Processes

18 pages, 1115 KB

Open AccessArticle

Effect of Diffusion Path Lengths on Effective Moisture Diffusivity and Activation Energy of Red Delicious Apple Slices Under Convective Drying

by Oldřich Dajbych, Abraham Kabutey, Čestmír Mizera, Aleš Sedláček and David Herak

Processes 2026, 14(12), 2015; https://doi.org/10.3390/pr14122015 (registering DOI) - 21 Jun 2026

Abstract

The study analyzed the effect of diffusion path lengths (initial, average, and final half-thicknesses) on the shrinkage, effective moisture diffusivity, activation energy, and pre-exponential factor of thin-layer red delicious apple slices under convective drying conditions (temperature from 40 °C to 80 °C at [...] Read more.

The study analyzed the effect of diffusion path lengths (initial, average, and final half-thicknesses) on the shrinkage, effective moisture diffusivity, activation energy, and pre-exponential factor of thin-layer red delicious apple slices under convective drying conditions (temperature from 40 °C to 80 °C at 10 h drying time). The results show that the shrinkage increased from 31.09% at 40 °C to a maximum of 42.65% at 70 °C, then slightly decreased to 36.77% at 80 °C, indicating that shrinkage did not increase linearly with drying temperature. The diffusion path lengths yielded effective moisture diffusivities ranging from 1.43 × 10⁻¹⁰ to 10.31 × 10⁻¹⁰ m²/s, with the average characteristic length providing the most realistic representation of the effective moisture diffusivity. The high coefficient of determination (R² = 0.965), consistent with the model efficiency value, confirms that the Arrhenius model fits the experimental diffusivity data across the temperature range studied. The mean absolute percentage error of 12% between the experimental and predicted diffusivities confirms the reliability of Fick’s and Arrhenius models. The activation energy ranged from 21.56 to 26.03 kJ/mol across diffusion path lengths, indicating a moderate sensitivity of moisture diffusion to temperature. Full article

(This article belongs to the Section Chemical Processes and Systems)

► Show Figures

Figure 1

9 pages, 453 KB

Open AccessReview

A Review on Numerical Simulation and Modeling Techniques in Blast Furnace Ironmaking

by Shanchao Gao, Xu Geng, Xiaobo Zhang, Zhe Jiang, Zhenghong Zhao and Yanhui Zhang

Processes 2026, 14(12), 2014; https://doi.org/10.3390/pr14122014 (registering DOI) - 20 Jun 2026

Abstract

Blast furnace (BF) ironmaking is a complex multiphase process involving gas–solid flow, heat transfer, chemical reactions, burden movement, and phase transformation under high-temperature conditions. Since many internal states of the blast furnace cannot be directly observed during operation, numerical simulation and mathematical modeling [...] Read more.

Blast furnace (BF) ironmaking is a complex multiphase process involving gas–solid flow, heat transfer, chemical reactions, burden movement, and phase transformation under high-temperature conditions. Since many internal states of the blast furnace cannot be directly observed during operation, numerical simulation and mathematical modeling have become important tools for understanding furnace behavior and optimizing operational parameters. This paper reviews recent advances in blast furnace numerical simulation and internal state reconstruction methods. Existing approaches, including packed-bed flow models, cohesive zone reconstruction methods, burden distribution models, and temperature field prediction methods, are summarized and discussed. In addition, the evolution of blast furnace mathematical models from early one-dimensional steady-state formulations to modern three-dimensional multifluid and hybrid simulation approaches is reviewed. Recent developments in computational fluid dynamics (CFD), the discrete element method (DEM), digital twin, and data-driven modeling are also discussed. Compared with traditional simplified models, modern multidimensional and hybrid approaches show improved capability in describing asymmetric furnace inner states, multiphase transport behavior, and operational parameter effects under industrial conditions. However, challenges still remain in achieving computational efficiency, parameter calibration, multiphase coupling, and real-time industrial application. Future studies are expected to focus on the integration of mechanism-based simulation and intelligent data-driven methods to improve prediction accuracy, operational adaptability, and intelligent control capability in blast furnace ironmaking. Full article

(This article belongs to the Special Issue Development, Optimization and Simulation of Metallurgical Extraction Processes)

► Show Figures

Figure 1

22 pages, 4652 KB

Open AccessArticle

Vacuum–Centrifugal Circulation Defoaming of High-Viscosity Sodium Alginate Solutions: Process Optimization and Kinetic Modeling

by Jianping Zhu, Minli Zheng, Hongxiang Xu, Sijun Feng, Hao Wang and Ming Song

Processes 2026, 14(12), 2013; https://doi.org/10.3390/pr14122013 (registering DOI) - 20 Jun 2026

Abstract

High-viscosity sodium alginate solutions (4.5% by mass, apparent viscosity 1 × 10⁴–2 × 10⁴ cP) are widely used in the preparation of hydrogels, wet spinning, and biomedical materials. Residual bubbles can cause internal voids in hydrogels, mechanical heterogeneity, fiber breakage [...] Read more.

