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Browsing ENERGIA by Sustainable Development Goals (SDG) "11:Cidades e Comunidades Sustentáveis"
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- Analysis of Techno-Economic and Social Impacts of Electric Vehicle Charging Ecosystem in the Distribution Network Integrated with Solar DG and DSTATCOMPublication . Bonela, Ramesh; Ghatak, Sriparna Roy; Swain, Sarat Chandra; Lopes, Fernando; Nandi, Sharmistha; Sannigrahi, Surajit; Acharjee, ParimalABSTRACT: In this work, a comprehensive planning framework for an electric vehicle charging ecosystem (EVCE) is developed, incorporating solar distributed generation (DG) and a distribution static compensator (DSTATCOM), to assess their long-term techno-economic and environmental impacts. The optimal locations and capacities of the EVCE, solar DG, and DSTATCOM are determined using an improved particle swarm optimization algorithm based on the success rate technique. The study aims to maximize the technical, financial, and social benefits while ensuring that all security constraints are met. To assess the financial viability of the proposed model over a 10-year horizon, a detailed economic analysis comprising installation cost, operation, and maintenance cost is conducted. To make the model more realistic, various practical parameters, such as the inflation rate and interest rate, are incorporated during the financial analysis. Additionally, to highlight the societal benefits of the approach, the study quantifies the long-term carbon emissions and the corresponding cost of emissions. The proposed framework is tested on both a 33-bus distribution network and a 108-bus Indian distribution network. Various planning scenarios are explored, with different configurations of the EVCE, solar-based DG, and DSTATCOM, to assist power system planners in selecting the most suitable strategy.
- Design optimisation of five pilot-scale two-stage vertical flow-constructed wetlands for piggery wastewater treatmentPublication . Karan, N.; Gogoi, Jayanta; Ganguly, Anasuya; Brito, António; Marques dos Santos, C.; de Oliveira Corrêa, Diego; Gouveia, Luisa; Mutnuri, SrikanthABSTRACT: With growing pig farming, sustainable piggery wastewater treatment methods are essential for environmental protection. This study evaluated five pilot-scale two-stage vertical flow-constructed wetlands (VFCWs) with varying configurations of aeration, plantation, and saturation zones. Three VFCW configurations (1VFCW, 2VFCW, and 3VFCW) were unsaturated, while 4VFCW and 5VFCW were saturated in the second stage (up to 60 and 90 cm, respectively). The 5VFCW featured a stacked configuration with no space between its two stages. Passive aeration was selectively applied in 2VFCW, 3VFCW, 4VFCW, and 5VFCW, while plants were present in most configurations except the control. Saturated 4VFCW achieved the highest removal efficiency for TN (77.03 ± 16.24%) and NO3− (46.06 ± 45.96%), while the stacked 5VFCW showed the highest removal for chemical oxygen demand (COD) (94.17 ± 4.85%) and Total ammoniacal nitrogen (TOC) (86.35 ± 6.78%). Unsaturated 1VFCW excelled in TAN removal (98.89 ± 0.33%), and the control system (C) showed the highest removal efficiency for PO43− (90.38 ± 6.52%) and TOC (87.52 ± 9.83%). Overall, 4VFCW emerged as the most balanced and effective system, supported by an optimal combination of aerobic and anaerobic conditions that facilitated sequential nitrification and denitrification, along with an extended hydraulic retention time due to saturation.
- Influence of Inhibitors Generated in Lignocellulosic Hydrolysates from Group of Acids on the Growth of Strains TG1 and Tuner of Escherichia coliPublication . Gaspar, Suelen S.; Alves Ferreira Caturra, Júnia Aparecida; Moniz, Patricia; Silva-Fernandes, Talita; Silvestre, Adriana I. R; Torrado, Ivone; Pesce, Gaetano R.; Carvalheiro, Florbela; Duarte, Luís; Fernandes, Maria da ConceiçãoABSTRACT: Concerns over fossil fuels are of increasing interest in biorefineries that utilize lignocellulosic residues. Besides sugars, inhibitors are formed during biomass pretreatment, including acetic acid (AI) and formic acid (FI), which can hinder microbial fermentation. The TG1 and Tuner strains of Escherichia coli were subjected to various acid concentrations. Samples were taken during fermentation to monitor growth, sugar consumption, biomass yield, and product yield. With increasing AI, the TG1 strain maintained stable growth (0.102 1/h), while xylose consumption decreased, and product formation improved, making it better suited for high-acetic-acid industrial applications. In contrast, the Tuner strain performed better under low-inhibitor conditions but suffered metabolic inhibition at high AI levels, compensating by increasing lactic acid production-an adaptation absent in TG1. However, Tuner showed greater resistance to formic acid stress, sustaining higher growth and ethanol production, whereas TG1 experienced a greater metabolic decline but maintained stable acetic acid output. Both strains experienced inhibition in formic acid metabolism, but TG1 had a higher yield despite its lower overall robustness in formic acid conditions. The use of TG1 for value-added compounds such as ethanol or formic acid may help to avoid the use of chemicals that eliminate acetic acid. Tuner could be used for lactic acid production, especially in hydrolysates with under moderate concentration.
- A Machine Learning Model for Procurement of Secondary Reserve Capacity in Power Systems with Significant vRES PenetrationsPublication . dos Santos, Joao; Algarvio, HugoABSTRACT: The growing investment in variable renewable energy sources is changing how electricity markets operate. In Europe, players rely on forecasts to participate in day-ahead markets closing between 12 and 37 h ahead of real-time operation. Usually, transmission system operators use a symmetrical procurement of up and down secondary power reserves based on the expected demand. This work uses machine learning techniques that dynamically compute it using the day-ahead programmed and expected dispatches of variable renewable energy sources, demand, and other technologies. Specifically, the methodology incorporates neural networks, such as Long Short-Term Memory (LSTM) or Convolutional neural network (CNN) models, to improve forecasting accuracy by capturing temporal dependencies and nonlinear patterns in the data. This study uses operational open data from the Spanish operator from 2014 to 2023 for training. Benchmark and test data are from the year 2024. Different machine learning architectures have been tested, but a Fully Connected Neural Network (FCNN) has the best results. The proposed methodology improves the usage of the up and down secondary reserved power by almost 22% and 11%, respectively.
- Mapping less sensitive areas with a view to the potential installation of solar and wind electricity generation units [Comunicação oral]Publication . Simoes, Sofia; Barbosa, Juliana Pacheco; Oliveira, Paula; Simões, Teresa; Quental, Lídia; Costa, Paula; Picado, Ana; Catarino, Justina; Patinha, Pedro
- Recovery of Nd3+ and Dy3+ from E-Waste Using Adsorbents from Spent Tyre Rubbers: Batch and Column Dynamic AssaysPublication . Nogueira, Miguel; Matos, Inês; Bernardo, Maria; Pinto, Filomena; Fonseca, Isabel Maria; Lapa, NunoABSTRACT: This paper investigates the use of spent tyre rubber as a precursor for synthesising adsorbents to recover rare earth elements. Through pyrolysis and CO2 activation, tyre rubber is converted into porous carbonaceous materials with surface properties suited for rare earth element adsorption. The study also examines the efficiency of leaching rare earth elements from NdFeB magnets using optimised acid leaching methods, providing insights into recovery processes. The adsorption capacity of the materials was assessed through batch adsorption assays targeting neodymium (Nd3+) and dysprosium (Dy3+) ions. Results highlight the superior performance of activated carbon derived from tyre rubber following CO2 activation, with the best-performing adsorbent achieving maximum uptake capacities of 24.7 mg.g(-1) for Nd3+ and 34.4 mg.g(-1) for Dy3+. Column studies revealed efficient adsorption of Nd3+ and Dy3+ from synthetic and real magnet leachates with a maximum uptake capacity of 1.36 mg.g(-1) for Nd3+ in real leachates and breakthrough times of 25 min. Bi-component assays showed no adverse effects when both ions were present, supporting their potential for simultaneous recovery. Furthermore, the adsorbents effectively recovered rare earth elements from e-waste magnet leachates, demonstrating practical applicability. This research underscores the potential of tyre rubber-derived adsorbents to enhance sustainability in critical raw material supply chains. By repurposing waste tyre rubber, these materials offer a sustainable solution for rare earth recovery, addressing resource scarcity while aligning with circular economy principles by diverting waste from landfills and creating value-added products.