. AIMS Energy, 2018, 6(3): 453-486. doi: 10.3934/energy.2018.3.453
    [8] Patrick Moriarty, Damon Honnery . The limits of renewable energy. AIMS Energy, 2021, 9(4): 812-829. doi: 10.3934/energy.2021037
    [9] Yadessa Gonfa Keneni, Jorge Mario Marchetti . Oil extraction from plant seeds for biodiesel production. AIMS Energy, 2017, 5(2): 316-340. doi: 10.3934/energy.2017.2.316
    [10] Muhammad Amir Raza, M. M. Aman, Abdul Ghani Abro, Muhammad Shahid, Darakhshan Ara, Tufail Ahmed Waseer, Mohsin Ali Tunio, Shakir Ali Soomro, Nadeem Ahmed Tunio, Raza Haider . Modelling and development of sustainable energy systems. AIMS Energy, 2023, 11(2): 256-270. doi: 10.3934/energy.2023014
  • Epidemic models are used to understand the dynamics of disease transmission and explore the possible measures for preventing the spread of infection in the population. Disease transmission is intrinsically random and severely affected by environmental factors. We investigated a stochastic population model of the susceptible-infected-susceptible (SIS) type, in which infection spreads via both vertical and horizontal transmission routes. To incorporate stochasticity to the system, white multiplicative noise was taken into account in the horizontal disease transmission term. We proved that noise intensity, disease transmission, and recovery rates are potential routes for eradicating the disease. Furthermore, the parasite population reduces its fitness for some fixed noise if the relative fecundity of infected hosts and the disease transmission are low. However, if either of these is increased, it observes enhanced fitness. A simulation study illustrated the system's analytically dynamic properties and provided different insights. A case study for the imperfect vertical and horizontal infection transmission is also presented, supporting some of our observed theoretical results.



    Population growth and changing lifestyle with industrialization and urbanization are the added sinks for soaring energy demand. Present energy security is predominantly drifting around the fossil resources and alternative fuels are being searched [1]. However, the faster depletion of fossil resources and accelerated accumulation of greenhouse gases (GHGs) in the environment that already has exceeded the "dangerously high" threshold of 450 ppm CO2, stressing the fossil fuel to be an unsustainable source of energy. The conventional fossil-based fuels contributed major share in the global primary energy consumption [2]. Global dependence on fossil fuels has led to the release of over 1,100 GtCO2 into the atmosphere since the mid-nineteenth century. Currently, energy related GHG emissions, mainly from fossil fuel combustion for heat supply, electricity generation and transport, account for around 70% of total emissions including carbon dioxide, methane and some traces of nitrous oxide [3].

    The concerns related to energy security, environmental safety and sustainability have encouraged researchers towards alternative, renewable, sustainable, efficient and cost effective energy sources with lesser emissions [4]. Renewable energy can play a decisive role at global and national levels in dealing with the concerns related to energy security, climate change, eco-friendliness and sustainability [5,6,7,8,9]. With the situation of increasing energy demand, its prices and implementation of policies for global warming reduction, the sources of renewable energy have popularized [10,11]. Renewable energy is not only providing the sustainable energy, but also considered as a tool to solve several other problems associated with the fossil energy, viz., improving the energy security, resolving the health and environmental anxiety, decreasing greenhouse gas emissions and reducing poverty by increasing employment [2].

    The increasing demand for biofuels has encouraged researchers and policy makers to find sustainable biofuel production systems in accordance with regional conditions and needs. The sustainability of a biofuel production system must include energy and greenhouse gas (GHG) saving along with environmental and social acceptability [12].

    The studies reviewed in various publications are mainly focused on very specific aspects of bio-hydrogen production such as reactor design, molecular tools, production pathway, etc., however, this article exclusively reviews the biological production of hydrogen and its sustainability as an economical clean fuel in present scenario and also discusses its future perspective.

    There are some recent publications on types of biofuels and their production from different substrates [13] and waste bioresources [10,14]. Biofuels can broadly be categorised into two groups:

    Primary biofuel: In this category, all those biomass materials can be included which were traditionally used i.e. firewood, wood chips, pellets, animal waste, forest, crop residue, landfill gas etc.

    Secondary biofuel: In this category, all other products and processes, which use biomass and provide fuel in the form of liquid, solid or gas. On the basis of substrate utilization, this group can further be divided into three categories viz. 1st, 2nd and 3rd generation biofuel (Figure 1).

    Figure 1.  Classification of biofuels [13].

    Though, research on the biohydrogen production is not new and basic photolytic hydrogen production during photosynthesis was explained long back, currently a possibility for industrial production of hydrogen from biological sources provides a boost in the field. After the possibility of hydrogen usage as transportation fuel, the research in this field has fuelled up and a tremendous improvement is seen since 2003. Published databases showed as much as 146 journals are publishing research results on biohydrogen. Among them, some are completely devoted towards the bioenergy i.e. International Journal of Hydrogen, Energy, Renewable and Sustainable Energy Reviews. Biohydrogen research are being conducted by more than 100 nations and around 7834 papers have been published since 1984 (Figure 2). Major contribution on this research have come from China followed by USA. India hold third position on this research (Figure 3) [2]. Most of the publications on biohydrogen are research articles (73%) followed by conference papers (11%) and Review articles (10%) (SCOPUS, 2016).

    Figure 2.  Year wise publication on biohydrogen (Source: SCOPUS).
    Figure 3.  Top ten countries publishing research on biohydrogen (Source: SCOPUS).

    Present hydrogen production system (Figure 4) is mainly based on electrolysis of water, and thermocatalytic reformation of hydrogen rich compounds, but these processes are energy intensive. Biological hydrogen production can solve this problem by utilizing biomass and microorganisms. Biohydrogen can be produced in two broad ways: By photosynthesis using microalgae, and by fermentation [15,16,17,18,19]. The organism involved and maximum H2 yield in different process are summarized in Table 1, and the pros and cons of different process are summarized in Table 2.

    Figure 4.  A systematic flow chart of bio-hydrogen production pathways.
    Table 1.  Comparison of production rate of biological H2 in different processes [20].
    Production-Process Maximum production rate (mmol H2/L h−1) Employed Microorganism
    Direct Bio-photolysis 0.07 Chlamydomonas reinhardtii
    Indirect Bio-photolysis 0.36 Anabaena variabilis
    Photo-Fermentation 0.16 Rhodobacter spheroides
    Dark-Fermentation 64.5–75.6 Enterobacter cloacae DM 11, Clostridium sp. strain No. 2
    Two-stage fermentation (Dark fermentation + Photolysis) 47.9–51.2 51.20 Enterobacter cloacae DM 11 + Rhodobacter Sphaeroides OU 001 Mixed microbial flora + Rhodobacter sphaeroides OU 001