High-viscosity sodium alginate solutions (4.5% by mass, apparent viscosity 1 × 10⁴–2 × 10⁴ cP) are widely used in the preparation of hydrogels, wet spinning, and biomedical materials. Residual bubbles can cause internal voids in hydrogels, mechanical heterogeneity, fiber breakage during spinning, and reduced strength, and can severely affect the cell compatibility and clinical safety of biomaterials. Due to the difficulty of bubble migration, coalescence, and rupture in high-viscosity systems, traditional vacuum-standing degassing takes up to 24 h and is extremely inefficient, severely limiting the quality of subsequent processing. To address this issue, this study proposes a novel vacuum-assisted centrifugal recirculating degassing method for highly viscous sodium alginate solutions and aims to establish a kinetic framework for describing its overall degassing behavior. Using the number density of bubbles larger than 0.5 mm in diameter as an evaluation metric, we conducted vacuum-standing control experiments and univariate experiments with different screen mesh apertures (5, 1.5, 0.3, and 0.07 mm). We experimentally verified a continuous kinetic model of bubble number decay based on vacuum bubble expansion, centrifugally enhanced migration, and removal probability during the cycle. The results indicate that the bubble removal effect of 40 min of vacuum–centrifugal cyclic degassing is equivalent to that of 4 h of vacuum static settling, representing a 450% increase in degassing efficiency. There is an optimal range for a screen aperture, with the best degassing effect observed at 0.3 mm, achieving a bubble removal rate of 83.69%. The established kinetic model exhibits good fitting accuracy (RMSE = 0.17, MAPE = 5.9%) and can accurately predict degassing efficiency under different process conditions. This study provides a quantifiable, modelable, and optimizable process scheme for rapid degassing of high-viscosity sodium alginate solutions, and offers a theoretical reference for the development of degassing technologies for high-viscosity polysaccharide fluids. Full article

(This article belongs to the Section Manufacturing Processes and Systems)

► Show Figures

Figure 1

27 pages, 18723 KB

Open AccessArticle

Physics-Guided Dual-Stream Fusion for Extreme Few-Shot Fault Diagnosis Under Massive Domain Shifts

by Shiqian Wu, Weiming Zhang, Huiyu Liu, Yuchen Lu and Yuxuan Zhang

Processes 2026, 14(12), 2012; https://doi.org/10.3390/pr14122012 (registering DOI) - 20 Jun 2026

Abstract

Reliable fault diagnosis of rotating machinery is critical for averting serious failures in modern industrial systems. While data-driven deep learning has advanced condition monitoring, its success is fundamentally predicated on the availability of independent and identically distributed (I.I.D.) datasets. In realistic operational environments, [...] Read more.

Reliable fault diagnosis of rotating machinery is critical for averting serious failures in modern industrial systems. While data-driven deep learning has advanced condition monitoring, its success is fundamentally predicated on the availability of independent and identically distributed (I.I.D.) datasets. In realistic operational environments, machinery frequently experiences massive domain shifts induced by varying rotational speeds. Concurrently, acquiring high-fidelity fault instances is limited compared to abundant healthy baseline data, often resulting in a long-tailed distribution. Under such data-starved conditions, conventional few-shot domain adaptation (FSDA) methodologies often may be affected by distributional erasure; global alignment objectives are mainly driven by the healthy majority, causing sparse fault signatures to be erroneously absorbed as noise and leading to severe diagnostic performance degradation. To address this setting, this study develops a physics-guided dual-stream fusion framework for extreme few-shot cross-domain fault diagnosis. The method does not treat the Laplace wavelet, STFT, CNNs, or AdaBN as newly introduced techniques. Instead, it integrates these components into a unified diagnostic pipeline designed for long-tailed target support sets under large speed shifts. A learnable Laplace wavelet convolution is used in the temporal branch to emphasize transient impact responses, while STFT spectrograms provide a complementary time-frequency representation for the two-dimensional branch. The two feature streams are then fused for target fault classification. For domain adaptation, a Strict AdaBN strategy is applied using only the target support set, rather than the target test data or a large unlabeled target pool. Under the evaluated 50 healthy + 12 fault support condition, the healthy samples provide target-domain operating-background statistics for BN recalibration, while the limited fault samples are used for supervised classifier adjustment. Experiments on the HUSTbearing and Torino DIRG datasets show that the proposed integrated framework achieves stable performance under the evaluated few-shot cross-speed settings. These results suggest that combining physics-guided Laplace convolution, time-frequency representations, and support-set-restricted BN recalibration can be useful for bearing fault diagnosis when target fault samples are limited. Full article

(This article belongs to the Section Manufacturing Processes and Systems)

30 pages, 11780 KB

Open AccessArticle

A Physics-Informed Neural Network for Unified Multi-Regime Pressure-Drop Representation of Inflow Control Devices in Reservoir–Wellbore Coupled Simulation

by Qingshuang Jin, Yongchao Xue, Junjian Li, Zhi Fan, Tao Jiao, Yan Lei, Jiangpeng Hu, Xiangyu Ren, Ying Zhang, Wenhao Zhang and Leihongbo Qiao

Processes 2026, 14(12), 2011; https://doi.org/10.3390/pr14122011 (registering DOI) - 20 Jun 2026

Abstract

Accurate representation of the pressure drop–flow rate (Δp–q) relationship of nozzle-type inflow control devices (ICDs) is critical for reliable reservoir–wellbore coupled simulation. Conventional ICD models in reservoir simulators rely primarily on empirical correlations or tabulated data, but commonly used formulations cannot consistently capture [...] Read more.

