cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24744947.v1
title: The thermal behavior during the co-combustion of bituminous coal and oil palm trunk hydrochars

description: Utilizing oil palm trunk (OPT) residue as a co-fuel in coal-fired plants offers an environmentally favorable solution to agricultural waste disposal and fossil fuel combustion impacts. This study investigates the potential of hydrochar from OPT (HTC-OPT) as a coal-like solid fuel for co-combustion. The combustion characteristics of coal, HTC-OPT, and their blends (10–50% HTC-OPT) were analyzed via thermogravimetric analysis with heating rates ranging from 10 to 30°C/min. The Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) models were employed to explore co-combustion interactions and kinetic parameters. Results reveal enhanced combustion efficiency with HTC-OPT addition to coal. Higher HTC-OPT ratios correlate with increased maximum combustion rates, ignition and comprehensive combustibility indices, and decreased ignition and burnout temperatures. Additionally, blended fuels exhibit a proportionally synergistic co-combustion interaction. Both KAS and FWO models indicate similar trends in activation energies, with the lowest values (49.94 kJ/mol for KAS and 60.90 kJ/mol for FWO) at a 10% HTC-OPT ratio.
DOI: 10.6084/m9.figshare.25134043
title: Biomass higher heating value prediction machine learning insights into ultimate, proximate, and structural analysis datasets

description: In this study machine learning (ML) models have been employed to predict the higher heating value (<i>HHV</i>) of biomass by utilizing input variables derived from ultimate, proximate, and structural analyses. In total, 180 models were developed, with 124 utilizing ultimate analysis data, 28 based on proximate analysis, and 28 relying on structural analysis. Various ML techniques, including polynomial models (SOP), support vector machines (SVM), random forest regression (RFR), and artificial neural networks (ANN), were employed for analysis. The study found that ANN models, when “fed” with FC and VM data, provided considerable accuracy in prediction results, with the best results obtained with 2-12-1 architecture (R<sup>2</sup> = 0.96). In addition, a separate model configuration that processed inputs on biomass constituents such as cellulose, lignin, and hemicellulose showed remarkable agreement with empirical data. Additional findings revealed that the models created using SOP (R<sup>2</sup> = 0.95), SVM (R<sup>2</sup> = 0.95), and RFR (R<sup>2</sup> = 0.90) demonstrated minimal discrepancies when predicting <i>HHV</i>. This study provides significant insights into the investigation of biomass analysis techniques employing ML tools, paving the way for future research aimed at constructing a robust tool for <i>HHV</i> prediction. Subsequent models may explore integrating inputs from diverse analysis methods and leveraging advanced machine learning techniques to enhance accuracy further.
DOI: 10.6084/m9.figshare.25134043.v1
title: Biomass higher heating value prediction machine learning insights into ultimate, proximate, and structural analysis datasets

description: In this study machine learning (ML) models have been employed to predict the higher heating value (<i>HHV</i>) of biomass by utilizing input variables derived from ultimate, proximate, and structural analyses. In total, 180 models were developed, with 124 utilizing ultimate analysis data, 28 based on proximate analysis, and 28 relying on structural analysis. Various ML techniques, including polynomial models (SOP), support vector machines (SVM), random forest regression (RFR), and artificial neural networks (ANN), were employed for analysis. The study found that ANN models, when “fed” with FC and VM data, provided considerable accuracy in prediction results, with the best results obtained with 2-12-1 architecture (R<sup>2</sup> = 0.96). In addition, a separate model configuration that processed inputs on biomass constituents such as cellulose, lignin, and hemicellulose showed remarkable agreement with empirical data. Additional findings revealed that the models created using SOP (R<sup>2</sup> = 0.95), SVM (R<sup>2</sup> = 0.95), and RFR (R<sup>2</sup> = 0.90) demonstrated minimal discrepancies when predicting <i>HHV</i>. This study provides significant insights into the investigation of biomass analysis techniques employing ML tools, paving the way for future research aimed at constructing a robust tool for <i>HHV</i> prediction. Subsequent models may explore integrating inputs from diverse analysis methods and leveraging advanced machine learning techniques to enhance accuracy further.
DOI: 10.6084/m9.figshare.26856908
title: An approach for minimizing annual energy loss by electric vehicle scheduling to optimal fast charging stations

