Processes, Volume 13, Issue 10 (October 2025) – 346 articles

Water scarcity remains a critical global challenge, driving the need for efficient small-scale desalination technologies. This study presents experimental research on the performance evaluation of an innovative ultrasonic humidifier designed for the humidification–dehumidification (HDH) desalination process. A prototype was designed, incorporating a 1.7 MHz piezoelectric transducer. The efficiency of the humidifier, the vapor production rate, and the specific energy consumption were evaluated based on two operating parameters: water temperature, ranging from 30 °C to 60 °C, and airflow rate, ranging from 20 to 120 L/min. The results show that humidification efficiency increases with airflow rate, reaching values above 95% at a temperature of 60 °C, with an airflow rate of 60 L/min, decreasing slightly at higher flow rates. The system demonstrated optimal performance at 60–80 L/min, balancing high efficiency and vapor production with moderate energy demand. These findings demonstrate that ultrasonic humidification is a viable alternative, especially in decentralized applications, due to its low thermal energy requirements, compact design, and adaptability to intermittent renewable energy sources. Full article

This study investigates wellbore stability in ultra-deep extended-reach wells (ERWs) in the East China Sea, where perforated pipes (a type of screen completion) are commonly used to support wellbore walls and prevent collapse. Cost constraints sometimes lead to the omission of this support, yet significant wellbore collapse is rarely observed. The instability is primarily attributed to variations in production pressure differences. A predictive model for critical pressure difference was developed based on immersion experiments and single-triaxial rock mechanics tests. The results from immersion tests revealed that, in water-bearing strata, the critical pressure difference decreased significantly, drop-ping by 20.07% after two days of rock core immersion and by 28.35% after seven days. Key factors influencing this difference, such as well inclination, rock cohesion, internal friction angle, Poisson’s ratio, and Biot coefficient, were identified. As production continues, pore pressure depletion reduces this difference, particularly when pore pressure falls below 23.5 MPa, leading to wellbore instability. On-site validation in three ultra-deep ERWs showed that the model’s predictions aligned well with actual conditions, with a confidence interval analysis further validating the model’s accuracy. The proposed model provides valuable guidance for future ultra-deep well development in the East China Sea. Full article

The rapid growth of Distributed Energy Resources (DERs) exerts significant pressure on distribution network margins, requiring predictive and safe coordination. This paper presents a closed-loop framework combining a topology-aware Spatio-Temporal Transformer (STT) for multi-horizon forecasting, a cooperative multi-agent reinforcement learning (MARL) controller under Centralized Training and Decentralized Execution (CTDE), and a real-time safety layer that enforces feeder limits via sensitivity-based quadratic programming. Evaluations on three SimBench feeders, with OLTC/capacitor hybrid control and a stress protocol amplifying peak demand and mid-day PV generation, show that the method reduces tail violations by 31% and 56% at the 99th percentile voltage deviation, and lowers branch overload rates by 71% and 90% compared to baselines. It mitigates tail violations and discrete switching while ensuring real-time feasibility and cost efficiency, outperforming rule-based, optimization, MPC, and learning baselines. Stress maps reveal robustness envelopes and identify MV–LV bottlenecks; ablation studies show that diffusion-based priors and coordination contribute to performance gains. The paper also provides convergence analysis and a suboptimality decomposition, offering a practical pathway to scalable, safe, and interpretable DER coordination. Full article

Statistical process control may lead to false detection results in the presence of measurement error, so it is necessary to deal with the effect of measurement error. The Bayesian exponentially weighted moving average (EWMA) variability control chart, first proposed by Lin et al., is a distribution-free control chart, and it can effectively monitor process variance even if the process skewness varies with time. This paper investigates the influence of measurement error on the Bayesian EWMA variability control chart, and it proposes two designs for the Bayesian EWMA variability control chart in the presence of measurement error. One is to modify the control limits based on the biased error-prone monitoring statistics, called the error-embedded control chart. The other is to design the control limits based on the error-corrected monitoring statistics, called the error-corrected control chart. Simulation results prove that both of the proposed control charts are reliable and have good detection performance in the presence of measurement error. Moreover, the average run lengths of the proposed control charts are exactly the same, indicating that both of them are equivalent control charts. Comparison results show that the existing control chart in Lin et al. is not in-control robust and fails to detect a downward shift in process variance when measurement error is present. Thus, using the error-embedded control chart or the error-corrected control chart to monitor processes with measurement errors is reliable and effective. Moreover, the proposed control charts, where π₁₁ = 1 and π₁₀ = 0, can be applied to monitor processes without measurement errors since their detection performance is equal to that of the existing control chart in Lin et al. Finally, we demonstrate the application of the error-embedded control chart and the error-corrected control chart to analyze the data from the service time system of a bank branch and the data from a semiconductor manufacturing process, showing that the proposed control charts can indeed be applied to data with measurement errors. Full article