Accurate representation of the pressure drop–flow rate (Δp–q) relationship of nozzle-type inflow control devices (ICDs) is critical for reliable reservoir–wellbore coupled simulation. Conventional ICD models in reservoir simulators rely primarily on empirical correlations or tabulated data, but commonly used formulations cannot consistently capture the linear behavior in the low-flow regime or the transition between flow regimes, which may reduce physical fidelity and numerical robustness. To overcome this limitation, this study proposes a unified characteristic-curve representation that integrates linear, transitional, and quadratic flow regimes into a single continuous and differentiable function through a physically constrained least-squares formulation, and further develops a physics-informed neural network (PINN) to learn the ICD pressure–flow relationship while enforcing physical consistency. The trained PINN model is embedded into a multi-segment well model within a reservoir–wellbore coupled simulation framework and evaluated using a mechanistic reservoir model containing permeability streaks with varying permeabilities. The results show that the proposed method improves numerical convergence and accurately reproduces ICD pressure–flow behavior across multiple flow regimes, providing a more physically consistent and robust representation of ICD performance for inflow control analysis and reservoir simulation. Full article

(This article belongs to the Topic Advances and Application in Intelligent Oil and Gas Field Development Technology)

► Show Figures

Figure 1

23 pages, 5222 KB

Open AccessArticle

Fracture Interferences in Combined Vertical–Horizontal Well Patterns and Their Field Application

by Shuai Li, Guangqing Zhang and Hu Cao

Processes 2026, 14(12), 2010; https://doi.org/10.3390/pr14122010 (registering DOI) - 20 Jun 2026

Abstract

Combined Vertical–Horizontal Well Patterns (CVHWPs) have been increasingly applied in mature and complex reservoirs, such as the C5 Block. Their application is attractive because they provide extensive reservoir coverage and high development efficiency. However, close well spacing and the three-dimensional configuration of vertical [...] Read more.

Combined Vertical–Horizontal Well Patterns (CVHWPs) have been increasingly applied in mature and complex reservoirs, such as the C5 Block. Their application is attractive because they provide extensive reservoir coverage and high development efficiency. However, close well spacing and the three-dimensional configuration of vertical and horizontal wells can induce strong stress-shadow interference. This interference makes fracture propagation difficult to control and may reduce stimulation effectiveness. To address this problem, a multi-well, multi-fracture induced-stress model for CVHWP stimulation was developed in this study. The model was validated using laboratory three-stage fracturing experiments, including two horizontal-well stages and one vertical-well stage, together with field observations. Across three stages, the calculated stress intensity factors at breakdown are closely matched, validating the induced-stress model. When the vertical well was fractured first, the horizontal principal-stress difference at the adjacent horizontal stage increased by 2.01 MPa, which was unfavorable for branched fracture development. In contrast, when the horizontal stage was fractured first, the stress difference decreased by 3.25 MPa at the subsequent horizontal stage and by 3.89 MPa at the vertical-well stage. This sequence is preferable because fractures generated from the vertical well impose a stronger stress perturbation on adjacent horizontal-well fractures than fractures generated from the horizontal well impose on the subsequent vertical-well fracture. Under the tested CVHWP conditions, the horizontal-well fractures tended to form nearly symmetric bi-wing planar fractures, whereas branched fractures were more likely to develop in the vertical well. Therefore, for CVHWP reservoirs with close vertical–horizontal well spacing and significant stress interference, fracturing the horizontal well before the vertical well is recommended to control fracture propagation and promote multiple-fracture formation. Field application of this sequence showed notable production improvement, indicating that the proposed method can provide practical guidance for unconventional well-pattern fracturing design. Full article

(This article belongs to the Special Issue Advances in Drilling and Excavation Theories and Technologies for Petroleum, Coal Seam and Sustainable Resources)

► Show Figures

Figure 1

28 pages, 2958 KB

Open AccessArticle

Carbon Responsibility Allocation Method and Optimal Scheduling Strategy for Park Integrated Energy Systems Considering User Heterogeneity

by Zhixin Fu, Hao Wang, Haixin Wu and Jian Wang

Processes 2026, 14(12), 2009; https://doi.org/10.3390/pr14122009 (registering DOI) - 20 Jun 2026

Abstract

Low-carbon operation and reasonable carbon responsibility allocation are essential for improving source-load coordinated emission reduction in park integrated energy systems (PIESs). Existing allocation methods usually trace carbon emissions or calculate marginal contributions, but they still have difficulty distinguishing heterogeneous park users with different [...] Read more.

Low-carbon operation and reasonable carbon responsibility allocation are essential for improving source-load coordinated emission reduction in park integrated energy systems (PIESs). Existing allocation methods usually trace carbon emissions or calculate marginal contributions, but they still have difficulty distinguishing heterogeneous park users with different load rigidity, demand response (DR) capability, payment capability and real carbon-reduction potential. To address this problem, this paper proposes a carbon responsibility allocation method for PIESs considering user heterogeneity and develops a carbon-cost-feedback-based bi-level low-carbon scheduling model. First, park users are classified into high-energy-consuming industrial users, commercial and public service users, and energy infrastructure users according to quantitative criteria related to energy consumption scale, load continuity, adjustable load proportion and distributed-resource interaction capability. A heterogeneity indicator system is then established, including DR elasticity, electricity utilization efficiency, payment capability, DR potential and actual carbon-reduction potential. Second, an improved Shapley value allocation model is constructed by combining coalition marginal contribution with entropy-weighted heterogeneity correction. The allocation results are converted into user-side carbon responsibility cost signals and embedded into a bi-level optimal scheduling model, where the upper level minimizes the system operating cost and the lower level minimizes users’ integrated energy-use cost. Case studies show that, compared with the conventional economic scheduling scenario, the proposed model reduces the total system cost from CNY 5.0782 million to CNY 4.3258 million and decreases carbon emissions from 14,994.39 t to 10,874.62 t, corresponding to reductions of 14.82% and 27.47%, respectively. The results indicate that the proposed method can coordinate fairness-oriented carbon responsibility allocation with incentive-oriented low-carbon scheduling, supporting both SDG 11 and SDG 12. Full article