description: In order to mitigate the effects of electric vehicles’ (EVs) high power consumption on the distribution network, this study proposes a two-stage approach. First, it uses node traffic density forecasts to select optimal locations for fast charging stations (FCS). Then, to reduce the power demand on the distribution system, it schedules EVs to these FCSs. Considering constraints such as charger number, station capacity, and desired battery state of charge (SOC), the objectives are to minimize annual energy loss, optimize load demand, decrease station development costs (SDC), and maximize charging station (CS) income. The best locations for FCS are determined using a gray wolf optimization (GWO) technique. Rather than allocating EVs to uniform base loads, the study recommends analyzing different initial SOC cases to minimize power demand at FCSs. For EV scheduling, a queuing-averse charging station algorithm is suggested to reduce annual energy loss and increase station income. For various SOC cases relative to the base load scenario, this suggested method achieves an average daily FCS charging time of 648 min, a power loss of 16.8 kW, and an annual energy loss of 9.38 kW.
DOI: 10.6084/m9.figshare.26856908.v1
title: An approach for minimizing annual energy loss by electric vehicle scheduling to optimal fast charging stations

description: In order to mitigate the effects of electric vehicles’ (EVs) high power consumption on the distribution network, this study proposes a two-stage approach. First, it uses node traffic density forecasts to select optimal locations for fast charging stations (FCS). Then, to reduce the power demand on the distribution system, it schedules EVs to these FCSs. Considering constraints such as charger number, station capacity, and desired battery state of charge (SOC), the objectives are to minimize annual energy loss, optimize load demand, decrease station development costs (SDC), and maximize charging station (CS) income. The best locations for FCS are determined using a gray wolf optimization (GWO) technique. Rather than allocating EVs to uniform base loads, the study recommends analyzing different initial SOC cases to minimize power demand at FCSs. For EV scheduling, a queuing-averse charging station algorithm is suggested to reduce annual energy loss and increase station income. For various SOC cases relative to the base load scenario, this suggested method achieves an average daily FCS charging time of 648 min, a power loss of 16.8 kW, and an annual energy loss of 9.38 kW.
DOI: 10.6084/m9.figshare.25425197
title: Yield and distribution of products of lignin liquefaction catalyzed by molybdenum-vanadium-phosphorus heteropolyacids

description: This paper focuses on the performance of Molybdenum-vanadium-phosphorus heteropolyacids (PMo<sub>12-x</sub>V<sub>x</sub>) with different V-doped amounts in catalyzing the conversion of lignin to bio-oil, and the distribution of liquefied products. Molybdenum-vanadium-phosphorus heteropolyacids (PMo<sub>11</sub>V<sub>1</sub>, PMo<sub>10</sub>V<sub>2</sub>, PMo<sub>9</sub>V<sub>3</sub>) with different amount (0, 2.5, 5, 10 w<sub>t</sub>%) were used to catalyze lignin liquefaction. The experimental results showed that PMo<sub>12-x</sub>V<sub>x</sub> could effectively catalyze the depolymerization of lignin to bio-oil, and the doping amount of the V atom in PMo<sub>12-x</sub>V<sub>x</sub> affects the distribution of lignin depolymerization products. The highest bio-oil yield (53.62w<sub>t</sub>%) and conversion (57.83w<sub>t</sub>%) could be obtained from lignin liquefaction at 2.5 w<sub>t</sub>% PMo<sub>9</sub>V<sub>3</sub>. With the increase of PMo<sub>12-x</sub>V<sub>x</sub> V-atom doped amount, the content of aliphatic hydrocarbons increased from 3.14% to 40.58%, the content of vanillin increased from none to 8.9%. Based on the analysis of bio-oil, FT-IR data, and DFT simulation results, it was found that the selective cleavage of the C<sub>α</sub>-C<sub>β</sub> bond and the ring-opening of the carbon at the methyl end of lignin were the main reasons for the high yield of vanillin and aliphatic hydrocarbons during the depolymerization of lignin catalyzed by PMo<sub>12-x</sub>V<sub>x</sub>. Finally, a possible reaction pathway for the PMo<sub>12-x</sub>V<sub>x</sub>-catalyzed depolymerization of lignin to vanillin and aliphatic hydrocarbons was proposed.
DOI: 10.6084/m9.figshare.25425197.v1
title: Yield and distribution of products of lignin liquefaction catalyzed by molybdenum-vanadium-phosphorus heteropolyacids