Gear defect detection directly affects the operational reliability of critical equipment in fields such as automotive and aerospace. Gear defect detection technology based on machine vision, leveraging the advantages of non-contact measurement, high efficiency, and cost-effectiveness, has become a key support for quality control in intelligent manufacturing. However, it still faces challenges including difficulties in semantic alignment of multimodal data, the imbalance between real-time detection requirements and computational resources, and poor model generalization in few-shot scenarios. This paper takes the paradigm evolution of gear defect detection technology as the main line, systematically reviews its development from traditional image processing to deep learning, and focuses on the innovative application of intelligent algorithms. A research framework of “technical bottleneck-breakthrough path-application verification” is constructed: for the problem of multimodal fusion, the cross-modal feature alignment mechanism based on Transformer network is deeply analyzed, clarifying its technical path of realizing joint embedding of visual and vibration signals by establishing global correlation mapping; for resource constraints, the performance of lightweight models such as MobileNet and ShuffleNet is quantitatively compared, verifying that these models reduce Parameters by 40–60% while maintaining the mean Average Precision essentially unchanged; for small-sample scenarios, few-shot generation models based on contrastive learning are systematically organized, confirming that their accuracy in the 10-shot scenario can reach 90% of that of fully supervised models, thus enhancing generalization ability. Future research can focus on the collaboration between few-shot generation and physical simulation, edge-cloud dynamic scheduling, defect evolution modeling driven by multiphysics fields, and standardization of explainable artificial intelligence. It aims to construct a gear detection system with autonomous perception capabilities, promoting the development of industrial quality inspection toward high-precision, high-robustness, and low-cost intelligence. Full article

The extraction of plant essences and volatile organic compounds has been performed using various methods throughout history. The production of essential oils is a significant industry. One notable ornamental flower in the Philippines is Jasminum sambac (L.), also known as Arabian Jasmine or Sampaguita, which is highly fragrant and used in various cosmetics, food, and medicine. Researchers developed a method to produce quality J. sambac (L.) concrete using the Supercritical Fluid Extraction (SFE). Among the parameters explored, it was noted that no drying method had more pleasant odors, while other drying methods had varying effects on the extract scent. A temperature of 35 °C produces fragrant and sweet concrete, and temperatures above 40 °C result in burnt-smelling extract. Higher pressure enhanced the aroma and yield. The drying method also affected the output. Plucking petals before drying resulted in low-quality outcomes. Using a blow dryer damages the petals. A combination of low temperature, moderate pressure, and no drying method produced the best aromas. However, the process requires winterization to remove waxes in the samples, which will decrease the yield. Full article

Numerous studies have utilized Petri nets for modeling, fault diagnosis, and deadlock control in manufacturing processes and systems. However, most are confined to specific subclasses of Petri nets, thus limiting their applicability. This paper extends the research scope by focusing on generalized Petri nets, analyzing system models from a novel perspective, and demonstrating the complete workflow from modeling to constraint establishment, controller integration, and deadlock avoidance. The methodology begins with an introduction to thread analysis, which involves identifying critical transitions within individual threads to locate resource cycles and minimal siphons corresponding to dead transitions, along with the presentation of a dedicated algorithm for this purpose. Next, optimal constraints are derived based on the thread analysis approach, with generalized patterns summarized for partially parameterized scenarios. Controllers are then incorporated into the system model according to the aforementioned constraints. Finally, derivative issues arising from controller integration are discussed, and pathways based on thread–circuit analysis are synthesized. The entire process described above is substantiated through illustrative case studies. Full article

4-vinylguaiacol (4-VG) is a commercially important compound due to its characteristic clove-like aroma and its use as a flavoring in the food, beverage, and cosmetics industries. However, its extraction from natural sources or by a chemical method is expensive. The bioconversion of ferulic acid (FA) to 4-VG via microorganisms is an alternative, considering the market trend toward biotechnological and environmentally friendly processes and products. This study aimed to evaluate the tolerance of the bacterial strain Brucella intermedia (basonym Ochrobactrum intermedium) TG 3.48 to FA, its bioconversion to 4-VG, and the activity of the FA decarboxylase enzyme (FADase), which is key to the 4-VG production process. The strain tolerated FA concentrations up to 700 mg L⁻¹. When the microorganism grew at 300 mg L⁻¹ FA in Mineral Liquid Medium (MLM), it converted 99.5% of FA to 4-VG within 12 h. The FADase activity was cell-associated with 5.17 U mL-1 in the whole cell, 4.40 U mL⁻¹ in the intracellular extract, and 3.54 U mL⁻¹ in the cell wall fragments, while the specific activity was 778.90 U mg⁻¹. Full article

