Next Article in Journal
On the Practical Evaluation of the Switching Loss in the Secondary Side Rectifiers of LLC Converters
Previous Article in Journal
A Novel Buck Converter with Constant Frequency Controlled Technique
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Energies
Volume 14
Issue 18
10.3390/en14185916
energies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

A Review of Enhancement of Biohydrogen Productions by Chemical Addition Using a Supervised Machine Learning Method

by
Yiyang Liu
1,†,
Jinze Liu
1,†,
Hongzhen He
1,
Shanru Yang
2,
Yixiao Wang
1,
Jin Hu
1,
Huan Jin
3,*,
Tianxiang Cui
3,
Gang Yang
4 and
Yong Sun
1,5,*
1
Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, Ningbo 315100, China
2
Centre for English Language Education (CELE), University of Nottingham Ningbo, Ningbo 315100, China
3
School of Computer Science, University of Nottingham Ningbo, Ningbo 315100, China
4
Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100864, China
5
School of Engineering, Edith Cowan University, 70 Joondalup Drive, Perth, WA 6027, Australia
*
Authors to whom correspondence should be addressed.
†
Authors have equal contributions.
Energies 2021, 14(18), 5916; https://doi.org/10.3390/en14185916
Submission received: 25 August 2021 / Revised: 9 September 2021 / Accepted: 14 September 2021 / Published: 17 September 2021
(This article belongs to the Topic Hydrogen Energy Technologies)
Browse Figures
Versions Notes

Abstract

:
In this work, the impact of chemical additions, especially nano-particles (NPs), was quantitatively analyzed using our constructed artificial neural networks (ANNs)-response surface methodology (RSM) algorithm. Fe-based and Ni-based NPs and ions, including Mg2+, Cu2+, Na+, NH4+, and K+, behave differently towards the response of hydrogen yield (HY) and hydrogen evolution rate (HER). Manipulating the size and concentration of NPs was found to be effective in enhancing the HY for Fe-based NPs and ions, but not for Ni-based NPs and ions. An optimal range of particle size (86–120 nm) and Ni-ion/NP concentration (81–120 mg L−1) existed for HER. Meanwhile, the manipulation of the size and concentration of NPs was found to be ineffective for both iron and nickel for the improvement of HER. In fact, the variation in size of NPs for the enhancement of HY and HER demonstrated an appreciable difference. The smaller (less than 42 nm) NPs were found to definitely improve the HY, whereas for the HER, the relatively bigger size of NPs (40–50 nm) seemed to significantly increase the H2 evolution rate. It was also found that the variations in the concentration of the investigated ions only statistically influenced the HER, not the HY. The level of response (the enhanced HER) towards inputs was underpinned and the order of significance towards HER was identified as the following: Na+ > Mg2+ > Cu2+ > NH4+ > K+.

Graphical Abstract

1. Introduction

The further rollback of globalization will ultimately reshape the current supply chain block, especially as more and more countries have realized how pivotal it is to have self-sufficient industries to produce strategic products such as medicine, energy, and even toilet paper rolls [1]. Aside from the public health emergency, energy security is another draconian challenge that countries across the world are reluctantly facing, although the price of crude oil did once plunge to USD 25 per barrel (158.98 L) in the middle of 2020 during the COVID-19 pandemic [2]. Whether to take bolder steps in the energy reliance transition from fossil fuel to renewable energy will make a great difference in the world that our children will be able to inherit in the future [3]. Consequently, by 2021, several developed countries already started to restrict the use of fossil fuels in order to eventually achieve a shift in fuel type [4,5].
Among all sources of energy, hydrogen (H2) is one of the most favorable candidates due to its inherent appealing features: (1) high energy yield (122 kJ kg−1), (2) generation of water as a result of combustion, and (3) electricity generation through the fuel cell [6,7]. However, the current predominant H2 generation still comes from fossil-based materials via existing mature industrial chemical processes such as natural gas steam reforming (NGSR), nature gas thermal cracking (NGTC), auto-thermal reforming (ATR), coal gasification, and partial oxidation of heavier-than-naphtha hydrocarbons [8]. Consequently, the paradox of sustainability of H2 utilization and the non-renewability of H2 generation will be encountered, although the development of carbon capture storage and utilization (CCSU) such as via a mature catalytic process like Fischer–Tropsch synthesis might alleviate environmental impacts from H2 generation [9,10,11,12].
Apart from the thermal process, the biological hydrogen (BioH2) generation process also plays a supplementary role in H2 generation due to features such as versatile feedstock (lignocellulose, wet kitchen organic waste, and wastewater) and no green-house gas emissions (GHE). Despite the appealing advantages that are mentioned above, BioH2 production is hampered by its relatively lower process performance [13]. To implement BioH2 in different applications either on a decentralized or centralized basis or both, different process intensification approaches have been proposed, such as hydrolysate detoxification, mixed continuous and batch operations, co-fermentation, process optimization, and chemical addition. Among these approaches, chemical addition is considered to be one of the most attractive and practical ones because of its operational simplicity (without any additional modifications) and relatively low energy consumption [14]. However, current reports are limited to focusing on the facilitation of BioH2 production by all types of chemical additives. In contrast, the nanoparticles (NPs) as a potential type of chemical additive still lack research on their addition and the corresponding quantitative relationships, such as hydrogen yield (HY) and hydrogen evolution rate (HER) with detailed incubation conditions, especially the concentration of different metal elements.
In this paper, instead of making a simple BioH2 production enhancement comparison using the addition of NPs across literature reports, the collected data (such as HY, HER, and the substrate concentrations from literature works) were used to construct the data matrix for supervised machine learning algorithm using the developed artificial neural networks (ANNs) coupled with statistical analysis using response surface methodology (RSM) for more insightful and quantitative correlations and analysis. The review of assessing the impact of NPs additions on BioH2 production in form of HY and HER using a developed ANNs-RSM algorithm, to the best of our knowledge, has not been reported before.

2. Materials and Methods

The literature used in this review was mainly collected from the scientific databases from Web of Science, Google Scholar and Science Direct via keyword search. Various keyword groups were comprised of several words, including “dark fermentation,” “biohydrogen,” and “nanoparticles.” With regard to the possible missing relevant literature, by using the abovementioned searching strategy, an extensive additional search process was conducted with more detailed keywords, including “trace metal,” “transitional metal,” “iron,” “nickel,” “gold,” “copper,” and “metal oxide.” During the additional search, these mentioned keywords were also combined with the keyword “biohydrogen.”
The ANNs (based on Python 2.7 platform) was deployed for data analysis. The detailed schematic diagram of the construction of the ANNs and data collection is shown in Figure S1. In this work, the widely used feed-forward three-layer networks were used. The simplified cross-out method was used for cross-validation during the data training step. The detailed descriptions of the standard procedures for this methodology can be found in our previous works [15]. During the data training, the mean square error (MSE) and mean average relative residual (MARR) were computed as follows:
M S E % = 1 N s a m ∑ j = 1 N s a m ( r i s a m − r i c a l ) 2 × 100 %
M A R R % = 1 N s a m ∑ j = 1 N e x p ( | r i s a m − r i c a l | r i s a m ) × 100 %
where Nsam is the number of data, and r i s a m and r i c a l are actual and calculated values, respectively. The setting for allowable accuracy was 95%. For the ANNs prediction data matrix, the widely used Box–Behnken design (BBD) and the central composite design (CCD) were used to predict the data matrix generation [16]. Once the supervised data learning was complete, the analysis of variation (ANOVA) based on commercial Design Expert® Version 11 software package (Stat-Ease, Inc., Minneapolis, MN, USA) was used for statistical analysis.

