The erosive wear encountered in some industrial processes results in economic loss and even disastrous consequences. Hitherto, the mechanism of the erosive wear is not clear, especially when the erosive wear is caused by large particles (>3.0 mm) carried by a flowing liquid. Current approaches of predicting erosive wear need improvement, and the optimization of relevant equipment and systems lacks a sound guidance. It is of significance to further explore such a subject based on the relevant literature. The present review commences with a theoretical analysis of the dynamics of large particles and the fundamental mechanism of erosion. Then the characteristics of the erosion of various equipment are explicated. Effects of influential factors such as particle size and properties of the target material are analyzed. Subsequently, commonly used erosion models, measurement techniques, and numerical methods are described and discussed. Based on established knowledge and the studies reported,... [more]

The classification of microseismic signals represents a fundamental preprocessing step in microseismic monitoring and early warning. A microseismic signal source rock classification method based on a convolutional neural network is proposed. First, the characteristic parameters of the microseismic signals are extracted, and a convolutional neural network is constructed for the analysis of these parameters; then, the mapping relationship model between the characteristic parameters of the microseismic signals and the rock class is established. The feasibility of the proposed method in differentiating acoustic emission signals under different load conditions is verified by using acoustic emission data from laboratory uniaxial compression tests, Brazilian splitting tests, and shear tests. In the three distinct laboratory experiments, the proposed method achieved a source rock classification accuracy of greater than 90% for acoustic emission signals. The proposed and verified method provide... [more]

With the increasing popularity of clean energy, the use of micro turbines to recover surplus energy in the water supply pipelines of high-rise buildings has attracted more attention. This study adopts a predictor model based on Radial Basis Function Neural Network (RBFNN) to optimize the draft tube shape for micro-Francis turbines. The predictor model is formed on a dataset provided by numerical simulations, which are validated by lab tests. Specifically, numerical investigations are carried out in the shape of a draft tube to determine an optimal model. Additionally, the superiority of the RBFNN model in nonlinear optimization is verified by comparing it with other models under the same date sets. After that, the design parameters are optimized using RBFNN and sequential quadratic programming algorithm (SQPA). Finally, the turbine prototype is fabricated and tested on a lab test rig. The experimental results indicate that the numerical method adopted in this research is accurate enoug... [more]

Methane (CH4), a non-polar molecule characterized by a tetrahedral structure, stands as the simplest organic compound. Predominantly constituting conventional natural gas, shale gas, and combustible ice, it plays a pivotal role as a carbon-based resource and a key raw material in the petrochemical industry. In natural formations, CH4 and H2O coexist in a synergistic system. This interplay necessitates a thorough examination of the phase equilibrium in the CH4-H2O system and CH4’s solubility under extreme conditions of temperature and pressure, which is crucial for understanding the genesis and development of gas reservoirs. This study synthesizes a comprehensive solubility database by aggregating extensive solubility data of CH4 in both pure and saline water. Utilizing this database, the study updates and refines the key parameters of Henry’s law. The updated Henry’s law has a prediction error of 22.86% at less than 40 MPa, which is an improvement in prediction accuracy compared to bef... [more]

Renewable energy sources are increasingly integrated into power systems, leading to significant variability in operations. This necessitates robust methods for assessing operational reliability. We propose a novel model−data hybrid approach that incorporates endogenous uncertainty into the reliability evaluation process. First, unlike traditional methods that treat uncertainties as external factors, this approach recognizes that operational decisions can significantly influence how uncertainties are resolved and impact reliability metrics. The proposed method integrates device reliability indices with operational decision variables. This allows us to evaluate the impact of endogenous uncertainty on operational reliability through a reliability-constrained stochastic unit commitment model. Additionally, a model−data hybrid algorithm is introduced for efficient solution of the formulated optimization problem. Case studies demonstrate the effectiveness of the proposed method. Results also... [more]

