Future Energy

Toward a 100% renewable energy future in Iceland: scenario analysis of geothermal, biofuel, and electric vehicle integration

Armita Fathi — 2025-08-01

This study investigates the transition paths towards a 100% renewable energy system in Iceland through scenario analysis and simulation using the Energy PLAN software. Because of its unique geographical location and abundant geothermal resources, Iceland is a case study for renewable energy. In the present research, three primary scenarios are considered. The EV scenario is the substitution of fossil-fuel vehicles with EVs, which would imply that the proportion of renewable energy rises to 91.2% and CO₂ emission falls from 1.98 million tons in 2022 to 1.27 million tons by 2035. The Hybrid scenario, beyond the expansion of EVs, also includes the use of biofuels in industrial and maritime sectors, leading to an increase in the share of renewable energy to 96.6% and reducing CO₂ emissions down to 0.49 million tons. In contrast, the Business as Usual (BAU) scenario keeps the current system without structural changes, resulting in only a marginal increase in renewable energy share and an escalation of CO₂ emissions to 2.58 million tons. Alongside technical and environmental analysis, this study assesses the economic, social, and political aspects of the transition to a sustainable energy system. It highlights the importance of supportive policies, stronger regulations, and greater public awareness as key factors for success. Overall, the comprehensive insights provided by this research offer valuable guidance for policymakers and stakeholders aiming to reduce reliance on fossil fuels and enhance Iceland’s environmental performance.

An extensive examination of the potential of waste cooking oil biodiesel in Bangladesh

Ahad Bin Azad — 2025-09-03

Waste cooking oil (WCO) has gained attention as a valuable resource for biodiesel production due to its availability and potential for waste management. This study examines the viability of WCO as a biodiesel feedstock in Bangladesh, addressing the increasing demand for sustainable energy alternatives. This paper explores multiple facets of WCO biodiesel, encompassing feedstock types, pretreatment techniques, and the production process. The study analyzes the physicochemical properties, emission characteristics, performance, and combustion behavior of biodiesel derived from WCO. The findings indicate that WCO biodiesel presents considerable potential as an economically viable and environmentally sustainable alternative fuel for diesel engines in Bangladesh. Challenges in WCO collection, commercialization, and public awareness must be addressed to realize its full potential. The paper concludes by proposing avenues for further research in Bangladesh, emphasizing the enhancement of collection systems, the refinement of policy frameworks, and the optimization of conversion technologies to facilitate the broader adoption of WCO biodiesel.

Managing risk and volatility in oil-dependent economies: the role of advanced predictive analytics

Mahmood Abdoos — 2025-09-06

The forecasting of oil production, demand, and prices holds critical significance for global economic stability and growth. Oil plays a crucial role in determining economic performance, making reliable price estimations essential for shaping public policy and guiding investment decisions. In this study, advanced neural network models were employed to enhance the accuracy of oil market forecasts, with a particular focus on their economic implications. Using Python-based implementations of Long Short-Term Memory (LSTM), Radial Basis Function (RBF), and multilayer perceptron (MLP) networks, the research compares the effectiveness of these approaches in crude oil price forecasting. The evaluation of model outputs using technical indicators revealed that the multilayer perceptron network yielded the best results. During training, it reached an average squared error of 55.28, a root mean squared error of 7.43, and a mean absolute error of 5.55; while in testing, the values were 116.01, 12.96, and 10.73, respectively. Overall, the comparative analysis indicates that the multilayer perceptron consistently surpassed both LSTM and RBF models in minimizing prediction errors. The economic relevance of these findings is underscored by the model's potential to enhance decision-making processes for investors, policymakers, and oil producers by offering more reliable forecasts. By improving accuracy by 20 to 30 percent compared to previous studies, this research provides valuable insights into optimizing resource allocation and mitigating the economic risks associated with oil price volatility.

Optimizing heliostat solar power plant field design: using the golden ratio of the Fibonacci sequence

Seyed Farhan Moosavian — 2025-09-12

Considering the importance of achieving sustainable development and using the potential of clean and renewable energy resources, such as solar energy with the highest efficiency, this research aims to optimize the design of the Heliostat solar power plant field with a central tower using the golden ratio of the Fibonacci sequence and investigate the effect of this ratio on the designed field results. The reference pattern used in this paper is the radial staggered pattern. The field under study is located in Naypyitaw (the capital of Myanmar). In this paper, with the approach of optimizing the heliostat field design, using the equations available in the design of solar power plants, the results of the new field pattern calculated using the golden ratio are investigated. The results of this research show that using the golden ratio increases the area required for the placement of heliostats by 27%, while increasing the optical efficiency by 77%. Also, in this case, it will be possible to increase the number of heliostats by 11% without increasing the current area.

Neutronic evolution and isotopic assessment of NIRR-1 under alternative fuel enrichment configurations: a WIMS-ANL/REBUS-ANL study

Dennis Solomon Balami — 2025-10-26

The Nigerian Research Reactor-1 (NIRR-1), a Miniature Neutron Source Reactor (MNSR), transitioned from highly enriched uranium (HEU) to low-enriched uranium (LEU) fuel to align with global non-proliferation objectives, necessitating a detailed evaluation of its neutronic behavior and isotopic evolution. This study employs the WIMS-ANL and REBUS-ANL computational framework to compare the burnup dynamics, reactivity profiles, and radionuclide inventories of NIRR-1’s HEU () and LEU core configurations under a representative operational schedule of 20 effective full-power days per year. Results reveal a steeper reactivity decline in the LEU core (17.02 pcm/EFPD) compared to the HEU core (13.97 pcm/EFPD), driven by enhanced resonance absorption, yet the LEU core’s higher initial mass extends its operational lifetime to 56.4 years versus 50.3 years for HEU. The LEU configuration produces 2.24 times more (0.767 g vs. 0.342 g at 252/282 EFPD), raising long-term waste management considerations. Both cores maintain robust safety through strongly negative temperature coefficients and low peak temperatures, supported by natural convection cooling. Comprehensive isotopic inventories and decay heat analyses (1620 W LEU vs. 1450 W HEU at shutdown) inform safety assessments and decommissioning strategies. Sensitivity studies highlight operational power and enrichment as critical lifetime influencers, with uncertainties validated against experimental data. These findings enhance NIRR-1’s operational strategy, support global MNSR conversion efforts, and provide critical data for safety and waste management planning, emphasizing proactive reactivity control and advanced fuel design exploration.