(This article belongs to the Section Energy Systems)

23 pages, 6843 KB

Open AccessArticle

Simulation of Purging and Injection in Long-Distance Liquid Ammonia Pipeline Commissioning Process

by Pengbo Yin, Bo Wang, Peiyan Zeng, Wen Yang, Junwen Chen, Zhenchao Li, Weidong Li, Jiaqing Li, Lin Teng and Lilong Jiang

Processes 2026, 14(12), 2008; https://doi.org/10.3390/pr14122008 (registering DOI) - 20 Jun 2026

Abstract

With the expansion of ammonia energy applications, long-distance liquid ammonia pipelines are expected to support large-scale cross-regional ammonia transport. In the liquid ammonia pipeline commissioning process, gaseous ammonia purging involves ammonia–nitrogen mixing and possible liquefaction, while liquid ammonia injection may induce flashing and [...] Read more.

With the expansion of ammonia energy applications, long-distance liquid ammonia pipelines are expected to support large-scale cross-regional ammonia transport. In the liquid ammonia pipeline commissioning process, gaseous ammonia purging involves ammonia–nitrogen mixing and possible liquefaction, while liquid ammonia injection may induce flashing and severe local cooling, all of which can affect commissioning safety. To characterize these thermodynamic phenomena, a transient gas–liquid two-phase flow model was established and validated using OLGA 2022.1.0 software for simulating the long-distance liquid ammonia pipeline commissioning. The model adopts the cross-sectionally averaged one-dimensional approach. A volume-corrected Soave–Redlich–Kwong (SRK) equation of state for ammonia was adapted, validated, and used to generate OLGA-compatible thermodynamic property tables. The results show that, during gaseous ammonia purging, a higher flowrate shortens the displacement time by accelerating nitrogen removal, and this effect is more pronounced at higher ambient temperatures due to enhanced molecular diffusion. Along the pipeline, pressure gradually decreases from frictional resistance, with a steeper drop near the outlet caused by gas acceleration, and temperature gradually approaches ambient through heat exchange with the pipe wall and surrounding soil. A high gaseous ammonia flowrate can cause partial liquefaction, regasification, and temperature fluctuations. During liquid ammonia injection, local condensation and slight liquid accumulation occur before the liquid front arrives, and the low-temperature region moves with the liquid front. The liquid ammonia mass flowrate has the strongest influence on the injection process, as it reduces the completion time but increases the outlet temperature, outlet pressure, and the low-temperature risk downstream of the valve. Therefore, it should be controlled within an appropriate range to balance efficiency and low-temperature safety risks. This work provides a rapid and efficient prediction model for key thermo-hydraulic parameters during liquid ammonia pipeline commissioning, and the overall analyses offer insights for on-site process design and safety control. Full article

(This article belongs to the Special Issue Process Modeling and Risk Assessment of Ammonia and Hydrogen Storage and Transport Systems)

► Show Figures

Figure 1

20 pages, 10688 KB

Open AccessArticle

A Study on the Thermal Behaviour of Micron-Sized Aluminium Powder in Contact with Water

by Xiaoliang Zhang, Haidan Cao, Jiawei Fang, Jun Zhang and Lingyun Wang

Processes 2026, 14(12), 2007; https://doi.org/10.3390/pr14122007 (registering DOI) - 20 Jun 2026

Abstract

Aluminium powder, an energetic material, is prone to thermal runaway upon water exposure under local heat sources, yet the nonadiabatic mechanisms of micron-sized accumulated aluminium powder under localised heating remain unclear. This study employs a proprietary characterisation platform to investigate the effects of [...] Read more.

Aluminium powder, an energetic material, is prone to thermal runaway upon water exposure under local heat sources, yet the nonadiabatic mechanisms of micron-sized accumulated aluminium powder under localised heating remain unclear. This study employs a proprietary characterisation platform to investigate the effects of particle size, water content, and local heat source power on heat transfer in the dry state and on parameters including induction time, onset temperature, peak heat release rate, and reaction heat during the induction and main reaction phases. In the dry state, decreasing particle size enhances effective thermal conductivity and accelerates temperature rise, whereas elevated local heat source power exacerbates thermal inertia. Under local heating upon water exposure, reduced particle size significantly enhances reactivity; the reaction heat of 2 μm powder reaches 983 J/g, approximately fourfold that of 106 μm powder. Water content exhibits a nonmonotonic effect, with the onset temperature reaching a minimum of 66.4 °C at a water content of 25%, while the reaction heat peaks at 33% water content. Interestingly, increasing local heat source power was found to suppress reaction intensity, and reaction heat at 10 W is one sixth of that at 2.5 W, attributed to rapid product layer densification and the possible steam-film barrier effect shifting the controlling mechanism from chemical to diffusion control. A coupled multifactorial predictive model incorporating the three factors was established with a correlation coefficient R² of 0.92, providing a theoretical basis and practical guidance for hazard assessment and safe storage of aluminium powder. Full article

