Processes

Vacuum arc remelting is the main production method of titanium alloy ingots at present. In order to obtain good quality ingots, it is of great significance to study the formation of the solidification structure of ingots via vacuum arc remelting. In order to select and optimize the nucleation parameters for the solidification microstructure simulation of an ingot, a 3D CAFE model for microstructure evolution during vacuum arc remelting was established, taking into account heat transfer, flow, and solute diffusion. The Gaussian distribution continuous nucleation model and extended KGT model were used to describe the grain nucleation and dendrite tip growth rates, respectively. The multi-point mass source and moving boundary method were used to simulate the ingot growth. The results show that there are three typical crystal regions in the solidification structure of vacuum arc remelting titanium alloy ingots, namely the surface fine crystal region, columnar crystal region, and central equiaxed crystal region. The proportion of the columnar crystal region in the solidification structure of an ingot increases gradually with the increase in the undercooling of the maximum bulk nucleation. With an increase in the maximum bulk nucleation density, the equiaxed grain zone gradually increases, and the grain size gradually decreases. The proportion of the columnar crystal region in the solidification structure of an ingot increases gradually with an increase in the undercooling of the maximum bulk nucleation. The maximum volume nucleation variance has no obvious effect on the change in the solidification structure. When the maximum volume nucleation undercooling is 5.5 K, the maximum volume nucleation standard deviation is 4 K, and the maximum volume nucleation density is 5 × 10⁸. The solidification structure simulation results are in good agreement with the experimental results. Full article

Industrial control systems are often used to assist and manage an industrial operation. These systems’ weaknesses in the various hierarchical structures of the system components and communication backbones make them vulnerable to cyberattacks that jeopardize their security. In this paper, the security of these systems is studied by employing a reinforcement learning extended attack graph to efficiently reveal the subsystems’ flaws. Specifically, an attack graph that mimics the environment is constructed for the system using the state–action–reward–state–action technique, in which the agent is regarded as the attacker. Attackers may cause the greatest amount of system damage with the fewest possible actions if they have the highest cumulative reward. The worst-case assault scheme with a total reward of 42.9 was successfully shown in the results, and the most badly affected subsystems were recognized. Full article

Cement concrete pavements are crucial to urban infrastructure, significantly influencing road safety and environmental sustainability with their anti-skid and noise reduction properties. However, while texturing techniques like transverse grooving have been widely adopted to enhance skid resistance, they may inadvertently increase road noise. This study addressed the critical need to optimize pavement textures to balance improved skid resistance with noise reduction. Tests were conducted to assess the influence of surface texture on skid resistance and noise, exploring the relationship between texture attributes and their performance in these areas. The investigation examined the effects of texture representation methods, mean profile depth, and the high-speed sideway force coefficient (SFC) on noise intensity and pavement skid resistance. The findings revealed that transverse grooves significantly improved the SFC, enhancing skid resistance. In contrast, longitudinal burlap drag, through its micro- and macro-texture adjustments, effectively reduced vibration frequencies between the tire and pavement, thus mitigating noise. Utilizing the TOPSIS multi-objective optimization framework, an optimization model for pavement textures was developed to augment skid resistance and noise reduction at varying speeds. The results indicated that at 60 km/h, an optimal balance of groove width, depth, and spacing yielded superior skid resistance with a minimal noise increase. At 80 km/h, increased groove spacing and depth were shown to effectively decrease noise while maintaining efficient water evacuation. The optimal pavement texture design must consider the specific context, including traffic volume, vehicle types, and operating speeds. This study provides essential guidance for optimizing urban cement concrete pavement textures, aiming to diminish traffic noise and bolster road safety. Full article

With the development of machine vision technology, deep learning and image recognition technology has become a research focus for agricultural product non-destructive inspection. During the ripening process, banana appearance and nutrients clearly change, causing damage and unjustified economic loss. A high-efficiency banana ripeness recognition model was proposed based on a convolutional neural network and transfer learning. Banana photos at different ripening stages were collected as a dataset, and data augmentation was applied. Then, weights and parameters of four models trained on the original ImageNet dataset were loaded and fine-tuned to fit our banana dataset. To investigate the learning rate’s effect on model performance, fixed and updating learning rate strategies are analyzed. In addition, four CNN models, ResNet 34, ResNet 101, VGG 16, and VGG 19, are trained based on transfer learning. Results show that a slower learning rate causes the model to converge slowly, and the training loss function oscillates drastically. With different learning rate updating strategies, MultiStepLR performs the best and achieves a better accuracy of 98.8%. Among the four models, ResNet 101 performs the best with the highest accuracy of 99.2%. This research provides a direct effective model and reference for intelligent fruit classification. Full article

This paper introduces a novel and cost-effective method for detecting adulterated diesel, specifically targeting contamination with kerosene, by leveraging machine learning and the refractive index values of mixed diesel samples. It proposes a laser-based sensor, employing COMSOL simulations for synthetic data generation to facilitate machine learning training. This innovative approach not only streamlines the detection process by eliminating the need for expensive equipment and specialized personnel but also enables on-site testing without extensive sample preparation. The sensor’s design, utilizing light refraction and reflection principles, allows for the accurate measurement of diesel adulteration levels. Validation results showcase the machine learning models’ high precision in predicting adulteration percentages, as evidenced by an R-squared value of 0.999 and a mean absolute error of 0.074. This research signifies a leap in sensor technology, offering a practical solution for rapid diesel adulteration detection, especially in developing countries, by minimizing reliance on advanced laboratory analyses. The sensor’s design aligns with the requirements for low-cost IoT technology, presenting a versatile tool for various applications. Full article

In order to remove high-concentration emulsified oil from wastewater, a chitosan-based magnetic flocculant, denoted as FS@CTS-P(AM-DMC), was employed in this present study. The effects of factors including the magnetic flocculant dose, pH values, and coexisting ions were investigated. A comparative dosing mode with the assistance of polyacrylamide (PAM) was also included. The evolution of floc size was studied using microscopic observation to investigate the properties of flocs under different pH values and dosing modes. Particle image velocimetry (PIV) and extended Deryaguin–Landau–Verwey–Overbeek models were utilized to illustrate the distribution and velocity magnitude of the particle flow fields and to delve into the mechanism of magnetic flocculation. The results showed that FS@CTS-P(AM-DMC) achieved values of 96.4 and 74.5% for both turbidity and COD removal for 3000 mg/L of simulated emulsified oil. In the presence of PAM, the turbidity and COD removal reached 95.7 and 71.6%. In addition, FS@CTS-P(AM-DMC) demonstrated remarkable recycling and reusability performances, maintaining effective removal after eight cycles. The strength and recovery factors of magnetic flocs without PAM reached 69.3 and 76.8%, respectively. However, with the addition of PAM, they decreased to 46.73 and 51.47%, respectively. During the magnetophoretic processes, FS@CTS-P(AM-DMC) and oil droplets continuously collided and aggregated, forming three-dimensional network aggregates. Moreover, the magnetic floc generated a swirling motion, and the residual emulsified oil droplets could be further captured. Emulsified oil droplets were primarily removed through charge neutralization under acidic conditions. Under neutral and alkaline conditions, magnetic interactions played a major role in magnetic flocculation. Full article

Water quality deterioration and eutrophication have become a global concern, while reservoir pollution caused by multiple factors has led to frequent algal blooms, posing a serious threat to rural drinking water security and urban water supply. The purpose of this paper is to analyze the current water quality of Waihu Reservoir and use the single index method, the weighted comprehensive scoring method, and the nutrient level index method (TLI) to evaluate eutrophication. On this basis, the pollution sources of the reservoir are comprehensively analyzed and discussed, and effective control strategies are proposed. The evaluation results indicate that the reservoir is of moderate eutrophication type. Therefore, reducing the input of nutrients such as nitrogen and phosphorus in water is the main goal of alleviating exogenous pollution. The combination of engineering intervention and ecological restoration strategies to remove nutrients from the aquatic environment is an effective strategy to manage endogenous pollution. From the point of view of the source of pollution, this study provides an in-depth analysis of exogenous and endogenous pollution, respectively, and the proposed treatment is instructive for the control and routine management of eutrophication in the Waihu Reservoir, as well as for the management of similar problems in different reservoirs. Full article

Surimi-based products typically demand cold storage and a cold chain distribution system, which not only affects their physical properties and flavor but also escalates production costs. In this study, we introduced a novel high-temperature and high-pressure retort processing method to enable room temperature storage and distribution of a surimi-based product, a fish paste cake. Our optimization efforts focused on refining the processing conditions for the fish paste cake. This included incorporating transglutaminase, sugar additives, natural herbal or seaweed extracts, and optimizing retort processing conditions to enhance textural properties, minimize browning and off flavor, and extend the shelf-life of the product. Our results demonstrated that the addition of 0.3% ACTIVA TG-K, 1.0% trehalose, and 0.5% sea tangle extract during the production process significantly enhanced the gel strength, minimized browning, and improved the overall flavor of the fish paste cake prototype. Importantly, the developed prototype exhibited favorable biochemical, textual, nutritional, and sensory properties, extending the shelf-life up to 160 days without compromising physical, chemical, or sensory attributes. In addition, the developed prototype exhibited improved elasticity, compared to control groups. The innovative process not only facilitates room temperature storage and distribution of surimi-based products but also holds potential for generating additional profits. Full article

Anaerobic co-digestion (AnCoD) presents several advantages over conventional mono-digestion. Various factors can impact the efficiency of the co-digestion process, including the mixing ratio of the feedstocks. This study primarily investigates the effects of different mixing ratios on methane production during the co-digestion of source-separated municipal organic waste (SSO) with thickened waste activated sludge (TWAS). While the C/N or COD/N ratio has generally been used for optimizing the mixing ratios of co-digested feedstocks, a new approach is introduced in this study to evaluate the effects of the lipid, protein, and carbohydrate (L:P:C) ratios on the efficiency of AnCoD with respect to methane production, kinetics, and synergism at mixing ratios of TWAS:SSO of 10:90, 30:70, 50:50, 70:30, and 10:90. AnCoD improved methane production and kinetics relative to TWAS at all mixing ratios, the highest of which was at the 10:90 ratio, corresponding to a methane yield, maximum methane production rate, and an L:P:C ratio of 353 mL CH₄/g COD, 25 mL CH₄/g COD/d, and 8:1:18, respectively. Improvements in methane yields and kinetics due to synergy were evident at all mixing ratios, with improvements in methane yields ranging from 11 to 23% and improvements in kinetics ranging from 18 to 58%. Improvements in methane yields and kinetics were insensitive to the feedstock composition beyond the 50:50 mixing ratio. Full article

Adequate power load data are the basis for establishing an efficient and accurate forecasting model, which plays a crucial role in ensuring the reliable operation and effective management of a power system. However, the large-scale integration of renewable energy into the power grid has led to instabilities in power systems, and the load characteristics tend to be complex and diversified. Aiming at this problem, this paper proposes a short-term power load transfer forecasting method. To fully exploit the complex features present in the data, an online feature-extraction-based deep learning model is developed. This approach aims to extract the frequency-division features of the original power load on different time scales while reducing the feature redundancy. To solve the prediction challenges caused by insufficient historical power load data, the source domain model parameters are transferred to the target domain model utilizing Kendall’s correlation coefficient and the Bayesian optimization algorithm. To verify the prediction performance of the model, experiments are conducted on multiple datasets with different features. The simulation results show that the proposed model is robust and effective in load forecasting with limited data. Furthermore, if real-time data of new energy power systems can be acquired and utilized to update and correct the model in future research, this will help to adapt and integrate new energy sources and optimize energy management. Full article

The reservoir in the Anyue gas field, located in the Sichuan basin of China, belongs to the second member of the Dengying formation and has distinctive geological features. It is characterized by strong heterogeneity, low porosity, low permeability, and locally developed natural fractures. The reservoir space consists primarily of corrosion holes, natural fractures, and similar voids. Moreover, the lower reservoir exhibits high water saturation and a homogeneous bottom-water interface. Since it is a carbonate-based hydrocarbon reservoir with low porosity and permeability, deep acid fracturing has proven to be an efficient method for enhancing individual well production. However, the reconstruction of the second member of the Dengying formation reservoir poses significant challenges. The reservoir contains high-angle natural fractures, small vertical stress differences, and is located in close proximity to the gas–water interface. As a result, it becomes difficult to control the height of the acid break. Improper acid break treatment may easily result in water production affecting gas well production. To explore ways to control the longitudinal extension of acid fractures, 3D numerical models focusing on the initiation and expansion of acid fractures have been developed. This model takes into account geological and engineering factors such as stress differences, acid fracture displacements and scales, and their effects on the longitudinal extension of acid fractures. It was revealed that the pressure difference is the main controlling factor for the acid fracture height, followed by the reservoir thickness, the interlayer thickness, and the viscosity of the working fluid. Technical countermeasures for controlled fracture and high-acid fracturing tailored to different reservoir characteristics have been proposed, and design parameters for controlled fracture and high-acid fracturing can be optimized. By effectively controlling the vertical extension of the acid fracture, it is possible to maximize production from a single well while avoiding interference from the lower water layer. This study provides theoretical guidance for the application of deep-acid-fracturing techniques in low-permeability bottom-water gas reservoirs. Full article

In view of the high salinity characteristics of reinjection oilfield wastewater in the Gasi Block of Qinghai Oilfield, with the polymer produced by Shandong Baomo as the research target, we systematically investigated the variations in the impact of six ions, Na⁺, K⁺, Ca²⁺, Mg²⁺, Fe²⁺, and Fe³⁺, in the produced water from polymer flooding on the viscosity and stability of the polymer solution. Additionally, we provided the primary research methods for complexation in reinjected wastewater. Experimental results indicate that the main factors leading to a decrease in polymer viscosity are high-valence cations, with the descending order of their influence being Fe²⁺ > Fe³⁺ > Mg²⁺ > Ca²⁺ > Na⁺ > K⁺. High-valent cations also effect the viscosity stability of polymer solutions, and their order from greatest to least impact is: Fe²⁺ > Ca²⁺(Mg²⁺) > Fe³⁺ > Na⁺(K⁺). This article is focused on investigating the influencing factors and extent of the impact of oilfield wastewater on the viscosity of polymer solutions. It illustrates the response mechanism of cations to the viscosity of polymer solutions in reinjection wastewater polymerization. Through this research, the goal is to provide reference control indicators and limits for the water quality of injected polymers at oilfield sites. This ensures the stability and controllability of polymers in field applications and offers theoretical guidance for polymer flooding technology. Full article

Protective coatings are typically applied to enhance their resistance to corrosion. There is considerable research on the corrosion resistance of coated casings. However, few research studies have focused on the erosion resistance on coated casings. In this work, the erosion resistance of resin- and metallic-coated casings in liquid–solid two-phase fluids were investigated using a self-made erosion facility. The results show that the resin coating tends to peel off the material base in the form of brittle spalling or coating bulge in the high-speed sand-carrying liquid. Both resin and metallic coatings were broken through within 20 min in a liquid–solid two-phase flow environment. Compared to resin coatings, metallic coatings exhibit weaker erosion resistance in similar liquid–solid flow. Through the analysis of experimental results and fitted curves, empirical constants for materials and sand content influencing factors were determined using non-dimensional processing. The erosion prediction model of metallic coatings and resin coatings was established based on the ECRC/Zhang model with the change in flow rate, angle, and sand content. This research contributes to a better understanding of the erosion resistance performance of casings used in oil and gas fields, thereby contributing to potential improvements in their production. Full article

Shale reservoirs typically exhibit high density, necessitating the use of horizontal wells and hydraulic fracturing techniques for efficient extraction. Proppants are commonly employed in hydraulic fracturing to prevent crack closure. However, limited research has been conducted on the impact of shale mineral composition and proppant filling patterns on shale stress sensitivity. In this study, shale cylindrical core samples from two different lithologies in Jimusaer, Xinjiang in China were selected. The mineral composition and microscopic structures were tested, and a self-designed stress sensitivity testing system was employed to conduct stress sensitivity tests on natural cores and fractured cores with different proppant filling patterns. The experimental results indicate that the stress sensitivity of natural shale porous cores is weaker, with a stress sensitivity coefficient below 0.03, significantly lower than that of fractured cores. The shale mineral composition has a significant impact on stress sensitivity, with the stress sensitivity of clayey argillaceous shale cores, characterized by higher clay mineral content, being higher than that of sandy argillaceous shale, characterized by higher quartz mineral content. This pattern is also applicable to fractured cores filled with proppants, but the difference gradually diminishes with increased proppant concentration. The choice of large particles and high-concentration proppant bedding can enhance crack conductivity. Within the experimental range, the crack conductivity of 20–40 mesh quartz sand is more than three times that of 70–120 mesh quartz sand. At an effective stress of 60 MPa, the conductivity of cores with a proppant concentration of 2 kg/m² is 3.61 times that of cores with a proppant concentration of 0.3 kg/m². Under different particle size combinations of proppant filling patterns, the crack conductivity at the crack front with large-particle proppants is 6.21 times that of mixed bedding. This study provides valuable insights for the hydraulic fracturing design of shale reservoirs and optimization of production system parameters in subsequent stages. Full article

This article describes the results of a comprehensive comparative study of the production of fatty acid ethyl esters (FAEEs) for use as biodiesel in perfect mixing reactors (PMRs) and plug flow reactors (PFRs). The products obtained on a laboratory scale at all stages of the separation and purification of the FAEE phase were analyzed using the FTIR, XRF and GC-MS methods. We compared distillation methods for the separation of stoichiometrically excessive ethanol from the reaction mixture. Neutralization methods with H₂SO₄ solution and carbonation with CO₂ were applied for FAEE phase purification from the catalyst. Emulsions formed during the water flushing stage were analyzed via the optical microscopy method. The optimal conditions of stirring speed and temperature were selected to maintain a high level of FAEE–water phase contact area with minimum phase separation time. The efficiency of the carbonation method for catalyst neutralization in the FAEE phase has been proven, allowing us to consider this method as an alternative to the traditional acid neutralization method. According to the results of experimental studies, we have developed a new high-performance technological scheme for the production of fatty acid esters in PFRs. The synthesis of FAEEs in a stoichiometric excess of ethanol of about 1:50 allowed us to increase the reaction rate and productivity of the synthesis unit after the transition from a PMR to a PFR. The yield of the product amounted to 86.7%. The purified FAEE fraction complied with most EN14214 specifications. Full article

In the era of rapid technological advancement and evolving industrial landscapes, embracing the concept of the factory of the future (FoF) is crucial for companies seeking to optimize efficiency, enhance productivity, and stay sustainable. This case study explores the concept of the FoF and its role in driving the energy transition and digital transformation within the automotive sector. By embracing advancements in technology and innovation, these factories aim to establish a smart, sustainable, inclusive, and resilient growth framework. The shift towards hybrid and electric vehicles necessitates significant adjustments in vehicle components and production processes. To achieve this, the adoption of lighter materials becomes imperative, and new technologies such as additive manufacturing (AM) and artificial intelligence (AI) are being adopted, facilitating enhanced efficiency and innovation within the factory environment. An important aspect of this paradigm involves the development and utilization of a modular, affordable, safe human–robot interaction and highly performant intelligent robot. The introduction of this intelligent robot aims to foster a higher degree of automation and efficiency through collaborative human–robot environments on the factory floor and production lines, specifically tailored to the automotive sector. By combining the strengths of human and robotic capabilities, the future factory aims to revolutionize manufacturing processes, ultimately driving the automotive industry towards a more sustainable and technologically advanced future. This study explores the implementation of automation and the initial strides toward transitioning from Industry 4.0 to 5.0, focusing on three recognized, large, and automotive companies operating in the north of Portugal. Full article

Nitrogen fixation, the conversion of atmospheric nitrogen into biologically useful compounds, is crucial for sustaining biological processes and industrial productivity. Recent advances have explored plasma-assisted processes as an innovative approach to facilitate nitrogen fixation. This review offers a comprehensive summary of the development, current state of the art, and potential future applications of plasma-based nitrogen fixation. The analysis encompasses fundamental principles, mechanisms, advantages, challenges, and prospects associated with plasma-induced nitrogen fixation. Full article

There has been a tendency in oil and gas industry towards the adoption of multilateral wells (MLWs) with completions that incorporate multiple types of flow control devices (FCDs). In this completion technique, passive inflow control devices (ICDs) or autonomous inflow control devices (AICDs) are positioned within the laterals, while interval control valves (ICVs) are installed at lateral junctions to regulate the overall flow from each lateral. While the outcomes observed in real field applications appear promising, the efficacy of this specific downhole completion combination has yet to undergo comparative testing against alternative completion methods that employ a singular flow control device type. Additionally, the design and current evaluations of such completions are predominantly based on analytical tools that overlook dynamic reservoir behavior, long-term production impacts, and the correlation effects among different devices. In this study, we explore the potential of integrating various types of flow control devices within multilateral wells, employing dynamic optimization process using numerical reservoir simulator while the Grey Wolf Optimizer (GWO) is used as optimization algorithm. The Egg benchmark reservoir model is utilized and developed with two dual-lateral wells. These wells serve as the foundation for implementing and testing 22 distinct completion cases considering single-type and multiple types of flow control devices under reactive and proactive management strategies. This comprehensive investigation aims to shed light on the advantages and limitations of these innovative completion methods in optimizing well and reservoir performance. Our findings revealed that the incorporation of multiple types of FCDs in multilateral well completions significantly enhance well performance and can surpass single-type completions including ICDs or AICDs. However, this enhancement depends on the type of the device implemented inside the lateral and the control strategy that is used to control the ICVs at the lateral junctions. The best performance of multiple-type FCD-based completion was achieved through combining AICDs with reactive ICVs which achieved around 75 million USD profit. This represents 42% and 22% increase in the objective function compared to single-type ICDs and AICDs installations, respectively. The optimal settings for ICD and AICD in individual applications may significantly differ from the optimal settings when combined with ICVs. This highlights a strong correlation between the different devices (control variables), proving that using either a common, simplified analytical, or a standard sequential optimization approach that do not explore this inter-dependence between devices would result in sub-optimal solutions in such completion cases. Notably, the ICV-based completion, where only ICVs are installed with lateral completion, demonstrated superior performance, particularly when ICVs are reactively controlled, resulting in an impressive 80 million USD NPV which represents 53% and 30% increase in the objective function compared to single-type ICDs and AICDs installations, respectively. Full article

This paper presents a coupled computational fluid dynamics–discrete element method (CFD–DEM) simulation to predict cuttings transport by the drilling fluid (mud) in different oil well drilling conditions. The mud rheology is expressed by the Herschel–Bulkley behaviour and modelled in a Eulerian framework (CFD), while the cuttings are modelled using the Lagrangian approach (DEM). In this work, the effects of drill string rotation, inclination angle, cutting size, mud rheology, and annular velocity on cleaning efficiency are investigated. It is found that increasing the well deviation from vertical to horizontal leads to a higher cuttings concentration. However, at low annular velocity, the cuttings concentration for the inclined (45-degree) annulus is found to be higher than the horizontal one due to the sliding motion of cuttings on the lower section of the annulus. Overall, the drill pipe rotation has little effect on decreasing the cuttings concentration, but the effect is more pronounced at low annular velocity. Full article