     | Show Table
    DownLoad: CSV
    Table 2.  Advantages and disadvantages of biohydrogen production processes [21].
    Production-Process Advantages in Process used Disadvantages in Process used
    Direct Bio-photolysis 1. water and sunlight produce hydrogen
    2. Ten-fold conversion of Solar energy than in crops and trees
    1. Requirement of High light intensity
    2. Oxygen can be hazardous for the system
    Indirect Bio-photolysis 1. Produces hydrogen from water
    2. Ability to fix nitrogen from atmosphere
    1. Removal of hydrogenase enzymes to avoid degradation of hydrogen
    2. Lower photochemical efficiency
    3. Oxygen presence at 30%
    4. Presence of oxygen is inhibitory for enzyme nitrogenase
    Dark-Fermentation 1. A range of feedstock can be used
    2. Hydrogen production in dark
    3. Metabolites can be used as added-value products
    1. Gas mixture requires cleaning from the presence of CO2
    2. Yields of hydrogen is low
    Photo-Fermentation 1. Wide-spectrum light energy is used
    2. A range of feedstock can be used
    1. Light conversion efficiency is low
    2. Presence of oxygen is inhibitory for enzyme hydrogenase

     | Show Table
    DownLoad: CSV

    Photosynthetic biohydrogen is either direct photolysis of water or indirect photolysis using sunlight. Unfortunately, H2 released by microorganisms during the photosynthetic process are in low yields. The increased order-of-magnitude of its volumetric productivity will be required before becoming reasonable for industrial-scale production. Further, the oxygen sensitivity of hydrogenase restricts the process in natural conditions. The Present photosynthetic biohydrogen production research is directed towards the enhancement of hydrogenase activity by identifying new organisms or using engineered organisms. Since the Rhodospirillum rubrum was found to produce H2 photosynthetically using organic acids as a carbon source and amino acid as a nitrogen source [22], many photosynthetic bacteria have been reported with hydrogen production efficiency under appropriate conditions [23,24,25,26]. Hwang et al. [27] reported enzymatic hydrogen production of Chlorella vulgaris YSL01 and YSL16 using CO2 as a carbon source under atmospheric conditions. Cyanothece sp. strain ATCC 51142 is capable of performing simultaneous oxygenic photosynthesis and H2 production by dinitrogenase NifHDK, an enzyme complex [28]. Bayro-Kaiser, V. and Nelson [29] randomly mutagenized the green microalgae Chlamydomonas reinhardtii to generate mutants that exhibited temperature-sensitive photoautotrophic growth. Eilenberg et al. [30] engineered the HydA enzyme and reported that the in vivo photosynthetic activity of the Fd-HydA enzyme surpasses that of the native HydA and shows higher oxygen tolerance. Recently, Batyrova and Hallenbeck reported another genetically modified Chlamydomonas reinhardtii strain cy6Nac2.49, which activates photosynthesis in a cyclical manner, so that photosynthesis will not be active in the presence of oxygen, but only in response to a metabolic trigger i.e. anaerobiosis [31]. Krassen et al. showed the stepwise assembly of a hybrid complex consisting of photosystem I and [NiFe] hydrogenase on a solid gold surface which can give rise to light-induced H2 evolution which converted solar energy to hydrogen energy [32].

    Present fermentative biohydrogen production research are concentrating on feedstock selection and process optimization. Azman et al. [33] used de-oiled rice bran, obtained after the extraction of oil content of rice bran, for dark fermentative biohydrogen production. Oil-extracted rice bran was hydrolyzed by dilute H2SO4 (1%, v/v) to obtain de-oiled rice bran hydrolyzate as a substrate for hydrogen generation. A recent comprehensive literature survey, on efficient biohydrogen generation and long-term operation in microbial fuel cell (MFC), concluded that to obtain high process efficiencies, cell design ought to be of primary concern [34]. Stanislaus et al. [35] used Ipomoea aquatica as a substrate with digested sludge as inoculum for biohydrogen production. Process optimization in this experiment through response surface methodology indicated 90 ℃ temperature for 60 min as the optimum pre-treatment condition of inoculum. Also, frozen dry I. aquatic demonstrated the highest hydrogen yield among all the other substrate pre-treatment conditions along with positive energy production. Kirili and Kapdan [36] applied a novel technique for biohydrogen production process enhancement, by adding microbial support particles namely; plastic scouring sponge pad, plastic nylon sponge, black porous sponge, plastic scouring sponge pad with metal mesh, and plastic nylon sponge with metal mesh, using waste wheat as feedstock. The experiment resulted into increased yield with decreasing retention time from 5 days to 1 day for all particles and achieved maximum yield with metal mesh covered plastic scouring sponge pad at 1 day retention time in repeated batch operation. Sarkar and Venkata Mohan [37] also concluded that application of pre-treated inoculum as biocatalyst and high substrate concentration resulted in substantial enhancement of both hydrogen and volatile fatty acid production. Radha and Murugesan [38] suggested that pre-treatment of marine microalgae is effective in removing phenolic content and enhancing biohydrogen production.

    Bharathiraja et al. [39] reviewed the variable feedstock resources and process enhancement criterion for dark fermentative biohydrogen production. Sivagurunatahn and Lin [40] conducted a process optimization experiment using beverage wastewater as a feedstock with enriched mixed microflora dominated by Clostridium sp. in a continuously-stirred tank reactor (CSTR) under mesophilic conditions. The results revealed that a peak hydrogen production rate was observed at HRT 1.5 h while, maximum hydrogen yield was achieved at HRT 6 h. Another experiment with psychrophilic G088 strain (EU636029), closely related to Polaromonas rhizosphaerae (EF127651) was evaluated for its hydrogen production efficiency using different carbon sources, such as xylose, glucose, fructose, galactose, lactose or sucrose [41]. Experiment results showed glucose as the substrate with the highest consumption rate, accompanied by the maximum values of biohydrogen production rate and yield. Wen et al. [42] achieved stable and efficient photo-fermentative hydrogen production by forming biofilm on the surface of carrier in the biofilm reactor.

    Dark fermentation process is considered to be promising and favourable over the photo fermentation process. Although, selection of one process over the other depends on several factors, such as availability of feedstock, selection of process organism, process condition for consortium, compatibility of feedstock with applied microorganism etc. Some workers also used the hybrid reactor to combine photo and dark fermentation processes. A combined process showed a positive result and an increase in total hydrogen yield. Several other researchers with variable feedstock and optimised process conditions also reported potential of industrial hydrogen production [43,44,45,46,47,48,49]. However, most of these results are limited to laboratory scales, none of them reached to the industrial production and failed to contribute in the hydrogen economy.

    Among the four strategically important alternative fuel sources viz. biofuels, hydrogen (H2), natural gas and syngas (synthesis gas), hydrogen emerges as superior, since it is renewable, does not emit the greenhouse gases, liberates large amount of energy per unit weight in combustion and can be easily converted into electricity by fuel cell. Biological H2 production delivers clean H2 in a sustainable manner with simple technology and more attractive potential than the current chemical production of H2. Biohydrogen holds the potential for a substantial contribution to the future renewable energy demands. Biohydrogen production delivers clean H2 in a sustainable manner using simple technology and has more attractive potential than the current chemical production of H2, since it is suited for the conversion of a wide spectrum of substrate utilization such as organic wastes, industrial manufacturing process by-products and biomass as feedstock, mostly available free or at a low cost [50]. The sustainability of all products depends mainly on its impact on society, economy and environment. Figure 5 summarizes the social, economic and environmental benefits of biohydrogen.

