• Energy Research
• Open Access close
• Closed Access close
• Open Source close
• Embargo close
• PK close

The global community has set intensive targets in Sustainable Development Goals (SDGs) to better people’s lives after closing the Millennium Development Goals (MDGs). It corresponds to the 2030 aspirations of the United Nations to enhance and promote the sustainable development of human society. The current paper explores the impact of fiscal hedging and R&D in energy Using a green-energy system in SDGs. To do this, we used TOPSIS and QARDL methodologies on a 21-year dataset of South and Southeast Asian economies from 2000 to 2020. The study results show that fiscal hedging contributes favourably to the environmental degradation of the underlying economy. Research and development (R&D) in renewables has contributed negatively to ecological degradation and SDGs in the economies of South & Southeast Asia. This study suggests policy guidelines for advanced and developing economies based on fiscal stability and technical innovation through R&D to meet SDG.

Sustainable food supply has gained considerable consumer concern due to the high percentage of spoilage microorganisms. Food industries need to expand advanced technologies that can maintain the nutritive content of foods, enhance the bio-availability of bioactive compounds, provide environmental and economic sustainability, and fulfill consumers’ requirements of sensory characteristics. Heat treatment negatively affects food samples’ nutritional and sensory properties as bioactives are sensitive to high-temperature processing. The need arises for non-thermal processes to reduce food losses, and sustainable developments in preservation, nutritional security, and food safety are crucial parameters for the upcoming era. Non-thermal processes have been successfully approved because they increase food quality, reduce water utilization, decrease emissions, improve energy efficiency, assure clean labeling, and utilize by-products from waste food. These processes include pulsed electric field (PEF), sonication, high-pressure processing (HPP), cold plasma, and pulsed light. This review describes the use of HPP in various processes for sustainable food processing. The influence of this technique on microbial, physicochemical, and nutritional properties of foods for sustainable food supply is discussed. This approach also emphasizes the limitations of this emerging technique. HPP has been successfully analyzed to meet the global requirements. A limited global food source must have a balanced approach to the raw content, water, energy, and nutrient content. HPP showed positive results in reducing microbial spoilage and, at the same time, retains the nutritional value. HPP technology meets the essential requirements for sustainable and clean labeled food production. It requires limited resources to produce nutritionally suitable foods for consumers’ health.

Abstract In this paper, a hybrid features based support vector machine (SVM) model is proposed using infrared thermography technique for hotspots detection and classification of photovoltaic (PV) panels. A novel hybrid feature vector consisting of RGB, texture, the histogram of oriented gradient (HOG), and local binary pattern (LBP) as features is formed using a data fusion approach. A machine learning algorithm SVM is employed to classify the obtained thermal images of PV panels into three different classes (i.e., healthy, non-faulty hotspot, and faulty). The comparison of different machine learning algorithms and datasets is also carried out to validate the superiority of the proposed model and hybrid feature dataset. The experimental results reveal that the proposed hybrid features with SVM resulted in 96.8% training accuracy and 92% testing accuracy with lesser computational complexity and storage space than other machine learning algorithms. The proposed approach is easily implementable for efficient monitoring and fault diagnosis of PV panels.

ABSTRACTWind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long range monthly‐to‐seasonal forecasting allows the prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However, it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here a model relating average weather conditions to average wind power output was built, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations was used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power were performed based on reanalysis data, making separate simulations based on monthly, daily and sub‐daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub‐optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub‐daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day‐to‐day variability.

Abstract This research focuses on parametric influence on product distribution and syngas production from conventional gasification. Three experimental parameters at three different levels of temperature (700, 800 and 900 °C), sugarcane bagasse loading (2, 3 and 4 g) and residence time (10, 20 and 30 min) were studied using horizontal axis tubular furnace. Response Surface Methodology supported by central composite design was adopted in order to investigate parameters impact on product distribution (i.e., gas, tar and char) and gaseous products (i.e., H2, CO, CO2 and CH4). The highest H2 fraction obtained was 42.88 mol% (36.91 g-H2 kg-biomass−1) at 3 g of sugarcane bagasse loading, 900 °C and 30 min reaction time. The temperature was identified as the most influential parameter followed by reaction time for H2 production and diminishing the bio-tar and char yields. An increase in sugarcane bagasse loading, on other hand, favored the production of bio-tar, CO2 and CH4 production. The statistical analysis verified temperature as most significant (p-value 0.0008) amongst the parameters investigated for sugarcane bagasse biomass gasification.

As population grows, demand for reliable and sustainable energy increases prompting the need for innovative solutions. The numerous strategies are needed to shift centralized power generation to distributed generation in the evolution of smart energy systems. The response and electricity market paradigms using multiple loads are missing in literature. In this paper, multiple loads are considered to propose optimal energy hub modeling comprised of the various sources and storage. The proposed system is modelled using demand response technique. The exact method-based optimization is used to simulate the proposed model in MATLAB. The results obtained show the effectiveness of the proposed model to reduce the operational cost and greenhouse gas (GHG) emissions. A case study is taken, where a poultry farm producing approximately 1.94 million tons of poultry waste annually, is used to evaluate the proposed model. For the industrial load, the optimized scheduling reduced the operational cost to $161.8 as compared to $1670 relying only on the utility grid. For the residential load, the optimized cost is reduced by -$3.3 from the pre-optimization cost of $2.7. This work highlights the economic and environmental benefits, particularly in developing countries where biomass-based energy generation is yet to be fully explored.

Here we investigate the irreversibility aspects in magnetohydrodynamics flow of viscous nanofluid by a variable thicked surface. Viscous dissipation, Joule heating and heat generation/absorption in energy expression is considered. Behavior of Brownian diffusion and thermophoresis are also discussed. The nanoliquid is considered electrical conducting under the behavior of magnetic field exerted transverse to the sheet. Using similarity variables the nonlinear PDEs are altered to ordinary one. The obtained system are computed through Newton built in shooting method. Significant behavior of various involving parameters on entropy generation rate, velocity, concentration, Bejan number and temperature are examined. Gradient of velocity and heat transfer rate are numerically computed through tabulated form. Velocity field is augmented versus power index (n). Temperature and velocity profiles have opposite characteristics for larger approximation of Hartmann number. Concentration profile has similar impact against Brownian diffusion variable and Lewis number. Entropy optimization is boost up via rising values of Brinkman and Hartmann numbers. Bejan number is declined for increasing value of Hartmann number.

The entropy production in 2-D heat transfer system has been analyzed systematically by using the finite volume method, to develop new criteria for the numerical simulation in case of multidimensional systems, with the aid of the CFD codes. The steady-state heat conduction problem has been investigated for entropy production, and the entropy production profile has been calculated based upon the current approach. From results for 2-D heat conduction, it can be found that the stability of entropy production profile exhibits a better agreement with the exact solution accordingly, and the current approach is effective for measuring the accuracy and stability of numerical simulations for heat transfer problems.