Processes

Due to the complexity of wind power, traditional prediction models are incapable of fully extracting the hidden features of multidimensional strong fluctuation data, which results in poor multi-step prediction performance. To predict continuous power effectively in the future, an improved wind power multi-step prediction model combining variational mode decomposition (VMD) with sequence-to-sequence (Seq2Seq) is proposed. Firstly, the wind power sequence is smoothed using VMD and the decomposition parameters of VMD are optimized by using the squirrel search algorithm (SSA) to effectively optimize the decomposition effect. Then, the subsequence obtained from decomposition, together with the original wind power data, is reconstructed into multivariate time series features. Finally, a Seq2Seq model is constructed, and convolutional neural networks (CNNs) with bidirectional gate recurrent units (BiGRUs) are used to learn the coupling and timing relationships of the input data and encode them. The gate recurrent unit (GRU) is decoded to achieve continuous power prediction. Based on the actual operating data of a wind farm, a case analysis is conducted. Experimental results show that SSA-VMD can effectively optimize the decomposition effect, and the subsequences obtained with its decomposition are highly accurate when applied to predictions. The Seq2Seq model has better multi-step prediction results than traditional prediction methods, and as the prediction step size increases, the advantages are more obvious. Full article

The lack of service lifetime of bearings has become a bottleneck that restricts the performance of aero engines. How to solve or improve this problem is the focus of most surface engineering researchers at present. In this study, plasma immersion ion implantation was conducted; in order to enhance the ion implantation efficiency and improve the wear resistance of 8Cr4Mo4V bearing steel, the dose-rate-enhanced method was adopted during ion implantation. The surface roughness, phase constituents, elemental concentration, hardness, contact angle and wear resistance of samples after ion implantation was determined by atomic force microscopy (AFM), grazing incidence X-ray diffraction (GIXRD), elemental dispersive spectroscopy (EDS), X-ray diffraction, nanoindentation tester, universal friction and wear tester, etc. The results showed that the high-dose-rate method had a significant enhancement influence on ion implantation efficiency. At the dose rate of 2.60 × 10¹⁷ ions/cm²·h, the roughness of Ra decreases from 24.8 nm to 10.4 nm, which is decreased by 58.1% for the dose rate of 7.85 × 10¹⁷ ions/cm²·h. XRD confirmed that the implanted samples consisted of the Fe(M) and Fe_2–3N phase and CrN which depends on the implantation dose rate. Meanwhile, the surface hardness was improved from 11.1 GPa to 16.9 GPa and enlarged the hardened region; more valuably, the surface state of samples via high-dose-rate implantation exhibits hydrophobicity with high roughness which is able to store debris and decrease the abrasive wear during testing; thereby, the wear resistance was greatly enhanced by high-dose-rate plasma immersion ion implantation. Full article

As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields. Full article

Aiming at the problem of the low reuse rate of mine water due to the high content of heavy metals in mine water, in this research, the microcharacterization means of EDX, XRD, BET, SEM, and FT-IR were used to characterize the nonstick coal in a mine in western China. The effects of solid–liquid ratio, solution pH, solution temperature, adsorption time, and initial concentration of the solution on the adsorption of Fe(II) by the nonstick coal were analyzed. The adsorption performance of nonstick coal on adsorbed Fe(II) was analyzed under different influencing factors. The results showed that the adsorption capacity and unit removal rate of the coal samples gradually decreased with the increase in the solid–liquid ratio; the adsorption amount increased with the increase in pH in an “S” shape, and the adsorption effect was better in the range of pH = 5~7; and the adsorption amount increased linearly with the temperature. The quasi-secondary kinetic model and Langmuir model could fit the adsorption kinetic curve and isothermal adsorption curve better, which indicated that the adsorption of Fe(II) by the nonstick coal was dominated by the chemical adsorption of the monomolecular layer. The quantitative analysis of the FT-IR results showed that the adsorption of Fe(II) was mainly by complexation with -OH detached from the coal samples to produce precipitation. Full article