    Figure 5.  Benefits of biohydrogen for its suitability to sustainable fuel.

    The main limitation of biological hydrogen production is lower rate and yields as compared to the other hydrogen production methods. Therefore, there is a necessity to develop strategies to increase the yield and production rate of biohydrogen. The main obstacles to achieve high rates and yields include, partial pressure of hydrogen gas in the produced gas mixture, competing reactions, bioprocess technology, insufficient active hydrogenase enzyme, and efficient hydrogen-producing cultures [51,52]. Hallenbeck [51] suggested that partial pressure of hydrogen can be reduced by sparging inert gas to derive electrons form NADH, while there is a need to develop a cost-effective and technologically sound method for the same [52].

    The metabolic shift from acetic acid generation to solvent or hydrogen-consuming organic acid generation, and consumption of hydrogen by uptake of hydrogenase and homoacetogens, reduces biohydrogen production [51,53]. The metabolic shift can be controlled by using a bioreactor, which has been reviewed in detail by Argun et al. [52]. The purity of hydrogen in gas phase is also a challenge, as it varies from 30–60%. The separation of hydrogen by using selective membranes in the production process could help in reducing hydrogen partial pressure and increasing purity of hydrogen [52,54].

    The sustainability of biohydrogen production is driven by production rate and its purity, to get biohydrogen at an affordable cost. Physicochemical methods are highly efficient in both productivity and purity of hydrogen but they are not cost-effective due to high energy demands during the production. However, the use of biological methods for hydrogen production have acquired significant attention in the last decades as they operate in mild conditions and have lower energy demands, which make the process cost-effective. There are some other factors seeking the attentions of researchers and industrialists for biohydrogen production, such as utilization of organic residues; but it involved high technologies to operate safely and to convert it into biohydrogen in an environmentally-acceptable form [52].

    The criteria for sustainability assessment concerns three aspects, including economics, environmental performance and social issues. Sustainability usually refers to simultaneously achieving economic prosperity, environmental cleanness and social parity [55]. Several investigations have been conducted to improve the biological hydrogen production and develop a biohydrogen economy. However, development of biofuel industries is recognized as a complex system and besides experimental studies, social, economical and environmental aspects of biofuel system in a country or region should be considered. Aspects are estimated by feasibility assessments, evaluation of biofuels sustainability, and life-cycle and techno-economic analysis [56]. The energy ratio and GHG emissions of biohydrogen compared favourably with diesel and other H2 production pathways. The energy ratio (may be called as Net Energy Ratio or Energy Balance) of biohydrogen production pathways must be positive for sustainable replacement of fossil sources. Consequently, biohydrogen is worthy of consideration in the planning and development of a H2 economy, both from an energy and from an environmental perspective [57].

    A detailed financial feasibility analysis by Lee [58], indicated that an attractive investment proposal or business plan, is critical in attracting investment for long-term biohydrogen production on a small or commercial scale. His results explained that the levelized cost of energy (LCOE) of biohydrogen will be approximately USD 2 to 3 kg. All financial indices revealed that biohydrogen is economically feasible of investment and will be commercialized successfully before the timelines in many official reports. Results revealed decision-making criteria should include the economic incentives. LCOE of biohydrogen is less sensitive to the cost of biomass feedstock, which is more sensitive to the capital cost, operating and maintenance cost. Another study by Lee [59] reported that the biohydrogen and biobutanol can replace fossil fuels with high economic feasibility. Biohydrogen has the most flexibility under variation in the production cost of biomass feedstock. Algae biodiesel is less financially competitive than biohydrogen and biobutanol.

    Three sources of bioenergy are cost-competitive with fossil fuels under ideal conditions. Lee and Chiu [60] investigated the development of the biohydrogen sector in four countries, US, Japan, China and India. His study stated China as the largest biohydrogen market with the highest total output multiplier by 2050, followed by the US, Japan and India (in that order). High investment will encourage the rapid development of the biohydrogen industry in all four countries. Therefore, investing US$1 in the biohydrogen industry will generate a total output of US$3.22, 3.50, 3.09 and 3.00 in the four economies, respectively, in 2011–2050. Study also revealed that investing in the development of biohydrogen technology will provide more benefits than investing in hydrogen infrastructure. Han et al. [61] conducted a techno-economic analysis for fermentative hydrogen production from food waste. Study exhibited 26.75% return on investment (ROI) and 24.07% internal rate of return (IRR) within 5 years of payback period (PBP).

    However, societal impact of biohydrogen production and its use were less quantified due to complexity in societal structure, a few reports suggested an edge of biohydrogen on other fuels. Ren et al. [55] described ten societal criteria to assess the societal aspect in a sustainability study. The criterion were: Inherent safety index, occupational index, social attractiveness, human health and safety of employees, per capita GDP contribution, taxes contribution, cultural influence, political acceptability, security of primary supply and contribution for energy sufficiency. Sun et al. [62] quantified potential societal benefits of hydrogen fuel cell vehicles (FCVs) using the societal lifetime cost (SLC). The study included the vehicle retail cost (a function of vehicle performance), the cost of energy use (a function of vehicle fuel economy), operating and maintenance costs, externality costs of oil-use, damage costs of noise and emissions from air pollutants and GHGs, and other factors. Results of the study showed the cost difference between FCVs and gasoline vehicles is initially very large. FCVs eventually become lifetime cost competitive with gasoline vehicles, as their production volume increases, even without accounting for externalities. High valuation of externalities and high oil price could reduce the buy-down cost (the cumulative investment needed to bring hydrogen FCVs to lifetime cost parity with gasoline vehicles by $10 billion relative to the reference case. According to Ogden et al. [63] "the hydrogen fuel cell car stands out as having the lowest externality costs of any option, and when mass produced with high valuations of externalities, the least projected lifecycle cost". These costs are estimated over the full fuel-cycle from "well-to-wheels" and the entire vehicle lifetime and include adjustments for non-cost social transfers, such as taxes and fees, and producer overhead costs associated with fuel and vehicles [62].

    In a study Stanislaus et al. [35] has studied the production of biohydrogen from Ipomoea aquatica using digested sludge as inoculum and reported that the energy consumed in the fermentation process was lesser than energy produced in the process, which shows a positive energy balance or NER. The biohydrogen system succeeds in obtaining a negative global warming impact with a low cumulative non-renewable energy demand. This indicates that biohydrogen can be produced with positive NER, which could be a sustainable approach. Sekoai and Daramola [64] and Singh et al. [65] reviewed the published reports and concluded hydrogen as the safest fuel due to its non-toxicity, dispersive in nature, and with the least dangers in terms of a fire hazard. Although, it can cause fire but the clear flame cannot perch skin at a distance because of the little thermal radiation emitted by the flame due its lack of soot content.