3. Literature Survey Comparisons

In this paper, for the convenience of discussion, four different types of NPs (Fe-based, Au-based, Cu-based, and Ni-based) were surveyed across different studies and the results are shown in Figure 1.
For each type of NPs, taking Ni-based NPs, for instance, all nickel-related species were included, such as nanoparticles such as zero-valent particles, metal oxide NiO2, etc. The number of reports on the topic of BioH2 enhancement by NPs additions has been increasing steadily since 2015. Among different NPs, the number of reports using iron-based NPs has presented a discernible trend in recent years. The impetus underlining this trend is possibly associated with its inherent appealing cost-effective feature compared to other NPs such as gold or nickel. Apart from Fe-based NPs, Ni-based NPs have experienced an appreciable increase in recent years, with an exception in 2015 [17,18]. The research interests that focus on Ni-based NPs might be pertinent to the metal cluster of hydrogenase [19]. According to recent classifications, there are three different hydrogenases, namely, [Fe], [NiFe], and [FeFe] [20,21]. During biological chemical reactions, these enzyme active centers play a pivotal role in the metabolism of proton ion-associated redox reactions. Studies have shown that [FeFe] hydrogenase catalyzes H2 generation, whereas [NiFe] hydrogenase catalyzes the consumption of H2. [NiFe] hydrogenase presents a relatively higher tolerance to the existence of oxygen and it widely exists in various types of microbial strains, whereas [FeFe] hydrogenase is relatively strict to the presence of oxygen and only exists in some algae and bacteria [22,23]. Regarding [Fe] hydrogenase, it only strictly exists in some methanogen strains [24,25,26].

4. Underlying Mechanisms of Metal Ions and Metal-Based Nanoparticles

Many extensively studied metal ions and metal-based nanoparticles are regarded as effective additives in culture medium to facilitate BioH2 production in the dark fermentation process, including Na+, K+, NH4+, Mg2+, Ca2+, Co2+, Zn2+, Cu2+, Fe2+/Fe3+, and Ni2+/Ni3+, among others [27,28,29]. Extensive studies have found that even small changes in the latter may have a significant impact on BioH2 production; hence, many strategies have been proposed based on them, such as concentration regulation, including concentration manipulation [30], size regulation [17,31], composites fabrication [23,32], and heteroatom doping [33]. In general, the enhancement of NPs addition lies in a few important facts: (i) the controllable release of mental ions that facilitates the passive transport across the membrane [34]; (ii) nanodots that facilitate the electron transport chain during metabolism, such as glycolysis [35]; and (iii) the appropriate level of NPs favorable to the hydrogenase activities (co-enzymes often contain the metal ions in the catalysis center, which ultimately enhances the rate of hydrogen generation [36]. The potential mechanisms of BioH2 enhancement are summarized in Figure 2. Therefore, in this part, this review will focus on the impact of the latter on BioH2 production and its mechanisms.

4.1. Fe-Based Ions and Nanoparticles

Iron is an important trace element in the formation of hydrogenases and other enzymes. The pre-addition of Fe in the culture medium is a widely used strategy to enhance BioH2 production in dark fermentation [37]. As illustrated in Figure 2, first, Fe is the essential element to form the metal content at the active sites of hydrogenase ([FeFe], [FeNi], and [Fe]), thus catalyzing the reduction reaction of H+ to H2 [38]. Second, the presence of Fe-based NPs improves the activity of ferredoxin oxidoreductase by reducing the dissolved oxygen (DO) level and enhancing electron transfer due to the surface and quantum size effects [39,40]. In addition, Fe-based components could participate in enriching the microbial community and enhancing the growth of H2-producing bacteria [41]. The oxidative stress increases when there is a higher Fe concentration, which results in the formation of abundant oxidative radicals, thus leading to the deactivation or decomposition of enzymes [17,30].

4.2. Ni-Based Ions and Nanoparticles

Similarly, nickel ions or Ni-based nanoparticles are another widely studied substance that can significantly enhance BioH2 production in dark fermentation. The mechanisms between Ni-ion/Ni-based nanoparticles and Fe-ion/Fe-based nanoparticles are largely identical but with minor differences. The key mechanisms for Ni include (a) facilitating the synthesis of [FeNi] hydrogenase [42], (b) improving the activity of ferredoxin oxidoreductase [43], and (c) Ni NPs controlling the concentration of Ni2+ at the optimum level. In addition, it is worth noting that [NiFe] hydrogenase exists in more bacteria than [FeFe] hydrogenase. Therefore, Ni can promote H2-producing bacteria in the dark fermentation process to a certain extent [44].

5. Results

5.1. Impact of Fe-Based Ions and NP Addition

To quantitatively unveil the impact of the concentration of Fe-ion/Fe NPs and size effects upon the HY and HER in BioH2 generation, the collected values from the literature (Table 1) were statistically analyzed through our previously established ANN-RSM method and the results are shown in Figure 3.
The effects of NPs size and NPs concentration together with the binary combined impact upon the HY and HER were extensively explored. Regarding HY, it was found that the size of the NPs together with the concentration of NPs were both statistically significant to the H2 yield amongst the surveyed literature’s reports of experimental conditions. From Figure 3A, it is indicated that the HY tended to approach the highest value in the range of NP size (81–100 nm) and NP concentration (406–604 mg L−1). For HER, it was found that the size of NPs, the concentration of NPs, and Fe2+/Fe3+ were all significant to HER. For the combined effects (NP size and concentration), on the other hand, these effects were found to be statistically insignificant to HER. The 3D plot of HER versus NPs size and NPs concentration (Figure 3B) also tended to show the highest region of HER located at the size range of 81–100 nm. Among the collected literature reports, the HER seemed to be more appreciably and directly related to the relatively larger size of the particle, which might be quite contradictory to some findings. This indicates that the manipulation of NPs ideally in size range of 81–100 nm is favorable for both high HY and HER. Reducing the size of NPs could improve the quantum dot effect, thus improving the electron transport. In contrast, the electron transport phenomena in extracellular media during cultivation is quite complicated and some factors such as osmosis condition and the activity of the fermentation broth might be counter-effective to the nanoparticle size effect for enhancing BioH2 generation. Currently, very few works have been done to elucidate the mechanisms of this size impact upon selective enhancement of HY and HER. From our statistical analysis, a reasonable explanation for the ideal size effect is that the nanoparticle size of 81–100 nm is more thermodynamically stable than NPs with a smaller size during fermentation, since Fe-based NPs with smaller size are easier to agglomerate and form large Fe-based particles and deteriorate the electron transport performance in extracellular conditions. The fabrication of composites (e.g., Fe@graphene) is a promising strategy to enable the stable existence of small-sized nanoparticles; however, it has not been widely investigated.

5.2. Impact of Ni-Based Ions and NP Addition

The impact of Ni-based ions and NPs upon HY and HER is summarized in Table 2 and the statistical analysis results are shown in Figure 4.
Among the collected literature reports, the size and concentration of NPs together with their combined effect were not statistically significant to either HY or HER according to the calculated p-value. Regarding HY (Figure 4A), it was found that both too low and too high levels of NPs size and concentration were not favorable. Indeed, an optimal range existed if the NP size and concentration were manipulated within 86–120 nm and (81–120 mg L−1, respectively. Similarly, the HER also presented the same variation patterns as those of HY. An optimal range of particle size (86–120 nm) and Ni-ion/NPs concentration (81–120 mg L−1) existed for HER. Unlike Fe, Ni presented more consistent responding patterns between HY and HER in regards to the variation in the size and concentration of NPs. In addition, studies have indicated that Ni-based ions and NPs tend to selectively enhance some BioH2 generation pathways, such as enhancing the acetate pathway while suppressing or inhibiting butyrate and propionate pathways. However, discrepancies still exist due to different strains of microbes inoculated, cultivation medium, experimental uncertainties, etc. Although the size of NPs was significant to the HER, the combined effects (NP size and concentration) were found to be insignificant. Among the collected literature reports, the HER seemed to be directly related to the relatively larger size of the particles. This indicates that the manipulation of NPs ideally in size range of 81–100 nm is favorable for both HY and HER. This might contradict the first impression that the reduction of NPs size significantly enhances the quantum dot effect that subsequently boosts electron transport. However, the preparation and large-scale deployment of small-sized NPs that can stably exist in the cultivation medium has always been a substantial challenge, which will inevitably increase fixing and operating costs. Fortunately, the enhancement of BioH2 generation seems to be linked to an ideal range of NPs at the size of 81–100 nm; therefore, blindly pursuing small nanoparticles may be meaningless.

