Current Issue : October-December Volume : 2024 Issue Number : 4 Articles : 5 Articles
In this industrialized world, in which the daily consumption of fossil fuels occurs, companies seek to prioritize energy generation through renewable energy sources with minimal environmental impact to improve their energy efficiency. The research objective was to calculate CO2 emissions for the pyrolysis process (conventional low-temperature pyrolysis) in two types of reactors, electric and traditional, where solar panels power the electric reactor. In addition, the amount of polluting gases and the energy consumption necessary to convert biomass into biochar were compared. Residual lignocellulosic biomass (RLB) from various species present in the southern region of Ecuador (eucalyptus, capuli, and acacia) was used, with three replicates per reactor. The electrical reactor (ER) consumed 82.60% less energy than the primary forest biomass fuel “traditional reactor (TR)” and distributed heat better in each pyrolytic process. The TR generated more pollution than the ER; it generated 40.48% more CO, 50% more NO2, 66.67% more SO2, and 79.63% more CH4. Undoubtedly, the pyrolysis process in an ER reduces environmental pollution and creates new bioproducts that could replace fossil fuels. This study provides relevant information on the residual biomass pyrolysis of plant species. These species are traditionally grown in the southern Ecuadorian region. In addition, an analysis of polluting gases for the TR and ER is presented....
This paper explores the pivotal role of data analysis and machine learning in advancing energy management strategies for New Energy Vehicles (NEVs) and Energy Storage Systems (ESS). Focused on the comprehensive journey from data collection and preprocessing to the application of dynamic programming, reinforcement learning, and genetic algorithms, our study underscores the transformational impact of these technologies on optimizing energy utilization and prolonging battery life. Initial stages involve meticulous data gathering and preprocessing to ensure the quality and usability of information derived from operational parameters. Subsequently, feature selection and engineering refine this data into meaningful insights, laying the groundwork for predictive modeling. These models forecast energy demands and system behavior, facilitating proactive maintenance and system efficiency improvements. We delve into optimization strategies, highlighting dynamic programming's role in decision-making, reinforcement learning's adaptability to environmental changes, and genetic algorithms' exploration of optimal charging/discharging strategies. These methodologies collectively contribute to sustainable energy practices and resource conservation, marking significant advancements in the field. The integration of machine learning not only enhances predictive maintenance and charging protocol optimization but also addresses challenges related to data scarcity, model generalizability, and interpretability. This paper provides a comprehensive analysis of current methodologies and future prospects, advocating for a multidisciplinary approach to further enrich the research landscape in energy management for NEVs and ESS....
An imperative environmental concern is escalating due to the widespread disposal of plastic waste in oceans and landfills, adversely impacting ecosystems and marine life. In this context, sustainable methods for plastic waste utilisation were evaluated, particularly for power generation. Two case studies were developed to assess the potential utilisation of waste plastic, specifically polyethylene and polypropylene, by integrating gasification with steam methane reforming (SMR) alongside two oxygen-supplying techniques for combustion including cryogenic air separation (ASU) and chemical looping combustion (CLC) for case 1 and case 2, respectively. For this, thorough process simulations of both case studies were performed to obtain detailed material and energy balances. The techno-economic analysis was performed to assess the economic performance of the processes by estimating levelized cost of electricity (LCOE). The results indicated that case 2 is more efficient (5.4%) due to the lower utility requirement of the CLC process as compared to ASU. Consequently, case 2 generated a LCOE of USD 137/MW. It was also seen from the results that the power output is directly proportional to the methane input while the increase in gasifier temperature enhances the H2 and CO content in syngas....
Egypt has the potential to generate a significant amount of energy from renewable technologies, in particular solar PV, concentrated solar power (CSP), and onshore and offshore wind. The energy sector is reliant on fossil fuels, particularly natural gas, for electricity production and is at risk of locking itself into a high carbon pathway. Globally, reducing greenhouse gas (GHG) emissions associated with national energy sectors is a target outlined in the UN’s Paris Agreement. To reduce carbon dioxide (CO2) emissions associated with a higher dependence on fossil fuels, Egypt must consider upscaling renewable energy technologies (RETs) to achieve a clean energy transition (CET). This research modelled six scenarios using clicSAND for OSeMOSYS to identify the technologies and policy target improvements that are needed to upscale RETs within Egypt’s energy sector. The results showed that solar PV and onshore wind are key technologies to be upscaled to contribute towards Egypt’s CET. The optimal renewable target is the International Renewable Energy Agency’s (IRENA) target of 53% of electricity being sourced from RETs by 2030, which will cost USD 16.4 billion more up to 2035 than Egypt’s current Integrated Sustainable Energy Strategy (ISES) target of 42% by 2035; it also saves 732.0 MtCO2 over the entire modelling period to 2070. Socio-economic barriers to this transition are considered, such as recent discoveries of natural gas reserves combined with a history of energy insecurity, political instability impacting investor confidence, and a lack of international climate funding. The paper concludes with policy recommendations that would enable Egypt to progress towards achieving a CET....
This paper considers the main modes of electricity consumption in the power system, taking into account their seasonal variability. The problem is solved using the methods of linear programming, product rules "IF, ... THEN, ..." and mathematical modelling for planning the modes of operation of electric power facilities. The study establishes that power consumers in certain regions of Uzbekistan, facing a shortage of electricity, can independently put into operation additional energy sources, such as wind power plants, solar photovoltaic stations and energy storage systems. A complete analysis of steady-state operation modes of the electric power system has been carried out. An algorithm for optimising the operation of electric power facilities based on the use of renewable and alternative energy sources has been created....
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