    Romangnoli et al. [66] conducted an LCA study of biohydrogen by photosynthesis and the results of the analysis, showed that using biohydrogen to produce electricity offers more environmental benefits than using a fossil fuel based source. Wulf and Kaltschmitt [67] estimated that total 29.9 Mio t CO2-eq could be reduced by using compact class hydrogen fuel cell vehicle over compact class gasoline vehicle over the 15 years life time. The life cycle study of Djomo and Blumberga [57] compared the energetic and environmental performances of hydrogen from wheat straw (WS-H2), sweet sorghum stalk (SSS-H2) and steam potato peels (SPP-H2), and found comparable energy ratios (ER) among the three raw materials used i.e. 1.08 for WS-H2, 1.14 for SSS-H2 and 1.17 for SPP-H2; and a GHG savings by 52–56% compared to diesel and by 54–57% compared to steam methane reforming production of H2.

    Dadak et al. [68] carried out an exergy analysis and concluded that the eco-exergy concept could provide unique insights beyond those of conventional exergy analysis, which thereby provides a useful design tool for photobiological hydrogen production. The researchers further added that the sodium acetate concentration of 1 g L−1 and light intensity of 1000 lux were found to be the most suitable conditions for biohydrogen production, according to the normalized exergy destruction obtained using both concepts. In another exergy analysis, no noticeable changes were observed in the conventional exergetic and eco-exergetic performance parameters of the bioreactor over 540 h of continuous operation. Nevertheless, eco-exergetic analysis owing to the inclusion of the work of information embedded in the genomes of living organisms is still recommended for improving the design features of photobioreactors for hydrogen production [68]. In a study with Rhodopseudomonas palustris PT, Hosseini et al. [69] provided a comprehensive insight into the exergetic parameters of a bioreactor for hydrogen production using a locally isolated light-dependent photosynthetic bacterium to select the best carbon source dosage for efficient and eco-friendly biohydrogen production. According to the experiment normalized exergy destruction and sustainability index, 1.5 g/L sodium acetate dosage was found to be an optimal carbon source for the industrial applications phase.

    Wulf et al. [70] has performed a life cycle assessment of different biohydrogen production processes to examine environmental impact, such as anthropogenic climate change, acidification, eutrophication and human toxicity. They considered biohydrogen production from biomass sources derived from forestry and short rotation coppice (SRC), herbaceous biomass, energy crops and biowaste in Germany. They reported that biomass source has significant influence on the environmental impact of biohydrogen production pathways, and concluded that the gasification and the reforming of biomass have the potential to be climate friendly. They also reported that steam methane reforming (SMR) technology is the most promising technology regarding the environmental impact [70].

    Though the biohydrogen production and its utilization seems to be an environmentally safe and feasible alternative for fossil based fuel however, shifting from present fuel economy to biohydrogen economy is still in its infancy stage. Despite being several research groups working on it, its industrial production, storage and transportation have not yet reached on a satisfactory level. The major constraints in biological hydrogen production processes are raw material cost, low hydrogen evolution rate and yield at large scale [71].

    Dadak et al. [68] suggested eco-exergy concept as an effective tool to assess the sustainability and productivity of biohydrogen production from a thermodynamic point of view. Bretner et al. [72] and Miandad et al. [20] summarised the technological challenges for sustainable use of biohydrogen fuel. Briefly these challenges are:

    1. Low Photochemical efficiency.

    2. Efficiency of employed bacterial strain.

    3. Instability of hydrogenase over-expression.

    4. Sensitivity of hydrogenase to oxygen and feedback inhibition.

    5. Competition for reductant from ferredoxin between hydrogenases and other cellular functions.

    6. Suitability of low cost substrates.

    7. Industrially feasible production process and yield. Substrate use competence of used strain.

    8. Kinetics suitable design of reactors.

    9. Thermodynamic barrier.

    10. Low cost material for hydrogen storage for economic feasibility.

    Biohydrogen production provides clean H2 with the help of simple technology and a more attractive potential than the current chemical production of H2, makes it sustainable. Although, present hydrogen production industries are based on chemical processing units, but the research trend on biohydrogen production promises a booming potential of industrial biohydrogen production in the near future. Global utilization of confined and trader hydrogen is projected to increase more than 300 billion cubic through 2018 with an annual growth rate of 3.5%. Recent global research trends showed that the world's largest hydrogen consumption will continue with US having the maximum share of growth, although in 2018 it is likely to occur in China.

    Biohydrogen production from the Asian countries is mainly focusing on dark fermentation while the European countries are focusing on dark and photo fermentation. So far, the current biohydrogen production system is appropriate for decentralized small scale systems, integrated with waste from agriculture and industries or from waste processing facilities, using reactors operating with mixed microflora (aerobic, anaerobic, thermophillic, photo non-sulphur producing bacteria) or pure cultures enriched from natural sources. Seed inocula for biohydrogen production have been obtained from heat sludge, compost, waste water, and food waste etc.

    Production of hydrogen using biological tool is the predominant challenge for biotechnology, concerning present and future environmental problems. Future of biological hydrogen production is not only determined by research advances, including genetic engineering of microorganisms for efficiency improvement and designing complications of bioreactor, but also by fuel economics (cost of fuel), societal adaptation and the development of systems for hydrogen energy [73]. Current strategies geared towards improving biohydrogen production include microbial culture immobilisation, bioreactor modifications, the optimisation of process conditions (temperature, pH, OLR and HRT), culture selection and enrichments, substrate choice and the metabolic engineering of biohydrogen specialists [74,75,76,77].

    Cost factor is an important aspect for sustainability of fuel. Metabolic and genetic engineering can play a vital role in bringing down the production cost and increasing the H2 yield. By an estimate 80 kg of hydrogen per acre per day could be produced by diverting the entire photosynthetic efficiency of the algae toward hydrogen production. In a realistic efficiency of 50%, hydrogen production cost comes close to a $2.80 a kilogram [78]. However, in the current scenario below, 10% of the algae's photosynthetic capacity was utilized for biohydrogen production [79,80]. Research on biotechnological approach to improve algal photosynthetic biohydrogen production are underway and demonstrating a promising result [81]. There are two major factors that would have an important impact on the cost of biohydrogen production for commercial use: The cost of the photo-bioreactor and storage system has to be brought down, which will depend on appropriate and less expensive materials to be used in the fabrication of the photo-bioreactors.

    The reports published by several researchers have clearly indicated that the cost-effective production of biohydrogen with a positive energy balance is the key feature to get sustainable biohydrogen production, as summarised in Table 3.