5.3. Impact of Other Metal and Non-Metal Nanoparticle Addition

The impact of other metal and non-metal NPs addition upon BioH2 generation is summarized in Table 3.
The addition of NPs was found to be effective at improving BioH2 generation due to the fact that NPs can facilitate electron transport in extracellular cultivation medium during fermentation [66,67]. With regard to the HY and HER, it was quite hard to find one individual NPs that positively enhanced both HY and HER simultaneously. This reflects the complex features of the BioH2 generation process, which generally involves many different steps of sub-metabolic pathways [43,68]. Among the investigated collected literature, CoO-NPs addition was among the most appreciable enhancement for HY and Ag-NPs addition was the most influential factor for HER enhancement. In addition, the impact of adding NPs prepared from hybrid approaches such as combining two different kinds of NPs, i.e., Cu and SiO2, was marginal. The correlation between BioH2 generation values (HER and HY) and the corresponding size of the NPs added to the fermentation broth was constructed and is plotted in Figure 5. The corresponding HY and HER varied from 0–30 (mmol g−1) and 0–80 (mmol L−1 h−1), respectively. Regarding to the enhancement of HY, some reported that smaller size (less than 42 nm) surely increased HY from 10 to 20–25 mmol g−1. On the other hand, for the enhancement of HER, some reported that a relatively bigger size of 40–50 nm seemed to significantly increase the H2 evolution rate. However, by considering the numbers of reports, the majority of works showed (i) the size of NPs seems to be more effective in enhancing HY than HER, and (ii) the rate of H2 evolution seems to be less responsive to the size of NPs, though some literature reported exceptionally higher values of HER after NPs (40–50 nm) addition.

5.4. Impact of Ion Addition

In this work, in order to assess the concentration impacts of different ions upon HY and HER BioH2 generation, ions including Mg2+, Cu2+, Na+, NH4+, and K+ were selected and all data are summarized in Table S1.
It is worth noting that some metal ions inevitably introduced into the culture medium due to the use of NPs addition are not in the scope of discussion. It was quite challenging to find out the detailed concentration ranges in each study due to the factor that many reports did not specify the detailed cultivation steps. Although this could be difficult for estimating the level of those ions during the cultivation, the type of defined and undefined cultivation media used in the studies could be utilized to indirectly estimate the range of those different ions accordingly. The level of different ions upon HY and HER BioH2 generation are summarized in Table S2 and Table 4, respectively, and the collected values from the literature were statistically analyzed through our previously established ANNs-RSM method.
By comparing the p-values, the impact of the variations in ion concentrations upon HY and HER of BioH2 generation could be identified accordingly [16,69]. It was found that the variations in the investigated ions only statistically influenced HER, but not HY. This suggests pivotal guidance for process intensification for BioH2 generation. The manipulations of ion concentrations in cultivation media can effectively improve or inhibit the rate but not the potential limit of BioH2 generation. In other words, the kinetics of BioH2 generation can be altered by varying some level of ionic concentration. The statistically significant impact of metal ion addition on HER is shown in Figure 6. Among the investigated ions, the single factor included Mg2+, Cu2+, and Na+ (Figure 6A,B) and the combined factor included Mg2+/Cu2+, Cu2+/Na+, Na+/NH4+, Na+/K+, and NH4+/K+ (Figure 6C–E) as the most influential factors for HER. The responding patterns of HER towards different kinds of ions appeared to be appreciably different. These effects can be broadly classified as counter-effective and synergistic. For instance, for the counter-effective impact, the binary Mg2+/Cu2+ belongs to this category, as does the binary NH4+/K+ (Figure 6A,E). For the synergistic effect, the binary Cu2+/Na+, Na+/NH4+, and Na+/K+ fall into this category (Figure 6B–D). These different ions will act as essential nutritious elements during metabolism at different stages of the growth of microbes [70,71,72]. For the growth pattern of microbes, there will normally be lagging, exponential, stationary, and death phases [73,74,75]. After inoculation, the microbes will experience a lagging phase with different duration [76,77]. The length of the lagging phase depends on many factors, such as the harshness of cultivation media, which contains lignocellulosic precursors and high levels of salt concentration [78,79,80].
The strategies for how to improve and shorten the length of the lagging phase will contribute to the improvement of the duration of the lagging phase [81]. For microbes to initiate their metabolism, elements such as Mg2+, Na+, NH4+, and K+ are essential [82,83,84]. These elements usually act as the major components of active centers in many enzymes [85,86,87]. Ensuring a sufficient amount of these necessary elements will facilitate the smooth and fast transition from the lagging phase to the growth phase [88,89,90]. It is commonly accepted that BioH2 generation will occur mainly in the exponential and stationary phases [91,92]. Clearly, these investigated literature reports provide useful guidance for the levels of these necessary ion elements in the cultivation media. More importantly, through statistical analysis from our developed ANN-RSM algorithm, the level of the response (the enhanced HER) for those inputs was underpinned. In addition, the order of significance for HER was also identified as the following: Na+ > Mg2+ > Cu2+ > NH4+ > K+. From a holistic point of view, all the steps involved in BioH2 generation metabolism could be targeted as steps to enhance BioH2 generation (HY and HER). Two major metabolic pathways, namely, butyrate and acetate, are mainly associated with the activities of hydrogenase and the generation of H2 during dark fermentation [93,94,95]. From a stoichiometric perspective, the metabolic route towards acetate generates two times that of butyrate pathways [96,97]. From a process intensification point of view, the facilitation of the metabolic pathway towards an acetate pathway is favorable. From our statistical analysis, of all the investigated ions among the literature reports, the yield of BioH2 generation for these chemical additions of ions is not significant, suggesting that the enhancement of BioH2 generation by simple chemical additions of ions might be ineffective at further improving the ceiling value of BioH2 generation yield. For the sake of skewing the delicate balance between butyrate and acetate pathways, the combination of other chemical additions such as activated carbon, biochars, or porous adsorbents will be more effective in enhancing BioH2 generation [98,99,100].

6. Conclusions

The statistical significance of these different NPs and ion additions were rigorously and quantitatively analyzed through a well-developed ANNs-RSM algorithm. As a result, this work provided effective guidance for the size optimization of NP additions and concentration regulation of ion additives in practice. For Fe-based NPs and ions, both the size of NPs and their corresponding concentration are statistically significant to HY. For HER, it was found that the combined effect of NP size and concentration is insignificant to HER. For Ni-based NPs and ions, neither size nor concentration is statistically significant to HY and HER, respectively. The variation in the size of NPs for the enhancement of HY and HER behaved differently. The smaller (less than 42 nm) were found to definitely improve HY. Simultaneously, for HER, most reported literature indicated that manipulating the size of NPs is ineffective. It was found that variations in the investigated ions only statistically influenced HER, but not HY. This discovery suggests very pivotal guidance for process intensification for BioH2 generation. Using the constructed algorithm, the level of responses (enhanced HER) towards inputs (other ion additions) was underpinned, and the order of significance towards HER was also identified as the following: Na+ > Mg2+ > Cu2+ > NH4+ > K+. However, the number of relevant literature reports is currently limited; with the support of more experimental data, the results predicted by the ANNs-RSM algorithm will be more credible.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/en14185916/s1, Figure S1: Schematic diagram of methodology: (A) The procedures flowchart, (B) ANNs construction: feed forward three layers networks; Table S1: Ion comparison upon BioH2 generation—refers to all data missing as, for convenience of calculation, the missing value was replaced by the averaged value during the artificial neuron network learning process; Table S2: ANOVA analysis for the effect of ion concentration on HY, where r2 = 0.94, adjusted r2 = 0.93, predicted r2 = 0.93, and adequate precision (AP) = 15.