The pursuit of low-energy-consumption CO₂ capture technology has promoted the renewal and iteration of absorbents for chemical absorption. In order to evaluate the regeneration energy consumption of absorbents and obtain the distribution of energy consumption, a coupling method combining rigorous energy balance and simple estimation is proposed in this study. The data regarding energy balance and material balance from process simulation are transformed into the model parameters required in the simple estimation model. Regenerative energy consumption and distribution are determined by the empirical estimation formula. Two CO₂ capture processes of an MEA aqueous solution and MEA–n-propanol aqueous solution (phase-change absorbent) were used to verify the feasibility and applicability of the coupling method. The effects of n-propanol concentration, CO₂ loading in the lean solution, and temperature on energy consumption were discussed. The results show that the energy consumption of 30 wt% MEA aqueous solution is the lowest at 3.92 GJ·t⁻¹-CO₂ when CO₂ load in the lean solution is 0.2 mol CO₂·mol⁻¹-MEA, and the reaction heat Q_rec, sensible heat Q_sen, and latent heat Q_latent were 1.97 GJ·t⁻¹-CO₂, 1.09 GJ·t⁻¹-CO₂, and 0.86 GJ·t⁻¹-CO₂, respectively. The lowest energy consumption of the phase-change absorbent with CO₂ loading of 0.35 mol CO₂·mol⁻¹-MEA in the lean solution is 2.32 GJ·t⁻¹-CO₂. Q_rec, Q_sen, and Q_latent were 1.9 GJ·t⁻¹-CO₂, 0.29 GJ·t⁻¹-CO₂, and 0.14 GJ·t⁻¹-CO₂, respectively. This study provides a simple and meaningful method for accurately assessing absorber performance and process improvement, which can accelerate the development of economically viable, absorption-based CO₂ capture processes. Full article

Accurate mathematical patterning of friction has always been a significant research project in the domains of machinery and control, and has played a crucial role in the analysis, control and compensation of mechanical systems containing friction. For high-property electrohydraulic servo control systems, friction compensation is an urgent problem to be solved. The LuGre friction pattern can represent most frictional behaviors, but the LuGre friction pattern is piecewise-continuous, making it non-differentiable. Therefore, the question of how to combine the LuGre friction pattern, enhancing its tracking capacity and robust performance problem, to friction perturbation in hydraulic backstep devices is an important focus for research. In this study, the conventional LuGre friction pattern was enhanced using the continuous differentiability of a friction of rest pattern that laid the foundation of a smooth tangent function. Laying the foundation of an electrohydraulic servo pump-controlled hydraulic roll-gap thickness automatic control system (pump-controlled AGC) pattern, a self-adapting friction compensation controller laid the foundation of the enhanced LuGre pattern. The gradual tracking capacity was determined academically using conditions of parameter, uncertainty and nonlinear friction, and the position control precision of the pump-controlled AGC system was enhanced. The steady-state error of the self-adapting friction compensation control system, which laid the foundation of the enhanced LuGre pattern, attained ±0.1 μm, and the tracking capacity was better than the LuGre pattern control and the conventional PID control strategy at low input speed. Full article

To obtain the macroscale Young’s modulus of glutenite under gravel inclusions, a numerical simulation of macroscale Young’s modulus prediction based on the mesoscale characteristics of glutenite was carried out. Firstly, the micron indentation test was used to obtain the meso-mechanical parameters of gravel and matrix in glutenite to ensure the reasonableness of the numerical simulation parameter settings; secondly, a two-dimensional glutenite physical model generation method based on the secondary development of Python was put forward; and then, the macroscale Young’s modulus variation rule of glutenite under different gravel sizes, particle size ratios, and content characteristics were analyzed using the finite element method (FEM). The results show that Young’s modulus of gravel is larger than Young’s modulus of the matrix, and Young’s modulus of different gravel and matrix has some differences. The gravel content is the main controlling factor affecting the macroscale Young’s modulus of glutenite; the gravel content and Young’s modulus of glutenite show a strong positive correlation, and the gravel size and particle size ratio have less influence on the macroscale Young’s modulus of glutenite. The difference in Young’s modulus between gravel and matrix causes the formation of local stress concentrations during loading and compression of glutenite. The smaller the gravel grain size, the higher the degree of non-uniformity of the grain size, the more likely it is to form higher local stresses. The results of the study provide a new prediction method for the prediction of the macroscale Young’s modulus of a glutenite reservoir. Full article