(This article belongs to the Section Chemical Processes and Systems)

► Show Figures

Figure 1

20 pages, 2491 KB

Open AccessArticle

Mechanical Mechanism of Abnormally High Pumping Pressure During Hydraulic Fracturing of Deep-to-Ultra-Deep Fine Sandstone Reservoirs in the Junggar Basin

by Liyan Pan, Han Song, Jian Zhou, Beibei Chen, Qi Chen, Yiyu Bao, Zerun Duan, Zewei Liu, Xiaohan Wang and Yan Peng

Processes 2026, 14(12), 2006; https://doi.org/10.3390/pr14122006 (registering DOI) - 20 Jun 2026

Abstract

To address the widespread issue of abnormally high pump pressure during hydraulic fracturing of deep-to-ultra-deep reservoirs (burial depth > 4500 m) in the Junggar Basin, this study systematically reveals the mechanical mechanism underlying this phenomenon by integrating well logging curve analysis and elastoplastic [...] Read more.

To address the widespread issue of abnormally high pump pressure during hydraulic fracturing of deep-to-ultra-deep reservoirs (burial depth > 4500 m) in the Junggar Basin, this study systematically reveals the mechanical mechanism underlying this phenomenon by integrating well logging curve analysis and elastoplastic mechanics theory. Statistical results demonstrate that the actual fracture initiation pressure of 60% of wells in the target block is significantly higher than the values predicted by traditional elastic theory, primarily attributed to plastic yielding and stress concentration effects around perforations induced by high in situ stress. An elastoplastic rock fracture initiation pressure model is established based on the Mohr–Coulomb criterion and the plastic zone radius criterion, which is applied to predict the fracture initiation pressure of selected wells in the target block. The relative error between the model predictions and field measurements is less than 2%, significantly improving the prediction accuracy of fracture initiation pressure in deep-to-ultra-deep formations. This provides precise guidance for subsequent optimization of operational parameters and selection of pressure ratings for wellhead equipment. The study further clarifies that in situ stress difference, rock yield stress, and the power-law hardening exponent are the key factors controlling the transition of fracture initiation modes. To mitigate the high pump pressure challenge in deep-to-ultra-deep reservoir fracturing, the field application of weighted fracturing fluid effectively increases the wellbore hydrostatic column pressure, reduces wellhead operational pressure, and ensures construction safety. The findings of this study provide critical theoretical and technical support for achieving the goal of “successful fracture initiation and effective fracture control” in deep-to-ultra-deep reservoir fracturing. Full article

(This article belongs to the Special Issue Hydraulic Fracturing Experiment, Simulation, and Optimization)

► Show Figures

Figure 1

19 pages, 20367 KB

Open AccessArticle

Sloshing-Induced Thermo-Hydrodynamic Characteristics of Onboard Liquid Hydrogen Cylinders: Effects of Filling Ratio

by Chenshu Xu, Hua Ding and Hui Wu

Processes 2026, 14(12), 2005; https://doi.org/10.3390/pr14122005 (registering DOI) - 20 Jun 2026

Abstract

The safety and stability of onboard Liquid Hydrogen (LH₂) storage systems depend strongly on gas–liquid two-phase flow, heat transfer, and phase change under sloshing; however, the coupled influence of filling ratio and sloshing on thermo-hydrodynamic behavior remains underexplored. We develop a [...] Read more.

The safety and stability of onboard Liquid Hydrogen (LH₂) storage systems depend strongly on gas–liquid two-phase flow, heat transfer, and phase change under sloshing; however, the coupled influence of filling ratio and sloshing on thermo-hydrodynamic behavior remains underexplored. We develop a Volume of Fluid (VOF)-based two-phase Computational Fluid Dynamics (CFD) model in ANSYS Fluent to quantify interfacial dynamics, pressure response, and temperature-field evolution in LH₂ tanks subjected to sinusoidal acceleration for filling ratios from 10% to 90%. Increasing the filling ratio strengthens net condensation in the ullage and thus intensifies depressurization. As the filling ratio increases from 10% to 90%, the pressure reduction over the 2.0 s sloshing process increases from 0.418 kPa to 2.410 kPa, and the corresponding initial depressurization rate rises from 0.209 to 1.205 kPa s⁻¹. Free-surface motion decreases with filling ratio: at 10%, large interface excursions can induce gas-cavity formation and splashing, increasing the risk of intermittent propellant supply, whereas at 90% the interface is constrained and oscillations are suppressed. Higher filling ratios lead to faster ullage cooling and larger temperature oscillations. The liquid warms modestly, and its warming rate decreases nonlinearly with filling ratio, consistent with the larger effective thermal mass at higher fillings. Overall, the obtained mechanistic understanding can support the engineering design of onboard LH₂ tanks, including filling-ratio selection and thermal-management optimization under sloshing conditions. Full article

(This article belongs to the Section Chemical Processes and Systems)

► Show Figures

Figure 1

25 pages, 15482 KB

Open AccessArticle

An Attention-Based Deep Learning Method for Acoustic Emission Arrival Picking in True Triaxial Hydraulic Fracturing Experiments

by Ji Lu and Botao Lin

Processes 2026, 14(12), 2004; https://doi.org/10.3390/pr14122004 (registering DOI) - 20 Jun 2026

Abstract

Accurate arrival picking of acoustic emission (AE) data is essential for AE event localization and hydraulic fracture characterization in true triaxial hydraulic fracturing experiments. However, conventional arrival picking methods are highly sensitive to manually defined thresholds, whereas existing deep learning models are constrained [...] Read more.