Author Contributions

Drafting and data collection, Y.L., J.L. and Y.W.; paper writing and data collection, H.H.; proofreading, S.Y.; programming and modelling, J.H.; supervision, H.J. and T.C.; drafting, G.Y.; funding acquisition and project management, Y.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province (2020E10018), the Qianjiang Talent Scheme (QJD1803014), the Ningbo Science and Technology Innovation 2025 Key Project (2020Z100), and the Ningbo Municipal Commonweal Key Program (2019C10033 & 2019C10104), UNNC FoSE Researchers Grant 2020 (I01210100011).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Acknowledgments

The authors would like to express their sincere appreciation for the critical and insightful comments raised by anonymous reviewers, which significantly improved the quality of this work.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ANOVAAnalysis of variation
ANNsArtificial neural networks
ATRAuto-thermal reforming
BioH2Biological hydrogen
BBDBox–Behnken design
CCSUCarbon capture storage and utilization
CCDCentral composite design
DODissolved oxygen
GHEGreenhouse gas emission
H2Hydrogen
HERHydrogen evolution rate
HYHydrogen yield
MSEMean square error
NGSRNatural gas steam reforming
NGTCNature gas thermal cracking
NPsNanoparticles
RSMResponse surface methodology

References

  1. Economist, T. Building up the pillars of state. The Economist, 28 March 2020. [Google Scholar]
  2. Bloomberg, Crude Oil (Nymex). In Bloomberg Energy Index, 2020/04/06 ed.; Bloomberg: New York, NY, USA, 2020.
  3. Carson, R. Silent Spring; Houghton Mifflin Harcourt: New York, NY, USA, 1962. [Google Scholar]
  4. Canadell, P.; Quéré, C.L.; Peters, G.; Korsbakken, J.I.; Andrew, R. Eighteen countries showing the way to carbon zero. The Conversation, 26 February 2019. [Google Scholar]
  5. Wang, Y.; Tang, M.; Yusuf, A.; Wang, Y.; Zhang, X.; Yang, G.; He, J.; Jin, H.; Sun, Y. Preparation of Catalyst from Phosphorous Rock Using an Improved Wet Process for Transesterification Reaction. Ind. Eng. Chem. Res. 2021, 60, 22. [Google Scholar] [CrossRef]
  6. Liu, Z.; Wang, K.; Chen, Y.; Tan, T.; Nielsen, J. Third-generation biorefineries as the means to produce fuels and chemicals from CO2. Nat. Catal. 2020, 3, 274–288. [Google Scholar] [CrossRef]
  7. Glenk, G.; Reichelstein, S. Economics of converting renewable power to hydrogen. Nat. Energy 2019, 4, 216–222. [Google Scholar] [CrossRef]
  8. Sun, Y.; He, J.; Yang, G.; Sun, G.; Sage, V. A review of the enhancement of bio-hydrogen generation by chemicals addition. Catalysts 2019, 9, 353. [Google Scholar] [CrossRef] [Green Version]
  9. Sun, Y.; Yang, G.; Zhang, L.; Sun, Z. Fischer-Tropsch synthesis in a microchannel reactor using mesoporous silica supported bimetallic Co-Ni catalyst: Process optimization and kinetic modeling. Chem. Eng. Process. Process. Intensif. 2017, 119, 44–61. [Google Scholar] [CrossRef]
  10. Sun, Y.; Jia, Z.; Yang, G.; Zhang, L.; Sun, Z. Fischer-Tropsch synthesis using iron based catalyst in a microchannel reactor: Performance evaluation and kinetic modeling. Int. J. Hydrog. Energy 2017, 42, 29222–29235. [Google Scholar] [CrossRef]
  11. Sun, Y.; Yang, G.; Wen, C.; Zhang, L.; Sun, Z. Artificial neural networks with response surface methodology for optimization of selective CO2 hydrogenation using K-promoted iron catalyst in a microchannel reactor. J. CO2 Util. 2018, 24, 10–21. [Google Scholar] [CrossRef]
  12. Sun, Y.; Wang, Y.; He, J.; Yusuf, A.; Wang, Y.; Yang, G.; Xiao, X. Comprehensive kinetic model for acetylene pretreated mesoporous silica supported bimetallic Co-Ni catalyst during Fischer-Trospch synthesis. Chem. Eng. Sci. 2021, 246, 116828–116844. [Google Scholar] [CrossRef]
  13. Wang, Y.X.; Tang, M.; Ling, J.; Wang, Y.; Liu, Y.; Jin, H.; He, J.; Sun, Y. Modeling biohydrogen production using different data driven approaches. Int. J. Hydrog. Energy 2021, 46, 29822–29833. [Google Scholar] [CrossRef]
  14. Kumar, G.; Mathimani, T.; Rene, E.R.; Pugazhendhi, A. Application of nanotechnology in dark fermentation for enhanced biohydrogen production using inorganic nanoparticles. Int. J. Hydrog. Energy 2019, 44, 13106–13113. [Google Scholar] [CrossRef]
  15. Wang, Y.; Yang, G.; Sage, V.; Xu, J.; Sun, G.; He, J.; Sun, Y. Optimization of dark fermentation for biohydrogen production using a hybrid artificial neural network (ANN) and response surface methodology (RSM) approach. Environ. Prog. Sustain. Energy 2021, 40, e13485. [Google Scholar] [CrossRef]
  16. Sun, Y.; Yang, G.; Xu, M.; Xu, J.; Sun, Z. A simple coupled ANNs-RSM approach in modeling product distribution of Fischer—Tropsch synthesis using a microchannel reactor with Ru-promoted Co/Al2O3 catalyst. Int. J. Energy Res. 2020, 44, 1046–1061. [Google Scholar] [CrossRef]
  17. Gadhe, A.; Sonawane, S.S.; Varma, M.N. Enhancement effect of hematite and nickel nanoparticles on biohydrogen production from dairy wastewater. Int. J. Hydrog. Energy 2015, 40, 4502–4511. [Google Scholar] [CrossRef]
  18. Taherdanak, M.; Zilouei, H.; Karimi, K. Investigating the effects of iron and nickel nanoparticles on dark hydrogen fermentation from starch using central composite design. Int. J. Hydrog. Energy 2015, 40, 12956–12963. [Google Scholar] [CrossRef]
  19. Trofanchuk, O.; Stein, M.; Geßner, C.; Lendzian, F.; Higuchi, Y.; Lubitz, W. Single crystal EPR studies of the oxidized active site of [NiFe] hydrogenase from Desulfovibrio vulgaris Miyazaki F. JBIC J. Biol. Inorg. Chem. 2000, 5, 36–44. [Google Scholar] [CrossRef]
  20. Morra, S.; Arizzi, M.; Allegra, P.; La Licata, B.; Sagnelli, F.; Zitella, P.; Gilardi, G.; Valetti, F. Expression of different types of [FeFe]-hydrogenase genes in bacteria isolated from a population of a bio-hydrogen pilot-scale plant. Int. J. Hydrog. Energy 2014, 39, 9018–9027. [Google Scholar] [CrossRef]
  21. Peters, J.W.; Schut, G.J.; Boyd, E.S.; Mulder, D.W.; Shepard, E.M.; Broderick, J.B.; King, P.W.; Adams, M.W. [FeFe]-and [NiFe]-hydrogenase diversity, mechanism, and maturation. Biochim. Biophys. Acta (BBA)-Mol. Cell Res. 2015, 1853, 1350–1369. [Google Scholar] [CrossRef] [Green Version]
  22. Kothari, R.; Singh, D.; Tyagi, V.; Tyagi, S. Fermentative hydrogen production–An alternative clean energy source. Renew. Sustain. Energy Rev. 2012, 16, 2337–2346. [Google Scholar] [CrossRef]
  23. Elreedy, A.; Ibrahim, E.; Hassan, N.; El-Dissouky, A.; Fujii, M.; Yoshimura, C.; Tawfik, A. Nickel-graphene nanocomposite as a novel supplement for enhancement of biohydrogen production from industrial wastewater containing mono-ethylene glycol. Energy Convers. Manag. 2017, 140, 133–144. [Google Scholar] [CrossRef]