Sodium sulfate (SS) is a neutral activator. SS-activated alkali-activated slag cement (AASC) has lower shrinkage. However, it sets slowly, and the mechanical property develops slowly. Slaked lime (SL) is an alkaline substance widely used in industry that can be used as an activator in AASC. In this paper, SL was used alone, and SL and SS were mixed together to prepare AASC. The effects of SL content on the setting time, shrinkage properties and mechanical strength of AASC were investigated. Furthermore, the mechanism was explored with the analysis of microscopic tests. The results showed that SS could not be used as an activator alone, while SL could be used as an activator alone, and SS could be combined with SL to prepare AASC. The setting time of the SL system or the SS-SL mix system decreased with the increase in SL. The mechanical properties of the SL system were poor. The SS-SL system showed the highest mechanical properties when SL was 3%. With the increase in SL, the autogenous and drying shrinkage of the SL system increased, while the former of the SS-SL system increased and the latter decreased. At the same time, due to the different changes in pore structure and mesoporous volume in the two systems, the drying shrinkage showed different changes. Compared with the SL system, ettringite (AFt) with a slight expansion property and more crystal phases were formed in the SS-SL system, which reduced the drying shrinkage. Full article

Packed bed reactors play a crucial role in various industrial applications. This paper utilizes the Discrete Element Method (DEM), an efficient numerical technique for simulating the behavior of packed beds of particles as discrete phases. The focus is on generating densely packed particle beds. To ensure the model accuracy, specific DEM parameters were studied, including sub-step and rolling resistance. The analysis of the packed bed model extended to a detailed exploration of void fraction distribution along radial and vertical directions, considering the impact of wall interactions. Three different samples, spanning particle sizes from 0.3 mm to 6 mm, were used. Results indicated that the number of sub-steps significantly influences void fraction precision, a key criterion for comparing simulations with experimental results. Additionally, the study found that both loosely and densely packed beds of particles could be accurately represented by incorporating appropriate values for rolling friction. This value serves as an indicator of both inter-particle friction and friction between particles and the walls. An optimal rolling friction coefficient has been thereby suggested for the precise representation for the densely packed bed of spherical char particles. Full article

This study investigates the effectiveness of using conditional generative adversarial networks (CGAN) to synthesize Optical Coherence Tomography (OCT) images for medical diagnosis. Specifically, the CGAN model is trained to generate images representing various eye conditions, including normal retina, vitreous warts (DRUSEN), choroidal neovascularization (CNV), and diabetic macular edema (DME), creating a dataset of 102,400 synthetic images per condition. The quality of these images is evaluated using two methods. First, 18 transfer-learning neural networks (including AlexNet, VGGNet16, GoogleNet) assess image quality through model-scoring metrics, resulting in an accuracy rate of 97.4% to 99.9% and an F1 Score of 95.3% to 100% across conditions. Second, interpretative analysis techniques (GRAD-CAM, occlusion sensitivity, LIME) compare the decision score distribution of real and synthetic images, further validating the CGAN network’s performance. The results indicate that CGAN-generated OCT images closely resemble real images and could significantly contribute to medical datasets. Full article

Wood pellets are a versatile ingredient to produce bioenergy and bioproducts. Wood pellet manufacturing in Canada started as a way of using the excess sawdust from sawmilling operations. With the recent dwindling availability of sawdust and the growth in demand for wood pellets, the industry uses more non-sawdust woody biomass as feedstock. In this study, woody biomass materials received from nine wood pellet plants in British Columbia (BC) and Alberta were analyzed for their properties, especially those used for fractionating feedstock to make pellets. Half of the feedstock received at the plants was non-sawdust. Moisture contents varied from 10 to 60% wet basis, with the hog having an average of 50%. Ash contents ranged from 0.3 to 4% dry basis and were highest in the hog fraction. Bulk density varied from 50 to 450 kg/m³, with shavings having the lowest bulk density. Particle density ranged from 359 kg/m³ for infeed mix to 513 kg/m³ for sawdust. In total, 25% of particles received were larger than 25 mm. The extraneous materials (sand, dirt) in the infeed materials ranged from 0.03% to 1.2%, except for one hog sample (8.2%). Plant operators use mechanical fractionation and blending to meet the required ash content. In conclusion, further instrumental techniques to aid in fractionation should be developed. Full article

A geographical information system (GIS) is an advanced tool for collecting, managing, and analyzing spatially-referenced data. The contribution of GIS use to process performance indicators can be improved by combining it with multi-criteria decision analysis (MCDA). Combining a GIS and MCDA is, in the scientific literature, rarely discussed for planning an internal transportation system in a manufacturing plant’s outdoor area. The purpose of this article is to clarify what mangers can expect from using a combined approach when deciding on a transport fleet and the operational routing of vehicles. Beside the simulation of MCDA, the computer software ArcGIS Pro 3.0.2 with the Network Analyst extension was used for modelling the transportation system in the form of a case study. The article demonstrates the feasibility and effectiveness of GIS and MCDA use and reveals the extent of the challenge of how decision makers could make the most of ArcGIS functionality. The final solution for an internal transportation system in a manufacturing plant’s outdoor area includes such a vehicle fleet and the set time windows of orders for transport services, so that there are no violations of time windows and the work is completed within the work shift while minimizing costs, time, and distance. Decision makers can use the program without advanced knowledge of optimization approaches, following a procedure that does not differ much from that of learning to use other business software tools. On the contrary, the listed disadvantages can be summarized as the rigidity of setting detailed boundary conditions for a specific simulation scenario. Full article

The production potential of highly deviated wells cannot be fully realized by conventional acid fracturing, as it can only generate a single fracture. To fully enhance the productivity of highly deviated wells, it is necessary to initiate multiple fractures along a prolonged well section to ensure the optimal number of fractures, thereby maximizing the economic returns post-stimulation. Thus, the number of fractures is a crucial parameter in the acid fracturing design of highly deviated wells. Considering factors such as the random distribution of natural fractures within the reservoir and interference between fractures during production, and, based on the oil–water two-phase flow equation, a three-dimensional reservoir–fracture production coupling model and its seepage difference model are established to simulate the production performance of highly deviated wells under varying conditions, including the number of fractures, fracture spacing, and conductivity parameters. A numerical model for the number of acid fracturing fractures in highly deviated wells is also established, in conjunction with an economic evaluation model. The simulation results indicate that the daily oil production of highly deviated wells increases with the increase in fracture number, fracture conductivity, fracture length, and reservoir permeability. However, over time, the daily oil production gradually decreases. Similarly, the cumulative production also increases with these parameters, but shows a downward trend over time. By conducting numerical simulations to evaluate the productivity and economy of highly deviated wells post-acid fracturing, it is determined that the optimal number of fractures to achieve maximum efficiency is six. The reliability of this result is confirmed by the pressure distribution cloud map of the formation after acid fracturing in highly deviated wells. Full article

In this paper, the dynamic deposition behavior of Na-enriching Zhundong coal ash on tube bundles with varying longitudinal and transverse pitches was numerically studied. By using a modified critical viscosity model, an improved CFD deposition model has been established and key parameters, including deposit mass and morphology, particle trajectories and impaction and sticking probabilities, as well as the heat flux distribution, have been analyzed. The results show that the ash deposited on tubes in the first row is, respectively, 1.74 and 3.80 times higher than that on the second and third rows, proving that ash deposition in the downstream is lessened. As the longitudinal pitch increased from 1.50 D to 2.50 D, deposit mass in the downstream increased two times, suggesting that an increase in longitudinal pitch would aggravate ash deposition. The effect of transverse pitch, however, with the least deposit propensity at S_t/D = 1.75, is non-linear due to the joint effect of adjacent tubes and walls in affecting particle trajectory. In addition, due to the non-uniform distribution of the deposit, heat flux across the tube is the smallest at the stagnation point but becomes six times higher at two sides and the leeward, which makes the thermal damage of these sides to be warranted as a practical concern. Full article

In the realm of sensing technologies, the appeal of sensors lies in their exceptional detection ability, high selectivity, sensitivity, cost-effectiveness, and minimal sample usage. Notably, molecularly imprinted polymer (MIP)-based sensors have emerged as focal points of interest spanning from clinical to environmental applications. These sensors offer a promising avenue for rapid, selective, reusable, and real-time screening of diverse molecules. The preparation technologies employed in crafting various polymer formats, ranging from microparticles to nanomaterials, wield a profound influence. These techniques significantly impact the assembly of simplified sensing systems, showcasing remarkable compatibility with other technologies. Moreover, they are poised to play a pivotal role in the realization of next-generation platforms, streamlining the fabrication of sensing systems tailored for diverse objectives. This review serves as a comprehensive exploration, offering concise insights into sensors, the molecular imprinting method, and the burgeoning domain of MIP-based sensors along with their applications. Delving into recent progress, this review provides a detailed summary of advances in imprinted-particle- and gel-based sensors, illuminating the creation of novel sensing systems. Additionally, a thorough examination of the distinctive properties of various types of MIP-based sensors across different applications enriches the understanding of their versatility. In the concluding sections, this review highlights the most recent experiments from cutting-edge studies on MIP-based sensors targeting various molecules. By encapsulating the current state of research, this review acts as a valuable resource, offering a snapshot of the dynamic landscape of MIP-based sensor development and its potential impact on diverse scientific and technological domains. Full article

In rural areas with higher agricultural energy consumption, ensuring low-carbon transformation and rapid penetration is crucial; therefore, the importance of rural energy system in energy transformation is even more prominent. In order to better understand the research progress of rural integrated energy system, the existing structure of rural energy system and design method are briefly introduced, and the bibliometric method is applied to analyze publications from 2013 to 2023. Based on the Scopus database, 915 publications have been retrieved. These publications are analyzed from the perspective of citation, author, address, and published journal. It is concluded that existing researches mostly use conventional energy technologies to achieve power supply in rural areas, lacking analysis of the potential application of emerging energy technologies and research on multi-energy demand. Furthermore, the review reveals the economy of grid-connected rural energy system is mainly related to geographical location, system configuration, and resource endowment. The bibliometric analysis indicated that these publications are mainly from India and China; the average citation is 24.98, and each article is co-published by 3.66 authors, 2.26 institutes, and 1.46 countries. This work is helpful for scholars to understand the research status on the rural integrated energy system. Full article

Thermo-rechargeable batteries, or tertiary batteries, are prospective energy-harvesting devices that are charged by changes in the battery temperature. Previous studies on tertiary batteries have utilized an electrolyte solution, yet the volume of this electrolyte solution could be a disadvantage in terms of the heat capacity given to the tertiary batteries. To overcome this drawback, the performance of an electrolyte-free tertiary battery consisting of physically joined Na_1.60Co[Fe(CN)₆]_0.902.9H₂O (NCF90) and Na_0.72Ni[Fe(CN)₆]_0.685.1H₂O (NNF68) thin films was investigated for the first time. During thermal cycling between 5 °C and 15 °C, the thermal voltage (V_TB) was observed to be 8.4 mV. This result is comparable to the V_TB of conventional tertiary batteries that use electrolyte solutions made of NCF90 and NNF68 thin films. Full article

During growth, plants produce bioactive compounds—secondary metabolites. Their concentration can be stimulated by the presence of a stressful factor—an elicitor. Since chlorine dioxide is commonly used in water plants to disinfect drinking water, its application as a plant elicitor seems to be very attractive. The aim of this work was to investigate the influence of a new elicitor, ClO₂, on the quality of seeds and bioactive compounds of sprouts. Elicitation of radish and broccoli seeds using ClO₂ solutions did not significantly reduce their germination percentage (GP remained over 90%). Radish sprouts sprouted from seeds elicited in chlorine dioxide solutions with concentrations up to 800 ppm did not differ statistically significantly in terms of polyphenol content. Sprouts which were grown in the presence of ClO₂ contained significantly fewer polyphenolic compounds. Elicitation of broccoli seeds in 800–1000 ppm ClO₂ solutions causes an increase in total phenolic content and concentration of ascorbic acid in sprouts. Elicitation in chlorine dioxide solutions not only increased concentrations of selected bioactive compounds but also improved the microbiological quality of sprouts. Full article

Design processes are always in motion, since more and more data-driven methods are used for various design and validation tasks. However, small and medium enterprises especially struggle with enhancing their processes with data-driven methods due to a lack of practical and easy-to-use analysis and redesign methods which can handle design process characteristics. In this paper, we present PADDME, which stands for process analysis for digital development in mechanical engineering, as a novel method that, in contrast to currently available analysis methods, considers those design process characteristics with respect to the integration of data-driven methods. Furthermore, a novel technology-readiness framework for digital engineering is introduced. Using the PADDME method, an industrial case study on introducing data-driven methods into the design and evaluation process chain is presented. The usability and novelty of the method are shown by the case study. Thus, PADDME allows a detailed capturing of current design processes and paves the way for process optimisation through data-driven methods. PADDME is a valuable method for advancing digital mechanical engineering processes in small and medium enterprises, and future work will focus on refining and expanding its application and evaluation. Full article