• Energy Research
• 7. Clean energy close
• 1. No poverty close
• 8. Economic growth close
• IN close
• Sustainability close

The conversion of methane into liquid biofuels using methane-consuming bacteria, known as methanotrophs, contributes to sustainable development, as it mitigates the problem of climate change caused by greenhouse gases and aids in producing cleaner and renewable energy. In the present research, an efficient methanotroph, Methylosarcina sp. LC-4, was studied as a prospective organism for biodiesel production using methane. The methane uptake rate by the organism was enhanced 1.6 times and 2.35 times by supplementing LC-4 with micronutrients, such as copper and tungstate, respectively. This unique ability of the isolated organism enables the deployment of methanotrophs-based processes in various industrial applications. A Plackett–Burman statistical (PBD) design was used to quantify the role of the micronutrients and other media components present in the nitrate minimal salt media (NMS) in biomass and fatty acid methyl esters (FAME) yields. Nitrate, phosphate, and tungstate had a positive effect, whereas copper, magnesium, and salinity had a negative effect. The modified NMS media, formulated according to the results from the PBD analysis, increased the FAME yield (mg/L) by 85.7%, with the FAME content of 13 ± 1% (w/w) among the highest reported in methanotrophs. The obtained FAME consisted majorly (~90%) of C14–C18 saturated and monounsaturated fatty acids, making it suitable for use as biodiesel.

Because of the current increase in energy requirement, reduction in fossil fuels, and global warming, as well as pollution, a suitable and promising alternative to the non-renewable energy sources is proton exchange membrane fuel cells. Hence, the efficiency of the renewable energy source can be increased by extracting the precise values for each of the parameters of the renewable mathematical model. Various optimization algorithms have been proposed and developed in order to estimate the parameters of proton exchange membrane fuel cells. In this manuscript, a novel hybrid algorithm, i.e., Hybrid Particle Swarm Optimization Puffer Fish (HPSOPF), based on the Particle Swarm Optimization and Puffer Fish algorithms, was proposed to estimate the proton exchange membrane fuel cell parameters. The two models were taken for the parameter estimation of proton exchange membrane fuel cells, i.e., Ballard Mark V and Avista SR-12 model. Firstly, justification of the proposed algorithm was achieved by benchmarking it on 10 functions and then a comparison of the parameter estimation results obtained using the Hybrid Particle Swarm Optimization Puffer Fish algorithm was done with other meta-heuristic algorithms, i.e., Particle Swarm Optimization, Puffer Fish algorithm, Grey Wolf Optimization, Grey Wolf Optimization Cuckoo Search, and Particle Swarm Optimization Grey Wolf Optimization. The sum of the square error was used as an evaluation metric for the performance evaluation and efficiency of the proposed algorithm. The results obtained show that the value of the sum of square error was smallest in the case of the proposed HPSOPF, while for the Ballard Mark V model it was 6.621 × 10−9 and for the Avista SR-12 model it was 5.65 × 10−8. To check the superiority and robustness of the proposed algorithm computation time, voltage–current (V–I) curve, power–current (P–I) curve, convergence curve, different operating temperature conditions, and different pressure results were obtained. From these results, it is concluded that the Hybrid Particle Swarm Optimization Puffer Fish algorithm had a better performance in comparison with the other compared algorithms. Furthermore, a non-parametric test, i.e., the Friedman Ranking Test, was performed and the results demonstrate that the efficiency and robustness of the proposed hybrid algorithm was superior.

Partial shading conditions (PSCs) are responsible for the root causes of photovoltaic (PV) system performance deprivation such as hotspots (damaged PV cells), mismatch power losses and multiple power maxima. Recently, PV array reconfiguration strategies have proven to be beneficial in improving PV system performance and achieving improved shade dispersion properties. This research analyzes the improved Su-Do-Ku (I-SDK) PV array configuration in order to counteract the shading effect. This approach implements a 6 × 6 size PV array configuration and performance evaluation under different realistic shading scenarios. The performance of the I-SDK configuration is assessed and compared to that of the total-cross-tied (TCT) and Su-Do-Ku (SDK) arrangements. The performance indices such as power loss (PL), power at global maximum power point (GMPP), fill-factor (FF), performance ratio (PR), power enhancement (PE) and execution ratio (ER) are analyzed to show comprehensive comparison. An experimental analysis confirms the MATLAB/Simulink findings, demonstrating that the I-SDK configuration outperforms both the TCT and SDK array setups. The GMPP values of 143.5 W, 141.7 W, 138.1 W and 129.3 W also show the superiority of I-SDK during four shading instances compared to conventional SP, TCT, SDK and SM arrangements. Moreover, under similar PSCs, higher %FF (74.61%, 76.10%, 77.1%, 75.92%) and lower PL (36.7 W, 38.5 W, 42.1 W, 50.9 W) support the adoptability of I-SDK for experimental validation/commercial viability.