    Table 3.  Sustainability assessment of biohydrogen.
    S. No. Methodology Social Sustainability Economical Sustainability Environmental Sustainability Reference
    1. Exergy Model Higher eco-exergy [68]
    2. Exergy Model High 1.5 g/L sodium acetate was an optimal carbon source for higher exergy [69]
    3. Input-output (IO) Model Investing US$1will generate US$3.22 to 3.50 [60]
    4. Societal lifetime cost (SLC) Positive lifetime cost in long term [62]
    5. Societal lifetime cost lowest externality costs of fuel cell vehicle [63]
    6. Financial feasibility analysis Low levelized cost of energy [58]
    7. Financial feasibility analysis High economic feasibility [59]
    8. Life cycle assessment (LCA) CO 2 saving [67]
    9. Life cycle assessment Environmental Benefit [66]
    10. Life cycle assessment Positive energy balance Reduction of GHG emission [57]
    11. Life cycle assessment GHG Saving [57]
    12. Techno-economic analysis 26.75% return on investment and 24.07% rate of return in 5 year payback period. [61]
    13. Literature review Safest fuel in term of toxicity and fire hazard Zero emission fuel with consequential local air quality benefits [65]
    14. Comparative LCA Positive energy balance of biohydrogen than non-renewable hydrogen Better performance of biohydrogen in term of abiotic depletion impact potential, cumulative non-renewable energy demand, and ozone layer depletion impact potential [82]
    15. LCA survey Biohydrogen in MECs provide environmental protection [34]
    16. Process optimization through response surface methodology Positive energy yield Water purification and clean energy [35]

     | Show Table
    DownLoad: CSV

    After reviewing all information this can be emphasized that the sustainable biohydrogen production can be generated in the future by exploration of the following technologies:

    a. Utilization of molecular tools for identification of robust hydrogen producing micro-organisms.

    b. Advancement in the bioreactor development.

    c. Fine tuning of pre-treatment techniques.

    d. Integration of other energy generation systems such as biogas, bioethanol, etc.

    The studies reviewed in various publications are mainly focused on very specific aspects of bio-hydrogen production, such as biomass gasification [82], reactor design [83], molecular tools [84], and production pathway [85], etc. However, this article has exclusively reviewed the biological production of hydrogen and its sustainability as an economical clean fuel currently and has also discussed its future perspective.

    This review with the following points concludes that biohydrogen is a renewable and sustainable source of energy and a clean economical fuel in comparison to other biofuels [86] for the near future:

    ● Various types of biomass can be used for sustainable biohydrogen production.

    ● The selection of pre-treatment technology for biohydrogen production process depends on substrate composition.

    ● The simultaneous use of more than one pre-treatment technology can lead to improvements in substrate-biodegradability along with increase in hydrogen production.

    ● Development of biohydrogen economy is a feasible alternative for sustainable fuel as it provides energy security, societal parity and environmental safety.

    ● Hybrid or biorefinery concept can lead to commercialization of biohydrogen production.

    ● Metabolic and genetic engineering can play a vital role in bringing down the production cost and increasing the yield of biohydrogen.

    The authors declare there are no conflicts of interest in this paper.