Curl propensity is a critical-to-quality (CTQ) property of paper, as it causes severe problems during printing and other final conversion operations. The main papermaking factor causing the curl phenomenon is the existence of a fiber orientation (FO) gradient across the thickness direction (or ZD), also known as two-sidedness. Therefore, a methodology that characterizes the FO across the ZD is fundamental for papermakers. In this work, we propose and validate an efficient and cost-effective protocol based on sheet splitting and image analysis. Besides assessing the level of FO two-sidedness, the methodology also provides insights into the flow dynamics in the draining zone of the forming section of the paper machine and the drying stresses built into the paper. This information is relevant for monitoring, optimizing, and troubleshooting activities in the paper industry.

The impact of COVID-19’s unexpected outbreak forced the scientific community to seek alternative treatment methods in order to overcome the hindrance of traditional medicine in terms of alleviating the symptoms of this virus. Erythromycin, which was introduced in 1952, is an antibiotic that is reported to pose as an effective substitute medication for various ailments such as skin, respiratory, bone, and female reproductive conditions, and cancer, as well as the newly added COVID-19. The importance of both the erythromycin molecule and the catalyst of its production, namely P450eryF of the cytochrome P450 family, in many health-concerned and environmentally related applications, has led several countries, the World Health Organization (WHO) and the health industry to recruit and cooperate with numerous universities and institutions, in an attempt to tackle the demand for efficient antibiotics. The aim of this study is to discuss and further analyze the overall structure and catalytic m... [more]

In the low-carbon era, photovoltaic power generation has emerged as a pivotal focal point. The inherent volatility of photovoltaic power generation poses a substantial challenge to the stability of the power grid, making accurate prediction imperative. Based on the integration of a backpropagation (BP) neural network and a genetic algorithm (GA), a prediction model was developed that contained two sub-models: no-rain and no-snow scenarios, and rain and snow scenarios. Through correlation analysis, the primary meteorological factors were identified which were subsequently utilized as inputs alongside historical power generation data. In the sub-model dedicated to rain and snow scenarios, variables such as rainfall and snowfall amounts were incorporated as additional input parameters. The hourly photovoltaic power generation output was served as the model’s output. The results indicated that the proposed model effectively ensured accurate forecasts. During no-rain and no-snow weather con... [more]

Principal Component Analysis (PCA) serves as a valuable tool for analyzing membrane processes, offering insights into complex datasets, identifying crucial factors influencing membrane performance, aiding in design and optimization, and facilitating monitoring and fault diagnosis. In this study, PCA is applied to understand operational features affecting pervaporation desalination performance of PVA-based TFC membranes. PCA-biplot representation reveals that the first two principal components (PCs) accounted for 62.34% of the total variance, with normalized permeation with selective layer thickness (Pnorm), water permeation flux (P), and operational temperature (T) contributing significantly to PC1, while salt rejection dominates PC2. Membrane clustering indicates distinct influences, with membranes grouped based on correlation with operational factors. Excluding outliers increases total variance to 74.15%, showing altered membrane arrangements. Interestingly, the adopted strategy show... [more]

Supramolecular solvent-based extraction (SUPRAS) stands out as a promising approach, particularly due to its environmentally friendly and efficient characteristics. This research explores the optimization of SUPRAS extraction for sango radish and kale microgreens, focusing on enhancing the extraction efficiency. The Taguchi experimental design and artificial neural network (ANN) modeling were utilized to systematically optimize extraction parameters (ethanol content, SUPRAS: equilibrium ratio, centrifugation rate, centrifugation time, and solid-liquid ratio). The extraction efficiency was evaluated by measuring the antioxidant activity (DPPH assay) and contents of chlorophylls, carotenoids, phenolics, and anthocyanidins. The obtained results demonstrated variability in phytochemical contents and antioxidant activities across microgreen samples, with the possibility of achieving high extraction yields using the prediction of optimized parameters. The optimal result for sango radish can... [more]

