bluuebunny/crossref_metadata_2025 huggingface dataset:
Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram
Authors: Han Hui , Yi Zhijian , Zhu Zhigang , Li Lin , Gong Shuaige , Li Bin , Wang Mingjie
Published: January 2023

In signal communication based on a non-cooperative communication system, the receiver is an unlicensed third-party communication terminal, and the modulation parameters of the transmitter signal cannot be predicted in advance. After the RF signal passes through the RF band-pass filter, low noise amplifier, and image rejection filter, the intermediate frequency signal is obtained by down-conversion, and then the IQ signal is obtained in the baseband by using the intermediate frequency band-pass filter and down-conversion. In this process, noise and signal frequency offset are inevitably introduced. As the basis of subsequent analysis and interpretation, modulation recognition has important research value in this environment. The introduction of deep learning also brings new feature mining tools. Based on this, this paper proposes a signal modulation recognition method based on multi-feature fusion and constructs a deep learning network with a double-branch structure to extract the features of IQ signal and multi-channel constellation, respectively. It is found that through the complementary characteristics of different forms of signals, a more complete signal feature representation can be constructed. At the same time, it can better alleviate the influence of noise and frequency offset on recognition performance, and effectively improve the classification accuracy of modulation recognition.
Filter-Based Ensemble Feature Selection and Deep Learning Model for Intrusion Detection in Cloud Computing
Authors: Kavitha C. , M. Saravanan , Gadekallu Thippa Reddy , K. Nimala , Kavin Balasubramanian Prabhu , Lai Wen-Cheng
Published: January 2023

In recent years, the high improvement in communication, Internet of Things (IoT) and cloud computing have begun complex questioning in security. Based on the development, cyberattacks can be increased since the present security techniques do not give optimal solutions. As a result, the authors of this paper created filter-based ensemble feature selection (FEFS) and employed a deep learning model (DLM) for cloud computing intrusion detection. Initially, the intrusion data were collected from the global datasets of KDDCup-99 and NSL-KDD. The data were utilized for validation of the proposed methodology. The collected database was utilized for feature selection to empower the intrusion prediction. The FEFS is a combination of three feature extraction processes: filter, wrapper and embedded algorithms. Based on the above feature extraction process, the essential features were selected for enabling the training process in the DLM. Finally, the classifier received the chosen features. The DLM is a combination of a recurrent neural network (RNN) and Tasmanian devil optimization (TDO). In the RNN, the optimal weighting parameter is selected with the assistance of the TDO. The proposed technique was implemented in MATLAB, and its effectiveness was assessed using performance metrics including sensitivity, F measure, precision, sensitivity, recall and accuracy. The proposed method was compared with the conventional techniques such as an RNN and deep neural network (DNN) and RNN–genetic algorithm (RNN-GA), respectively.
XDLL: Explained Deep Learning LiDAR-Based Localization and Mapping Method for Self-Driving Vehicles
Authors: Charroud Anas , El Moutaouakil Karim , Palade Vasile , Yahyaouy Ali
Published: January 2023

Self-driving vehicles need a robust positioning system to continue the revolution in intelligent transportation. Global navigation satellite systems (GNSS) are most commonly used to accomplish this task because of their ability to accurately locate the vehicle in the environment. However, recent publications have revealed serious cases where GNSS fails miserably to determine the position of the vehicle, for example, under a bridge, in a tunnel, or in dense forests. In this work, we propose a framework architecture of explaining deep learning LiDAR-based (XDLL) models that predicts the position of the vehicles by using only a few LiDAR points in the environment, which ensures the required fastness and accuracy of interactions between vehicle components. The proposed framework extracts non-semantic features from LiDAR scans using a clustering algorithm. The identified clusters serve as input to our deep learning model, which relies on LSTM and GRU layers to store the trajectory points and convolutional layers to smooth the data. The model has been extensively tested with short- and long-term trajectories from two benchmark datasets, Kitti and NCLT, containing different environmental scenarios. Moreover, we investigated the obtained results by explaining the contribution of each cluster feature by using several explainable methods, including Saliency, SmoothGrad, and VarGrad. The analysis showed that taking the mean of all the clusters as an input for the model is enough to obtain better accuracy compared to the first model, and it reduces the time consumption as well. The improved model is able to obtain a mean absolute positioning error of below one meter for all sequences in the short- and long-term trajectories.
Transformer Fault Early Warning Analysis Based on Hierarchical Clustering Combined with Decision Trees
Authors: Liu Xiaoqiang , Li Ji , Shao Lei , Liu Hongli , Ren Lei , Zhu Lihua
Published: January 2023

The issues of low accuracy, poor generality, high cost of transformer fault early warning, and the subjective nature of empirical judgments made by field maintenance personnel are difficult to solve with the traditional measurement methods used during the development of the transformer. To construct a transformer fault early warning analysis, this study recommends a data-fusion-based decision tree approach for merging electrical quantity signals with a non-electrical amount of vibration signals. By merging a decision tree inference with actual operation data, a clustering center, and an early warning model, this method creates a transformer fault early warning model with self-learning ability and adaptive capabilities. After reasonable verification, the method becomes more universal and interpretable, and it can successfully conduct an early warning of transformer faults.
Review on the Life Cycle Assessment of Thermal Energy Storage Used in Building Applications
Authors: Hayatina Isye , Auckaili Amar , Farid Mohammed
Published: January 2023