Aiming at the speed chattering problem caused by high-frequency square wave injection in permanent magnet synchronous motors (PMSMs) during low-speed operation (200–500 r/min), this study intends to improve the rotor position estimation accuracy of sensorless control systems as well as the system’s ability to resist harmonic interference and sudden load changes. The goal is to enhance the control performance of traditional control schemes in this scenario and meet the requirement of stable low-speed operation of the motor. First, the study analyzes the harmonic error propagation mechanism of high-frequency square wave injection and finds that the traditional PI phase-locked loop (PI-PLL) is susceptible to high-order harmonic interference during demodulation, which in turn leads to position estimation errors and periodic speed fluctuations. Therefore, the extended state observer phase-locked loop (ESO-PLL) is adopted to replace the traditional PI-PLL. A third-order extended state observer (ESO) is used to uniformly regard the system’s unmodeled dynamics, external load disturbances, and harmonic interference as “total disturbances”, realizing real-time estimation and compensation of disturbances, and quickly suppressing the impacts of harmonic errors and sudden load changes. Meanwhile, a dynamic pole placement strategy for the speed loop is designed to adaptively adjust the controller’s damping ratio and bandwidth parameters according to the motor’s operating states (loaded/unloaded, steady-state/transient): large poles are used in the start-up phase to accelerate response, small poles are switched in the steady-state phase to reduce errors, and a smooth attenuation function is used in the transition phase to achieve stable parameter transition, balancing the system’s dynamic response and steady-state accuracy. In addition, high-frequency square wave voltage signals are injected into the dq axes of the rotating coordinate system, and effective rotor position information is extracted by combining signal demodulation with ESO-PLL to realize decoupling of high-frequency response currents. Verification through MATLAB/Simulink simulation experiments shows that the improved strategy exhibits significant advantages in the low-speed range of 200–300 r/min: in the scenario where the speed transitions from 200 r/min to 300 r/min with sudden load changes, the position estimation curve of ESO-PLL basically overlaps with the actual curve, while the PI-PLL shows obvious deviations; in the start-up and speed switching phases, dynamic pole placement enables the motor to respond quickly without overshoot and no obvious speed fluctuations, whereas the traditional fixed-pole PI control has problems of response lag or overshoot. In conclusion, the “ESO-PLL + dynamic pole placement” cooperative control strategy proposed in this study effectively solves the problems of harmonic interference and load disturbance caused by high-frequency square wave injection in the low-speed range and significantly improves the accuracy and robustness of PMSM sensorless control. This strategy requires no additional hardware cost and achieves performance improvement only through algorithm optimization. It can be directly applied to PMSM control systems that require stable low-speed operation, providing a reliable solution for the promotion of sensorless control technology in low-speed precision fields. Full article

This review addresses the environmental and health risks caused by antibiotic abuse, focusing on the inefficiency of traditional treatment methods and their tendency to cause secondary pollution, as well as the limitations of g-C₃N₄ in photocatalytic antibiotic degradation, such as insufficient visible light utilization and high carrier recombination rates. It systematically summarizes modification strategies and application advances of g-C₃N₄. Compared with previous reviews on carbon nitride, this work distinguishes itself by precisely targeting the cutting-edge application scenario of antibiotic-specific degradation, providing an in-depth analysis of how precursor selection and preparation methods regulate material properties, and emphasizing the role of modification approaches—including crystal optimization, element doping, surface modification, and heterojunction construction—in enhancing catalytic efficiency. It offers targeted and forward-looking insights for the practical application of this material in controlling antibiotic pollution in complex water environments. Full article

By emulating the morphological structures of organisms, bionic robots achieve enhanced locomotion efficiency, stability, and environmental adaptability. Inspired by insect morphology and biological locomotion mechanisms, a wheel-legged transformation device for a hexapedal robot is proposed in this work. First, an iris-type wheel-legged transformation mechanism is designed. Subsequently, the operational principle of the iris–link composite mechanism is analyzed, and kinematic modeling of the transformation process is conducted. Finally, joint angle rotation, positional variation, and their effects under different gait states are examined through simulation of three typical gait patterns. Experimental results demonstrate that the proposed design significantly improves the motion stability of the bionic hexapedal robot. Furthermore, the adoption of a hollow leg structure reduces weight while enhancing locomotion flexibility, thereby strengthening the robot’s overall capability to respond to external disturbances. In summary, this study offers a valuable reference for the future development of wheel-legged transformable bionic robots. Full article

