• Energy Research
• 2021-2025close
• other engineering and technologies close
• PT close
• AO close

AbstractThe widespread use of petroleum products in modern times has led to a search for alternative resources. Biofuel is a promising alternative to petroleum fuel, but biodiesel has a lower calorific value and is slightly more denser than diesel. To address this, a novel combination of GNA emulsified MME20 fuel is being investigated. This study aims to analyze the impact of a novel Nano additive blended biodiesel on engine performance and optimize the best compression ratio for the selected blend. The novelty of the study lies in the production of novel GNA emulsified MME fuel and its influence on a conventional CI engine. To achieve the objectives of the study, MME was produced using a two-phase transesterification method, and GNA was added to the MME20 at concentrations of 30, 60, and 90 ppm using the ultrasonication method. Engine experiments were then conducted using the prepared samples at CRs of 16, 17.5, and 19, and the results were compared with the standard diesel and MME20 blend. The results showed that the CP of the MME20 + GNA30 fuel at a CR 19 revealed a 14% increase compared to diesel. The ID of the fuel decreased by 20% compared to diesel at CR19, and there was a 23.5% increase in the CD for the MME20 + GNA30 blend compared to diesel at CR19. The BTHE for the MME20 + GNA30 fuel showed increases of 2.64% and BSFC and EGT decreases of 3.6% and 3.9%, respectively, at CR19 compared to the other blends. In summary, the study found that MME20 with GNA30, along with VCR, significantly enhanced the engine attributes compared to the pure diesel-operated standard CI engine conditions.

AbstractIn aviation, accurate wind prediction is crucial, especially during takeoff and landing at complex sites like Gran Canaria Airport. This study evaluated five Deep Learning models: Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), One-Dimensional Convolutional Neural Network (1dCNN), Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), and Gated Recurrent Unit (GRU) for forecasting wind speed and direction. The LSTM model demonstrated the highest precision, particularly for extended forecasting periods, achieving a mean absolute error (MAE) of 1.23 m/s and a circular MAE (cMAE) of 15.80° for wind speed and direction, respectively, aligning with World Meteorological Organization standards for Terminal Aerodrome Forecasts (TAF). While the GRU and CNN-LSTM also showed promising results, and the 1dCNN excelled in wind direction forecasting over shorter intervals, the vRNN lagged in performance. Additionally, the autoregressive integrated moving average model underperformed relative to the DL models, underscoring the potential of DL, particularly LSTM, in enhancing TAF accuracy at airports with intricate wind patterns. This study not only confirms the superiority of DL over traditional methods but also highlights the promise of integrating artificial intelligence into TAF automation.

Industry is a fundamental sector that allows mass production to support a large population. As population grows, many industries produce large amounts of industrial effluents with different pollutants, that must be removed at the industrial wastewater treatment plants, with the consequent production of large amounts of sludge. The present study was conducted to identify and evaluate different sludge treatment/valorisation methodologies, being given priority to the valorisation in detriment of the elimination operations, like incineration or landfill. Therefore, sludges from the wastewater treatment plant of a resin industry, after dehydration operation by a press, were submitted to several valorisation methodologies, such as: application in anaerobic digestion aiming the production of biogas and allowing energy recovery, use in the preparation of adsorbents for the treatment of industrial wastewater, use as cement replacement in mortar production, and application of heterogeneous catalysts to produce biodiesel. The results revealed that all methodologies can be applied as sludge recovery. However, it is possible to conclude that the most promising industrial sludge treatment/recovery hypothesis is anaerobic co-digestion followed by the production of heterogeneous catalysts for biodiesel production.

The purpose of this study is to evaluate the economic and environmental gains that result from the implementation of cleaner production in a small enterprise (SE) from the metal and mechanics industry, allowing for overcoming barriers and contributing to sustainable development goals. The research work builds on a case study that involved several data sources, including semi structured interviewing and non-participant observation. Data analysis involved the calculation of mass balance, building solid and liquid waste that were minimized in the process. The results suggest that cleaner production led to economic gains, by allowing for reduction in losses and promoting a better use of raw materials. It also led to environmental improvements by means of the implementation of a wastewater treatment station that allowed for the reuse of water in the manufacturing process. It was concluded that the economic gain in the project played a determinant role for the SE to overcome the barriers for the implementation of CP and leading to the reduction of the environmental impact in the abiotic, biotic, water and air compartments, as estimated with the Mass Intensity Factor. The study offers a timely and relevant contribution for the literature in the field, while offering insights for o managerial practice, and the attainment of the Sustainable Development Goals defined by the United Nations Development Program.

The use of residual biomass of forest and/or agricultural origin is an increasingly common issue regarding the incorporation of materials that, until recently, were out of the typical raw material supply chains for the production of biomass pellets, mainly due to the quality constraints that some of these materials present. The need to control the quality of biomass-derived fuels led to the development of standards, such as ENplus®, to define the permitted limits for a set of parameters, such as the ash or alkali metal content. In the present study, samples of vine pruning, and ENplus®-certified pellets were collected and characterized, and the results obtained were compared with the limits presented in the standard. The values presented from vine pruning approximated the values presented by Pinus pinaster wood, the main raw material used in the production of certified pellets in Portugal, except for the values of ash, copper (Cu), and nitrogen (N) contents, with vine pruning being out of the qualifying limits for certification. However, it was found that the incorporation of up to 10% of biomass from vine pruning allowed the fulfillment of the requirements presented in the ENplus® standard, indicating a path for the implementation of circular economy processes in the wine industry.