To reduce building sector CO2 emissions, integrating renewable energy and thermal energy storage (TES) into building design is crucial. TES provides a way of storing thermal energy during high renewable energy production for use later during peak energy demand in buildings. The type of thermal energy stored in TES can be divided into three categories: sensible, latent, and sorption/chemical. Unlike sensible TES, latent TES and sorption/chemical TES have not been widely applied; however, they have the advantage of a higher energy density, making them effective for building applications. Most TES research focuses on technical design and rarely addresses its environmental, social, and cost impact. Life cycle assessment (LCA) is an internationally standardized method for evaluating the environmental impacts of any process. Life cycle sustainability assessment (LCSA) is an expansion of LCA, including economic and social sustainability assessments. This paper aims to provide a literature review of the LCA and LCSA of TES, specifically for building applications. Concerning the low technology readiness level (TRL) of several TES systems, the challenges and benefits of conducting LCA for these systems are highlighted. Furthermore, based on published studies on emerging technologies for LCA, a suggested procedure to carry out the LCA of TES with low TRL is presented.
Development of a Method for Sizing a Hybrid Battery Energy Storage System for Application in AC Microgrid
Authors: Costa Tatiane , Arcanjo Ayrlw , Vasconcelos Andrea , Silva Washington , Azevedo Claudia , Pereira Alex , Jatobá Eduardo , Filho José Bione , Barreto Elisabete , Villalva Marcelo Gradella , Marinho Manoel
Published: January 2023

This article addresses the development of the energy compensation method used for the design of hybrid energy storage systems—HBESS. The combination of two battery technologies offers better cost and performance when considering microgrid systems to provide uninterrupted power to sensitive loads (substation auxiliary system) and also provides greater energy security. In the event of a failure, the load needs to continue operating, and batteries such as lithium ions have a fast response, but are expensive for large-scale systems. However, some technologies offer low-cost and good availability of energy for long hours of discharge, such as lead–acid batteries. Consequently, different battery technologies can be used to meet all the needs of the sensitive loads. A specific method for sizing a HBESS was developed for islanded microgrids to support sensitive loads. This method was developed to meet the demand for substations outside the Brazilian standard of power systems that lack an uninterrupted and reliable energy source. The method is validated by designing a microgrid to support the auxiliary systems of a transmission substation in northeastern Brazil. The results showed a system with a capacity of 1215 kWh of lead-carbon and 242 kWh of lithium ions is necessary to maintain an islanded microgrid for at least 10 h. Furthermore, the microgrid comprises a PV plant with an AC output power of 700 kW in connected operation and 100 kW when islanded from the grid.
Molecular Dynamics Simulation Studies of Properties, Preparation, and Performance of Silicon Carbide Materials: A Review
Authors: Yan Zefan , Liu Rongzheng , Liu Bing , Shao Youlin , Liu Malin
Published: January 2023

Silicon carbide (SiC) materials are widely applied in the field of nuclear materials and semiconductor materials due to their excellent radiation resistance, thermal conductivity, oxidation resistance, and mechanical strength. The molecular dynamics (MD) simulation is an important method to study the properties, preparation, and performance of SiC materials. It has significant advantages at the atomic scale. The common potential functions for MD simulations of silicon carbide materials were summarized firstly based on extensive literatures. The key parameters, complexity, and application scope were compared and analyzed. Then, the MD simulation of SiC properties, preparation, and performance was comprehensively overviewed. The current studies of MD simulation methods and applications of SiC materials were systematically summarized. It was found that the Tersoff potential was the most widely applied potential function for the MD simulation of SiC materials. The construction of more accurate potential functions for special application fields was an important development trend of potential functions. In the MD simulation of SiC properties, the thermal properties and mechanical properties, including thermal conductivity, hardness, elastic modulus, etc., were mainly studied. The correlation between MD simulations of microscopic processes and the properties of macroscopic materials, as well as the methods for obtaining different property parameters, were summarized. In the MD simulation of SiC preparation, ion implantation, polishing, sputtering, deposition, crystal growth, amorphization, etc., were mainly studied. The chemical vapor deposition (CVD) and sintering methods commonly applied in the preparation of SiC nuclear materials were reported rarely and needed to be further studied. In the MD simulation of SiC performance, most of the present studies were related to SiC applications in the nuclear energy research. The irradiation damage simulation in the field of nuclear materials was studied most widely. It can be found that SiC materials in the field of nuclear materials study were a very important topic. Finally, the future perspective of MD simulation studies of SiC materials were given, and development suggestions were summarized. This paper is helpful for understanding and mastering the general method of computation material science aimed at the multi-level analysis. It also has a good reference value in the field of SiC material study and MD method study.
Tenerife’s Infrastructure Plan for Electromobility: A MATSim Evaluation
Authors: Rojano-Padrón Alejandro , Metais Marc Olivier , Ramos-Real Francisco J. , Perez Yannick
Published: January 2023

According to the Canarian government’s plans, a complete decarbonization of the Canary Islands economy is foreseen from 2040 onwards, which includes the electrification of land transport in the archipelago. However, due to the current low penetration rate of electric vehicles (EVs) on the islands, the number of EVs in circulation is expected to grow significantly in the coming years. Despite this, the network of charging points in Tenerife is currently totally insufficient, which is why it is essential to carry out a study to design the network of charging points in such a way that it can absorb the entire fleet of EVs that is expected to be in place by 2040. To this end, there are studies on the capacity, in terms of parking space, available for the installation of these charging points, but to date there are no studies on this subject supported by mobility data. For this reason, a simulation of traffic in Tenerife in 2040 has been carried out using MATSim (Multi-Agent Transport Simulation) to determine the ideal places to install these charging points and to find the number of charging points needed for the network.
A Review of Biomass-to-Bioenergy Supply Chain Research Using Bibliometric Analysis and Visualization
Authors: Helal Md Abu , Anderson Nathaniel , Wei Yu , Thompson Matthew
Published: January 2023

Based on current trends and policies aimed at decarbonizing energy systems, the conversion of biomass to bioenergy has the potential to grow rapidly, but such growth depends on the development of efficient, sustainable, and competitive biomass supply chains. As a result, the biomass supply chain has stimulated the interest of a diverse group of researchers across academia, government, and industry, and there is a need to synthesize and categorize the rapidly expanding literature in this field. We conducted a literature review using advanced bibliometric analysis and visualization of 1711 peer-reviewed articles published from January 1992 to August 2022 with the aim of promoting impactful research in both growing and neglected areas of investigation. The results show that there are potential research gaps and opportunities in six critical areas: globalization of supply chain research; incorporation of uncertainty, stochasticity, and risk into supply chain models; investigation of multi-feedstock supply systems; strengthening supply chain resilience; application of inventory control methods; and broader use of machine learning and artificial intelligence in this field. By providing a holistic examination of how biomass-to-bioenergy supply chain research has grown and evolved over this period, our results and subsequent framework and recommendations can aid researchers in developing future studies and can guide stakeholder strategies to identify, diagnose, and address modern challenges that face the bioenergy industry.
An Improved Phase-Disposition Pulse Width Modulation Method for Hybrid Modular Multilevel Converter
Authors: Zhou Fayun , Xiang Xinxing , Ma Fujun , Wang Yichao , Zhou Fangyuan , Peng Peng
Published: January 2023

The hybrid modular multilevel converter (MMC) consisting of half-bridge submodules (HBSMs) and full-bridge submodules (FBSMs) is a promising solution for overhead lines high-voltage direct current systems (HVDC) due to the advantages of direct current short circuit fault ride-through (DC-FRT) capability. This paper proposes an improved phase-disposition pulse width modulation (PDPWM) method for the hybrid modular multilevel converter. The number of carriers can be reduced from 3N (N is the number of submodules in each arm) to 6. The theoretical harmonic analysis of the improved PDPWM method for hybrid MMC is performed by using double Fourier integral analysis. The influence of three carrier displacement angles between HBSMs and FBSMs in the upper and lower arms on harmonic characteristics is investigated. The output voltage harmonics minimization PDPWM scheme and circulating current harmonics cancellation PDPWM scheme can be achieved by selecting the optimum carrier displacement angles, respectively. The proposed method for hybrid MMC is verified by the simulation and experimental results.
An Improved Optimally Designed Fuzzy Logic-Based MPPT Method for Maximizing Energy Extraction of PEMFC in Green Buildings
Authors: Aly Mokhtar , Mohamed Emad A. , Rezk Hegazy , Nassef Ahmed M. , Elhosseini Mostafa A. , Shawky Ahmed
Published: January 2023

Recently, the concept of green building has become popular, and various renewable energy systems have been integrated into green buildings. In particular, the application range of fuel cells (FCs) has become widespread due to the various government plans regarding green hydrogen energy systems. In particular, proton exchange membrane fuel cells (PEMFCs) have proven superiority over other existing FCs. However, the uniqueness of the operating maximum power point (MPP) of PEMFCs represents a critical issue for the PEMFC control systems. The perturb and observe, incremental conductance/resistance, and fuzzy logic control (FLC) represent the most used MPP tracking (MPPT) algorithms for PEMFC systems, among which the FLC-based MPPT methods have shown improved performance compared to the other methods. Therefore, this paper presents a modified FLC-based MPPT method for PEMFC systems in green building applications. The proposed method employs the rate of change of the power with current (dP/dI) instead of the previously used rate of change of power with voltage (dP/dV) in the literature. The employment of dP/dI in the proposed method enables the fast-tracking of the operating MPP with low transient oscillations and mitigated steady-state fluctuations. Additionally, the design process of the proposed controller is optimized using the enhanced version of the success-history-based adaptive differential evolution (SHADE) algorithm with linear population size reduction, known as the LSHADE algorithm. The design optimization of the proposed method is advantageous for increasing the adaptiveness, robustness, and tracking of the MPP in all the operating scenarios. Moreover, the proposed MPPT controller can be generalized to other renewable energy and/or FCs applications. The proposed method is implemented using C-code with the PEMFC model and tested in various operating cases. The obtained results show the superiority and effectiveness of the proposed controller compared to the classical proportional-integral (PI) based dP/dI-based MPPT controller and the classical FLC-based MPPT controller. Moreover, the proposed controller achieves reduced output waveforms ripple, fast and accurate MPPT operation, and simple and low-cost implementation.
Review of Natural Hazard Risks for Wind Farms
Authors: Patil Atul , Pathak Chaitanya , Alduse Bejoy
Published: January 2023

Technological advancement in recent years has resulted in larger and taller wind turbines (WTs) with enhanced power generation capacities. Application of natural hazard risk quantification for WTs helps stakeholders plan, design, install, and operate wind farms safely and profitably. This study focuses on a review of the risks to WTs from earthquakes, strong wind, hurricanes, tsunamis, and lightning. The structural failure of the blades, towers, and foundations in response to these hazards was investigated. Furthermore, research from the past few decades covering modes of failures, such as foundation overturning, tower tilting, tower buckling, blade buckling, deformations, and delamination of blades, was investigated. It was found that the methodologies used by researchers include analytical, statistical, and data-based models, as well as experimental research. This study shows that, while seismic, wind, and hurricane risks have been explored with analytical, experimental, and statistical models in the past, future research could focus on the latest methods involving data-based models, integration of monitored data, and physics-based models. Tsunami risk assessment focuses on experimental methods, and future research may benefit from data-integrated models and a focus on the transient nature of the risks.
Analysis of Temperature Control Strategy on Energy Consumption in Buildings with Intermittent Occupancy
Authors: Ferretti Ivan , Marchi Beatrice , Zanoni Simone , Zavanella Lucio Enrico
Published: January 2023

The increase in energy costs has led researchers and practitioners to investigate the energy consumption of buildings and propose solutions for its improvement. This research aimed at estimating the effect of different temperature control strategies on the energy consumption of a building. This study belongs to the field of research that considers both the structural characteristics and the conditioning systems incorporated into the design or renovation of buildings. The objective was to identify the optimal settings that led to the maximization of energy performance under intermittent occupancy considering the building’s structural and conditioning system characteristics and environmental conditions. To reach this objective, it was necessary to use simulation software that considered all these characteristics simultaneously in a dynamic environment. Specifically, the effect of the geometric and structural characteristics on the thermal profile of the building was investigated by implementing a dynamic hourly regime with temperature fluctuations around an assigned set-point. The study showed that for models with heavy masonry walls, the annual thermal dispersion of the hourly regime was much lower than for the corresponding models with light masonry walls. These results and the related method could be considered by researchers and practitioners for the design and/or renovation of buildings.
Investigation on the Mobile Wheeled Robot in Terms of Energy Consumption, Travelling Time and Path Matching Accuracy
Authors: Szeląg Piotr , Dudzik Sebastian , Podsiedlik Anna
Published: January 2023

The task of controlling a wheeled mobile robot is an important element of navigation algorithms. The control algorithm manages the robot’s movement in accordance with the path determined by the planner module, where the accuracy of mapping the given route is very important. Most often, mobile robots are battery-powered, which makes minimizing energy consumption and shortening travel time an important issue. For this reason, in this work, the mobile robot control algorithm was tested in terms of energy consumption, travel time and path mapping accuracy. During the research, a criterion was developed, thanks to which it was possible to select the optimal parameters of the pure pursuit algorithm that controls the movement of the tested robot. The research was carried out in the Laboratory of Intelligent Mobile Robots using the QBot2e mobile robot operating on the basis of differential drive kinematics. As a result of the research, optimal values of the control parameters were obtained, minimizing the travel time, energy consumption and mapping error of the given paths.
Free-Base Porphyrin Aggregates Combined with Nickel Phosphite for Enhanced Alkaline Hydrogen Evolution
Authors: Taranu Bogdan-Ovidiu , Fagadar-Cosma Eugenia , Sfirloaga Paula , Poienar Maria
Published: January 2023

In response to the escalating global energy requests and the need to address them in a sustainable manner, researchers have identified hydrogen as an energy vector that provides a practical way to store and use energy from renewable sources. To make a step forward, the electrocatalytic properties for the hydrogen evolution reaction in an alkaline medium of graphite electrodes modified with combinations of nickel phosphite and free-base porphyrins were investigated voltammetrically. The sample obtained by combining the respective phosphite with 5,10,15,20-tetrakis(4-methoxyphenyl)porphyrin exhibited the highest catalytic activity, surpassing that observed for the specimens manufactured using the individual catalysts and thus providing enhanced water-splitting performance. The best electrode displayed an overpotential of 0.43 V (at i = −10 mA/cm2) and a Tafel slope of 0.14 V/dec. Since the catalytic activity of the compositions containing the metal salt and the porphyrins has not been previously studied, the investigation and its outcome constitute an original contribution to the growing water-splitting literature, providing new opportunities for obtaining better results than the ones reported for electrodes modified solely with the phosphite.
Strongly Heated Turbulent Flow in a Channel with Pin Fins
Authors: Lee Chien-Shing , Shih Tom I. -P. , Bryden Kenneth Mark , Dalton Richard P. , Dennis Richard A.
Published: January 2023

Large-eddy simulations (LES) were performed to study the turbulent flow in a channel of height H with a staggered array of pin fins with diameter D = H/2 as a function of heating loads that are relevant to the cooling of turbine blades and vanes. The following three heating loads were investigated—wall-to-coolant temperatures of Tw/Tc = 1.01, 2.0, and 4.0—where the Reynolds number at the channel inlet was 10,000 and the back pressure at the channel outlet was 1 bar. For the LES, two different subgrid-scale models—the dynamic kinetic energy model (DKEM) and the wall-adapting local eddy-viscosity model (WALE)—were examined and compared. This study was validated by comparing with data from direct numerical simulation and experimental measurements. The results obtained show high heating loads to create wall jets next to all heated surfaces that significantly alter the structure of the turbulent flow. Results generated on effects of heat loads on the mean and fluctuating components of velocity and temperature, turbulent kinetic energy, the anisotropy of the Reynolds stresses, and velocity-temperature correlations can be used to improve existing RANS models.
DC Microgrids: Benefits, Architectures, Perspectives and Challenges
Authors: Pires Vitor Fernão , Pires Armando , Cordeiro Armando
Published: January 2023

One of the major paradigm shifts that will be predictably observed in the energy mix is related to distribution networks. Until now, this type of electrical grid was characterized by an AC transmission. However, a new concept is emerging, as the electrical distribution networks characterized by DC transmission are beginning to be considered as a promising solution due to technological advances. In fact, we are now witnessing a proliferation of DC equipment associated with renewable energy sources, storage systems and loads. Thus, such equipment is beginning to be considered in different contexts. In this way, taking into consideration the requirement for the fast integration of this equipment into the existing electrical network, DC networks have started to become important. On the other hand, the importance of the development of these DC networks is not only due to the fact that the amount of DC equipment is becoming huge. When compared with the classical AC transmission systems, the DC networks are considered more efficient and reliable, not having any issues regarding the reactive power and frequency control and synchronization. Although much research work has been conducted, several technical aspects have not yet been defined as standard. This uncertainty is still an obstacle to a faster transition to this type of network. There are also other aspects that still need to be a focus of study and research in order to allow this technology to become a day-to-day solution. Finally, there are also many applications in which this kind of DC microgrid can be used, but they have still not been addressed. Thus, all these aspects are considered important challenges that need to be tackled. In this context, this paper presents an overview of the existing and possible solutions for this type of microgrid, as well as the challenges that need to be faced now.
A Study on Performance and Characteristic Analysis According to the Operating Point of IPMSM Drive
Authors: Park Hyun-Jong , Ahn Han-Woong , Go Sung-Chul
Published: January 2023

This study presents the characteristics and performance of a motor at the operating point based on pulse-width modulation (PWM). PWM is used in modulating the output voltage of a three-phase inverter. The PWM technique has advantages and disadvantages depending on the system size and operating range. For example, some discontinuous pulse-width modulation (DPWM) techniques are advantageous in low-cost systems because they can reduce the number of switches. Some PWM techniques are complicated for software construction and require a lot of computation. In this paper, PWM techniques were constructed by using the offset voltage injection method. Therefore, the construction of the PWM technique is simplified by the method. In an experimental setup, an interior permanent-magnet synchronous motor (IPMSM) and a three-phase inverter were implemented to test motor control performance depending on the PWM technique. In addition, the current control characteristics and inverter efficiency were analyzed and compared depending on the speed and load of the motor. According to the results of this paper, it is possible to compare the motor and inverter control characteristics using various types of PWM rapidly. Additionally, it will help to select an appropriate PWM technique for a specific system.
Combined Heat and Power Economic Dispatching within Energy Network using Hybrid Metaheuristic Technique
Authors: Kaur Paramjeet , Chaturvedi Krishna Teerth , Kolhe Mohan Lal
Published: January 2023

Combined heat and power (CHP) plants have opportunities to work as distributed power generation for providing heat and power demand. Furthermore, CHP plants contribute effectively to overcoming the intermittence of renewable energy sources as well as load dynamics. CHP plants need optimal solution(s) for providing electrical and heat energy demand simultaneously within the smart network environment. CHP or cogeneration plant operations need appropriate techno-economic dispatching of combined heat and power with minimising produced energy cost. The interrelationship between heat and power development in a CHP unit, the valve point loading effect, and forbidden working regions of a thermal power plant make the CHP economic dispatch’s (CHPED) objective function discontinuous. It adds complexity in the CHPED optimisation process. The key objective of the CHPED is operating cost minimisation while meeting the desired power and heat demand. To optimise the dispatch operation, three different algorithms, like Jaya algorithm, Rao 3 algorithm, and hybrid CHPED algorithm (based on first two) are adopted containing different equality and inequality restrictions of generating units. The hybrid CHPED algorithm is developed by the authors, and it can handle all of the constraints. The success of the suggested algorithms is assessed on two test systems; 5-units and 24-unit power plants.
Study on Source Identification of Mixed Gas Emission and Law of Gas Emission Based on Isotope Method
Authors: Xu Gang , Hou Yaping , Jin Hongwei , Wang Zhongwei
Published: January 2023

It is of great significance to obtain the source of mixed gas emission from the working face and the law of gas emission from each coal seam for the targeted implementation of gas control measures. Based on the principle that the hydrocarbon isotope values of gas in different coal seams have significant variability, a hydrocarbon isotope method for identifying the source of gas emission is proposed. Taking Pingmei No. 6 Coal Mine as the study area, the distribution characteristics of each value were obtained by testing the values of carbon and hydrogen isotopes in the gas of mined coal seams and adjacent coal seams; by testing the hydrocarbon isotope value of CH4 in the mixed gas of coal seam, the proportion of gas emission in each coal seam is determined and the law of gas emission in each coal seam is studied. The results show that the variation law of the proportion of gas emission in each coal seam can be divided into three stages: the dominant stage of gas emission in the mining layer (stage I), the stage of gas emission in the long-distance adjacent coal seam (stage II), and the dynamic equilibrium stage of gas emission in each coal seam (stage III). In the process of working face mining, the amount of gas emission in the mining layer remains in a small fluctuation state, and the proportion of gas emission decreases rapidly in stage I and stage II, and remains stable in stage III; the amount of gas emission and the proportion of gas emission in adjacent coal seams increase rapidly in stage I and stage II, and remain stable in stage III; the mixed gas emission of the working face increases rapidly in stage I and stage II, and remains stable in stage III. The calculation formula of the gas emission rate of the adjacent coal seam is established; during the development of the height of the mining fractured zone, the gas emission rate of the adjacent coal seam increases exponentially, and the gas emission ratio and gas emission amount of the adjacent coal seam increase; after the height of mining fracture zone tends to be stable, the gas emission rate, the proportion of gas emission, and the amount of gas emission remain of adjacent coal seams remain in a small fluctuation state.
Assessing Different Inoculum Treatments for Improved Production of Hydrogen through Dark Fermentation
Authors: Al-Haddad Saleh , Okoro-Shekwaga Cynthia Kusin , Fletcher Louise , Ross Andrew , Camargo-Valero Miller Alonso
Published: January 2023

Hydrogen gas (H2) is an energy carrier that does not generate carbon dioxide emissions during combustion, but several processes in use for its production demand high energy inputs associated with fossil fuels and greenhouse emissions. Biological processes, such as dark fermentation (DF), have the potential to remove the dependency on fossil fuels in H2 production. DF is a process that encourages fermentative bacteria to ferment organic substrates to produce H2 as a truly clean energy carrier, but its success depends on removing the presence of competing H2−consuming microorganisms in the inoculum consortia. This paper addresses a strategy to enhance H2 production from different types of substrates by testing inoculum pre-treatment processes to inactivate H2−consuming bacteria, including acid-shock (pH 3), basic-shock (pH 10) and heat-shock (115 °C) methods. Digestate from anaerobic digesters processing sewage sludge was used to produce pre-treated inocula, which were subsequently tested in a batch bio-H2 potential (BHP) test using glucose as a substrate. The results show that heat-shock pre-treatment was the best method, reporting a H2 yield of 191.8 mL-H2/gVS added (the untreated inoculum reported 170.91 mL-H2/gVS added). Glucose conversion data show a high concentration of butyric acid in both treated and untreated inocula during BHP tests, which indicate that the butyrate pathway for H2 production was dominant; shifting this to the formate route could further enhance net H2 production. A standardised inoculum-conditioning method can help to consistently assess the biohydrogen potential of suitable feedstock for DF and maximise H2 yields.
Chemical, Crystallinity and Morphological Changes of Rubberwood (Hevea brasiliensis (Willd. ex A. Juss.) Müll. Arg.) Hydrothermally Treated in Different Buffered Media
Authors: Ali Md. Rowson , Abdullah Ummi Hani , Gerardin Philippe , Ashaari Zaidon , Hamid Norul Hisham , Kamarudin Siti Hasnah
Published: January 2023

Hydrothermal treatment is an efficient and environmentally friendly method of enhancing the dimensional stability and durability of wood. The destructive effects of formic and acetic acids generated during thermal treatment, on the other hand, are known to have a negative impact on wood strength. As a result, hydrothermal treatment in different buffered media (acidic, neutral, and alkaline buffer) was performed in this study to mitigate the effects of the acids. As heating media, acidic buffer (pH 4 and 6), alkaline buffer (pH 8 and 10), and tap water (pH 7.43) were used to treat rubberwood at three different temperatures (160 °C, 180 °C, and 200 °C). The effects of different buffered media and treatment temperatures on the chemical components, crystallinity, and morphological changes in treated and untreated rubberwood were studied. The results of the experiments revealed that the chemical constituents of rubberwood changed along with the pH of the buffered media and treatment temperature. Rubberwood treated in alkaline media showed the least degradation, whereas water medium caused the most severe degradation. The crystallinity of wood increased initially (from 160 to 180 °C) and then decreased further at 200 °C. In addition, minimal damage to the wood cell was observed in an alkaline medium.
Automatic Estimation of Drill Wear Based on Images of Holes Drilled in Melamine Faced Chipboard with Machine Learning Algorithms
Authors: Jegorowa Albina , Kurek Jarosław , Antoniuk Izabella , Krupa Artur , Wieczorek Grzegorz , Świderski Bartosz , Bukowski Michał , Kruk Michał
Published: January 2023

In this article, an approach to drill wear evaluation is presented. Tool condition monitoring is an important problem in furniture manufacturing and similar industries. At the same time, approaches that rely on sets of sensors, often tend to be to robust or complex for the production environment. Instead of signals acquired from dedicated sensors, presented approach uses images of drilled holes as input data. Initial pictures are processed and enhanced in order to highlight the crucial properties. A set of selected features is then calculated on the resulting images, and later used during the training of 5 state-of-the-art classifiers. Presented research also evaluates number of images for consecutive drillings that needs to be taken into account in order to produce accurate results. From the selected set, the best performing classifier was Random Forest and it achieved close to 100% accuracy.
Development and Challenges of China’s Ecological Non-Commercial Forest Certification Policy
Authors: Zhao Liang , Zhang Jiao , Duan Ruiqi , Xu Chang
Published: January 2023

Since its implementation in China, ecological non-commercial forest certification (ENCFC) has achieved certain ecological, social, and economic effects. In this study, we review domestic and foreign literature to examine the development of ENCFC in China, which is led and enforced by the Chinese government. We found that the ENCFC program is facing several key challenges, including insufficient compensation, single compensation fund sources, and a lack of post-certification evaluation and supervision systems. The government should introduce market mechanisms to optimize ENCFC and consider whether it is necessary to continue to expand the ENCFC program. In addition, more research is needed on the performance evaluation of ENCFC implementation.
Methane and Hydrogen Sulfide Production from the Anaerobic Digestion of Fish Sludge from Recirculating Aquaculture Systems: Effect of Varying Initial Solid Concentrations
Authors: Choudhury Abhinav , Lepine Christine , Good Christopher
Published: January 2023

Recirculating aquaculture systems (RAS) are efficient at solid waste capture and collection but generate a concentrated waste stream. Anaerobic digestion (AD) could be one potential treatment option for RAS facilities. However, the concentration of organic matter in the sludge can significantly affect the biogas quality from AD. This study evaluated the effect of fish sludge (FS) solid concentration on biogas quality. Three FS treatments consisted of different initial total solid concentrations (1.5%, 2.5%, and 3.5%) from a mixture of sludge produced by Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss). Methane (CH4) production was measured, quantified, and normalized on a volatile solids (VS) basis. The highest solid concentration treatment produced 23% more CH4 than the lowest solid concentration (519 mL/g VS versus 422 mL/g VS, respectively). Peak CH4 production occurred on Day 7 for the lowest FS concentration (78.2 mL/day), while the highest FS concentration peaked on Day 11 (96 mL/day). Peak hydrogen sulfide (H2S) concentrations ranged from 1803–2074 ppm across treatments, signifying the requirement of downstream unit processes for H2S removal from biogas. Overall, this study demonstrated that increasing the FS concentration can significantly enhance CH4 production without affecting the stability of the digestion process.
Characteristics of Soil Amendment Material from Food Waste Disposed of in Bioplastic Bags
Authors: Kwanyun Padtaraporn , Praditwattana Nontawat , Phutthimethakul Lalitsuda , Chart-asa Chidsanuphong , Intaravicha Nuttakorn , Supakata Nuta
Published: January 2023

Effective food waste management is key to a sustainable future. We herein aimed at assessing the composition and the amount of food waste generated in the Chamchuri (CU) Terrace condominium (floors 18–22) in Bangkok (Thailand), producing soil amendment material from this same food waste and examining the effect of bioplastic bags on composting. The condominium generated 29.01 kg of general waste per day. The food waste (6.26 kg/day) was classified into “available” and “unavailable” food waste, accounting for 3.26 and 3.00 kg/day, respectively. The composting of the food waste lasted 45 days and was undertaken under three experimental conditions: (i) control (no food waste), treatment 1 (T1: food waste), and treatment 2 (T2: food waste along with pieces of bioplastic bags). The physicochemical analysis of the final composts of these treatments revealed that T2 could be used as soil amendment material after enrichment of its macronutrient composition and an increase in fermentation time. Interestingly, the T2 bioplastics were characterized by a lack of holes or were fragmented into pieces larger than 5 mm. In conclusion, food waste management in the CU Terrace condominium can use food waste collected in bioplastic bags as soil amendment material.
Metabolomics Analysis Reveals the Effect of Fermentation to Secondary Metabolites of Chenopodium album L. Based on UHPLC-QQQ-MS
Authors: Liu Na , An Xiaoping , Wang Yuan , Qi Jingwei
Published: January 2023

Chenopodium album L. (CAL) is an excellent vegetable crop that is rich in nutrients and possesses potential pharmaceutical value. However, the research on the secondary metabolites and the processing utilization of CAL has been rarely reported. In this study, the polyphenol content, microstructure and secondary metabolite composition of aerial parts of CAL (AC), including stems, leaves, inflorescence and grain, before and after fermentation were investigated. The results showed that the polyphenol content of fermented AC (FAC) was significantly higher than that of AC (increased by 38.62%). The AC had a compact surface, while FAC had a loose and cracked surface with large holes. A total of 545 secondary metabolites, including 89 alkaloids, 179 flavonoids, 25 lignans and coumarins, 163 phenolic acids, 35 terpenoids, 9 quinones, 6 tannins and 39 others, were identified in the AC and FAC by UHPLC-QQQ-MS metabolomics. Differential metabolites analysis reviewed 285 differential metabolites (117 upregulated and 168 downregulated) between AC and FAC. The decrease in parts of toxic alkaloids accompanied with the increase in some biologically active substances with small molecules, such as quercetin, kaempferol, p-coumaric acid and protocatechuic acid, indicated that fermentation is beneficial to enhance the bioavailability of AC. This study provides a reference value for the identification of secondary metabolites from AC and the application of fermentation in the deepness development of AC.
Storage Fungi and Mycotoxins Associated with Rice Samples Commercialized in Thailand
Authors: Laut Seavchou , Poapolathep Saranya , Piasai Onuma , Sommai Sujinda , Boonyuen Nattawut , Giorgi Mario , Zhang Zhaowei , Fink-Gremmels Johanna , Poapolathep Amnart
Published: January 2023

The study focused on the examination of the different fungal species isolated from commercial rice samples, applying conventional culture techniques, as well as different molecular and phylogenic analyses to confirm phenotypic identification. Additionally, the mycotoxin production and contamination were analyzed using validated liquid chromatography–tandem mass spectrometry (LC-MS/MS). In total, 40 rice samples were obtained covering rice berry, red jasmine rice, brown rice, germinated brown rice, and white rice. The blotting paper technique applied on the 5 different types of rice samples detected 4285 seed-borne fungal infections (26.8%) for 16,000 rice grains. Gross morphological data revealed that 19 fungal isolates belonged to the genera Penicillium/Talaromyces (18 of 90 isolates; 20%) and Aspergillus (72 of 90 isolates; 80%). To check their morphologies, molecular data (fungal sequence-based BLAST results and a phylogenetic tree of the combined ITS, BenA, CaM, and RPB2 datasets) confirmed the initial classification. The phylogenic analysis revealed that eight isolates belonged to P. citrinum and, additionally, one isolate each belonged to P. chermesinum, A. niger, A. fumigatus, and A. tubingensis. Furthermore, four isolates of T. pinophilus and one isolate of each taxon were identified as Talaromyces (T. radicus, T. purpureogenum, and T. islandicus). The results showed that A. niger and T. pinophilus were two commonly occurring fungal species in rice samples. After subculturing, ochratoxin A (OTA), generated by T. pinophilus code W3-04, was discovered using LC-MS/MS. In addition, the Fusarium toxin beauvericin was detected in one of the samples. Aflatoxin B1 or other mycotoxins, such as citrinin, trichothecenes, and fumonisins, were detected. These preliminary findings should provide valuable guidance for hazard analysis critical control point concepts used by commercial food suppliers, including the analysis of multiple mycotoxins. Based on the current findings, mycotoxin analyses should focus on A. niger toxins, including OTA and metabolites of T. pinophilus (recently considered a producer of emerging mycotoxins) to exclude health hazards related to the traditionally high consumption of rice by Thai people.
Volatile Compounds in Green and Roasted Arabica Specialty Coffee: Discrimination of Origins, Post-Harvesting Processes, and Roasting Level
Authors: Vezzulli Fosca , Lambri Milena , Bertuzzi Terenzio
Published: January 2023

The aroma of coffee is a complex mixture of more than 1000 compounds. The volatile compounds in green and roasted coffee were analyzed to detect several features related to quality, roasting level, origins, and the presence of specific defects. With respect to specialty coffee, the flavor profile and peculiarities of the aforementioned characteristics are even more relevant knowing the expectations of consumers to find, in a cup of coffee, unicity bestowed by its origin and post-harvesting processes. In this work, which dealt with 46 lots of specialty Arabica coffee, we used HS-SPME/GC–MS to detect the volatile compounds in green coffees together with those in the same coffees roasted at three different levels to identify whether differences in headspace composition were ascribable to the origin, the post-harvesting processes, and the roasting profiles. The main results are related to the discriminant power of the volatile compounds in green coffee, which are impacted by the origins more than the post-harvesting processes. Compounds such as linalool and 2,3-butanediol were more concentrated in natural coffees, while hexanal was more concentrated in washed varieties (p < 0.05). In roasted coffees, the differences in composition were due to roasting levels, countries of origin, and the post-harvesting processes, in descending order of significance.