  24. Pohorelic, B.K.; Voordouw, J.K.; Lojou, E.; Dolla, A.; Harder, J.; Voordouw, G. Effects of deletion of genes encoding Fe-only hydrogenase of Desulfovibrio vulgaris Hildenborough on hydrogen and lactate metabolism. J. Bacteriol. 2002, 184, 679–686. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Vignais, P.M.; Billoud, B. Occurrence, classification, and biological function of hydrogenases: An overview. Chem. Rev. 2007, 107, 4206–4272. [Google Scholar] [CrossRef]
  26. Kucharska, K.; Hołowacz, I.; Konopacka-Łyskawa, D.; Rybarczyk, P.; Kamiński, M. Key issues in modeling and optimization of lignocellulosic biomass fermentative conversion to gaseous biofuels. Renew. Energy 2018, 129, 384–408. [Google Scholar] [CrossRef]
  27. Mohanraj, S.; Kodhaiyolii, S.; Rengasamy, M.; Pugalenthi, V. Phytosynthesized iron oxide nanoparticles and ferrous iron on fermentative hydrogen production using Enterobacter cloacae: Evaluation and comparison of the effects. Int. J. Hydrog. Energy 2014, 39, 11920–11929. [Google Scholar] [CrossRef]
  28. Mohanraj, S.; Kodhaiyolii, S.; Rengasamy, M.; Pugalenthi, V. Green synthesized iron oxide nanoparticles effect on fermentative hydrogen production by Clostridium acetobutylicum. Appl. Biochem. Biotechnol. 2014, 173, 318–331. [Google Scholar] [CrossRef] [PubMed]
  29. Patel, S.K.; Lee, J.-K.; Kalia, V.C. Nanoparticles in biological hydrogen production: An overview. Indian J. Microbiol. 2018, 58, 8–18. [Google Scholar] [CrossRef] [PubMed]
  30. Gadhe, A.; Sonawane, S.S.; Varma, M.N. Influence of nickel and hematite nanoparticle powder on the production of biohydrogen from complex distillery wastewater in batch fermentation. Int. J. Hydrog. Energy 2015, 40, 10734–10743. [Google Scholar] [CrossRef]
  31. Mishra, P.; Thakur, S.; Mahapatra, D.M.; Ab Wahid, Z.; Liu, H.; Singh, L. Impacts of nano-metal oxides on hydrogen production in anaerobic digestion of palm oil mill effluent–A novel approach. Int. J. Hydrog. Energy 2018, 43, 2666–2676. [Google Scholar] [CrossRef]
  32. Sun, Y.; Yang, G.; Zhang, J.; Wen, C.; Sun, Z. Optimization and kinetic modeling of an enhanced bio-hydrogen fermentation with the addition of synergistic biochar and nickel nanoparticle. Int. J. Energy Res. 2019, 43, 983–999. [Google Scholar] [CrossRef]
  33. Kodhaiyolii, S.; Mohanraj, S.; Rengasamy, M.; Pugalenthi, V. Phytofabrication of bimetallic Co–Ni nanoparticles using Boerhavia diffusa leaf extract: Analysis of phytocompounds and application for simultaneous production of biohydrogen and bioethanol. Mater. Res. Express 2019, 6, 095051. [Google Scholar] [CrossRef]
  34. Jiang, X.C.; Hu, J.S.; Lieber, A.M.; Jackan, C.S.; Biffinger, J.C.; Fitzgerald, L.A.; Ringeisen, B.R.; Lieber, C.M. Nanoparticle Facilitated Extracellular Electron Transfer in Microbial Fuel Cells. Nano Lett. 2014, 14, 6737–6742. [Google Scholar] [CrossRef] [Green Version]
  35. El-Naggar, M.Y.; Wanger, G.; Leung, K.M.; Yuzvinsky, T.D.; Southam, G.; Yang, J.; Lau, W.M.; Nealson, K.H.; Gorby, Y.A. Electrical transport along bacterial nanowires from Shewanella oneidensis MR-1. Proc. Natl. Acad. Sci. USA 2010, 107, 18127–18131. [Google Scholar] [CrossRef] [Green Version]
  36. Viggi, C.C.; Rossetti, S.; Fazi, S.; Paiano, P.; Majone, M.; Aulenta, F. Magnetite Particles Triggering a Faster and More Robust Syntrophic Pathway of Methanogenic Propionate Degradation. Environ. Sci. Technol. 2014, 48, 7536–7543. [Google Scholar] [CrossRef]
  37. Wang, J.; Wan, W. Effect of Fe2+ concentration on fermentative hydrogen production by mixed cultures. Int. J. Hydrog. Energy 2008, 33, 1215–1220. [Google Scholar] [CrossRef]
  38. Frey, M. Hydrogenases: Hydroge—activating enzymes. ChemBioChem 2002, 3, 153–160. [Google Scholar] [CrossRef]
  39. Shanmugam, S.; Hari, A.; Pandey, A.; Mathimani, T.; Felix, L.; Pugazhendhi, A. Comprehensive review on the application of inorganic and organic nanoparticles for enhancing biohydrogen production. Fuel 2020, 270, 117453. [Google Scholar] [CrossRef]
  40. Nadeem, F.; Jiang, D.; Tahir, N.; Alam, M.; Zhang, Z.; Yi, W.; Chaoyang, L.; Zhang, Q. Defect engineering in SnO2 nanomaterials: Pathway to enhance the biohydrogen production from agricultural residue of corn stover. Appl. Mater. Today 2020, 21, 100850. [Google Scholar] [CrossRef]
  41. Shanmugam, S.; Krishnaswamy, S.; Chandrababu, R.; Veerabagu, U.; Pugazhendhi, A.; Mathimani, T. Optimal immobilization of Trichoderma asperellum laccase on polymer coated Fe3O4@SiO2 nanoparticles for enhanced biohydrogen production from delignified lignocellulosic biomass. Fuel 2020, 273, 117777. [Google Scholar] [CrossRef]
  42. Braga, J.K.; Stancari, R.A.; Motteran, F.; Malavazi, I.; Varesche, M.B.A. Metals addition for enhanced hydrogen, acetic and butyric acids production from cellulosic substrates by Clostridium butyricum. Biomass Bioenergy 2021, 150, 105679. [Google Scholar] [CrossRef]
  43. Bhatia, S.K.; Jagtap, S.S.; Bedekar, A.A.; Bhatia, R.K.; Rajendran, K.; Pugazhendhi, A.; Rao, C.V.; Atabani, A.; Kumar, G.; Yang, Y.-H. Renewable biohydrogen production from lignocellulosic biomass using fermentation and integration of systems with other energy generation technologies. Sci. Total Environ. 2020, 765, 144429. [Google Scholar] [CrossRef]
  44. Wang, J.; Wan, W. Influence of Ni2+ concentration on biohydrogen production. Bioresour. Technol. 2008, 99, 8864–8868. [Google Scholar] [CrossRef]
  45. Yang, G.; Wang, J. Improving mechanisms of biohydrogen production from grass using zero-valent iron nanoparticles. Bioresour. Technol. 2018, 266, 413–420. [Google Scholar] [CrossRef]
  46. Dolly, S.; Pandey, A.; Pandey, B.K.; Gopal, R. Process parameter optimization and enhancement of photo-biohydrogen production by mixed culture of Rhodobacter sphaeroides NMBL-02 and Escherichia coli NMBL-04 using Fe-nanoparticle. Int. J. Hydrog. Energy 2015, 40, 16010–16020. [Google Scholar] [CrossRef]
  47. Hsieh, P.-H.; Lai, Y.-C.; Chen, K.-Y.; Hung, C.-H. Explore the possible effect of TiO2 and magnetic hematite nanoparticle addition on biohydrogen production by Clostridium pasteurianum based on gene expression measurements. Int. J. Hydrog. Energy 2016, 41, 21685–21691. [Google Scholar] [CrossRef]
  48. Yin, Y.; Wang, J. Enhanced biohydrogen production from macroalgae by zero-valent iron nanoparticles: Insights into microbial and metabolites distribution. Bioresour. Technol. 2019, 282, 110–117. [Google Scholar] [CrossRef] [PubMed]