    [1] R. M. Anderson, R. M. May, The population dynamics of microparasites and their invertebrate hosts, Philos. Trans. R. Soc. B Biol. Sci., 291 (1981), 451–524. https://doi.org/10.1098/rstb.1981.0005 doi: 10.1098/rstb.1981.0005
    [2] M. Lipsitch, M. A. Nowak, D. Ebert, R. M. May, The population dynamics of vertically and horizontally transmitted parasites, Proc. R. Soc. B, 260 (1995), 321–327. https://doi.org/10.1098/rspb.1995.0099 doi: 10.1098/rspb.1995.0099
    [3] A. M. Dunn, J. E. Smith, Microsporidian life cycles and diversity: The relationship between virulence and transmission, Microbes Infect., 3 (2001), 381–388. https://doi.org/10.1016/S1286-4579(01)01394-6 doi: 10.1016/S1286-4579(01)01394-6
    [4] R. Fayer, Effect of high temperature on infectivity of Cryptosporidium parvum oocysts in water, Appl. Environ. Microbiol., 60 (1994), 2732–2735. https://doi.org/10.1128/aem.60.8.2732-2735.1994 doi: 10.1128/aem.60.8.2732-2735.1994
    [5] F. Memarzadeh, Literature review of the effect of temperature and humidity on viruses, ASHRAE Trans., 118 (2012), 1049–1060.
    [6] A. Gray, D. Greenhalgh, L. Hu, X. Mao, J. Pan, A stochastic differential equation SIS epidemic model, SIAM J. Appl. Math., 71 (2011), 876–902. https://doi.org/10.1137/10081856X doi: 10.1137/10081856X
    [7] Y. Zhao, D. Jiang, The threshold of a stochastic SIRS epidemic model with saturated incidence, Appl. Math. Lett., 34 (2014), 90–93. https://doi.org/10.1016/j.aml.2013.11.002 doi: 10.1016/j.aml.2013.11.002
    [8] A. Majumder, D. Adak, N. Bairagi, Phytoplankton-zooplankton interaction under environmental stochasticity: Survival, extinction and stability, Appl. Math. Model., 89 (2021), 1382–1404. https://doi.org/10.1016/j.apm.2020.06.076 doi: 10.1016/j.apm.2020.06.076
    [9] M. Lipsitch, S. Siller, M. A. Nowak, The evolution of virulence in pathogens with vertical and horizontal transmission, Evolution, 50 (1996), 1729–1741. https://doi.org/10.1111/j.1558-5646.1996.tb03560.x doi: 10.1111/j.1558-5646.1996.tb03560.x
    [10] Y. Chen, J. Evans, M. Feldlaufer, Horizontal and vertical transmission of viruses in the honey bee, Apis mellifera, J. Invertebr. Pathol., 92 (2006), 152–159. https://doi.org/10.1016/j.jip.2006.03.010 doi: 10.1016/j.jip.2006.03.010
    [11] D. H. Clayton, D. M. Tompkins, Ectoparasite virulence is linked to mode of transmission, Proc. R. Soc. B, 256 (1994), 211–217. https://doi.org/10.1098/rspb.1994.0072 doi: 10.1098/rspb.1994.0072
    [12] P. W. Ewald, Evolution of Infectious Disease, Oxford University Press on Demand, 1994.
    [13] J. Antonovics, A. J. Wilson, M. R. Forbes, H. C. Hauffe, E. R. Kallio, H. C. Leggett, et al., The evolution of transmission mode, Philos. Trans. R. Soc. B Biol. Sci., 372 (2017), 20160083. https://doi.org/10.1098/rstb.2016.0083 doi: 10.1098/rstb.2016.0083
    [14] N. Gao, Y. Song, X. Wang, J. Liu, Dynamics of a stochastic SIS epidemic model with nonlinear incidence rates, Adv. Contin. Discrete Models, 41 (2019), 1–19. https://doi.org/10.1186/s13662-019-1980-0 doi: 10.1186/s13662-019-1980-0
    [15] G. Lan, Y. Huang, C. Wei, S. Zhang, A stochastic SIS epidemic model with saturating contact rate, Phys. A Stat. Mech. Appl., 529 (2019), 121504. https://doi.org/10.1016/j.physa.2019.121504 doi: 10.1016/j.physa.2019.121504
    [16] A. Miao, T. Zhang, J. Zhang, C. Wang, Dynamics of a stochastic SIR model with both horizontal and vertical transmission, J. Appl. Anal. Comput., 8 (2018), 1108–1121. https://doi.org/10.11948/2018.1108 doi: 10.11948/2018.1108
    [17] D. Li, J. Cui, M. Liu, S. Liu, The evolutionary dynamics of stochastic epidemic model with nonlinear incidence rate, Bull. Math. Biol., 77 (2015), 1705–1743. https://doi.org/article/10.1007/s11538-015-0101-9 doi: 10.1007/s11538-015-0101-9
    [18] K. Mangin, M. Lipsitch, D. Ebert, Virulence and transmission modes of two microsporidia in Daphnia magna, Parasitology, 111 (2009), 133–142. https://doi.org/10.1017/S0031182000064878 doi: 10.1017/S0031182000064878
    [19] D. Ebert, M. Lipsitch, K. L. Mangin, The effect of parasites on host population density and extinction: Experimental epidemiology with Daphnia and six microparasites, Am. Nat., 156 (2000), 459–477. https://doi.org/10.1086/303404 doi: 10.1086/303404
    [20] R. E. Sorensen, D. J. Minchella, Parasite influences on host life history Echinostoma revolutum parasitism of Lymnaea elodes snails, Oecologia, 115 (1998), 188–195. https://doi.org/10.1007/s004420050507 doi: 10.1007/s004420050507
    [21] D. Tompkins, M. Begon, Parasites can regulate wildlife populations, Trends Parasitol., 15 (1999), 311–313. https://doi.org/10.1016/s0169-4758(99)01484-2 doi: 10.1016/s0169-4758(99)01484-2
    [22] J. C. HOLMES, Modification of intermediate host behaviour by parasites, Behav. Asp. Parasite Transm., (1972), 123–149. https://doi.org/10.5555/19730804260 doi: 10.5555/19730804260
    [23] K. D. Lafferty, A. K. Morris, Altered behavior of parasitized killifish increases susceptibility to predation by bird final hosts, Ecology, 77 (1996), 1390–1397. https://doi.org/10.2307/2265536 doi: 10.2307/2265536
    [24] P. Saha, N. Bairagi, Dynamics of vertically and horizontally transmitted parasites: Continuous vs discrete models, preprint, arXiv: 1906.03026. https://doi.org/10.48550/arXiv.1906.03026