A technology for the hydrochemical processing of the kaolinite fraction of bauxite has been developed, and it involves preliminary chemical activation in a sodium bicarbonate solution and alkaline leaching in a recycled high-modulus solution with the addition of an active form of calcium oxide. Chemical activation allows for the transformation of the difficult-to-explore kaolinite phase to form easily soluble phases of dawsonite, sodium hydroaluminosilicate and bemite. An active, finely dispersed form of calcium oxide was obtained as a result of CaO quenching in Na2SO4 solution at elevated temperature and pressure. Using a central composite design (CCD) via response surface methodology (RSM), the technological leaching mode was achieved. The influence on the leaching process was studied by adjusting the CaO/SiO2 ratio, temperature, alkaline solution concentration and duration. It was found that the determining factors are the concentration of the leaching solution and the temperature.... [more]

The present work focuses on predicting the steel melt temperature following the vacuum treatment step in a vacuum tank degasser (VTD). The primary objective is to establish a comprehensive methodology for developing and validating machine learning (ML) models within this context. Another objective is to evaluate the model by analyzing the alignment of the SHAP values with metallurgical domain expectations, thereby validating the model’s predictions from a metallurgical perspective. The proposed methodology employs a Random Forest model, incorporating a grid search with domain-informed variables grouped into batches, and a robust model-selection criterion that ensures optimal predictive performance, while keeping the model as simple and stable as possible. Furthermore, the Shapley Additive Explanations (SHAP) algorithm is employed to interpret the model’s predictions. The selected model achieved a mean adjusted R2 of 0.631 and a hit ratio of 75.3% for a prediction error within ±5 °C. De... [more]

With the rapid development of industrialization, the problem of concentration determination based on the copper production process has been widely concerned, and the accurate determination of high-concentration copper ions (Cu2+) is of great significance for enterprise production, resource utilization, and pollution prevention. The characteristics of different spectrophotometric methods for the determination of Cu2+ are discussed, and it is found that these methods are suitable for the determination of trace or low concentration of Cu2+ (0.5 μg/L−5 mg/L), whereas for the determination of high Cu2+ concentration pre-treatments such as dilution, complexation, and coloring are required. In this study, a method based on ultraviolet-visible spectroscopy (UV-Vis) combined with partial least squares regression analysis (PLS) was proposed for the determination of high copper ions (>100 mg/L), which performs rapid and accurate determination of high Cu2+ concentration by preprocessing and featur... [more]

This article addresses the problem statement and objective by exploring the necessity, scope, and execution of digital transformation in the oil and gas industry from a psychological perspective. It highlights the cognitive barriers faced by non-ICT professionals, which are often overlooked in traditional approaches. The study integrates case studies and empirical evidence from a mixed-methods approach, including qualitative interviews with industry experts and quantitative surveys among employees, to provide a comprehensive understanding of the transformation process. The research emphasizes the integration of psychological theories with practical digital transformation strategies, illustrating key obstacles and solutions. By adopting a holistic approach that incorporates both technological advancements and psychological insights, the study aims to enhance the effectiveness and sustainability of digital transformation efforts. Major contributions include identifying cognitive barriers... [more]

Shear wave velocity is one of the important parameters reflecting the lithological and physical properties of reservoirs, and it is widely used in the fields of lithology and fluid property identification, reservoir evaluation, seismic data processing, and interpretation. However, due to the high cost and challenge of obtaining shear wave velocity, only a few key wells are measured. Considering the intricate nonlinear mapping relationship between shear wave velocity and conventional logging data, an integrated network incorporating an attention mechanism, a convolutional neural network, and a bidirectional gated recurrent unit (STACBiN) is proposed for predicting shear wave velocity. The impact of conventional logging data on shear wave velocity is analyzed, thus employing the attention mechanism to focus on data correlated with shear wave velocity, which can enable the prediction results of the method proposed superior to those of conventional methods. Additionally, the prediction res... [more]

The main objective of this study is to analyze the drying kinetics of Citrus medica by using the freeze-drying method at various thicknesses (3, 5, and 7 mm) and cabin pressures (0.008, 0.010, and 0.012 mbar). Additionally, the study aims to evaluate the efficacy of an artificial neural network (ANN) in estimating crucial parameters like dimensionless mass loss ratio (MR), moisture content, and drying rate. Feedforward multilayer perceptron (MLP) neural network architecture was employed to model the freeze-drying process of Citrus medica. The ANN architecture was trained using a dataset covering various drying conditions and product characteristics. The training process, including hyperparameter optimization, is detailed and the performance of the ANN is evaluated using robust metrics such as RMSE and R2. As a result of comparing the experimental MR with the predicted MR of the ANN modeling created by considering various product thicknesses and cabin pressures, the R2 was found to be 0... [more]

Gravels are widely distributed in the Baikouquan formation in the Mabei area of the Junggar Basin. However, conventional logging methods cannot quantitatively characterize gravel development, which limits the identification of lithology, structure, and sedimentary facies in this region. This study proposes a new method for automatically identifying gravels from electric imaging images and calculating gravel parameters utilizing the salient object detection (SOD) network. Firstly, a SOD network model (U2-Net) was constructed and trained using electric imaging data from the Baikouquan formation at the Mahu Sag. The blank strips in the images were filled using the U-Net convolutional neural network model. Sample sets were then prepared, and the gravel areas were labeled in the electric imaging images with the Labelme software in combination with image segmentation and human−machine interaction. These sample sets were used to train the network model, enabling the automatic recognition of g... [more]

Shaped charge perforation is an important technology for sandstone gas reservoirs. In the process of shaped charge perforating, the initial permeability and porosity of the formation are greatly reduced, directly affecting oil and gas production. This paper uses smooth particle hydrodynamics (SPH) and finite element methods (FEMs) to study the formation damage caused by the shaped charge perforation. In this perforation simulation, the impact characteristics of the metal jet formed by the liner are coupled with the damage characteristics of the sandstone. A new mathematical model is proposed to describe the damage of permeability and porosity based on the Morris and Xue models. The simulated results show that a large-scale abdominal section appears at the perforation site, and the axis of the hole tilts with the increase in the perforation depth. The porosity damage at the perforation site is the greatest, up to 60%, while the permeability recovers to 90% of its initial state after pre... [more]

The ever-increasing needs of the working population have led to the development of various branches of industry, an increase in the number of employees, and a rise in the number of work-related accidents. The welder occupation is one of the most sought after occupations in Europe, according to the EURopean Employment Services (EURES) statistics. Taking into account the work system in which welders conduct their activity (uncomfortable working positions, splashes, high temperatures, mechanical factors, gases and fumes, magnetic fields due to electric current), the paper presents the risk factors identified for the welder occupation, based on the occupational injury and illness risk assessments. Following the analysis of 25 risk assessments, carried out by the assessment team that must include qualified evaluators, process specialists, the workers’ representative, occupational health and safety responsible at various industrial economic agents, a total of 70 main risk factors of occupati... [more]

Copolymers are commonplace in various industries. Nevertheless, fine-tuning their properties bears significant cost and effort. Hence, an ability to predict polymer properties a priori can significantly reduce costs and shorten the need for extensive experimentation. Given that the physical and chemical characteristics of copolymers are correlated with molecular arrangement and chain topology, understanding the reactivity ratios of monomers—which determine the copolymer composition and sequence distribution of monomers in a chain—is important in accelerating research and cutting R&D costs. In this study, the prediction accuracy of two Artificial Neural Network (ANN) approaches, namely, Multi-layer Perceptron (MLP) and Graph Attention Network (GAT), are compared. The results highlight the potency and accuracy of the intrinsically interpretable ML approaches in predicting the molecular structures of copolymers. Our data indicates that even a well-regularized MLP cannot predict the reacti... [more]

This paper presents the development of algorithms for mass-energy constrained neural network (MECNN) models that can exactly conserve the overall mass and energy of distributed chemical process systems, even though the noisy steady-state/transient data used for optimal model training violate the same. For developing dynamic mass-energy constrained network models for distributed systems, hybrid series and parallel dynamic-static neural networks are used as candidate architectures. The proposed approaches for solving both the inverse and forward problems are validated considering both steady-state and dynamic data in presence of various noise characteristics. The proposed network structures and algorithms are applied to the development of data-driven models of a nonlinear non-isothermal reactor that involves an exothermic reaction making it significantly challenging to exactly satisfy the mass and energy conservation laws of the system only by using the available input and output boundar... [more]

Water chemistry plays a critical role in the design and operation of water treatment processes. Detailed chemistry modeling tools use a combination of advanced thermodynamic models and extensive databases to predict phase equilibria and reaction phenomena. The complexity and formulation of these models preclude their direct integration in equation-oriented modeling platforms, making it difficult to use their capabilities for rigorous water treatment process optimization. Neural networks (NN) can provide a pathway for integrating the predictive capability of chemistry software into equation-oriented models and enable optimization of complex water treatment processes across a broad range of conditions and process designs. Herein, we assess how NN architecture and training data impact their accuracy and use in equation-oriented water treatment models. We generate training data using PhreeqC software and determine how data generation and sample size impact the accuracy of trained NNs. The... [more]

Process simulation problems often involve systems of nonlinear and nonconvex equations and may run into convergence issues due to the existence of recycle loops within such models. To that end, surrogate models have gained significant attention as an alternative to high-fidelity models as they significantly reduce the computational burden. However, these models do not always provide a guarantee on the prediction accuracy over the domain of interest. To address this issue, we strike a balance between computational complexity by developing a data-driven branch and prune-based framework that progressively leads to a guaranteed solution to the original system of equations. Specifically, we utilize interval arithmetic techniques to exploit Hessian information about the model of interest. Along with checking whether a solution can exist in the domain under consideration, we generate error-bounded convex hull surrogates using the sampled data and Hessian information. When integrated in a bran... [more]

Four types of non-dairy (plant) drinks—almond, oat, rice, and soy—as well as cow milk with varying fat contents (1.5%, 2.0%, and 3.2%), were examined and compared in terms of the total concentrations of Al, As, B, Ba, Ca, Cd, Cr, Cu, Fe, K, Mg, Na, Mn, Ni, P, Pb, Sb, Se, Sr, and Zn using inductively coupled optical emission spectrometry (ICP OES). Additionally, in vitro gastrointestinal digestion was used to determine the bio-accessible fraction of selected elements, evaluating the nutritional value and risk assessment involved with the consumption of these beverages. A significant difference in the mineral profile was observed depending on the type of plant drink, with the highest content of elements noted in the soy drink and the lowest in the rice drink. Except for Ca and P, the soy drink appears to be a much better source of essential nutrients, including Cu, Fe, and Mn, than cow’s milk. A similar Ca content in plant beverages can be obtained only by adding calcium salt at the stag... [more]

Alginate, a promising biopolymer in the food, biomedical, pharmaceutical, and electronic materials industries, is characterized by its biodegradability, biocompatibility, low toxicity, and gel-forming properties. It is most abundantly found in brown algae. However, conventional dilute acid and alkali extraction methods face limitations in commercialization due to their long processing time, low throughput, and high solvent requirements. In this study, a microwave-assisted extraction (MAE) process for sodium alginate was designed to improve extraction efficiency. The solid/liquid ratio, extraction temperature, and extraction solvent concentration were major variables affecting sodium alginate extraction from Undaria pinnatifida (sea mustard). They were then statistically optimized using response surface methodology. Under optimal conditions (13.27 g/L, 91.86 °C, 2.51% (w/v), and 15 min), the yield was 38.41%, which was 93.43% of the theoretical content of sodium alginate in Undaria pinn... [more]