  49. Engliman, N.S.; Abdul, P.M.; Wu, S.-Y.; Jahim, J.M. Influence of iron (II) oxide nanoparticle on biohydrogen production in thermophilic mixed fermentation. Int. J. Hydrog. Energy 2017, 42, 27482–27493. [Google Scholar] [CrossRef]
  50. Malik, S.N.; Pugalenthi, V.; Vaidya, A.N.; Ghosh, P.C.; Mudliar, S.N. Kinetics of nano-catalysed dark fermentative hydrogen production from distillery wastewater. Energy Procedia 2014, 54, 417–430. [Google Scholar] [CrossRef] [Green Version]
  51. Elreedy, A.; Fujii, M.; Koyama, M.; Nakasaki, K.; Tawfik, A. Enhanced fermentative hydrogen production from industrial wastewater using mixed culture bacteria incorporated with iron, nickel, and zinc-based nanoparticles. Water Res. 2019, 151, 349–361. [Google Scholar] [CrossRef]
  52. Lin, R.; Cheng, J.; Ding, L.; Song, W.; Liu, M.; Zhou, J.; Cen, K. Enhanced dark hydrogen fermentation by addition of ferric oxide nanoparticles using Enterobacter aerogenes. Bioresour. Technol. 2016, 207, 213–219. [Google Scholar] [CrossRef] [PubMed]
  53. Zaidi, A.A.; RuiZhe, F.; Shi, Y.; Khan, S.Z.; Mushtaq, K. Nanoparticles augmentation on biogas yield from microalgal biomass anaerobic digestion. Int. J. Hydrog. Energy 2018, 43, 14202–14213. [Google Scholar] [CrossRef]
  54. Wang, J.; Wan, W. The effect of substrate concentration on biohydrogen production by using kinetic models. Sci. China Ser. B Chem. 2008, 51, 1110–1117. [Google Scholar] [CrossRef]
  55. Reddy, K.; Nasr, M.; Kumari, S.; Kumar, S.; Gupta, S.K.; Enitan, A.M.; Bux, F. Biohydrogen production from sugarcane bagasse hydrolysate: Effects of pH, S/X, Fe2+, and magnetite nanoparticles. Environ. Sci. Pollut. Res. 2017, 24, 8790–8804. [Google Scholar] [CrossRef]
  56. Han, H.; Cui, M.; Wei, L.; Yang, H.; Shen, J. Enhancement effect of hematite nanoparticles on fermentative hydrogen production. Bioresour. Technol. 2011, 102, 7903–7909. [Google Scholar] [CrossRef]
  57. Mullai, P.; Yogeswari, M.; Sridevi, K. Optimisation and enhancement of biohydrogen production using nickel nanoparticles–A novel approach. Bioresour. Technol. 2013, 141, 212–219. [Google Scholar] [CrossRef]
  58. Taherdanak, M.; Zilouei, H.; Karimi, K. The effects of Fe0 and Ni0 nanoparticles versus Fe2+ and Ni2+ ions on dark hydrogen fermentation. Int. J. Hydrog. Energy 2016, 41, 167–173. [Google Scholar] [CrossRef]
  59. Sun, Y.; Wang, Y.; Yang, G.; Sun, Z. Optimization of biohydrogen production using acid pretreated corn stover hydrolysate followed by nickel nanoparticle addition. Int. J. Energy Res. 2020, 44, 1843–1857. [Google Scholar] [CrossRef]
  60. Zhang, J.; Zhao, W.; Yang, J.; Li, Z.; Zhang, J.; Zang, L. Comparison of mesophilic and thermophilic dark fermentation with nickel ferrite nanoparticles supplementation for biohydrogen production. Bioresour. Technol. 2021, 329, 124853. [Google Scholar] [CrossRef]
  61. Zhao, W.; Zhang, Y.; Du, B.; Wei, D.; Wei, Q.; Zhao, Y. Enhancement effect of silver nanoparticles on fermentative biohydrogen production using mixed bacteria. Bioresour. Technol. 2013, 142, 240–245. [Google Scholar] [CrossRef] [PubMed]
  62. Mohanraj, S.; Anbalagan, K.; Rajaguru, P.; Pugalenthi, V. Effects of phytogenic copper nanoparticles on fermentative hydrogen production by Enterobacter cloacae and Clostridium acetobutylicum. Int. J. Hydrog. Energy 2016, 41, 10639–10645. [Google Scholar] [CrossRef]
  63. Mohanraj, S.; Anbalagan, K.; Kodhaiyolii, S.; Pugalenthi, V. Comparative evaluation of fermentative hydrogen production using Enterobacter cloacae and mixed culture: Effect of Pd (II) ion and phytogenic palladium nanoparticles. J. Biotechnol. 2014, 192, 87–95. [Google Scholar] [CrossRef] [PubMed]
  64. Zhang, Y.; Shen, J. Enhancement effect of gold nanoparticles on biohydrogen production from artificial wastewater. Int. J. Hydrog. Energy 2007, 32, 17–23. [Google Scholar] [CrossRef]
  65. Beckers, L.; Hiligsmann, S.; Lambert, S.D.; Heinrichs, B.; Thonart, P. Improving effect of metal and oxide nanoparticles encapsulated in porous silica on fermentative biohydrogen production by Clostridium butyricum. Bioresour. Technol. 2013, 133, 109–117. [Google Scholar] [CrossRef] [PubMed]
  66. Ameen, F.; Alsamhary, K.; Alabdullatif, J.A.; ALNadhari, S. A review on metal-based nanoparticles and their toxicity to beneficial soil bacteria and fungi. Ecotoxicol. Environ. Saf. 2021, 213, 112027. [Google Scholar] [CrossRef]
  67. Pu, Y.; Laratte, B.; Marks, R.S.; Ionescu, R.E. Impact of copper nanoparticles on porcine neutrophils: Ultrasensitive characterization factor combining chemiluminescence information and USEtox assessment model. Mater. Today Commun. 2017, 11, 68–75. [Google Scholar] [CrossRef] [Green Version]
  68. El-Dalatony, M.M.; Zheng, Y.; Ji, M.-K.; Li, X.; Salama, E.-S. Metabolic pathways for microalgal biohydrogen production: Current progress and future prospectives. Bioresour. Technol. 2020, 318, 124253. [Google Scholar] [CrossRef]
  69. Sun, Y.; Yang, G.; Zhang, L.; Sun, Z. Fischer-Trospch synthesis using iron-based catalyst in a microchannel reactor: Hybrid lump kinetic with ANNs/RSM. Chem. Eng. Process. Process. Intensif. 2017, 122, 181–189. [Google Scholar] [CrossRef]
  70. Rambabu, K.; Show, P.-L.; Bharath, G.; Banat, F.; Naushad, M.; Chang, J.-S. Enhanced biohydrogen production from date seeds by Clostridium thermocellum ATCC 27405. Int. J. Hydrog. Energy 2020, 45, 22271–22280. [Google Scholar] [CrossRef]
  71. Ulhiza, T.A.; Puad, N.I.M.; Azmi, A.S. Optimization of culture conditions for biohydrogen production from sago wastewater by Enterobacter aerogenes using Response Surface Methodology. Int. J. Hydrog. Energy 2018, 43, 22148–22158. [Google Scholar] [CrossRef]
  72. Zainal, B.S.; Zinatizadeh, A.A.; Chyuan, O.H.; Mohd, N.S.; Ibrahim, S. Effects of process, operational and environmental variables on biohydrogen production using palm oil mill effluent (POME). Int. J. Hydrog. Energy 2018, 43, 10637–10644. [Google Scholar] [CrossRef]
  73. Usman, M.; Kavitha, S.; Kannah, Y.; Yogalakshmi, K.; Sivashanmugam, P.; Bhatnagar, A.; Kumar, G. A critical review on limitations and enhancement strategies associated with biohydrogen production. Int. J. Hydrog. Energy 2021, 46, 31. [Google Scholar]
  74. Sethupathy, A.; Kumar, P.S.; Sivashanmugam, P.; Arun, C.; Banu, J.R.; Ashokkumar, M. Evaluation of biohydrogen production potential of fragmented sugar industry biosludge using ultrasonication coupled with egtazic acid. Int. J. Hydrog. Energy 2021, 46, 1705–1714. [Google Scholar] [CrossRef]