Accurate arrival picking of acoustic emission (AE) data is essential for AE event localization and hydraulic fracture characterization in true triaxial hydraulic fracturing experiments. However, conventional arrival picking methods are highly sensitive to manually defined thresholds, whereas existing deep learning models are constrained by low signal-to-noise ratios (SNRs) and limited AE dataset sizes. To address these challenges, this study proposes an attention-based deep learning method for AE arrival picking. The proposed method introduces an attention mechanism into the PhaseNet framework to suppress noise feature transmission in the skip connections. In addition, a kernel density estimation (KDE)-based label smoothing strategy was adopted to alleviate label imbalance and account for arrival-time uncertainty. The results demonstrate that the proposed method reduced the mean absolute error (MAE) by 10.58%, 92.92%, and 98.25% compared with PhaseNet, STA/LTA, and AR-AIC, respectively. The proposed method exhibited superior picking accuracy, robustness, and computational efficiency relative to the other methods, providing a reliable foundation for AE event localization and high-precision AE monitoring in hydraulic fracturing experiments. Full article

(This article belongs to the Special Issue Applications of Intelligent Models in the Petroleum Industry, 2nd Edition)

► Show Figures

Figure 1

21 pages, 19187 KB

Open AccessArticle

Optimization Design Methods for Development Parameters of Tight Oil and Gas Reservoirs

by Xiangwu Bai, Zhiping Li and Fengpeng Lai

Processes 2026, 14(12), 2003; https://doi.org/10.3390/pr14122003 (registering DOI) - 19 Jun 2026

Abstract

Tight oil and gas reservoirs have become an important alternative to conventional hydrocarbon resources worldwide. They are characterized by dense formations, strong heterogeneity, and the low natural productivity of individual wells, making well pattern deployment and injection–production parameter optimization highly challenging. In real [...] Read more.

Tight oil and gas reservoirs have become an important alternative to conventional hydrocarbon resources worldwide. They are characterized by dense formations, strong heterogeneity, and the low natural productivity of individual wells, making well pattern deployment and injection–production parameter optimization highly challenging. In real development, tight oil and gas fields usually involve hundreds or even thousands of wells. If each well is analyzed and optimized individually, a large amount of computation is required. Meanwhile, uncertainty in geological models further increases the complexity of development scheme design. Traditional manual adjustment methods based on engineering experience are inefficient and make it difficult to obtain an optimal well pattern suitable for the efficient development of tight oil and gas reservoirs under complex constraints, thus showing obvious limitations. To address these problems, this study first analyzes the strengths, weaknesses, and applicability of existing well placement optimization methods. Based on this analysis, we propose an optimization design method that integrates numerical simulation software for tight oil and gas reservoirs with modern intelligent optimization algorithms, enabling rapid and effective integrated optimization of horizontal well placement and fracturing in tight reservoirs. After being applied to Block X of a tight oil field, this optimization method achieved favorable field results, with an average cumulative oil and gas equivalent production of 31,400 metric tons per well, providing a new approach for the effective development of similar tight oil and gas reservoirs. Full article

(This article belongs to the Section Petroleum and Low-Carbon Energy Process Engineering)

► Show Figures

Figure 1

19 pages, 3863 KB

Open AccessArticle

Heterologous Expression and Enzymatic Characterization of a Stable β-Galactosidase from Aspergillus niger

by Yuanyuan Dong, Jiamin Qian, Haiyang Huang, Yang Liu, Jingwen Zhang, Xiangwen Yin and Zhiqiang Cai

Processes 2026, 14(12), 2002; https://doi.org/10.3390/pr14122002 (registering DOI) - 19 Jun 2026

Abstract

β-Galactosidase is an important enzyme for lactose hydrolysis because it catalyzes the conversion of lactose into glucose and galactose. In this study, Aspergillus niger C18, which showed β-galactosidase-producing ability during preliminary screening, was selected as the gene source. A β-galactosidase gene from this [...] Read more.

β-Galactosidase is an important enzyme for lactose hydrolysis because it catalyzes the conversion of lactose into glucose and galactose. In this study, Aspergillus niger C18, which showed β-galactosidase-producing ability during preliminary screening, was selected as the gene source. A β-galactosidase gene from this strain was cloned into the pET28a vector and heterologously expressed in Escherichia coli. Solid-state fermentation conditions were optimized to produce the native enzyme as a reference for comparison. The enzymatic properties of the recombinant enzyme were then systematically characterized and compared with those of the native enzyme. The recombinant β-galactosidase exhibited favorable thermal and pH stability. After incubation for 2 h at its optimal pH and optimal temperature, the recombinant enzyme retained 88.9% and 94.1% of its initial activity, respectively; specifically, 88.9% corresponded to pH stability and 94.1% corresponded to thermal stability. These results indicate favorable stability of the recombinant enzyme under the tested conditions. Thin-layer chromatography and high-performance liquid chromatography analyses confirmed that the recombinant enzyme efficiently hydrolyzed lactose in a model lactose solution, achieving more than 99.0% lactose degradation after 12 h of reaction. These findings suggest that β-galactosidase derived from A. niger C18 is a promising candidate for lactose hydrolysis. Full article

(This article belongs to the Section Catalysis Enhanced Processes)

► Show Figures

Figure 1

18 pages, 3878 KB

Open AccessArticle

CFD Analysis of the Thermal-Hydraulic Performance in a Fin Channel of a Solar Air Heater with Various Block Shapes

by Byeong-Hwa An, Eflita Yohana, Kwang-Am Moon and Hwi-Ung Choi

Processes 2026, 14(12), 2001; https://doi.org/10.3390/pr14122001 (registering DOI) - 19 Jun 2026

Abstract

A solar air heater generates heated air using solar energy. This system has a relatively simple design, which reduces the initial cost and facilitates maintenance compared with other solar systems. However, its thermal conversion efficiency is limited by the poor thermal conductivity of [...] Read more.

A solar air heater generates heated air using solar energy. This system has a relatively simple design, which reduces the initial cost and facilitates maintenance compared with other solar systems. However, its thermal conversion efficiency is limited by the poor thermal conductivity of air. Previous studies have improved thermal efficiency by enhancing either the heat transfer area or the heat transfer coefficient, but most have applied only one of these approaches. In this work, a novel solar air heater with longitudinal fins and blocks, designed to simultaneously enhance the heat transfer area and heat transfer coefficient, is investigated for various block shapes (rectangular, forward-chamfered, backward-chamfered, and triangular blocks) utilizing computational fluid dynamics. Compared to the smooth fin channel, heat transfer is enhanced by a maximum of 1.61 times with the backward-chamfered block, while the corresponding enhancement factors for the rectangular, forward-chamfered, and triangular blocks are 1.52, 1.46, and 1.54, respectively. The thermo-hydraulic performance parameter, which simultaneously evaluates heat transfer augmentation and frictional penalty, further indicates that the backward-chamfered block is most effective at Reynolds numbers below 6000, while the rectangular block performs best above 9000. Full article

(This article belongs to the Special Issue Solar Energy and Heat Transfer Monitoring and Simulation)

18 pages, 1546 KB

Open AccessArticle

Niobium Recovery from Eschynite-Type Niobium Ore via Flotation

by Hongjing Li, Mannian Li, Feng Jiang, Hui Ren, Jianfei Liu, Jia Jia, Zhuohan Song and Guixia Fan

Processes 2026, 14(12), 2000; https://doi.org/10.3390/pr14122000 (registering DOI) - 19 Jun 2026

Abstract

As a rare metal element, niobium is widely used in steel, electronics, aerospace and many other fields. Eschynite is one of the most important niobium-bearing minerals in Bayan Obo niobium ores. Investigating the beneficiation process and associated reagents is of great significance for [...] Read more.

As a rare metal element, niobium is widely used in steel, electronics, aerospace and many other fields. Eschynite is one of the most important niobium-bearing minerals in Bayan Obo niobium ores. Investigating the beneficiation process and associated reagents is of great significance for improving niobium resource utilization. In this study, mixed ore with eschynite as the main niobium-bearing mineral was used as the research object. Under the condition that the Nb₂O₅ grade of the feed ore was 0.37%, a niobium concentrate with an Nb₂O₅ grade of 5.250% was obtained through one rougher stage and four cleaner stages, followed by magnetic separation. The content of eschynite in the niobium concentrate increased from 0.76% in the run-of-mine ore to 26.32%, with an enrichment ratio of 34.63 times, and the proportion of eschynite in all niobium-bearing minerals rose from 50.67% to 86.10%. Experimental results show that the combined flotation–magnetic separation process can realize the efficient concentration of niobium minerals dominated by eschynite, providing a technical reference for the subsequent development and utilization of eschynite-type niobium ore resources. Full article

(This article belongs to the Special Issue Mineral Processing Equipments and Cross-Disciplinary Approaches)

51 pages, 4795 KB

Open AccessArticle

A Parametric Life Cycle–Energy Modeling Framework for Evaluating Plastic Waste-to-Energy Systems Under Variable Grid Carbon Intensity

by Lydia Pérez Pastrana, David A. Buentello-Montoya, Jorge A. Ascencio and Iván García Kerdan

Processes 2026, 14(12), 1999; https://doi.org/10.3390/pr14121999 (registering DOI) - 19 Jun 2026

Abstract

Waste-to-energy (WtE) systems are frequently proposed as complementary waste-management strategies; however, their climate performance depends on the interaction between thermodynamic efficiency, material circularity, and electricity-system characteristics. Existing life-cycle assessments generally provide static comparisons between landfill and WtE but rarely identify the operating conditions [...] Read more.

Waste-to-energy (WtE) systems are frequently proposed as complementary waste-management strategies; however, their climate performance depends on the interaction between thermodynamic efficiency, material circularity, and electricity-system characteristics. Existing life-cycle assessments generally provide static comparisons between landfill and WtE but rarely identify the operating conditions under which WtE remains environmentally competitive. To address this gap, a parametric life cycle–energy framework was developed by integrating attributional LCA with an analytical energy model capable of evaluating critical efficiency thresholds under varying recovery rates and electricity-grid conditions. Four representative thermoplastics (PET, HDPE, PP, and LDPE) were evaluated using ReCiPe 2016 Midpoint (H) in SimaPro under Mexican electricity conditions (

E F_{grid} = 0.444

kg CO₂eq/kWh). Results indicate that total life-cycle climate impacts are dominated by upstream polymer production, whereas end-of-life management contributes only marginally to overall GWP. Critical-efficiency analysis revealed strong sensitivity to both recovery rate and electricity-grid carbon intensity. For PET, the minimum efficiency required for WtE to outperform landfill increased from 13.1% to 73.5% across the evaluated scenarios, whereas HDPE remained competitive at efficiencies below 1.3%. Monte Carlo simulations (10,000 realizations) further demonstrated that avoided emissions decline systematically with increasing recovery rates, with LDPE exhibiting the highest mean avoided emissions (1735 kg CO₂eq) and PET the lowest (811 kg CO₂eq). These results demonstrate that WtE climate performance is governed primarily by residual waste availability and electricity-system evolution rather than thermodynamic efficiency alone. Consequently, WtE should be interpreted as a transitional residual-waste management strategy whose long-term climate relevance decreases as material circularity and electricity-grid decarbonization advance. Full article

(This article belongs to the Special Issue Optimization and Analysis of Energy System)

► Show Figures

Figure 1

14 pages, 2995 KB

Open AccessArticle

Preparation of a SiO₂@PDA/CS Coated Stainless Steel Mesh with Superhydrophilicity and Underwater Superoleophobicity for Oil–Water Separation

by Zhuangzhuang Zhang, Lingling Ma, Yang Shao, Diandou Xu and Min Luo

Processes 2026, 14(12), 1998; https://doi.org/10.3390/pr14121998 (registering DOI) - 19 Jun 2026

Abstract

To tackle the environmental challenges associated with industrial oily wastewater discharges and recurrent marine oil spill incidents, developing high-efficiency oil–water separation technologies represents a pressing environmental challenge. This research presents a novel design approach comprising the deposition of a stable SiO₂ anchoring [...] Read more.

To tackle the environmental challenges associated with industrial oily wastewater discharges and recurrent marine oil spill incidents, developing high-efficiency oil–water separation technologies represents a pressing environmental challenge. This research presents a novel design approach comprising the deposition of a stable SiO₂ anchoring layer followed by the fabrication of a PDA/CS crosslinked coating, thereby achieving successful construction of a superhydrophilic/underwater superoleophobic (SH/UWSO) coating on stainless steel meshes (SSM). In the first step, SiO₂ microspheres were deposited via vapor deposition to create a micro-rough surface architecture. Subsequently, a dopamine/chitosan (DA/CS) reaction solution was introduced to form a Polydopamine/chitosan (PDA/CS) coating, yielding a SiO₂@PDA/CS-SSM separation membrane. The resulting membrane exhibited separation efficiencies surpassing 99% for various oil–water mixtures, achieving a flux of 1.24 × 10⁵ L·m⁻²·h⁻¹ in petroleum ether systems. Notably, the membrane maintained high efficiency and structural stability even after 25 separation cycles, immersion in strong acid and base solutions for 72 h, and 100 abrasion tests. The rational design of the anchoring and crosslinking layers endows SiO₂@PDA/CS-SSM with high efficiency and stability, making it an effective oil–water separation material. Full article

(This article belongs to the Section Separation Processes)

► Show Figures

Figure 1

88 pages, 1951 KB

Open AccessReview

Non-Thermal Plasma-Ozonation in Water Treatment—Synergistic Effect and Reactor Systems for Organic Micropollutant Removal (Phenolics, Pesticides and Dyes): A Review

by Paul Kaweesa, Michael O. Daramola and Samuel A. Iwarere

Processes 2026, 14(12), 1997; https://doi.org/10.3390/pr14121997 (registering DOI) - 19 Jun 2026

Abstract

Many sectors that sustain humanity’s daily life and wellbeing contribute to the occurrence and accumulation of organic micropollutants (OMPs) in the environment, making them a global concern. This manuscript presents an appraisal of existing scientific literature on removal of OMPs from water by [...] Read more.

Many sectors that sustain humanity’s daily life and wellbeing contribute to the occurrence and accumulation of organic micropollutants (OMPs) in the environment, making them a global concern. This manuscript presents an appraisal of existing scientific literature on removal of OMPs from water by non-thermal plasma-ozonation (NTPO) synergy with specific attention on phenolics, pesticides and herbicides and organic dyes. An overview of non-thermal plasma (NTP) degrading agents in gas and aqueous phases has been given, complemented with diagnostic systems and reactive species detection methods. A scrutiny of reactor systems and their influencing operating parameters has also been discussed. For the analysed types of OMPs, the kinetics, reaction mechanisms and the synergistic degradation effects have been explored. Several studies showed NTPO and NTP/other process synergy resulting in higher degradation efficiency than the individual processes. Most removal reactions followed pseudo-first-order and second-order kinetics while the mechanistic breakdown mainly involved the action of the nonselective OH radical. This scientific critique brings to light utilisable data, provides novel insights on NTPO of OMPs, unveils science gaps for further investigation and presents a wide spectrum of points to consider in plasma water research on OMPs. Full article

(This article belongs to the Special Issue Advances in Plasma Technology for Environmental and Energy Process Engineering)

Journal Description

Processes

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI