Sustainable development of fuel cell using enhanced weighted mean of vectors algorithm

Using the mathematical model of a Direct Methanol Fuel Cell (DMFC) stack, a new optimum approach is presented for estimating the seven unknown parameters i.e., ( e o , α , R , j e i d , C 1 , β ,r_eq) optimally. Specifically, a method is proposed for minimization of the Sum of Squared Errors (SSE) associated with the estimated polarization profile, based on the experimental data from simulations. The Enhanced Weighted mean of vectors (EINFO) algorithm is a novel metaheuristic method that is proposed to achieve this goal. An analysis of the results of this method is then compared to various metaheuristic algorithms such as the Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Dragonfly Algorithm (DA), Atom Search Optimization (ASO), and Weighted mean of vectors (INFO) well known in literature. As a final step to confirm the proposed approach's effectiveness, the sensitivity analysis is carried out using temperature changes, along with comparison against different approaches described in the literature to demonstrate its superiority. After comparison of parameter estimation and different operating temperature a non-parametric test is also performed and compared with the rest of the metaheuristic algorithms used in the manuscript. From these tests it is concluded that the proposed algorithm is superior to the rest of the compared algorithms.

Optimal sizing of different configuration of photovoltaic, fuel cell, and biomass-based hybrid energy system

The need for power is rising on a daily basis all across the world. Due to the finite supply of fossil fuels, it is critical to develop innovative non-renewable energy systems that can reduce reliance on conventional energy sources. A hybrid off-grid renewable energy system might be utilized to reduce reliance on traditional energy supplies, and to enhance the reliability of the renewable energy system. The process of selecting the appropriate combinations of components and their costs in order to produce an affordable, dependable, and effective alternative energy supply is known as hybrid system optimization. Hybrid energy technology can meet the energy needs of community very effectively. The goal of improving hybrid energy system control, size, and component selection is to offer society with a cost-effective electric power solution. The main aim of this paper is to use the proposed algorithm, i.e., hybrid chaotic particle swarm optimization and slime mould algorithm (HCPSOSMA), and Hybrid Optimization Model for Electric Renewable (HOMER) Software (Version 3.14.0) to minimize the levelized cost of energy (LCOE) supply and annualized cost system (ACS). The findings show that the proposed algorithm has an excellent convergence characteristic and the capacity to provide high-quality output. According to the simulation findings, the suggested off-grid-connected hybrid (solar PV/biomass/FC) power system is the most suitable and cost-effective option for the selected location, Patiala in Indian sub-continent.

Parameter estimation of proton exchange membrane fuel cell using a novel meta-heuristic algorithm

In recent years, proton exchange membrane fuel cells (PEMFCs) have been known to be a viable method for meeting the electrical energy needs, thereby enhancing the overall reliability of renewable energy systems. PEMFCs demonstrate various promising attributes like pollution-free, totally sustainable, non-self-discharging. These need hydrogen as fuel, and air for their operation, while the final product is pure water only. Thus, under varying operating conditions, the appropriate modeling and parameter optimization of PEMFCs have gained considerable importance in recent times. The evolutionary optimization approaches had been utilized in recent past for estimating PEMFCs parameters as exact modeling of the same does not exist in the literature. For the evaluation of PEMFCs performance criteria, a newly proposed algorithm is developed in this manuscript i.e. black widow optimization (BWO). Firstly, the performance of this proposed algorithm is checked by complex benchmark results. After that, this proposed algorithm is applied to extract the parameters of PEMFCs models under different operating temperatures. The parameter optimization results are obtained using BWO and are further compared with those obtained with five other algorithms, i.e., particle swarm optimization (PSO), multi-verse optimizer (MVO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), and grey wolf optimization (GWO). The complete error analysis is carried out for the two data sheets of the PEMFCs to establish the superiority of BWO. It has been observed that the developed proposed algorithm gives better results when compared to those obtained with rest of the algorithms considered in this work. After calculating the error, non-parametric test is performed which suggests that the BWO is better than the rest of the compared algorithms.

Hydrogen fuel and fuel cell technology for cleaner future: a review

One of the main problems facing our planetary bodies is unexpected and sudden climate change due to continuously increasing global energy demand, which currently is being met by fossil fuels. Hydrogen is considered as one of the major energy solutions of the twenty-first century, capable of meeting future energy needs. Being 61a zero-emission fuel, it could reduce environmental impacts and craft novel energy opportunities. Hydrogen through fuel cells can be used in transport and distributed heating, as well as in energy storage systems. The transition from fossil-based fuels to hydrogen requires intensive research to overcome scientific and socio-economic barriers. The purpose of this paper is to reflect the current state, related issues, and projection of hydrogen and fuel elements within the conceptual framework of 61a future sustainable energy vision. An attempt has been made to compile in this paper the past hydrogen-related technologies, present challenges, and role of hydrogen in the future.

Hydrogen fuel and fuel cell technology for cleaner future: a review

One of the main problems facing our planetary bodies is unexpected and sudden climate change due to continuously increasing global energy demand, which currently is being met by fossil fuels. Hydrogen is considered as one of the major energy solutions of the twenty-first century, capable of meeting future energy needs. Being 61a zero-emission fuel, it could reduce environmental impacts and craft novel energy opportunities. Hydrogen through fuel cells can be used in transport and distributed heating, as well as in energy storage systems. The transition from fossil-based fuels to hydrogen requires intensive research to overcome scientific and socio-economic barriers. The purpose of this paper is to reflect the current state, related issues, and projection of hydrogen and fuel elements within the conceptual framework of 61a future sustainable energy vision. An attempt has been made to compile in this paper the past hydrogen-related technologies, present challenges, and role of hydrogen in the future.

Tuberculosis latent infection in health care workers: oxidative stress and Quantiferon-TB Plus

Background

This study moves from recent evidences highlighting: 1) the high sensitivity of Mycobacterium tuberculosis (MT) to perturbation of redox homeostasis induced by oxidative stress; 2) the improvement of Tuberculosis (TB) diagnosis following the introduction of Quantiferon-TB Plus (QFT-Plus) assay.

Methods

The QFT-Plus diagnostic performance and the blood antioxidant capacity, expressed as ratio between oxidized (GSSG) and reduced (GSH) forms of glutathione, were determined on three selected populations (40 Health care workers (HCWs) controls, 63 latent TB HCWs, 8 active TB patients). Quantitative Real Time PCR analysis on leukocytes of active TB patients was also performed, in order to identify “redox profiles” of genes mainly involved in the antioxidant response.

Results

1) The glutathione homeostasis was shifted towards an oxidative status in active TB patients respect to controls, as evidenced by the significant decrease of the ratio between free and total GSH, an indirect index of oxidative stress. More reducing conditions were observed in latent TB subjects. 2) The expression profiles of antioxidant genes confirmed the major susceptibility of active TB patients to oxidative stress compared to controls, and highlighted a great individual variability. 3) The diagnostic performance of QFT-Plus test present a moderate concordance with QFT-GIT one, in this preliminary phase.

Conclusions

Glutathione has anti-mycobacterial effects in its reduced form GSH, thus the quantification of Free/Total GSH ratio may represent a systemic marker of TB infection and be useful in developing combined therapies. Moreover, the identification of personalized redox profiles will additionally provide an individual antioxidant response to the infection.

This project was funded by the Ministry of Health (RF 2016)

Key messages

LTBI management represents an objective of primary importance in the field of occupational medicine in order to define a personalized prevention in HCW. A new approach that combines biochemical determinations of redox biomarkers and gene expression in blood will be a novel biomarkers of tuberculosis.

Metabolism of low molecular weight ribonucleic acids in early sea urchin embryos

There are three major low molecular weight RNAs (150-300 nucleotides) larger than 5S rRNA present in sea urchin embryos. Two of these are localized in the nucleus and one is localized in the cytoplasm. The nuclear species contain "capped" 5' termini, with a cap I structure. These RNAs are synthesized starting in late cleavage and continuing through pluteus. Relative to 5S RNA there is a 10-fold change in the rate of synthesis of these RNAs, due primarily to a decrease in their rate of transcription after blastula. The RNAs are metabolically stable and the nuclear RNA genes are reiterated 50--100 times in the genome. Significant amounts of these RNAs are present in sea urchin eggs, enough to supply the embryo during early cleavage, prior to initiation of their synthesis.