    [25] N. C. Grassly, C. Fraser, Mathematical models of infectious disease transmission, Nat. Rev. Microbiol., 6 (2008), 477–487. https://doi.org/10.1038/nrmicro1845 doi: 10.1038/nrmicro1845
    [26] A. S. Mikhailov, A. Y. Loskutov, Foundations of Synergetics II: Complex Patterns, Springer Nature, 2012. https://link.springer.com/book/10.1007/978-3-642-80196-9
    [27] R. M. Anderson, B. Anderson, R. M. May, Infectious Diseases of Humans: Dynamics and Control, Oxford Academic, 1991. https://doi.org/10.1093/oso/9780198545996.001.0001
    [28] P. Van den Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48. https://doi.org/10.1016/s0025-5564(02)00108-6 doi: 10.1016/s0025-5564(02)00108-6
    [29] N. Bairagi, D. Adak, Complex dynamics of a predator–prey–parasite system: An interplay among infection rate, predator's reproductive gain and preference, Ecol. Complex., 22 (2015), 1–12. https://doi.org/10.1016/j.ecocom.2015.01.002 doi: 10.1016/j.ecocom.2015.01.002
    [30] M. Kot, Elements of Mathematical Ecology, Cambridge University Press, 2001. https://doi.org/10.1017/CBO9780511608520
    [31] D. Valenti, A. Fiasconaro, B. Spagnolo, Stochastic resonance and noise delayed extinction in a model of two competing species, Phys. A Stat. Mech. Appl., 331 (2004), 477–486. https://doi.org/10.1016/j.physa.2003.09.036 doi: 10.1016/j.physa.2003.09.036
    [32] X. Mao, Stochastic Differential Equations and Applications, Elsevier, 2007.
    [33] M. Liu, K. Wang, Persistence and extinction in stochastic non-autonomous logistic systems, J. Math. Anal. Appl., 375 (2011), 443–457. https://doi.org/10.1016/j.jmaa.2010.09.058 doi: 10.1016/j.jmaa.2010.09.058
    [34] C. Ji, D. Jiang, Threshold behaviour of a stochastic SIR model, Appl. Math. Model., 38 (2014), 5067–5079. https://doi.org/10.1016/j.apm.2014.03.037 doi: 10.1016/j.apm.2014.03.037
    [35] V. V. Petrov, On the strong law of large numbers, Theory Probab. Appl., 14 (1969), 183–192. https://doi.org/10.1137/1114027 doi: 10.1137/1114027
    [36] R. M. May, Stability and Complexity in Model Ecosystems, Princeton University Press, 2019. https://doi.org/10.2307/j.ctvs32rq4
    [37] S. Olaniyi, O. S. Obabiyi, Qualitative analysis of malaria dynamics with nonlinear incidence function, Appl. Math. Sci., 8 (2014), 3889–3904. http://dx.doi.org/10.12988/ams.2014.45326 doi: 10.12988/ams.2014.45326
    [38] S. Mushayabasa, C. Bhunu, Modeling HIV transmission dynamics among male prisoners in sub-saharan africa, Int. J. Appl. Math., 41 (2011).
    [39] I. M. Sobol, Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates, Math. Comput. Simul., 55 (2001), 271–280. https://doi.org/10.1016/S0378-4754(00)00270-6 doi: 10.1016/S0378-4754(00)00270-6
    [40] O. Kaltz, J. C. Koella, Host growth conditions regulate the plasticity of horizontal and vertical transmission in Holospora undulata, a bacterial parasite of the protozoan paramecium caudatum, Evolution, 57 (2003), 1535–1542. https://doi.org/10.1111/j.0014-3820.2003.tb00361.x doi: 10.1111/j.0014-3820.2003.tb00361.x
    [41] D. Lunn, R. J. Goudie, C. Wei, O. Kaltz, O. Restif, Modelling the dynamics of an experimental host-pathogen microcosm within a hierarchical Bayesian framework, PLOS One, 8 (2013), e69775. https://doi.org/10.1371/journal.pone.0069775 doi: 10.1371/journal.pone.0069775
    [42] W. O. Kermack, A. G. McKendrick, A contribution to the mathematical theory of epidemics, Proc. R. Soc. Lond. A, 115 (1927), 700–721. https://doi.org/10.1098/rspa.1927.0118 doi: 10.1098/rspa.1927.0118
    [43] M. Liu, K. Wang, Global stability of a nonlinear stochastic predator-prey system with Beddington-DeAngelis functional response, Commun. Nonlinear Sci. Numer. Simul., 16 (2011), 1114–1121. https://doi.org/10.1016/j.cnsns.2010.06.015 doi: 10.1016/j.cnsns.2010.06.015
    [44] S. Liu, Y. Pei, C. Li, L. Chen, Three kinds of TVS in a SIR epidemic model with saturated infectious force and vertical transmission, Appl. Math. Model., 33 (2009), 1923–1932. https://doi.org/10.1016/j.apm.2008.05.001 doi: 10.1016/j.apm.2008.05.001
    [45] J. Tanimoto, Sociophysics Approach to Epidemics, Springer, 23 (2021). https://link.springer.com/book/10.1007/978-981-33-6481-3
    [46] B. Zhou, D. Jiang, B. Han, T. Hayat, Threshold dynamics and density function of a stochastic epidemic model with media coverage and mean-reverting Ornstein-Uhlenbeck process, Math. Comput. Simul., 196 (2022), 15–44. https://doi.org/10.1016/j.matcom.2022.01.014 doi: 10.1016/j.matcom.2022.01.014
    [47] E. Allen, Environmental variability and mean-reverting processes, Discrete Contin. Dyn. Syst. Ser. B, 21 (2016), 2073–2089. https://www.aimsciences.org/article/doi/10.3934/dcdsb.2016037
    [48] K. Mamis, M. Farazmand, Stochastic compartmental models of the Covid-19 pandemic must have temporally correlated uncertainties, Proc. R. Soc. A, 479 (2023), 20220568. https://doi.org/10.1098/rspa.2022.0568 doi: 10.1098/rspa.2022.0568
    [49] K. Mamis, M. Farazmand, Modeling correlated uncertainties in stochastic compartmental models, Math. Biosci., 374 (2024), 109226. https://doi.org/10.1016/j.mbs.2024.109226 doi: 10.1016/j.mbs.2024.109226
    [50] J. Nocedal, S. J. Wright, Numerical optimization, 2nd edition, Springer, New York, 2006.
    [51] M. I. Lourakis, A brief description of the Levenberg-Marquardt algorithm implemented by levmar, Foundation Res. Technol., 4 (2005), 1–6. http://www.ics.forth.gr/lourakis/levmar
  • This article has been cited by:

