Fermentative hydrogen production from wastewaters: A review and prognosis

Biohydrogen is a promising candidate which can replace a part of our fossil fuels need in day-to-day life due its perceived environmental benefits and availability through dark fermentation of organic substrates. Moreover, advances in biohydrogen production technologies based on organic wastewater conversion could solve the issues related to food security, climate change, energy security and clean development in the future. An evaluation of studies reported on biohydrogen production from different wastewaters will be of immense importance in economizing production technologies. Here we have reviewed biohydrogen production yields and rates from different wastewaters using sludges and microbial consortiums and evaluated the feasibility of biohydrogen production from unexplored wastewaters and development of integrated bioenergy process. Biohydrogen production has been observed in the range of substrate concentration 0.25–160 g COD/L, pH 4–8, temperature 23–60 °C, HRT 0.5–72 h with various types of reactor configuration. The most efficient hydrogen production has been obtained at an organic loading rate (OLR) 320 g COD/L/d, substrate concentration 40 g COD/L, HRT 3 h, pH 5.5–6.0, temperature 35 °C in a continuously-stirred tank reactor system using mixed cultures and fed with condensed molasses fermentation soluble wastewater. The net energy efficiency analysis showed vinasse wastewater has the highest positive net energy gain followed by glycerin wastewater and domestic sewage as 140.39, 68.65, 51.84 kJ/g COD feedstock with the hydrogen yield (HY) of 10 mmol/g COD respectively.     

Semiconducting allotrope of graphene

From first-principles calculations, we predict a planar stable graphene allotrope composed of a periodic array of tetragonal and octagonal (4, 8) carbon rings. The stability of this sheet is predicted from the room-temperature molecular dynamics study and the electronic structure is studied using state-of-the-art calculations such as the hybrid density functional and the GW approach. Moreover, the mechanical properties of (4, 8) carbon sheet are evaluated from the Young's modulus and intrinsic strength calculations. We find this is a stable planar semiconducting carbon sheet with a bandgap between 0.43 and 1.01?eV and whose mechanical properties are as good as graphene's.     

Transverse conductance of DNA nucleotides in a graphene nanogap from first principles

The fabrication of nanopores in atomically thin graphene has recently been achieved, and translocation of DNA has been demonstrated. Taken together with an earlier proposal to use graphene nanogaps for the purpose of DNA sequencing, this approach can resolve the technical problem of achieving single-base resolution in electronic nucleobase detection. We have theoretically evaluated the performance of a graphene nanogap setup for the purpose of whole-genome sequencing, by employing density functional theory and the nonequilibrium Green's function method to investigate the transverse conductance properties of nucleotides inside the gap. In particular, we determined the electrical tunneling current variation at finite bias due to changes in the nucleotides orientation and lateral position. Although the resulting tunneling current is found to fluctuate over several orders of magnitude, a distinction between the four DNA bases appears possible, thus ranking the approach promising for rapid whole-genome sequencing applications.     

Overcoming propionic acid inhibition of hydrogen fermentation by temperature shift strategy

A novel temperature shift strategy has been proposed to overcome an inhibition on hydrogen fermentation of beverage industry wastewater (BW) due to the accumulation of propionic acid (HPr) during continuous reactor operation. The continuous performance at constant pH 5.5, temperature 37 °C and hydraulic retention time (HRT) 8 h with BW concentration of 20 g/L_{hexose-equivalent} in a stirred tank reactor (2 L) showed an accumulation of HPr to 2.36 g/L leading to a drop in hydrogen production rate (HPR) from 10 to 8.5 L L⁻¹ d⁻¹. To overcome the HPr inhibition, a temperature shift (from 37 °C) to 45 °C for 8 h was applied. This significantly improved the inhibited HPR and HY to 13.6 L L⁻¹ d⁻¹ and 1.68 mol-H₂ mol⁻¹ hexose, respectively, with a simultaneous reduction in the HPr concentration to 0.7 g/L. Microbial community analysis based on PCR-DGGE after temperature shift revealed the non-dominance of Selenomonas lacticifex and Bifidobacterium catenulatum (involved in HPr formation), and dominance of hydrogen producing bacteria namely Clostridium butyricum, Clostridium perfringenes, Clostridium acetobutylicum, and Ethanoligenens harbinense. This study demonstrated that temperature shift strategy could overcome the HPr inhibition and significantly improve the hydrogen fermentation of an industrial wastewater.     

An indirect model of SSSC for reducing complexity of coding in Newton power flow algorithm

In this paper, an indirect modeling approach for static synchronous series compensator (SSSC) is proposed for Newton power flow analysis. This model shows that addition of ‘p’ SSSCs to an existing ‘n’ bus power system can be represented as an equivalent ‘(n + p)’ bus power system, without any SSSC. As a result, standard power flow analysis of the ‘(n + p)’ bus system can be carried out to calculate the steady state operating point of the original system containing SSSCs. Absence of any SSSC in the transformed system eliminates the need of writing new codes for computing the power flow equations and the Jacobian elements pertaining to SSSCs. This results in a substantial reduction in the programming complexity. The proposed model can also easily account for converter switching losses and various practical device constraints. Application of the proposed model for multiple SSSCs in the IEEE 30-bus, IEEE 118-bus and IEEE 300-bus systems demonstrates the feasibility of the model and its excellent convergence characteristics.     

Mechanism of Versatile Catalytic Activities of Quaternary CuZnFeS Nanocrystals Designed by a Rapid Synthesis Route

Quaternary alloyed nanocrystals (NCs) composed of earth abundant, environment friendly elements are of interest for energy‐harvesting applications. These complex NCs are useful as catalysts for the degradation of multiple refractory organic pollutants as well as nitro‐organic reduction at a rapid rate. Here, a remarkably fast (～30 s) and facile synthesis of crystalline quaternary chalcopyrite copper‐zinc‐iron‐sulfide (CZIS) NCs is reported. These NCs show excellent catalytic properties by degrading a number of refractory organic dyes and converting nitro‐compounds at a rapid rate. The valence and conduction band information of the newly designed NCs are extracted using scanning tunneling spectroscopy and ultraviolet photoelectron spectroscopy, which reveal energy levels suitable for performing redox chemistry by generating reactive radicals establishing NCs as efficient catalyst with multiple uses. Rapid synthesis of high quality phase‐controlled CZIS NCs with robust catalytic activities could be useful for organic waste treatment.     

Uncertainty quantification and reliability analysis by an adaptive sparse Bayesian inference based PCE model

An adaptive Bayesian polynomial chaos expansion (BPCE) is developed in this paper for uncertainty quantification (UQ) and reliability analysis. The sparsity in the PCE model is developed using automatic relevance determination (ARD) and the PCE coefficients are computed using the variational Bayesian (VB) inference. Further, Sobol sequence is utilized to evaluate a response quantity sequentially. Finally, leave one out (LOO) error is used to obtain the adaptive BPCE model. UQ and reliability analysis are performed of some numerical examples by the adaptive BPCE model. It is found that the optimal number of model evaluations and the optimal PCE degree are suitably selected simultaneously for a problem by the adaptive BPCE model. A highly accurate result is predicted by the proposed approach using very few model evaluation. Further, highly sparse PCE models are obtained by the ARD approach for most of the numerical examples. Additionally, distribution parameters of the predicted response quantity are also obtained by the VB inference, which are used to compute the confidence interval of the predicted response quantities.

     

Hybrid density functional study on SrTiO3 for visible light photocatalysis

Hybrid Density Functional calculations have been performed on the electronic structure of anionic mono- (S, N, P, and C) and co-doped (N–N, N–P, N–S, P–P) SrTiO₃ to improve their visible light photocatalytic activity. The electronic band position of doped system has been aligned with respect to the water oxidation/reduction potential. The electronic band position and optical absorption study shows that the mono- (S) and co-doped (N–N, N–P and P–P) SrTiO₃ systems are promising materials for the visible-light photocatalysis. The calculated binding energies show that the co-doped systems are more stable than their respective mono-doped systems.     

Investigation for Improved Artificial Intelligence Techniques for Thyristor-controlled Series-compensated Transmission Line Fault Classification with Discrete Wavelet Packet Entropy Measures

A new transmission line fault-classification algorithm based on half-cycle post-fault current data is presented for an advanced series-compensated transmission line equipped with a thyristor-controlled series compensator. The proposed scheme was developed with the signal feature enhancement tool of discrete wavelet packet entropy measures. The Chebyshev neural network is presented as network-growing technique for protective classification, the single-layer structure of which is a more powerful classifier that eliminates the need for complicated network design. A comparative implementation study of the multi-layer perceptron and Chebyshev neural network authenticates benefits gained by the Chebyshev neural network. To demonstrate the advantage gained by Chebyshev neural networks compared to support vector machines, a comparative study is presented with a support vector machine based classification technique. The fault datawere obtained by dynamic simulation of a sample system using the real-time power system simulator PSCAD (Manitoba HVDC Research Centre, Winnipeg, Manitoba, Canada). Extensive testing reveals the effectiveness of the Chebyshev neural network for fault classification; a comparative study brings out the superiority of the Chebyshev neural network for neural network design and implementation against the multi-layer perceptron. The Chebyshev neural network proved advantageous against support vector machines as being insensitive to the classification parameter.