With the deepening of unconventional oil and gas resource development, the gas–liquid two-phase flow phenomenon in horizontal gas wells is becoming increasingly complex. Accurate and efficient prediction of the flow state has become key to optimizing production. While traditional numerical simulation methods are highly accurate, their long calculation times make them unsuitable for real-time applications. Conversely, purely data-driven methods struggle with accuracy under sparse data conditions. This paper proposes a deep operator network method (PI-DeepONet) that integrates physical prior knowledge—specifically the drift-flux model—to rapidly predict two-phase flow parameters. By jointly training the network with both data loss and physical loss, the model’s accuracy and generalization are significantly enhanced. Comparing the results with the OLGA numerical simulator verifies the model’s high performance. The average relative error of the PI-DeepONet on the test set is less than 1%, with the error of some physical quantities controlled within 0.2%. Critically, the single prediction time is less than 0.1 s, achieving a calculation speed nearly 50,000 times higher than the traditional numerical simulation method. The model significantly improves prediction speed while ensuring accuracy, making it ideal for real-time simulation and rapid response requirements in horizontal wells. This study provides a new path for intelligent diagnosis and prediction of underground working conditions and demonstrates broad engineering application potential. Full article

This study investigated the drying–grinding–extrusion processing of camel compound feeds enriched with locally available botanicals. A 2 × 2 × 3 full factorial design was applied to evaluate the effects of infrared drying temperature (two levels), grinding time (two levels), and extrusion screw speed (three levels) on process efficiency and product quality. Moisture calibration was performed using gravimetric reference values. Drying kinetics were modeled with Page and Midilli equations, while specific energy consumption (SEC) and specific moisture extraction rate (SMER) were calculated. Particle-size distribution, extrusion parameters, and extrudate properties (expansion ratio, bulk density, water absorption index (WAI), water solubility index (WSI), hardness, and color) were analyzed. Infrared drying resulted in faster moisture removal and greater energy efficiency compared with convective drying. The Midilli model provided the best fit to drying kinetics data. The results indicate that optimized combinations of drying, grinding, and extrusion conditions can enhance the technological and nutritional potential of camel compound feeds; however, biological validation is required. Limitations: These findings are limited to processing and compositional outcomes; biological validation in camels (in vivo or in vitro) remains necessary to confirm effects on digestibility, health, or performance. Full article

With industrial development, food safety problems occur frequently. The contamination of harmful elements in food has received widespread attention, especially heavy metal elements such as lead, cadmium, mercury, arsenic, and other heavy metals proven toxic to human health. As one of the most sensitive and accurate analytical techniques for trace element detection, inductively coupled plasma mass spectrometry (ICP-MS) has become an indispensable key technology in the field of food safety testing due to its ability to accurately determine the ppb/ppt level toxic elements in food and analyze the morphology of the elements, and the number of applications in the literature continues to grow remarkably (e.g., the average annual growth rate in the last decade has reached 12–15%), which supports the risk assessment and regulation. It has become an indispensable key technology in this field. In this review, the research progress of ICP-MS in the detection of hazardous elements in food is summarized, focusing on the basic principles of the technique, sample pretreatment methods, and common interference issues. The specific applications of ICP-MS in different types of food (e.g., cereals, aquatic products, vegetables, and dairy products) are also summarized. The main challenges in the current application of ICP-MS are also discussed, including matrix effect, stability of morphological transformation, and standardization issues. It is expected that the development of ICP-MS in portability, automation, and high-throughput detection has brought potential for its applications in food safety detection. Full article

The rising penetration of electric vehicles is driving huge demand for lithium batteries, making low-carbon manufacturing a critical objective. This goal is challenged by insufficient production scheduling flexibility and the neglect of carbon-reduction technologies. To address these challenges, this paper develops a low-carbon planning methodology for lithium battery plant energy systems by leveraging manufacturing chain flexibilities. First, a lithium battery energy–carbon material modeling approach is developed that accounts for process production delays and intermediate product storage to capture schedulable process energy consumption patterns. A nitrogen–oxygen coupling production framework is introduced to facilitate oxygen-enriched combustion technology application, while energy recovery pathways are incorporated given the high energy consumption of the formation stage. Subsequently, a process scheduling-driven planning model for lithium battery industrial integrated energy systems (IIES) is developed. Finally, the planning model is validated through four contrasting case studies and systematically evaluated using multi-criteria decision analysis (MCDA). The results demonstrate three principal conclusions: (1) incorporating process scheduling effectively enhances process energy flexibility and reduces total system costs by 19.4%, with MCDA closeness coefficient improving from 0.257 to 0.665; (2) oxygen-enriched combustion increases maximum combustion and carbon capture (CCS) rates from 90% to 95%, reducing carbon tax to 40.5% of the baseline; (3) energy recovery on the basis of process scheduling further reduces costs and carbon emissions, with battery recovery achieving an additional 30.2% cost reduction compared to 24.1% for heat recovery, and MCDA identifies this integrated approach as the optimal solution with a closeness coefficient of 0.919. Full article

Tea-flavor baijiu, in which the aroma combines the tea note and the typical profile of baijiu, has brought a fresh flavor to the market. Yet its flavor evolution during the storage period and the associated changes in volatile compounds remain poorly characterized. To systematically address the flavor profile dynamics during storage, the study evaluated tea-flavor baijiu of varying ages using integrated sensory and instrumental analyses. Through napping with ultra-flash profiling (Napping-UFP) and check-all-that-apply (CATA), the sensory attributes from aroma, flavor, and mouthfeel profiles of tea-flavor baijiu were established, and quantitative descriptive analysis (QDA) was employed to distinguish the distinct sensory profiles among samples with different aging durations. The overall aroma patterns were examined using an electronic nose (E-nose), and the distinction of sample A401 with the longest storage period was notable. Headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS) and headspace solid-phase microextraction–gas chromatography–mass spectrometry (HS-SPME-GC-MS) were used to identify and quantify the volatile compounds, while aging notably altered volatile composition with increased ester levels and reduced alcohol content; hence, the short-aged (one to three years), mid-aged (four to six years), and long-aged (seven and eight years) samples could be easily differentiated. Through the analysis of the data, 12 key odor-active compounds, namely (E)-2-methyl-2-butenal, ethyl caproate, 3-methylbutanal, 2-pentanone, ethyl acetate, ethyl heptanoate, ethyl 2-methylbutanoate, ethyl pentanoate, ethyl butyrate, ethyl hexanoate, ethyl octanoate, and 2,4-di-tert-butylphenol, were identified as major contributors to shifts. Furthermore, Pearson correlation analysis revealed a strong negative association between the accumulation of esters and the intensity of tea aroma in long-aged samples, clarifying the chemical mechanism underlying the diminished tea note in aged tea-flavor baijiu. This study provides new insights into the impact of aging on the flavor profile of tea-flavor baijiu and offers a scientific foundation for improving its production, storage, and quality management. Full article

In high-voltage direct-current (HVDC) transmission and large-scale power-system operation, DC-bias effects can drive current-transformer (CT) cores into premature saturation, distorting the secondary current and seriously jeopardizing the reliability of protective relaying and metering. To address the limited fitting capability and robustness of conventional compensation approaches in the presence of nonlinear distortion, this paper proposes a convolutional neural network (CNN)-based inversion method for CT saturation current. First, a simulation model is built on the PSCAD/EMTDC platform to generate a dataset of saturated, distorted currents covering DC components from −50 A to +50 A. Then, a CNN with a three-layer one-dimensional convolutional architecture is designed; leveraging local convolutions and parameter sharing, it extracts features from current sequences and reconstructs the true primary current. Simulation results show that the proposed method accurately recovers the primary-current waveform under mild-to-severe saturation, with errors within 2%, and exhibits strong adaptability and robustness with respect to both the polarity and magnitude of the DC component. These findings verify the effectiveness of CNNs for CT-saturation compensation. Full article

Chlorpyrifos (CHP) is a persistent organophosphate pesticide whose presence in water poses serious ecological and health risks. Here, we report a sustainable adsorbent obtained by high-temperature carbonization of immature walnuts (Juglans regia). The adsorbent’s structure, surface chemistry, and charge properties were comprehensively characterized using FTIR, SEM-EDX, zeta potential measurement, BET analysis, and XPS. The synthesis yielded a mesoporous carbon material with a BET surface area of 303 m² g⁻¹. Its performance in CHP removal was assessed under batch and dynamic conditions. Adsorption followed pseudo-second-order kinetics (k₂ = 0.122 mg min⁻¹ g⁻¹; contact time 0–120 min). Isotherm experiments performed at 20, 25, and 30 °C, with equilibrium data best described by the Langmuir and Sips models, reaching a maximum capacity of 43.2 mg g⁻¹. Thermodynamic analysis indicated a spontaneous and endothermic process. The adsorbent demonstrated selectivity for CHP over chlorpyrifos-oxon (CPO) in binary mixtures, retained its efficiency over at least ten regeneration cycles with ethanol, and removed up to 90% of CHP toxicity, as measured by acetylcholinesterase inhibition. Dynamic filtration confirmed its applicability under flow conditions. These findings demonstrate that the investigated adsorbent is an effective, reusable, and selective adsorbent, offering a low-cost and eco-friendly approach to pesticide removal from contaminated waters. Full article