    1. Salma Mukhtar, Mehwish Aslam, 2020, Chapter 7, 978-3-030-53932-0, 115, 10.1007/978-3-030-53933-7_7
    2. Galal El-Habaak, Rafat Khalaphallah, Mokhles Hassan, Mohamed Askalany, Mahmoud Abdel-Hakeem, Characterization and exploitation of black shale as unconventional source of biohydrogen: a case study from the Abu-Tartur mine, Western Desert, Egypt, 2020, 13, 1866-7511, 10.1007/s12517-020-05482-9
    3. S. Chozhavendhan, G. Karthiga Devi, B. Bharathiraja, R. Praveen Kumar, S. Elavazhagan, 2020, 9780128189962, 195, 10.1016/B978-0-12-818996-2.00009-0
    4. Prakash K. Sarangi, Sonil Nanda, Biohydrogen Production Through Dark Fermentation, 2020, 43, 0930-7516, 601, 10.1002/ceat.201900452
    5. Shikha Dahiya, Sulogna Chatterjee, Omprakash Sarkar, S. Venkata Mohan, Renewable hydrogen production by dark-fermentation: Current status, challenges and perspectives, 2021, 321, 09608524, 124354, 10.1016/j.biortech.2020.124354
    6. Anoop Singh, Dheeraj Rathore, Deepak Pant, Shaili Srivastava, 2020, Chapter 1, 978-981-15-6020-0, 1, 10.1007/978-981-15-6021-7_1
    7. M. Kamaraj, K. K. Ramachandran, J. Aravind, Biohydrogen production from waste materials: benefits and challenges, 2020, 17, 1735-1472, 559, 10.1007/s13762-019-02577-z
    8. Shiv Prasad, Anoop Singh, Nicholas E. Korres, Dheeraj Rathore, Surajbhan Sevda, Deepak Pant, Sustainable utilization of crop residues for energy generation: A life cycle assessment (LCA) perspective, 2020, 303, 09608524, 122964, 10.1016/j.biortech.2020.122964
    9. Lakshana G Nair, Komal Agrawal, Pradeep Verma, An overview of sustainable approaches for bioenergy production from agro-industrial wastes, 2022, 6, 27724271, 100086, 10.1016/j.nexus.2022.100086
    10. M. Rajamehala, Renugaa Su, B. Gopalakrishnan, A. Muthu Kumara Pandian, M. Vijay Pradhap Singh, S. Chozhavendhan, 2022, 9780323900409, 3, 10.1016/B978-0-323-90040-9.00019-9
    11. Shruti Choudhary, Harmeet Singh Bakala, Loveleen Kaur Sarao, Sandeep Kaur, 2023, Chapter 1, 978-981-19-6229-5, 1, 10.1007/978-981-19-6230-1_1
    12. 2021, 9781119042761, 173, 10.1002/9781119042792.part2
    13. Bishwambhar Mishra, Rajasri Yadavalli, Y. Vineetha, C. Nagendranatha Reddy, 2021, 9780128224014, 7, 10.1016/B978-0-12-822401-4.00014-3
    14. Anita Šalić, Bruno Zelić, A Game Changer: Microfluidic Technology for Enhancing Biohydrogen Production—Small Size for Great Performance, 2022, 15, 1996-1073, 7065, 10.3390/en15197065
    15. Palas Samanta, Tarakeshwar Senapati, Sukhendu Dey, Apurba Ratan Ghosh, Sandipan Pal, 2022, Chapter 165-1, 978-981-16-4921-9, 1, 10.1007/978-981-16-4921-9_165-1
    16. Dolores Hidalgo, Jesús M. Martín-Marroquín, David Díez, 2022, Chapter 8, 978-981-19-1994-7, 181, 10.1007/978-981-19-1995-4_8
    17. R. Thiruchelvi R. Thiruchelvi, N. Kabila Kumari, K. N. Rajnish, Potential of Bio Hydrogen Production from Dark Fermentation of Sewage Waste Water – A Review, 2022, 19, 24562602, 347, 10.13005/bbra/2989
    18. Rahul Gautam, Jagdeep K. Nayak, Neil V. Ress, Robert Steinberger-Wilckens, Uttam Kumar Ghosh, Bio-hydrogen production through microbial electrolysis cell: Structural components and influencing factors, 2023, 455, 13858947, 140535, 10.1016/j.cej.2022.140535
    19. G. Allegretti, M.A. Montoya, L.A.S. Bertussi, E. Talamini, When being renewable may not be enough: Typologies of trends in energy and carbon footprint towards sustainable development, 2022, 168, 13640321, 112860, 10.1016/j.rser.2022.112860
    20. Arunachalam Bose Sathya, Arunachalam Thirunavukkarasu, Rajarathinam Nithya, Abhishek Nandan, Krishnamoorthy Sakthishobana, Anand Kishore Kola, Raja Sivashankar, Hoang Anh Tuan, Balakrishnan Deepanraj, Microalgal biofuel production: Potential challenges and prospective research, 2023, 332, 00162361, 126199, 10.1016/j.fuel.2022.126199
    21. Chayanika Putatunda, Manya Behl, Preeti Solanki, Samriti Sharma, Shashi Kant Bhatia, Abhishek Walia, Ravi Kant Bhatia, Current challenges and future technology in photofermentation-driven biohydrogen production by utilizing algae and bacteria, 2022, 03603199, 10.1016/j.ijhydene.2022.10.042
    22. Nitish Venkateswarlu Mogili, Nithya Murugesan, Seenivasan Ayothiraman, Rahul Gautam, Narendra Naik Deshavath, Rajeswara Reddy Erva, 2022, 9780323853873, 165, 10.1016/B978-0-323-85387-3.00009-4
    23. Nisha Singh, Reeta Rani Singhania, Poonam S. Nigam, Cheng-Di Dong, Anil Kumar Patel, Munish Puri, Global status of lignocellulosic biorefinery: Challenges and perspectives, 2022, 344, 09608524, 126415, 10.1016/j.biortech.2021.126415
    24. Santhana Krishnan, Hesam Kamyab, Mohd Nasrullah, Zularisam Abdul Wahid, Krishna Kumar Yadav, Alissara Reungsang, Sumate Chaiprapat, Recent advances in process improvement of dark fermentative hydrogen production through metabolic engineering strategies, 2023, 343, 00162361, 127980, 10.1016/j.fuel.2023.127980
    25. M. Rajamehala, A. Kaviprabha, A. Muthu Kumara Pandian, M. Vijay Pradhap Singh, S. Karthikadevi, B. Gopalakrishnan, S. Chozhavendhan, 2022, 9780323900409, 207, 10.1016/B978-0-323-90040-9.00013-8
    26. Richa Kothari, Har Mohan Singh, Kajol Goria, Shubham Raina, V. V. Tyagi, Shamshad Ahmad, Ramkishore Singh, Atul Sharma, Shane Sheoran, Frank Bruno, D. Buddhi, Utilization of rice crop residue to fortify biogas production with mitigation of aerosols for sustainable environment: mechanism, potential strategies, and opportunities, 2024, 2190-6815, 10.1007/s13399-024-05571-9
    27. Ashfaq Ahmad, Rambabu K, Shadi W. Hasan, Pau Loke Show, Fawzi Banat, Biohydrogen production through dark fermentation: Recent trends and advances in transition to a circular bioeconomy, 2024, 52, 03603199, 335, 10.1016/j.ijhydene.2023.05.161
    28. Chandra Tejaswi Padigala, Gour Gopal Satpati, Mamata Singhvi, Lalit Goswami, Anamika Kushwaha, Sheetal Oraon, Kristine Aleksanyan, Regina S. Smykovskaya, Hemamalini Rawindran, Lim Jun Wei, Rajiv Rajak, Soumya Pandit, Pritam Kumar Dikshit, Nanotechnological advancement in green hydrogen production from organic waste: Recent developments, techno–economic, and life cycle analyses, 2024, 92, 03603199, 672, 10.1016/j.ijhydene.2024.10.216
    29. Abdulkarem I. Amhamed, Anwar Hamdan Al Assaf, Laurent M. Le Page, Odi Fawwaz Alrebei, Alternative sustainable aviation fuel and energy (SAFE)- A Review with selected simulation cases of study, 2024, 11, 23524847, 3317, 10.1016/j.egyr.2024.03.002
    30. Nitesh Premchand Machhirake, Kumar Raja Vanapalli, Sunil Kumar, Bijayananda Mohanty, Biohydrogen from waste feedstocks: An energy opportunity for decarbonization in developing countries, 2024, 252, 00139351, 119028, 10.1016/j.envres.2024.119028
    31. Narasiman Nirmala, Ghodke Praveen, Sharma AmitKumar, PanneerSelvam SundarRajan, Athmanathan Baskaran, Packiyadas Priyadharsini, SivaPerumal SanjayKumar, SelvananthamShanmuganatham Dawn, Kirubanandam Grace Pavithra, Jayaseelan Arun, Arivalagan Pugazhendhi, A review on biological biohydrogen production: Outlook on genetic strain enhancements, reactor model and techno-economics analysis, 2023, 896, 00489697, 165143, 10.1016/j.scitotenv.2023.165143
    32. Emisha L., Prince D., S.J. Vijay, Jebasingh Bhagavathsingh, Prathap Somu, Nagaraj Basavegowda, Dibyajyoti Haldar, Technological advancement in the production of biohydrogen from lignocellulosic biomass: A review, 2024, 12, 22133437, 113084, 10.1016/j.jece.2024.113084
    33. Gayathri Priya Iragavarapu, Syed Shahed Imam, Omprakash Sarkar, Srinivasula Venkata Mohan, Young-Cheol Chang, Motakatla Venkateswar Reddy, Sang-Hyoun Kim, Naresh Kumar Amradi, Bioprocessing of Waste for Renewable Chemicals and Fuels to Promote Bioeconomy, 2023, 16, 1996-1073, 3873, 10.3390/en16093873
    34. Manoj Kumar, Raj Kumar Tiwari, Gunjan Vasant Bonde, Kaushik Das, Ranjan Kumar Bhagobaty, Suvendu Manna, Current trends on the advancement and limitation of biohydrogen research: An exclusive overview, 2024, 1944-7442, 10.1002/ep.14521
    35. Fatima Akram, Taseer Fatima, Ramesha Ibrar, Ikram ul Haq, Biohydrogen: Production, promising progressions and challenges of a green carbon-free energy, 2024, 69, 22131388, 103893, 10.1016/j.seta.2024.103893
    36. Yen-Ju Lin, Lee-Feng Chien, Hydrogen production in the Chlorella sp. DT mutants carrying heterologous electron donor ferredoxin 1 of Chlamydomonas reinhardtii, 2024, 69, 07173458, 11, 10.1016/j.ejbt.2024.03.001
    37. Abeer Fawad, Muhammad Abdul Qyyum, Absaar Ul Jabbar, A comprehensive simulation study of integrated gasification, enrichment, and separation processes for biohydrogen production from sugarcane bagasse, 2024, 03603199, 10.1016/j.ijhydene.2024.05.332
    38. Ankush Rani, Saurabh Kumar Gupta, Suraj Kumar Singh, Gowhar Meraj, Pankaj Kumar, Shruti Kanga, Bojan Đurin, Dragana Dogančić, Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models, 2023, 4, 2673-4834, 728, 10.3390/earth4030039
    39. Akshay Loyte, Jiwak Suryawanshi, Sacheth Sri Kiran Bellala, Roshan V. Marode, Yuvarajan Devarajan, Current Status and Obstacles in the Sustainable Synthesis of Biohydrogen from Microalgal Species, 2024, 25901230, 103455, 10.1016/j.rineng.2024.103455
    40. Palas Samanta, Tarakeshwar Senapati, Sukhendu Dey, Apurba Ratan Ghosh, Sandipan Pal, 2025, Chapter 165, 978-981-97-4617-0, 1328, 10.1007/978-981-97-4618-7_165
    41. Vijayata Singh, Swati Mishra, Deepjyoti Singh, 2025, Chapter 13, 978-981-97-9872-8, 389, 10.1007/978-981-97-9873-5_13
    42. Ciro Vasmara, Stefania Galletti, Stefano Cianchetta, Enrico Ceotto, Hydrogen Production from Renewable and Non-Renewable Sources with a Focus on Bio-Hydrogen from Giant reed (Arundo donax L.), a Review, 2025, 18, 1996-1073, 709, 10.3390/en18030709
    43. Jayachitra Murugaiyan, Anantharaman Narayanan, Samsudeen Naina Mohamed, Influence of zinc ferrite nanocomposites for enhancing biohydrogen production during distillery wastewater treatment in microbial electrolysis cell, 2025, 392, 00162361, 134872, 10.1016/j.fuel.2025.134872
  • Reader Comments
  • © 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(790) PDF downloads(61) Cited by(0)

Figures and Tables

Figures(11)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog