Although the calcination-based activation of coal gangue is important for its valorization in the form of cementitious materials, the related works mainly focus on high-quality coal gangue, neglecting its low-quality counterpart. To bridge this gap, we herein conducted the pilot-scale suspension calcination of low-quality coal gangue; explored the effects of calcination temperature, particle size, and O2 content on the phase composition of the calcined product, kaolinite decomposition, decarbonization, and silica/alumina dissolution; and evaluated calcination-product-based cementitious materials. Under optimal conditions (temperature = 875−900 °C; particle size = 39.71−46.84 μm; and O2 content = 12−14%), the carbon content of the calcined product equaled 1.24−1.87 wt%, and the dissolution rates of activated alumina and silica were 77.6−79.5% and 49.4−51.1%, respectively. The 28 d compressive strength (50.8−55.7 MPa) and true activity index (98.8−108.4%) of the cementitious material pre... [more]

The inherent volatility of PV power introduces unpredictability to the power system, necessitating accurate forecasting of power generation. In this study, a machine learning (ML) model based on Gaussian process regression (GPR) for short-term PV power output forecasting is proposed. With its benefits in handling nonlinear relationships, estimating uncertainty, and generating probabilistic forecasts, GPR is an appropriate approach for addressing the problems caused by PV power generation’s irregularity. Additionally, Bayesian optimization to identify optimal hyper-parameter combinations for the ML model is utilized. The research leverages solar radiation intensity data collected at 60-min and 30-min intervals over periods of 1 year and 6 months, respectively. Comparative analysis reveals that the data set with 60-min intervals performs slightly better than the 30-min intervals data set. The proposed GPR model, coupled with Bayesian optimization, demonstrates superior performance compar... [more]

In response to the urgent need for efficient pneumonia diagnosis—a significant health challenge that has been intensified during the COVID-19 era—this study introduces the RCGAN-CTL model. This innovative approach combines a coupled generative adversarial network (GAN) with relativistic and conditional discriminators to optimize performance in contexts with limited data resources. It significantly enhances the efficacy of small or incomplete datasets through the integration of synthetic images generated by an advanced RCGAN. Rigorous evaluations using a wide range of lung X-ray images validate the model’s effectiveness. In binary classification tasks that differentiate between normal and pneumonia cases, RCGAN-CTL demonstrates exceptional accuracy, exceeding 99%, with an area under the curve (AUC) of around 95%. Its capabilities extend to a complex triple classification task, accurately distinguishing between normal, viral pneumonia, and bacterial pneumonia, with precision scores of 89... [more]

CO2 geological storage combined with deep saline water recovery technology (CO2-EWR) is one of the most effective ways to reduce carbon emissions. Due to the complex structural features, it is difficult to use CO2-EWR technology in Huaiyin Sag, Subei basin, East China. In this study, the multi-source information superposition evaluation technology of GIS was utilized for the selection of CO2 storage sites and water displacement potential target areas in this area, which mainly focused on the sandstone reservoirs of Cretaceous Pukou Formation. Based on the results, a three-dimensional injection−extraction model was established. Various scenarios with different production/injection well ratios (PIR) were simulated. Research has shown that the suitability of the surrounding site of Huaiyin Power Plant can be divided into two levels: relatively suitable and generally suitable; the area in the generally suitable level accounts for more than 80%. At a PIR of 1, CO2 is distributed asymmetrica... [more]

Biowaste, which often accounts for more than 50% of municipal waste, is an environmental problem if disposed of improperly in landfills but has great potential to achieve the recycling targets set out in Directive (EU) 2018/851. Despite the knowledge in theory and practice about the processing of biowaste and the benefits of recycling, there is a lack of methodological approaches in describing the process of aerobic biodegradation in a concise and suitable way for decision makers, environmental engineers, and project designers. This paper presents how basic data on the properties of biowaste can be used, using theoretical models, to determine basic indicators of the dynamics and material balance of the process. The maximum rate of CO2 generation on the 4th day was Rm = 45.3 g/d, with the potential of available, readily biodegradable components of the biowaste sample of P = 526 g CO2/kg VS. A substrate conversion of 51.7% was achieved in the bioreactor by the 17th day of treatment. The... [more]

Cold atmospheric pressure plasma (CAP) has attracted increased interest in recent years for possible biomedical, environmental and agricultural applications. A wide range of cold plasma treatment effects is observed in agricultural applications, like effects on the seed germination and seedling growth, but more systematic investigations are needed. The aim of this study was to identify the most appropriate combinations of the plasma source and duration of treatment positively affecting seed germination. In addition, the effect of cold plasma on the seedling growth and osmotic stress tolerance was studied. The seeds of three Bulgarian durum wheat cultivars were treated with cold plasma in twelve variants. The results obtained were processed statistically via two-way ANOVA. The treatment of seeds with a plasma torch for 20 s and the treatment with underwater diaphragm discharge for 5 min when the seeds were placed in both cameras in two different positions (relative to the electrodes bet... [more]

The pressure distribution characteristics of an apple subjected to compressive loading were investigated using the pressure-sensitive film (PSF) technique combined with apple bruise measurements. Pressure was unevenly distributed in the elliptical contact region. The average pressure had no effect on bruising because it changed slightly in the range of 0.26−0.31 MPa with increasing load. Pressures of 0.20−0.40 MPa accounted for 72% of the total pressure area. Comparatively, the area where pressure over 0.50 MPa was distributed could be ignored and showed little contribution to the bruise area. The contact edge subjected to pressure below 0.10 MPa showed that no bruising occurred. As a result, the relationship between the ≥0.10 MPa pressure area strongly correlated with the bruise area according to a linear equation, with a correlation coefficient of ≥0.99. When this relationship was applied to determine the bruise area with FE, satisfactory predicted results were obtained with minor er... [more]

In the traditional C3MR process (T-C3MR), the boiling gas (BOG) output from the last stage of the gas−liquid separator is directly discharged, in which the excellent low-temperature capability is not utilized, and the system efficiency is decreased. In liquefied natural gas (LNG), single-objective optimization methods are commonly used to optimize system parameters, which may result in incomplete system analysis. To solve the above problems, this paper proposes a multi-objective optimization strategy for the improved C3MR process(I-C3MR) based on a new multi-objective optimization algorithm called EHR-GWO-GA. Firstly, the main work proposes an I-C3MR structure. Secondly, an optimization strategy of the I-C3MR with the maximization of liquefaction amount, minimization of unit energy consumption and minimization of exergy loss as objective functions are proposed. Based on the optimization results, the influence of decision variables on liquefaction amount, unit energy consumption and exe... [more]

This study focuses on a renewable energy power plant equipped with electrolytic hydrogen production system, aiming to optimize energy management to smooth renewable energy generation fluctuations, participate in peak shaving auxiliary services, and increase the absorption space for renewable energy. A multi-objective energy management model and corresponding algorithms were developed, incorporating considerations of cost, pricing, and the operational constraints of a renewable energy generating unit and electrolytic hydrogen production system. By introducing uncertain programming, the uncertainty issues associated with renewable energy output were successfully addressed and an improved particle swarm optimization algorithm was employed for solving. A simulation system established on the Matlab platform verified the effectiveness of the model and algorithms, demonstrating that this approach can effectively meet the demands of the electricity market while enhancing the utilization rate o... [more]

This paper proposes an observer-based proportional Derivative (O-BPD) fuzzy controller for uncertain discrete-time nonlinear descriptor systems (NDSs). Representing NDSs with the Takagi−Sugeno fuzzy model (T-SFM), the proportional derivative (PD) feedback method can be utilized in the fuzzy controller design via the Parallel Distributed Compensation (PDC) concept, such that the noncausal problem and impulse behavior are avoided. A fuzzy observer is proposed to obtain unmeasured states to fulfill the PD fuzzy controller. Moreover, uncertainties and transient response performances are taken into account for the NDSs. Then, a stability analysis process and corresponding stability conditions are derived from the Lyapunov theory with the robust control method and the pole constraint. Different from existing research, the Singular Value Decomposition (SVD) and the projection lemma are utilized to transfer the stability conditions into the Linear Matrix Inequation (LMI) form. Because of this... [more]

In this work, four hundred and forty experimental solubility data points of 14 systems composed of methane and ionic liquids are considered to train a multilayer perceptron model. The main objective is to propose a simple procedure for the prediction of methane solubility in ionic liquids. Eight machine learning algorithms are tested to determine the appropriate model, and architectures composed of one input layer, two hidden layers, and one output layer are analyzed. The input variables of an artificial neural network are the experimental temperature (T) and pressure (P), the critical properties of temperature (Tc) and pressure (Pc), and the acentric (ω) and compressibility (Zc) factors. The findings show that a (4,4,4,1) architecture with the combination of T-P-Tc-Pc variables results in a simple 45-parameter model with an absolute prediction deviation of less than 12%.

In order to explore the influence of the side duct position and venting position on the premixed combustion and explosion characteristics of methane/air, a premixed combustion and explosion experiment of methane/air and a simulation of an explosion of the same size were carried out in a tube with an internal size of 2000 mm × 110 mm × 110 mm. The results showed that the side duct could change the flame structure and accelerate the flame inside the tube. The maximum increase ratio of the flame propagation speed was 106.1%. The side duct had a certain venting effect on the explosion pressure. For different position cases, when the venting film was placed over the bottom section, the maximum overpressure first decreased and then increased. When the venting film was placed over the middle section and the top section, the maximum overpressure first increased and then decreased, and the change trend of the top section was stronger. Turbulence mostly occurred inside the side duct when the ven... [more]

In the context of carbon reduction and emission reduction, the new process of electric arc furnace (EAF) steelmaking based on direct hydrogen reduction is an important potential method for the green and sustainable development of the steel industry. Within an electric furnace for the hydrogen-based direct reduction of iron, after hydrogen-based directly reduced iron (HDRI) is produced through a shaft furnace, HDRI is melted or smelted in an EAF to form final products such as high-purity iron or high-end special steel. As smelting proceeds in the electric furnace, it is easy for pieces of HDRI to bond to each other and become larger pieces; they may even form an “iceberg”, and this phenomenon may then worsen the smelting working conditions. Therefore, the melting of HDRI is the key to affecting the smelting cycle and energy consumption of EAFs. In this study, based on the basic characteristics of HDRI, we established an HDRI melting model using COMSOL Multiphysics 6.0 and studied the HD... [more]

Robotic friction stir welding (RFSW), with its wide application range, ample working space, and task flexibility, has emerged as a vital development in friction stir welding (FSW) technology. However, the low stiffness of serial industrial robots can lead to end-effector deviations and vibrations during FSW tasks, adversely affecting the weld quality. This paper proposes a dynamic dual particle swarm optimization (DDPSO) algorithm through a new comprehensive stability index that considers both the stiffness and vibration stability of the robot to optimize the installation position of complex space curve weldments, thereby enhancing the robot’s stability during the FSW process. The algorithm employs two independent particle swarms for exploration and exploitation tasks and dynamically adjusts task allocation and particle numbers based on current results to fully utilize computational resources and enhance search efficiency. Compared to the standard particle swarm optimization (PSO) algo... [more]

A scramjet engine consisting of several components is a highly coupled system that urgently needs a universal performance metric. Exergy is considered as a potential universal currency to assess the performance of scramjet engines. In this paper, a control-volume-based exergy method for the Reynolds-averaged Navier−Stokes solution of truncated and corrected Busemann inlets was proposed. An exergy postprocessing code was developed to achieve this method. Qualitative and quantitative analyses of exergies in the Busemann inlets were performed. A complete understanding of the evolution process of anergy and the location where anergy occurs in the inlet at various operation conditions was also obtained. The results show that the exergy destroyed in the Busemann inlet can be decomposed into shock wave anergy, viscous anergy and thermal anergy. Shock wave anergy accounts for less than 4% of the total exergy destroyed while thermal anergy and viscous anergy, in a roughly equivalent magnitude,... [more]

With the continuous expansion of grid-connected wind, photovoltaic, and other renewable energy sources, their volatility and uncertainty pose significant challenges to system peak regulation. To enhance the system’s peak-load management and the integration of wind (WD) and photovoltaic (PV) power, this paper introduces a distributionally robust optimization scheduling strategy for a WD−PV thermal storage power system incorporating deep peak shaving. Firstly, a detailed peak shaving process model is developed for thermal power units, alongside a multi-energy coupling model for WD−PV thermal storage that accounts for carbon emissions. Secondly, to address the variability and uncertainty of WD−PV outputs, a data-driven, distributionally robust optimization scheduling model is formulated utilizing 1-norm and ∞-norm constrained scenario probability distribution fuzzy sets. Lastly, the model is solved iteratively through the column and constraint generation algorithm (C&CG). The outcomes dem... [more]

Strong competition in the automotive industry has required manufacturers to implement lean production, both with methods and techniques specific to Industry 4.0. At the same time, universities must provide graduates with specific skills for applying these new production methods and techniques. In this context, a lean learning factory was developed in the Pitesti University Center that allows students to learn about, experiment with, and research new lean manufacturing methods and techniques as well as Industry 4.0 in an environment similar to that of enterprises. The research presented in this study aimed to identify the minimum number of repetitions necessary to train operators to perform the same assembly operation while working at two differently organized workstations: one classic and the other including digital techniques. Several indicators were considered in our analysis, such as the number of errors, the number of stops, the effective duration of the work cycle, and the percent... [more]

This study explores the existing literature on specific energy consumption (SEC) use for paddy drying and consolidates all relevant data for comparisons across technologies. Energy consumption data for a range of drying technologies are consolidated from published literature and normalized to enable comparison. A large proportion of the source data are generated from operational performance in industrial or laboratory settings, while the remainder is derived from computer simulations. The SEC of paddy drying is driven primarily by technology type; however, operational factors (such as the system size, temperature, and airflow) and external factors (such as the local climate and paddy moisture content) also heavily influence system energy use. The results of our analysis show that the industrial drying technologies explored in this study have an average SEC of 5.57 ± 2.21 MJ/kg, significantly lower than the 20.87 ± 14.97 MJ/kg observed in a laboratory setting, which can potentially be a... [more]

This study aimed to assess the changes occurring during the storage of tablets of three effervescent preparations available in Polish pharmacies containing calcium and quercetin from various manufacturers under stressful conditions (45 °C, UV radiation) using a hyperspectral Specim IQ camera (Finland), X-ray microtomography (Germany), and selected pharmacopoeial parameters. All measurements were made three times at the beginning of the experiment (day 0) and then on days 3 and 10. In general, for all analyzed preparations, the values of reflectance (within a range from visible light to near-infrared) were significantly higher on day 0 than after 10 days of heat and UV (p < 0.001 each). The hardness of the tablets of all analysed preparations was higher on days 3 and 10 compared to day 0. Significant differences were found in the density of the internal structure of the tested preparations (p < 0.001), but in Preparations 1 and 2 on day 10, the density was higher compared to the i... [more]

The recycling of metals from waste printed circuit boards (WPCBs) has been presented as a solid−liquid extraction process using two deep eutectic solvents (DESs) and four ionic liquids (ILs). The extraction and separation of Cu(II), Ag(I), and other metals, such as Al(III), Fe(II), and Zn(II), from the solid WPCBs (after the physical, mechanical, and thermal pre-treatments) with different solvents are demonstrated. Two popular DESs were used to recover valuable metal ions: (1) choline chloride + malonic acid, 1:1, and (2) choline chloride + ethylene glycol, 1:2. The extraction efficiencies of DES 1 after two extraction and two stripping stages were only 15.7 wt% for Cu(II) and 17.6 wt% for Ag(I). The obtained results were compared with those obtained with four newly synthetized ILs as follows: didecyldimethylammonium propionate ([N10,10,1,1][C2H5COO]), didecylmethylammonium hydrogen sulphate ([N10,10,1,H][HSO4]), didecyldimethylammonium dihydrogen phosphate ([N10,10,1,1][H2PO4]), and t... [more]

The conditions in the combustion zones, i.e., the raceways, are crucial for the operation of the blast furnace. In recent years, advancements in tuyere cameras and image processing and interpretation techniques have provided a better means by which to obtain information from this region of the furnace. In this study, a comprehensive approach is proposed to visually monitor the status of the pulverized coal cloud at the tuyeres based on a carefully designed processing strategy. Firstly, tuyere images are preprocessed to remove noise and enhance image quality, applying the adaptive Otsu algorithm to detect the edges of the coal cloud, enabling precise delineation of the pulverized coal region. Next, a Swin−Unet model, which combines the strengths of Swin Transformer and U-Net architecture, is employed for accurate segmentation of the coal cloud area. The extracted pulverized coal cloud features are analyzed using RGB super-pixel weighting, which takes into account the variations in color... [more]

To investigate the characteristics and generation potential of gas generated from over-mature shale, hydrous and anhydrous pyrolysis experiments were carried out on the Longmaxi Formation in the Anwen 1 well of the Sichuan Basin of China at temperatures of 400−598 °C and pressures of 50 Mpa, with (hydrous) and without (anhydrous) the addition of liquid water. The results show that in the presence of water, the total yield of carbon-containing gases (i.e., the sum of methane, ethane, and carbon dioxide) was increased by up to 1.8 times when compared to the total yield from the anhydrous pyrolysis experiments. The increased yield of carbon dioxide and methane accounted for 89% and 10.5% of the total increased yield of carbon-containing gases. This indicated that the participation of water could have promoted the release of carbon from over-mature shale, like we used in this study. The methane generated in the hydrous pyrolysis experiments was heavier, with a δ13C value of −21.27‱ (544 °C... [more]

The Sulige gas field is a typical “three lows” (low permeability, low pressure, and low abundance) tight sandstone gas reservoir, with formation pressures often characterized by abnormally high or low pressures. The complex geological features of the reservoir further deviate from conventional understanding, impacting the effective implementation of wellbore blockage removal measures. Therefore, it is imperative to establish the wellbore blockage mechanism, prediction model, and effective prevention measures for the target area. In this study, based on field data, we first experimentally analyzed the water quality and types of blockage in the target area. Subsequently, utilizing a BP neural network model, we established a model for predicting the risk of wellbore blockage and analyzing mitigation measures in the target reservoir. The model’s prediction results, consistent with on-site actual results, demonstrate its reliability and accuracy. Experimental results show that the water qua... [more]

This study presents a method for predicting the volume flow output of external gear pumps using neural networks. Based on operational measurements across the entire energy chain, the neural network learns to map the internal leakage of the pumps in use and consequently to predict the output volume flow over the entire operating range of the underlying dosing process. As a consequence, the previously used volumetric flow sensors become obsolete within the application itself. The model approach optimizes the higher-level dosing system in order to meet the constantly growing demands of industrial applications. We first describe the mode of operation of the pumps in use and focus on the internal leakage of external gear pumps, as these primarily determine the losses of the system. The structure of the test bench and the data processing for the neural network are discussed, as well as the architecture of the neural network. An error flow rate of approximately 1% can be achieved with the pre... [more]

A new manufacturing process has been developed that involves drawing circular sheets of thin metal through a conical die to create square cups. This technique produces deep square cups with a height-to-punch-side length ratio of approximately 2, as well as high dimensional accuracy and a nearly uniform height. The study investigated how various factors, including the sheet material properties and process geometric parameters, affect the limiting drawing ratio (LDR). The researchers used finite element analysis to determine the optimal die design for achieving a high LDR and found that the proposed technique is advantageous for producing long square cups with high dimensional accuracy.