  75. Mirza, S.S.; Qazi, J.I.; Liang, Y.; Chen, S. Growth characteristics and photofermentative biohydrogen production potential of purple non sulfur bacteria from sugar cane bagasse. Fuel 2019, 255, 115805. [Google Scholar] [CrossRef]
  76. Urbaniec, K.; Bakker, R.R. Biomass residues as raw material for dark hydrogen fermentation–A review. Int. J. Hydrog. Energy 2015, 40, 3648–3658. [Google Scholar] [CrossRef]
  77. Hu, J.; Nagarajan, D.; Zhang, Q.; Chang, J.-S.; Lee, D.-J. Heterotrophic cultivation of microalgae for pigment production: A review. Biotechnol. Adv. 2018, 36, 54–67. [Google Scholar] [CrossRef]
  78. Sun, Y.; Mang, J.-P.; Yang, G.; Li, Z.-H. Study on the spectra of spruce lignin with chlorine dioxide oxidation. Spectrochim. Acta Part A Mol. Spectrosc. 2007, 27, 1551–1554. [Google Scholar]
  79. Tan, M.; Ma, L.; Rehman, M.S.U.; Ahmed, M.A.; Sajid, M.; Xu, X.; Sun, Y.; Cui, P.; Xu, J. Screening of acidic and alkaline pretreatments for walnut shell and corn stover biorefining using two way heterogeneity evaluation. Renew. Energy 2019, 132, 950–958. [Google Scholar] [CrossRef]
  80. Meghana, M.; Shastri, Y. Sustainable valorization of sugar industry waste: Status, opportunities, and challenges. Bioresour. Technol. 2020, 303, 122929. [Google Scholar] [CrossRef]
  81. Shuler, M.L. Bioprocess Enzineering: Basic Concepts; Prentice-Hall: New York, NY, USA, 2017; pp. 412–420. [Google Scholar]
  82. Zhao, X.; Xing, D.; Qi, N.; Zhao, Y.; Hu, X.; Ren, N. Deeply mechanism analysis of hydrogen production enhancement of Ethanoligenens harbinense by Fe2+ and Mg2+: Monitoring at growth and transcription levels. Int. J. Hydrog. Energy 2017, 42, 19695–19700. [Google Scholar] [CrossRef]
  83. Palomo-Briones, R.; Razo-Flores, E.; Bernet, N.; Trably, E. Dark-fermentative biohydrogen pathways and microbial networks in continuous stirred tank reactors: Novel insights on their control. Appl. Energy 2017, 198, 77–87. [Google Scholar] [CrossRef]
  84. Wimonsong, P.; Llorca, J.; Nitisoravut, R. Catalytic activity and characterization of Fe–Zn–Mg–Al hydrotalcites in biohydrogen production. Int. J. Hydrog. Energy 2013, 38, 10284–10292. [Google Scholar] [CrossRef]
  85. Woodward, J.; Orr, M.; Cordray, K.; Greenbaum, E. Enzymatic production of biohydrogen. Nature 2000, 405, 1014–1015. [Google Scholar] [CrossRef]
  86. Ergal, İ.; Gräf, O.; Hasibar, B.; Steiner, M.; Vukotić, S.; Bochmann, G.; Fuchs, W.; Simon, K.-M.R. Biohydrogen production beyond the Thauer limit by precision design of artificial microbial consortia. Commun. Biol. 2020, 3, 1–12. [Google Scholar] [CrossRef]
  87. Wang, S.; Tang, H.; Peng, F.; Yu, X.; Su, H.; Xu, P.; Tan, T. Metabolite-based mutualism enhances hydrogen production in a two-species microbial consortium. Commun. Biol. 2019, 2, 1–11. [Google Scholar] [CrossRef] [Green Version]
  88. Lu, Y.; Zhao, H.; Zhang, C.; Xing, X.-H. Insights into the global regulation of anaerobic metabolism for improved biohydrogen production. Bioresour. Technol. 2016, 200, 35–41. [Google Scholar] [CrossRef] [PubMed]
  89. Banu, J.R.; Ginni, G.; Kavitha, S.; Kannah, R.Y.; Kumar, S.A.; Bhatia, S.K.; Kumar, G. Integrated biorefinery routes of biohydrogen: Possible utilization of acidogenic fermentative effluent. Bioresour. Technol. 2021, 319, 124241. [Google Scholar] [CrossRef] [PubMed]
  90. Wang, L. Sustainable Bioenergy Production; CRC Press: Boca Raton, FL, USA, 2019. [Google Scholar]
  91. Banu, J.R.; Kavitha, S.; Kannah, R.Y.; Bhosale, R.R.; Kumar, G. Industrial wastewater to biohydrogen: Possibilities towards successful biorefinery route. Bioresour. Technol. 2020, 298, 122378. [Google Scholar] [CrossRef] [PubMed]
  92. Nawaz, M.Z.; Bilal, M.; Tariq, A.; Iqbal, H.M.; Alghamdi, H.A.; Cheng, H. Bio-purification of sugar industry wastewater and production of high-value industrial products with a zero-waste concept. Crit. Rev. Food Sci. Nutr. 2020, 1–18. [Google Scholar] [CrossRef] [PubMed]
  93. Liu, D.; Sun, Y.; Li, Y.; Lu, Y. Perturbation of formate pathway and NADH pathway acting on the biohydrogen production. Sci. Rep. 2017, 7, 1–8. [Google Scholar]
  94. Gutekunst, K.; Hoffmann, D.; Westernströer, U.; Schulz, R.; Garbe-Schönberg, D.; Appel, J. In-vivo turnover frequency of the cyanobacterial NiFe-hydrogenase during photohydrogen production outperforms in-vitro systems. Sci. Rep. 2018, 8, 1–10. [Google Scholar]
  95. Mangayil, R.; Karp, M.; Lamminmäki, U.; Santala, V. Recombinant antibodies for specific detection of clostridial [Fe-Fe] hydrogenases. Sci. Rep. 2016, 6, 1–9. [Google Scholar] [CrossRef] [Green Version]
  96. Oladokun, O.; Ahmad, A.; Abdullah, T.A.T.; Nyakuma, B.B.; Kamaroddin, M.F.A.; Nor, S.H.M. Biohydrogen production from Imperata cylindrica bio-oil using non-stoichiometric and thermodynamic model. Int. J. Hydrog. Energy 2017, 42, 9011–9023. [Google Scholar] [CrossRef]
  97. Show, K.-Y.; Lee, D.-J.; Chang, J.-S. Bioreactor and process design for biohydrogen production. Bioresour. Technol. 2011, 102, 8524–8533. [Google Scholar] [CrossRef] [PubMed]
  98. Wang, Y.; Yang, G.; He, J.; Sun, G.; Sun, Z.; Sun, Y. Preparation of biochar catalyst from black liquor by spray drying and fluidized bed carbonation for biodiesel synthesis. Process. Saf. Environ. Prot. 2020, 141, 333–343. [Google Scholar] [CrossRef]
  99. Park, J.-H.; Kim, D.-H.; Kim, H.-S.; Wells, G.F.; Park, H.-D. Granular activated carbon supplementation alters the metabolic flux of Clostridium butyricum for enhanced biohydrogen production. Bioresour. Technol. 2019, 281, 318–325. [Google Scholar] [CrossRef] [PubMed]
  100. Jamali, N.S.; Jahim, J.M.; Isahak, W.N.R.W.; Abdul, P.M. Particle size variations of activated carbon on biofilm formation in thermophilic biohydrogen production from palm oil mill effluent. Energy Convers. Manag. 2017, 141, 354–366. [Google Scholar] [CrossRef]
Energies 14 05916 g001 550
Figure 1. Statistics of publications from Scopus and Google Scholar in regard to BioH2 production by chemical nanoparticle additions.
Figure 1. Statistics of publications from Scopus and Google Scholar in regard to BioH2 production by chemical nanoparticle additions.
Energies 14 05916 g001
Energies 14 05916 g002 550
Figure 2. Potential mechanism of BioH2 enhancement by NPs addition.
Figure 2. Potential mechanism of BioH2 enhancement by NPs addition.
Energies 14 05916 g002
Energies 14 05916 g003 550