description: This paper focuses on the performance of Molybdenum-vanadium-phosphorus heteropolyacids (PMo<sub>12-x</sub>V<sub>x</sub>) with different V-doped amounts in catalyzing the conversion of lignin to bio-oil, and the distribution of liquefied products. Molybdenum-vanadium-phosphorus heteropolyacids (PMo<sub>11</sub>V<sub>1</sub>, PMo<sub>10</sub>V<sub>2</sub>, PMo<sub>9</sub>V<sub>3</sub>) with different amount (0, 2.5, 5, 10 w<sub>t</sub>%) were used to catalyze lignin liquefaction. The experimental results showed that PMo<sub>12-x</sub>V<sub>x</sub> could effectively catalyze the depolymerization of lignin to bio-oil, and the doping amount of the V atom in PMo<sub>12-x</sub>V<sub>x</sub> affects the distribution of lignin depolymerization products. The highest bio-oil yield (53.62w<sub>t</sub>%) and conversion (57.83w<sub>t</sub>%) could be obtained from lignin liquefaction at 2.5 w<sub>t</sub>% PMo<sub>9</sub>V<sub>3</sub>. With the increase of PMo<sub>12-x</sub>V<sub>x</sub> V-atom doped amount, the content of aliphatic hydrocarbons increased from 3.14% to 40.58%, the content of vanillin increased from none to 8.9%. Based on the analysis of bio-oil, FT-IR data, and DFT simulation results, it was found that the selective cleavage of the C<sub>α</sub>-C<sub>β</sub> bond and the ring-opening of the carbon at the methyl end of lignin were the main reasons for the high yield of vanillin and aliphatic hydrocarbons during the depolymerization of lignin catalyzed by PMo<sub>12-x</sub>V<sub>x</sub>. Finally, a possible reaction pathway for the PMo<sub>12-x</sub>V<sub>x</sub>-catalyzed depolymerization of lignin to vanillin and aliphatic hydrocarbons was proposed.
DOI: 10.6084/m9.figshare.26383776
title: Thermo-kinetic behavior of closed-loop heat pump dryer with different drying chambers for holy basil leaves drying application: an experiment approach

description: A closed-loop heat pump drier was designed and constructed with three different drying chambers (tray, fluidized bed, and integrated) to investigate the drying kinetics (drying rate and specific moisture evaporation rate) of holy basil leaves in this research work. The significance of drying temperature, air velocity, and mass of leaves on the performance of the dryer chambers was explored and compared through an experimental investigation. In addition, the present research focuses on analyzing the significance of drying air temperature (45, 50, and 55°C), air velocity (1.5, 2.0, and 2.5 m/s), and mass of holy basil leaves (5, 6, and 7 kg) on the performance of the dryer chambers. In tested combinations, the specific moisture evaporation rate and drying rate are higher in the integrated drying chamber. Notably, the effective moisture diffusivity coefficient exhibited a range of values from 2.68166 × 10<sup>−10</sup> to 6.10243 × 10<sup>−10</sup> m<sup>2</sup>/s. The design of the experiment approach is applied to optimize the number of experiments, and an analysis of variance was conducted to identify the most influential parameter. The results showed that temperature had a greater effect, accounting for 32% of the variation in the moisture evaporation rate.
DOI: 10.6084/m9.figshare.26383776.v1
title: Thermo-kinetic behavior of closed-loop heat pump dryer with different drying chambers for holy basil leaves drying application: an experiment approach

description: A closed-loop heat pump drier was designed and constructed with three different drying chambers (tray, fluidized bed, and integrated) to investigate the drying kinetics (drying rate and specific moisture evaporation rate) of holy basil leaves in this research work. The significance of drying temperature, air velocity, and mass of leaves on the performance of the dryer chambers was explored and compared through an experimental investigation. In addition, the present research focuses on analyzing the significance of drying air temperature (45, 50, and 55°C), air velocity (1.5, 2.0, and 2.5 m/s), and mass of holy basil leaves (5, 6, and 7 kg) on the performance of the dryer chambers. In tested combinations, the specific moisture evaporation rate and drying rate are higher in the integrated drying chamber. Notably, the effective moisture diffusivity coefficient exhibited a range of values from 2.68166 × 10<sup>−10</sup> to 6.10243 × 10<sup>−10</sup> m<sup>2</sup>/s. The design of the experiment approach is applied to optimize the number of experiments, and an analysis of variance was conducted to identify the most influential parameter. The results showed that temperature had a greater effect, accounting for 32% of the variation in the moisture evaporation rate.
DOI: 10.6084/m9.figshare.18866037
title: Intrusion detection and mitigation of attacks in microgrid using enhanced deep belief network

description: The convergence from the electric grid to the smart microgrid motivates the incorporation of the intrusion detection system to identify intruders and mitigate the resultant damages to ensure system stability. It is planned to employ the Deep Belief Network (DBN), which is one of the deep learning techniques with some improvement to detect the attacks in a microgrid. To improve the accuracy of the detection, a rule-based detection technique is added to enhance the detection of intruders using DBN. The proposed technique is supported with the layered micro-grid architecture that makes the system flexible and simple toward the implementation. The proposed Enhanced DBN (EDBN) performance is measured in different bus representations for identifying the higher hit rate and rejection rate, lesser miss rate and false-positive rate. Two attacks, such as False Data Injection and Denial of Service attacks, are generated by Greedy Algorithm and are detected by the proposed technique. Compared to the existing detection and control system, the proposed EDBN technique provides accuracy higher than 92%, false alarm rate less than 1%. Thus, the experimental results show that the proposed technique accuracy is higher than the existing intrusion detection techniques in a microgrid.
DOI: 10.6084/m9.figshare.18866037.v1
title: Intrusion detection and mitigation of attacks in microgrid using enhanced deep belief network

description: The convergence from the electric grid to the smart microgrid motivates the incorporation of the intrusion detection system to identify intruders and mitigate the resultant damages to ensure system stability. It is planned to employ the Deep Belief Network (DBN), which is one of the deep learning techniques with some improvement to detect the attacks in a microgrid. To improve the accuracy of the detection, a rule-based detection technique is added to enhance the detection of intruders using DBN. The proposed technique is supported with the layered micro-grid architecture that makes the system flexible and simple toward the implementation. The proposed Enhanced DBN (EDBN) performance is measured in different bus representations for identifying the higher hit rate and rejection rate, lesser miss rate and false-positive rate. Two attacks, such as False Data Injection and Denial of Service attacks, are generated by Greedy Algorithm and are detected by the proposed technique. Compared to the existing detection and control system, the proposed EDBN technique provides accuracy higher than 92%, false alarm rate less than 1%. Thus, the experimental results show that the proposed technique accuracy is higher than the existing intrusion detection techniques in a microgrid.
DOI: 10.6084/m9.figshare.25403441
title: The utilization of novel NaF/Al<sub>2</sub>O<sub>3</sub> catalysts in the process of hydrothermal liquefaction of Crambe orientalis: synthesis and characterization

description: This research study explores the influence of NaF/Al<sub>2</sub>O<sub>3</sub> catalysts on the process of hydrothermal liquefaction (HTL) of Crambe Orientalis. Three distinct catalysts were prepared and examined through a multitude of methodologies including FT-IR, BET, XRD and SEM/EDX/EDS. The HTL were made at temperatures ranging from 275 to 350°C for a duration of 0 to 60 minutes. The liquid products obtained were subjected to elemental, GC-MS, FT-IR, and<sup>1</sup>H NMR analyses. The findings of this study demonstrate that NaF/Al<sub>2</sub>O<sub>3</sub> catalysts effectively enhance the yield of liquid products. The liquid products that were obtained displayed an exceptionally elevated carbon content, with a maximum value of 80.97% achieved at a temperature of 350°C. These liquid products exhibit superior heating values (HHV), which vary between 37.64 and 29.31 MJ/kg. The catalysts demonstrated efficacy in enhancing the liquid product yield, characterized by a heightened proportion of aliphatic compounds and a diminished ratio of oxygen constituents. It was determined that the obtained catalysts may have the potential to obtain liquid products with high carbon content and energy values.
DOI: 10.6084/m9.figshare.25403441.v1
title: The utilization of novel NaF/Al<sub>2</sub>O<sub>3</sub> catalysts in the process of hydrothermal liquefaction of Crambe orientalis: synthesis and characterization

description: This research study explores the influence of NaF/Al<sub>2</sub>O<sub>3</sub> catalysts on the process of hydrothermal liquefaction (HTL) of Crambe Orientalis. Three distinct catalysts were prepared and examined through a multitude of methodologies including FT-IR, BET, XRD and SEM/EDX/EDS. The HTL were made at temperatures ranging from 275 to 350°C for a duration of 0 to 60 minutes. The liquid products obtained were subjected to elemental, GC-MS, FT-IR, and<sup>1</sup>H NMR analyses. The findings of this study demonstrate that NaF/Al<sub>2</sub>O<sub>3</sub> catalysts effectively enhance the yield of liquid products. The liquid products that were obtained displayed an exceptionally elevated carbon content, with a maximum value of 80.97% achieved at a temperature of 350°C. These liquid products exhibit superior heating values (HHV), which vary between 37.64 and 29.31 MJ/kg. The catalysts demonstrated efficacy in enhancing the liquid product yield, characterized by a heightened proportion of aliphatic compounds and a diminished ratio of oxygen constituents. It was determined that the obtained catalysts may have the potential to obtain liquid products with high carbon content and energy values.
DOI: 10.6084/m9.figshare.26404521
title: Cumulative energy demand and environmental impact analysis in the manufacture of recycled fiber reinforced composites panels for use in truck bodies

description: The reuse of fiber-reinforced composite materials has become increasingly important with the tremendous waste produced by aerospace, wind turbine blades, and automobile components. Recycling fiber-reinforced composites from parts at their end-of-life provides the benefit of reinforcing strength and integrity. The life cycle assessment (LCA) method has been widely used to examine the embodied energy (EE) and the environmental impact of the manufacturing process. Two different types of composite panels made via hand layup and compression molding are considered for a comparative LCA investigation using SimaPro software. Some panels are made with a mixture of 50% virgin glass fiber (GF) and 50% polyester resin. Others are produced with a mixture of 25% virgin GF, 25% shredded recyclates from wind turbine blade, and 50% of polyester resin. Panels from the recyclates termed Recycled Glass Fiber Reinforced Polyester (rGFRP) had 2.44% lower EE as compared to their counterparts vGFRP. Ozone depletion proved to be the greatest impact on the environment as regard to the manufacturing of rGFRP (4.38E–6 kg CFC-11eq). rGFRP panels manufacture generated 3.8% less greenhouse gas emissions than the vGFRP. LCA analysis suggests that the manufacture of panels for truck body/floor using recyclates from shredded wind turbine blades involves less energy use and has less environmental impact.
DOI: 10.6084/m9.figshare.26404521.v1
title: Cumulative energy demand and environmental impact analysis in the manufacture of recycled fiber reinforced composites panels for use in truck bodies

description: The reuse of fiber-reinforced composite materials has become increasingly important with the tremendous waste produced by aerospace, wind turbine blades, and automobile components. Recycling fiber-reinforced composites from parts at their end-of-life provides the benefit of reinforcing strength and integrity. The life cycle assessment (LCA) method has been widely used to examine the embodied energy (EE) and the environmental impact of the manufacturing process. Two different types of composite panels made via hand layup and compression molding are considered for a comparative LCA investigation using SimaPro software. Some panels are made with a mixture of 50% virgin glass fiber (GF) and 50% polyester resin. Others are produced with a mixture of 25% virgin GF, 25% shredded recyclates from wind turbine blade, and 50% of polyester resin. Panels from the recyclates termed Recycled Glass Fiber Reinforced Polyester (rGFRP) had 2.44% lower EE as compared to their counterparts vGFRP. Ozone depletion proved to be the greatest impact on the environment as regard to the manufacturing of rGFRP (4.38E–6 kg CFC-11eq). rGFRP panels manufacture generated 3.8% less greenhouse gas emissions than the vGFRP. LCA analysis suggests that the manufacture of panels for truck body/floor using recyclates from shredded wind turbine blades involves less energy use and has less environmental impact.
DOI: 10.6084/m9.figshare.26389067
title: Layered production prediction of oil wells based on ConvLSTMA

description: With the continuous development of oilfield, oilfield production prediction is becoming more and more important. Most of the current methods are based on machine learning prediction methods and predict the oil production of a single well with relatively low accuracy. In this paper, we focus on dividing single wells into specific layer segments and propose a layered production prediction method for oil wells based on ConvLSTMA model with convolutional neural network, long- and short-term memory network, and fine-grained attention mechanism. The method can effectively utilize stratified historical data to predict future oilfield production and can be adapted to various complex production prediction scenarios. Through experiments, it can be seen that the model performs better than existing models in predicting oil production data, reducing the MSE index by about 2%, MAE index by about 3%, and R2 coefficient by 2%.