The shale oil reservoir in Block Y of the Ordos Basin exhibits low porosity and low permeability, yet it features distinct stratification and developed micro-fractures. During the development process using “horizontal wells + volume fracturing”, the differential in geostress exerts a certain influence on the initiation and propagation of fractures. This paper employs the Cohesive element simulation method to investigate the formation patterns of fracture networks in fractured formations. By prefabricating natural fractures, the study explores the impact of natural fractures on the direction of hydraulic fractures during the hydraulic fracturing process. The study considers the fracture initiation and propagation patterns as well as the interaction between hydraulic fractures and natural fractures under differential geostress conditions of 0 MPa, 1 MPa, and 5 MPa. The numerical simulation results reveal that the presence of natural fractures significantly affects the direction of hydraulic fractures, with the tip of the hydraulic fracture deflecting towards the natural fracture. The smaller the geostress difference, the more complex the fractures become with more branching fractures. Conversely, a larger geostress difference leads to the formation of a single double-wing fracture perpendicular to the minimum principal stress, resulting in a simpler fracture morphology. The pore pressure variation at the injection point generally experiences a rapid increase followed by a slight decrease, subsequently undergoing wavy changes. The occurrence of wavy pressure variations indicates the continuous generation of micro-fractures. The fracture width at the injection point generally exhibits an increasing trend followed by a decreasing trend. When the stress difference is 0 MPa, 1 MPa, and 5 MPa, the peak rupture pressures are 12.63 MPa, 13.42 MPa, and 18.33 MPa, respectively; the maximum crack openings are 0.797 cm, 0.779 cm, and 0.771 cm, respectively. The study on fracture initiation and propagation in shale reservoirs provides guidance for the field application of multi-cluster fracturing in horizontal well sections. Full article

Effect-based toxicity assessments are crucial for evaluating the risks of disinfection byproducts (DBPs), particularly unknown species, generated during drinking water chlorination. However, the accuracy of this approach is highly dependent on unbiased sample extraction. Conventional methods often employ single, low-pH extraction, which may fail to recover pH-sensitive amphoteric DBPs derived from amphoteric precursors (e.g., nitrogenous compounds). This study investigated how extraction pH affects the measured biotoxicity of DBPs formed from three model precursors: biopterin (Bip), cytosine (Cyt), and tryptophan (Trp). Under excess chlorine conditions, all three precursors degraded rapidly. The formation of aliphatic DBPs followed the order Trp > Cyt > Bip, and the maximum toxicity of the non-volatile extracts, assessed via a Vibrio fischeri bioassay, followed the reverse order: Bip > Trp > Cyt. This toxicity profile was significantly influenced by extraction pH, with maximum toxicity observed for Bip at around pH 4.0, under weakly acidic conditions for Trp, and under neutral to alkaline conditions for Cyt. For all precursors, the total organic carbon concentration remained constant throughout chlorination, indicating negligible mineralization and the predominant formation of non-aliphatic, likely heteroaromatic, products. These findings demonstrate that conventional extractions at a single low pH can lead to the incomplete recovery of toxic DBPs from amphoteric precursors. Therefore, pH-optimized extraction protocols are necessary for a more accurate risk assessment of chlorinated drinking water. Full article

Ammonia is an ideal candidate for clean energy in the future, and its large-scale production has long relied on the Haber–Bosch process, which operates at a high temperature and pressure. However, this process faces significant challenges due to the growing demand for ammonia and the increasing need for environmental protection. The high energy consumption and substantial CO₂ emissions associated with the Haber–Bosch method have greatly limited its application. Consequently, increasing research efforts have been devoted to developing green ammonia synthesis technologies. Among these, the electrocatalytic nitrogen reduction reaction (NRR), which uses water and nitrogen as raw materials to synthesize NH₃ under mild conditions, has emerged as a promising alternative. This method offers the potential for carbon neutrality and decentralized production when coupled with renewable electricity. However, it is important to note that the current energy efficiency and ammonia production rates of NRR under ambient aqueous conditions generally lag behind those of modern Haber–Bosch processes integrated with green hydrogen (H₂). As the core of the NRR process, the performance of electrocatalysts directly impacts the efficiency, energy consumption, and product selectivity of the entire reaction. To date, significant efforts have been made to identify the most suitable electrocatalysts. In this paper, we focus on the current research status of metal catalysts—including both precious and non-precious metals—as well as non-metal catalysts. We systematically review important advances in performance optimization, innovative design strategies, and mechanistic analyses of various catalysts. We clarify innovative optimization strategies for different catalysts and summarize and compare the catalytic effects of various catalyst types. Finally, we discuss the challenges facing electrocatalysis research and propose possible future development directions. Through this paper, we aim to provide guidance for the preparation of high-efficiency NRR catalysts and the future industrial application of electrochemical ammonia synthesis. Full article

This study aims to investigate the sedimentary facies characteristics of the coal seam roof in the Qinglong and Longfeng coal mines and their control over water abundance. By collecting core samples and well logging data from both mining areas, multiple methods were employed, including core observation, thin-section analysis, sedimentary microfacies distribution mapping, nitrogen adsorption tests, and nuclear magnetic resonance analysis, to systematically analyze the depositional environments, types of sedimentary microfacies, and their distribution patterns. Results indicate that the roof of Qinglong Coal Mine is predominantly composed of sandy microfacies with well-developed faults, which not only increase fracture porosity but also provide water-conducting pathways between surface water and aquifers, significantly enhancing water abundance. In contrast, Longfeng Coal Mine is characterized mainly by muddy microfacies, with small-scale faults exhibiting weak water-conducting capacity and relatively low water abundance. Hydrochemical analysis indicates that consistent water quality between Qinglong’s working face, karst water, and goaf water confirms fault-induced aquifer–surface water connectivity, whereas Longfeng’s water quality suggests weak aquifer–coal seam hydraulic connectivity. The difference in water hazard threats between the two mining areas primarily stems from variations in sedimentary microfacies and fault structures. Full article

Ziziphus joazeiro Mart., commonly known as juazeiro, is a tree species native to the Brazilian semi-arid region. It produces yellow drupe fruits with white, mucilaginous pulp known as juá. Various parts of the plant, such as its leaves and stem bark, have been widely used in traditional medicine to treat wounds, ulcers, and dermatitis, as well as to combat fever and bacterial infections. While its fruits are highly recommended for human consumption due to their nutritional composition, they also present significant potential for exploration as a raw material rich in biological properties. This is evidenced by their traditional use in folk medicine and other phytotherapeutic applications, such as the utilization of the trunk bark for oral hygiene and in the production of cosmetics and shampoos, owing to its saponin content, which possesses foaming properties. This review aims to present the relevant literature on Z. joazeiro Mart. while highlighting the comparatively limited attention given to the fruit, particularly regarding its bioactive and functional properties associated with the presence of antioxidant compounds. Full article

A greenhouse solar dryer was used to study the drying behavior of mesquite pods, and a radiation model for participating media was numerically solved to predict the air temperature in the dryer. The model was solved for a stationary state by considering the environmental conditions. The transfer coefficients were calculated for natural and forced convection. In the case of forced convection, an average airflow of 0.5668 m/s (SD = 0.1121) was provided over the trays. The weight of the pods, their temperature, air temperature, ambient temperature, relative humidity, and solar irradiation were recorded. The average heat transfer coefficients for natural and forced convection were 2.9294 W/m² °C and 6.3772 W/m² °C, respectively. The average mass transfer coefficients for natural and forced convection were 0.002987 kg/m² s and 0.00601 kg/m² s, respectively. The greenhouse dryer showed a high dependence on the weather conditions, showing important disturbances to air temperature. For the experiment with forced convection, a reabsorption of moisture was observed during the night; nevertheless, the final moisture content of the pods was below 0.05 g moisture/g dry matter, which was convenient for the subsequent grinding process. The radiation model correctly describes the average air temperature in the greenhouse volume. A reduction in thermal fluctuations will be important to improve the process. Full article

As urbanization accelerates, solid waste volume increases, making municipal solid waste incineration (MSWI) a primary disposal method. However, low combustion efficiency and harmful gas emissions, such as nitrogen oxides (NO_X), contribute to significant environmental pollution. Improving combustion efficiency and reducing pollutants are critical challenges in waste incineration. Due to the process’s complexity and operational fluctuations, traditional PID and model-based methods often fail to deliver optimal results, making this a key research focus. To address this, this paper proposes an optimal control method for the solid waste incineration process, aimed at improving combustion efficiency and reducing emissions. By establishing Radial Basis Function (RBF) neural network prediction models for CO, CO₂, and NO_X, and integrating an improved Pelican Optimization Algorithm (IPOA), an optimized control strategy for air volume and pressure settings is developed. Experimental results demonstrate that the proposed method significantly enhances combustion efficiency while effectively reducing NO_X emissions. Furthermore, under varying operational conditions, the method can adaptively adjust the air volume and pressure settings, ensuring system adaptability to new conditions and maintaining both combustion efficiency and NO_X emission concentrations within target ranges. Full article

This paper presents the development and experimental verification of a second-order polynomial regression model for predicting the adhesion strength of coatings produced by electric arc metallization (EAM). The aim of the study is to optimize three key process parameters: current strength (I), carrier gas pressure (P) and nozzle-to-substrate distance (L) in order to maximize the adhesion strength of the coating to the substrate. Experimental data were obtained from the central composite plan within the response surface method (RSM) and processed using analysis of variance (ANOVA). A pronounced synergistic interaction between pressure and distance was found (P × L), whereas current strength had no statistically significant effect in the range investigated. Optimal parameters (I = 200 A, P = 6.5 bar, L = 190 mm) provided an adhesion strength of ~15.4 kN, which was within 8.5% of the model’s prediction, confirming its accuracy. The proposed two-stage approach—combining statistical modeling with experimental fine-tuning in the global extremum zone—made it possible to improve the accuracy of the forecast and link statistical dependencies with the physical and mechanical mechanisms of adhesion formation (kinetic energy of particles, residual thermoelastic stresses). This method provides engineering-based recommendations for industrial application of EAM, reduces the cost of parameter selection, and improves the reproducibility of coating properties. Full article

Shale gas pressure post-production accompanies the entire production process. The depth of the tubing is crucial for the entire life cycle of production, especially influencing the production dynamics in the middle and later stages of downward-inclined Wells. The full dynamic multiphase flow simulation method is adopted, combined with wellbore structure, fluid composition (gas), gas layer temperature and pressure gradient, production dynamic data, etc., to establish the wellbore structure model of the gas well, simulate the production dynamics under different formation pressures and tubing depths, and determine a reasonable tubing depth. Considering the material balance of the constant-volume gas reservoir and the critical formation pressure of the gas well’s self-injection, the cumulative gas production of the gas well at different tubing depths was analyzed. Taking 11210-1-well as an example, it was believed that when the tubing depth reached 4000 m, the self-injection production time could be extended by 206 days, and the cumulative gas production increased by 5.1 × 10⁶ m³, compared with the tubing depth of 2983 m. The gas production is increased by approximately 12.2 × 10⁶ cubic meters when the tubing depth is 2000 m. The research conclusion can provide theoretical guidance for the optimization of tubing depth during the drainage and production process of shale gas downward-inclined horizontal Wells. Full article

Selective alkaline leaching was evaluated to remove arsenic (As) and antimony (Sb) from a polymetallic copper concentrate from Zimapán, Mexico, where these metalloids cause environmental risk and smelter penalties. Batch tests used sodium sulfide (Na₂S) in alkaline media, varying reagent concentrations and temperature; kinetic modeling identified the rate-controlling step, and X-ray diffraction (XRD) plus scanning electron microscopy/energy-dispersive spectroscopy (SEM–EDS) assessed phase changes. The kinetic analysis indicated chemical control with a higher reaction order for Na₂S than for NaOH. A quadratic regression described the process and identified Na₂S concentration and temperature as the dominant factors. Maximum extractions reached 91.9% for As and 72.1% for Sb while limiting dissolution of value-bearing sulfides, as supported by XRD and SEM–EDS. Environmental indices (I_geo, EF) classified As and Sb as highly contaminating and geochemically enriched in the feed, underscoring the need for selective removal. Overall, alkaline leaching with Na₂S provides a technically feasible and environmentally favorable route to remediate metalloids and upgrade polymetallic concentrates. Full article

This article takes the perspective of Hydrogen Load Aggregator (HLA) to optimize the declaration strategy of peak shaving market, improve the flexible regulation capability of power system and HLA economy as the research objectives, and proposes an optimization strategy method for HLA to participate in peak shaving market. Firstly, an improved Convolutional Neural Networks–Long Short-Term Memory (CNN-LSTM) time series prediction model is developed to address peak shaving demand uncertainty. Secondly, a bidding strategy model incorporating dynamic pricing is constructed by comprehensively considering electrolyzer regulation costs, market supply–demand relationships, and system constraints. Thirdly, a market clearing model for peak shaving markets with HLA participation is designed through analysis of capacity contribution and marginal costs among different regulation resources. Finally, the capacity allocation model is designed with the goal of minimizing the total cost of peak shaving among various stakeholders within HLA, and the capacity won by HLA in the peak shaving market is reasonably allocated. Simulations conducted on a Python3.12-based experimental platform demonstrate the following: the improved CNN-LSTM model exhibits strong adaptability and robustness, the bidding model effectively enhances HLA market competitiveness, and the clearing model reduces system operator costs by 5.64%. Full article

In this study, we investigate the recovery of Li and Co from spent LiCoO₂ cathodes of spent lithium batteries using a combined approach of mechanochemical activation (MA) and ammoniacal leaching. High-energy ball milling disrupts the layered structure of LiCoO₂, introduces defects, and increases surface area, which strongly improves subsequent dissolution. Leaching experiments in an ammonia–ammonium sulphate–sulphite medium were optimized by varying the solid-to-liquid ratio, sodium sulfite concentration, and temperature. Under the best conditions (90 °C, 120 min, S/L = 10 g/L, 0.5 M Na₂SO₃), nearly complete recoveries were obtained: 99.5% Li and 96.5% Co. Kinetic modeling based on the shrinking-core model confirmed that dissolution of both metals is controlled by chemical reaction, with activation energies of 45.7 kJ·mol⁻¹ for Li and 60.7 kJ·mol⁻¹ for Co. Structural and morphological analyses (XRD, SEM) supported the enhanced reactivity of the activated material. The study demonstrates that MA coupled with optimized ammoniacal leaching provides an efficient process for LiCoO₂ recycling, without using aggressive mineral acids and long treatment times. Full article