Solid-state transformers (SSTs) have emerged as a superior alternative to conventional transformers and are regarded as the building block of the future smart grid. They incorporate power electronics circuitry and high-frequency operation, which allows high controllability and enables bi-directional power flow, overcoming the limitations of conventional transformers. This paper presents a detailed analysis of the solid-state transformer, expounding the fundamentals, converter topologies, applications, and future challenges of the SST in a systematic manner. The paper discusses the necessity of improved replacement of the low-frequency transformers (LFTs) and presents the configuration of SST. It presents SST fundamentals in individual stages and explores its origin and evolution. The basic topologies, their specifications, and control strategies are also described. The applications of SST as a replacement of LFTs are discussed along with recent applications. The future challenges for real-time implementation of SSTs are explored, and research directions are proposed.

The share of biofuels has increased significantly over the last decade, which has lead to several negative impacts on the environment. As a solution, several governments worldwide have promoted the use of certification systems, which have been implemented and in some cases have even been established as mandatory regulations. Due to the focus of the public debate, standard-setting has mainly been limited to developing and newly industrializing countries. Hence, the issues of environmental impacts as a consequence of agricultural intensification in Germany has been given little attention, and the question whether existing biofuel certification systems sufficiently cover ecological issues remains. In order to answer this question, this study performs a benchmarking analysis of selected certification systems, whereby their ability to ensure ecological sustainability is evaluated and compared. The assessment reveals that the currently existing national ordinances, like Cross Compliance, are in many aspects insufficient to ensure sustainability. Contrarily, they often deter necessary discussions to tackle these issues.

One major concern about foreign direct investment (FDI) is the potential negative environmental impact due to increased CO2 emissions. However, there is a possibility that FDI mitigates CO2 emissions through green innovation and creates a cleaner environment. In the existing literature, there is no significant empirical evidence on the linkage among FDI, green innovation and CO2 emissions in the context of BRICS countries. Hence, this study aims to analyze the impact of FDI and green innovation on the environmental quality of BRICS economies for 1990–2014. The study employed Augmented Mean Group (AMG) estimators for empirical data analysis. The study’s findings depict that foreign direct investment, energy use, and economic growth have a significant and positive impact on the CO2 emissions of BRICS economies. Moreover, green innovation has a significant inverse impact on CO2 emissions. The results show bidirectional causalities between CO2 emissions and green innovation, trade openness and CO2 emissions, energy use and CO2 emissions, and urbanization and CO2 emissions. Additionally, the findings reveal a one-way causality from CO2 emissions to GDP and CO2 emissions to urbanization. This study offers essential policy recommendations for the environmental sustainability of BRICS countries through green innovation.

This paper presents atechnical and economic analysis of the proposed solar PV/diesel generator smart hybrid power plant for a part of SRM IST, Delhi-NCR campus. The analysis was performed using five battery storage technologies: lead-acid, lithium-ion, vanadium flow, zinc bromide and nickel-iron. The analysis also used the HOMER Pro software. The analysis was conducted to assess performance parameters such as initial cost, simple payback period, return on investment, energy produced, renewable penetration and emission of air pollutants. The optimal solution was obtained as SPP(200 kW)/DG(82 kW)/ZB(2000 kWh), with cycle charging dispatch strategy. The initial cost of this configuration is estimated to be USD163,445, and the operating cost is USD534 per year. The net present cost is estimated to be USD170,348, and the estimated cost of energy with this configuration has been obtained as USD0.090 per kWh. It is estimated that with this optimal solution, the diesel generator may consume only 110 L/year of diesel, which is the minimum of all configurations. Sensitivity analysis was performed between the size of the solar PV array and the size of the battery, along with variations in the battery’s nominal capacity and renewable fraction.

Today’s society is faced with many upfront challenges such as the energy crisis, water pollution, air pollution, and global warming. The greenhouse gases (GHGs) responsible for global warming include carbon dioxide (CO2), methane (CH4), nitrous oxide (NOx), water vapor (H2O), and fluorinated gases. A fraction of the increased emissions of CO2 in the atmosphere is due to agricultural and municipal solid waste (MSW) management systems. There is a need for a sustainable solution which can degrade the pollutants and provide a technology-based solution. Hence, the present work deals with the custom design of a loop photobioreactor with 34 L of total volume used to handle different inlet CO2 concentrations (0.03%, 5%, and 10% (v/v)). The obtained values of biomass productivity and CO2 fixation rate include 0.185 ± 0.004 g L−1 d−1 and 0.333 ± 0.004 g L−1 d−1, respectively, at 10% (v/v) CO2 concentration and 0.084 ± 0.003 g L−1 d−1 and 0.155 ± 0.003 g L−1 d−1, respectively, at 5% (v/v) CO2 concentration. The biochemical compositions, such as carbohydrate, proteins, and lipid content, were estimated in the algal biomass produced from CO2 mitigation studies. The maximum carbohydrate, proteins, and lipid content were obtained as 20.7 ± 2.4%, 32.2 ± 2.5%, and 42 ± 1.0%, respectively, at 10% (v/v) CO2 concentration. Chlorophyll (Chl) a and b were determined in algal biomass as an algal physiological response. The results obtained in the present study are compared with the previous studies reported in the literature, which indicated the feasibility of the scale-up of the process for the source reduction of CO2 generated from waste management systems without significant change in productivity. The present work emphasizes the cross-disciplinary approach for the development of bio-mitigation of CO2 in the loop photobioreactor.