Figure 3. Statistical analysis of HY and HER. HY refers to H2 yield; HER refers to the H2 evolution rate. (A) Particle size and NP concentrations versus HY, (B) particle size and NP concentrations versus HER.
Figure 3. Statistical analysis of HY and HER. HY refers to H2 yield; HER refers to the H2 evolution rate. (A) Particle size and NP concentrations versus HY, (B) particle size and NP concentrations versus HER.
Energies 14 05916 g003
Energies 14 05916 g004 550
Figure 4. Statistical analysis of HY and HER. (A) Particle size and nanoparticle concentration versus HY, (B) particle size and nanoparticles concentration versus HER.
Figure 4. Statistical analysis of HY and HER. (A) Particle size and nanoparticle concentration versus HY, (B) particle size and nanoparticles concentration versus HER.
Energies 14 05916 g004
Energies 14 05916 g005 550
Figure 5. Impact of size of NPs upon HER and HY.
Figure 5. Impact of size of NPs upon HER and HY.
Energies 14 05916 g005
Energies 14 05916 g006 550
Figure 6. ANNs-RSM analysis of statically significant ion concentrations for HER. (A) Mg2+/Cu2+ nanoparticle concentration versus HER, (B) Na+/Mg2+ nanoparticle concentration versus HER, (C) Na+/NH4+ nanoparticle concentration versus HER, (D) Na+/K+ nanoparticle concentration versus HER, (E) NH4+/K+ nanoparticle concentration versus HER.
Figure 6. ANNs-RSM analysis of statically significant ion concentrations for HER. (A) Mg2+/Cu2+ nanoparticle concentration versus HER, (B) Na+/Mg2+ nanoparticle concentration versus HER, (C) Na+/NH4+ nanoparticle concentration versus HER, (D) Na+/K+ nanoparticle concentration versus HER, (E) NH4+/K+ nanoparticle concentration versus HER.
Energies 14 05916 g006
Table
Table 1. Comparison of BioH2 production with the addition of Fe-based nanoparticles.
Table 1. Comparison of BioH2 production with the addition of Fe-based nanoparticles.
NPsOpt/mg L−1SubstrateSC/g L−1Size/nmHY
/mmol g−1		HER
/mmol L−1h−1		Reference
Fe (NPs)400Grass10.7502.95.4[45]
Fe (NPs)25Starch5353-[18]
Fe (NPs)300Malate316200.4[46]
Fe (NPs)50Xylose307513.32[47]
Fe (NPs)200MSJ10500.92.4[48]
Fe (NPs)200Sucrose7.55015.910.1[27]
Fe (NPs)175Glucose7.55912.95.69[28]
Fe (NPs)50Starch6355-[43]
Fe (NPs)250Malate41224.20.8[44]
Fe2O3 (NPs)50Glucose5501.922.5[49]
Fe2O3 (NPs)50CDW15.33316.75102.5[17]
Fe2O3 (NPs)200DW56237.8562.4[30]
Fe2O3 (NPs)50Wastewater1106.51.949.4[50]
Fe2O3 (NPs)200MEG41008.40.6[51]
Fe2O3 (NPs)300CAS10203.8751.92[52]
Fe2O3 (NPs)200Glucose10209.23.1[52]
Fe2O3 (NPs)60Glucose6601.922.5[49]
Fe3O4(NPs)10Glucose2.510010.10.23[53]
Fe3O4(A-C-NPs)250Glucose53011.6563.2[38]
GT-INP (Fe2O4 and FeO(OH)(NPs)1000CO1.008701.580.0662[54]
Magnetite (NPs)200SJ3506.70.23[55]
Hematite (NPs)200Sucrose12.55510.46[56]
In this table, MEG refers to mono ethylene glycol, SC refers to substrate concentration, MSJ denotes Marcroalgea Saccharina Japonica, NMBL refers to R. sphaeroides NMBL-02 and E. coli NMBL-04, MC refers to mixed consortia, BA refers to Bacillus anthracis PUNAJAN 1, CP refers to C. pasteurianum, EA refers to E. aerogenes ATCC13408, EC refers to E. cloacae, Cl refers to Clostridium, Ca refers to C. acetobutylicum NCIM 2337, SJ refers to sugarcane juice, CAS refers to cassava starch.
Table
Table 2. Comparison of BioH2 production with the addition of Ni-based nanoparticles.
Table 2. Comparison of BioH2 production with the addition of Ni-based nanoparticles.
NanoparticlesOpt/mg L−1SubstrateSC/g L−1Size/nmHY
/mmol g−1		HER
/mmol L−1h−1		Reference
Ni (NPs)5.7Glucose14.0113.614.111.5[57]
Ni (NPs)32Starch8802.410.3[18]
Ni (NPs)60MEG4.7601.111.5[23]
Ni (NPs)10Glucose1259.530[32]
Ni (NPs)1Glucose2.510011.70.28[53]
Ni (NPs)4.3Glucose13.922812.710.4[57]
Ni (NPs)2.5Glucose542.510.81.3[58]
Ni (NPs)25Starch10402.711.5[18]
Ni (NPs)11Glucose2.71201.210.22[59]
NiO (NPs)20MEG41007.250.5[51]
NiO (NPs)10CDW15.32315.744.9[17]
NiO (NPs)1.5Wastewater9.623.60.512[31]
Ni (NPs)100CS2050200.27[60]
In this table, MEG refers to mono ethylene glycol, CS: cornstalk.
Table
Table 3. Comparison of BioH2 production with the addition of other nanoparticles, where POME: palm oil mill effluent.
Table 3. Comparison of BioH2 production with the addition of other nanoparticles, where POME: palm oil mill effluent.
NPsOpt/mg L−1SubstrateSC/g L−1Size/nmHY
/mmol g−1		HER
/mmol L−1h−1		Reference
Ag0.002Glucose12.51513.810.5[61]
Cu2.5Glucose2.5972.85.4[62]
Pd5Glucose101008.16.7[63]
Au0.002Sucrose1557.57.3[64]
Co1Glucose2.51004.850.16[53]
CoO1POME76.51722.50.7[31]
TiO2100Xylose3030121.8[47]
ZnO10MEG41007.30.58[51]
MgO1Glucose1001004.30.1[53]
Cu/SiO20.064Glucose52.55.80.54[65]
Ag/SiO20.107Glucose52.55.40.5[65]
Pd/SiO20.207Glucose52.55.40.52[65]
Table
Table 4. ANOVA analysis for the effect of ion concentration upon HER.
Table 4. ANOVA analysis for the effect of ion concentration upon HER.
SourceSum of SquaresDFMean SquareF-Valuep-Value
Model38,286.08201914.304.160.0005
A-Mg2+2467.7312467.735.360.0291
B-Cu2+1729.5011729.503.750.0640
C-Na+7543.8417543.8416.380.0004
D-NH4+496.571496.571.080.3091
E-K+261.491261.490.56770.4582
AB7903.3517903.3517.160.0003
AC1957.2711957.274.250.0498
AD513.911513.911.120.3009
AE1109.5111109.512.410.1332
BC41.84141.840.09080.7656
BD330.261330.260.71700.4052
BE16.50116.500.03580.8514
CD4919.6614919.6610.680.0031
CE2100.8312100.834.560.0427
DE1719.7911719.793.730.0647
A2801.661801.661.740.1990
B23897.0913897.098.460.0075
C22148.8012148.804.670.0406
D2387.391387.390.84100.3679
E21539.5411539.543.340.0795
Residue11,515.2225460.61
Lack of fit11,515.2220575.76
Pure Error0.000050.0000
Cor total49,801.3145
In this table, r2 = 0.94, adjusted r2 = 0.93, predicted r2 = 0.93, and adequate precision (AP) = 15.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Liu, Y.; Liu, J.; He, H.; Yang, S.; Wang, Y.; Hu, J.; Jin, H.; Cui, T.; Yang, G.; Sun, Y. A Review of Enhancement of Biohydrogen Productions by Chemical Addition Using a Supervised Machine Learning Method. Energies 2021, 14, 5916. https://doi.org/10.3390/en14185916

AMA Style

Liu Y, Liu J, He H, Yang S, Wang Y, Hu J, Jin H, Cui T, Yang G, Sun Y. A Review of Enhancement of Biohydrogen Productions by Chemical Addition Using a Supervised Machine Learning Method. Energies. 2021; 14(18):5916. https://doi.org/10.3390/en14185916

Chicago/Turabian Style

Liu, Yiyang, Jinze Liu, Hongzhen He, Shanru Yang, Yixiao Wang, Jin Hu, Huan Jin, Tianxiang Cui, Gang Yang, and Yong Sun. 2021. "A Review of Enhancement of Biohydrogen Productions by Chemical Addition Using a Supervised Machine Learning Method" Energies 14, no. 18: 5916. https://doi.org/10.3390/en14185916

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop