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Extraction of silica and biochar from biomass waste for the synthesis of aerogel and its application for the removal of dye

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BIOMASS CONVERSION AND BIOREFINERY 2025年 1-19页

作者： Samanta, Ashmita Ghosh, Subhasis Das, Papita Jadavpur Univ Dept Chem Engn 188 Raja SC Mullick Rd Kolkata 700032 West Bengal India Jadavpur Univ Sch Adv Studies Ind Pollut Control Engn 188 Raja Subodh Chandra Mallick Rd Kolkata 700032 West Bengal India

Contamination o f wastewater is one of the rising global issues, and several studies are being conducted to perform efficient treatment processes with cost-effective methods. In this study, sugarcane bagasse was used as biomass to extract silica (Si) and biochar (BC) and was used further as fillers for aerogel formation with polyvinyl alcohol (PVA) polymer to eliminate Congo red dye from the solution. A batch study was conducted in each case, and the efficiency of dye removal, by different aerogel PVA, PVA-Si, and PVA-BC was studied. Experimental parameters were varied to analyze the effect of the various parameters in each case. The highest removal was 94.98% using PVA-Si aerogel at 300 min with 10 mg/L initial concentration of Congo red, pH 7, and temperature 30 degrees C. The adsorption capacity of Congo red was 29.49 mg/g by PVA-Si aerogel. The characterization of the aerogel showed the occupancy of various functional groups, modification in crystallographic structure, and surface alteration of aerogels. The analysis for the study of kinetics exhibited that the process of adsorption fitted the pseudo-second-order reaction for all the aerogels and also the thermodynamics data demonstrated that the adsorption process was spontaneous and endothermic. Also, from the isotherm modeling, it was observed that the process best fitted with the Freundlich isotherm model.

关键词： Aerogel Silica Biochar PVA Congo red Adsorption modeling Sugarcane bagasse

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Simultaneous Fault Diagnosis for control Valve Using Feature Fusion and Multilabel Classification Framework Considering Fault Similarity

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Dai, Wei Fan, Ruipeng Nan, Jing China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

With the increasing complexity of industrial systems, the frequency of simultaneous faults within these systems is also on the rise, posing a challenge to traditional fault diagnosis methods that rely solely on sensor data. To this end, a novel simultaneous fault diagnosis method for the whole process from data feature extraction to diagnosis model construction is proposed in this article. First, a multi-feature fusion feature extraction method integrating cloud model (CM) and dynamic-inner principal component analysis (DiPCA) is proposed, termed CM-DiPCA, which effectively captures the hidden uncertainties and dynamic characteristics embedded in the fault signals, thereby improving the ability of the diagnosis model to identify and distinguish various fault modes. Subsequently, a dual stochastic configuration network (dualSCN) framework is constructed to identify the fault category while determining the number of faults, significantly enhancing the self-adaptive capabilities and modeling efficiency of the diagnostic model. Moreover, considering the potential correlation between single and simultaneous faults, the CM-based overlap degree (OD) calculation is introduced into dualSCN, thereby reducing the adverse effects predicted only from a probabilistic perspective. The experimental results show that the proposed method exhibits advantages in identifying simultaneous faults and modeling efficiency compared with the existing methods.

关键词： Cloud model (CM) control valve fault diagnosis fault diagnosis potential correlation potential correlation simultaneous fault simultaneous fault stochastic configuration network (SCN) stochastic configuration network (SCN) stochastic configuration network (SCN)

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An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

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ACTA GEOPHYSICA 2025年第2期73卷 1865-1889页

作者： Rafiei-Sardooi, Elham Azareh, Ali Ghazanfarpour, Hossein Parteli, Eric Josef Ribeiro Faryabi, Mohammad Barkhori, Saeed Univ Jiroft Fac Nat Resources Dept Ecol Engn Kerman Iran Univ Jiroft Dept Geog Kerman Iran Shahid Bahonar Univ Kerman Dept Geog Kerman Iran Univ Duisburg Essen Fac Phys D-47057 Duisburg Germany

Groundwater contamination risk mapping constitutes an important component of groundwater management and quality control. In the present study, we describe a method for such mapping that is more suitable for arid regions than other methods developed in previous work. Specifically, we integrate machine learning tools, interpolation and process-based models with a modified version of DRASTIC-AHP to evaluate groundwater vulnerability to nitrate contamination, and to map this contamination in Jiroft plain, Iran. The DRASTIC model provides a tool for evaluating aquifer vulnerability by using seven parameters related to the hydrogeological setting (depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone and hydraulic conductivity), while the criteria ratings and weights of these parameters are evaluated by means of an analytic hierarchy process (AHP). However, to obtain the risk map, the model predictions related to groundwater vulnerability are combined here with a contamination hazard map, which we estimate by applying ensemble modeling. This modeling builds on the occurrence probability predicted by means of a modeling framework that is based on generalized linear modeling (GLM), flexible discriminant analysis (FDA) and support vector machine (SVM). We find that the application of our ensemble modeling to predicting groundwater contamination in Jiroft plain leads to better results (AUC = 0.916, Kappa = 0.89, MSE = 0.18 and RMSE = 0.11) compared to the separated employment of the various machine learning (ML) methods, i.e., either SVM (AUC = 0.847, Kappa = 0.86, MSE = 0.19 and RMSE = 0.29), GLM (AUC = 0.829, Kappa = 0.81, MSE = 0.23 and RMSE = 0.37) or FDA (AUC = 0.816, Kappa = 0.8, MSE = 0.26 and RMSE = 0.42). Our integrated modeling framework provides an assessment of both regional patterns of groundwater contamination and an estimate of contamination impacts based on socio-environmental variables, being particularly suitable for appl

关键词： Groundwater Contamination Hazard Risk Vulnerability Iran

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Real-Time Auto controlling of Viable Cell Density in Perfusion Cultivation Aided by In-Line Dielectric Spectroscopy With Segmented Adaptive PLS Model

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BIOTECHNOLOGY AND BIOENGINEERING 2025年第4期122卷 858-869页

作者： Sun, Yunpeng Zhang, Qiongqiong He, Yunfei Chen, Dongliang Wang, Zheyu Zheng, Xiang Fang, Mingyue Zhou, Hang WuXi Biol Cell Culture Proc Dev CCPD Shanghai Peoples R China WuXi Biol NonGMP Pilot Plant NPP Shanghai Peoples R China WuXi Biol Bioproc Res & Dev BRD Shanghai Peoples R China

Serving as a dedicated process analytical technology (PAT) tool for biomass monitoring and control, the capacitance probe, or dielectric spectroscopy, is showing great potential in robust pharmaceutical manufacturing, especially with the growing interest in integrated continuous bioprocessing. Despite its potential, challenges still exist in terms of its accuracy and applicability, particularly when it is used to monitor cells during stationary and decline phases. In this study, data pre-processing methods were first evaluated through cross-validation, where the first-order derivative emerged as the most effective method to diminish variability in prediction accuracy across different training datasets. Subsequently, a segmented adaptive partial least squares (SA-PLS) model was developed, and its accuracy and universality were demonstrated through several validation studies using different clones and culture processes. Furthermore, a real-time viable cell density (VCD) auto-control system in perfusion culture was established, where the VCD was maintained around the target with notable precision and robustness. This model enhanced the monitoring capabilities of capacitance-based PAT tools throughout the cultivation, expanded their application in cell-specific automatic control strategies, and contributed vitally to the advancement of continuous manufacturing paradigms.

关键词： biomass capacitance modeling multivariate data analysis perfusion

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Optimization of process parameters in metal injection molding for turbine blade using response surface methodology

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第11-12期137卷 5899-5912页

作者： Rosli, Mohd Uzair Khor, Chu Yee Zailani, Zainal Abidin Univ Malaysia Perlis Fac Mech Engn & Technol Arau 02600 Perlis Malaysia

Manufacturing turbine blades using metal injection molding (MIM) is a complex process that requires precise control over parameters to achieve high dimensional accuracy. Inadequate management of shrinkage, frozen volume, and volume filled leads to dimensional deviations, resulting in defects or reduced performance of turbine blades in operation. Optimizing these response factors ensures reliable production and high-quality turbine blades. This study investigates the influence of process parameters in metal injection molding by evaluating their significance and interaction. A three-level central composite design (CCD) approach-based response surface methodology analysis was applied to statistically specify the effect of important numerical and categorical process variables: mold temperature, melt temperature, injection time, flow rate on the critical response process output variables concerning product quality, namely shrinkage, frozen volume, and volume filled. By using a face-centered design, a total of 30 simulation data was fitted. analysis of variance (ANOVA) was then performed to assess the significance of factors and their interactions at a 95% confidence level (p < 0.05). Subsequently, empirical models were developed and rigorously validated against the simulation results. The optimum process parameters of the metal part were characterized as follows: mold temperature of 15 degrees C, 138 degrees C of melt temperature, 2.5 s of injection time, and 94 cm3/s flow rate. The results are expected to advance the metal injection molding industry by providing valuable references and enhancing the understanding of the optimization process.

关键词： Metal injection molding Response surface methodology Simulation and modeling Optimization

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Agent System Event data: Concepts, Dimensions, Applications 43rd

Agent System Event Data: Concepts, Dimensions, Applications

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43rd International conference on Conceptual modeling

作者： Shen, Qingtan Polyvyanyy, Artem Lipovetzky, Nir Kampik, Timotheus Univ Melbourne Parkville Vic 3010 Australia SAP Walldorf Germany

ISBN: (纸本)9783031758713;9783031758720

Event data is a collection of recorded events that capture performed actions and observed states of business processes supported by information systems. It describes the times of event occurrences, event types, event attributes, and process cases of events identified by one or more objects the events relate to. process mining uses event data to analyze and improve the processes in organizations. These processes are often performed by actors or agents, such as employees, resources, and systems, in different roles within organizations. In this paper, we present Agent System Event data (ASED), a new type of event data that describes business processes as interactions of agents. ASED provides a new scope for analyzing individual agents involved in multiple processes, interactions of agents, and systems of agents that enact the processes. We formalize ASED as a conceptual data model, discuss its dimensional data modeling aspects, and argue that event data, in general, benefits from dimensional representation. We review existing event data types and discuss the complementary nature of existing models and ASED. Finally, we validate ASED by demonstrating its ability to express existing business process compliance rules, significantly expanding the scope of compliance analysis addressed by existing data models.

关键词： process mining event logs event data agent system mining

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A Max-type Run Sum Chart for Monitoring a Shifted Exponential process

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2025年

作者： Antzoulakos, Demetrios L. Fountoukidis, Konstantinos G. Rakitzis, Athanasios C. Univ Piraeus Dept Stat & Insurance Sci Piraeus Greece

The shifted (or two-parameter) exponential distribution is a probability model that is widely used in many practical applications to model time-to-event data. In reliability analysis it has been frequently used for modeling the lifetime of products with a warranty period. In this paper, we propose and study the properties of a run sum chart for monitoring shifted exponential lifetimes in order to detect a change in either or in both process parameters. Using the Markov chain method, we evaluate several performance measures, based on the run length distribution of the proposed chart, and investigate its performance for increasing and/or decreasing shifts in process parameters. The results of an extensive numerical study show that a properly designed run sum chart has an improved detection ability compared to that of a Shewhart-type Max chart for shifted exponential distribution. In addition, the proposed chart can be considered as a viable alternative to the CUSUM-type Max chart for shifted exponential distribution, since for a large number of out-of-control situations it requires approximately the same, if not less, time to detect them. Finally, for supporting the use of the proposed run sum chart in practice, we provide guidelines and empirical rules for selecting the values of its parameters, along with an illustrative numerical example.

关键词： average run length expected average run length expected quadratic average run length expected quadratic loss run sum statistic shifted exponential distribution single control chart

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A Differentiable Physics-Informed Machine Learning Approach to Model Laser-Based Micro-Manufacturing process

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JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 2025年第5期147卷 051002页

作者： Oddiraju, Manaswin Cleeman, Jeremy Malhotra, Rajiv Chowdhury, Souma Univ Buffalo Dept Mech & Aerosp Engn Buffalo NY 14260 USA Rutgers State Univ Dept Mech & Aerosp Engn Piscataway NJ 08854 USA

Advanced manufacturing processes are often based on complex multiphysics phenomena that are either poorly understood or are computationally too expensive to simulate in the context of process design, control, or planning. Traditionally, simplified physics models with prescribed heuristics or purely data-driven surrogate models are used as alternatives in such applications. The concept of physics-informed machine learning (PIML) has been shown to have unique advantages over both of these alternatives in various fields of complex system analysis. In this paper, a new PIML approach is presented to model the geometry of the cut produced by a magnetically assisted laser-induced plasma micro-machining (M-LIPMM) process. This PIML architecture uses a neural network to auto-adapt the parametric boundary condition and physical properties used in a simplified finite difference-based physics model (of 2D heat conduction), as a function of the inputs namely the laser settings. This network also estimates the scaling and shifting parameters used by a convolutional neural network that takes the temperature profile predicted by the simplified heat conduction model to predict the width and depth of the machined cut. Trained on physical experiment data, the PIML approach compares favorably to a pure data-driven neural network model in extrapolation tests, while also providing physical insights (that the latter cannot). The PIML approach also provides an 85% better accuracy overall compared to the simplified physics model with heuristic settings.

关键词： convolutional neural networks extrapolation laser-induced plasma micro-manufacturing physics-informed machine learning thermal model micro- and nano-machining and processing modeling and simulation nontraditional manufacturing processes process planning

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E²AG: Entropy-Regularized Ensemble Adaptive Graph for Industrial Soft Sensor modeling

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IEEE-CAA JOURNAL OF AUTOMATICA SINICA 2025年第4期12卷 745-760页

作者： Chen, Zhichao Pan, Licheng Ma, Yiran Yang, Zeyu Yao, Le Qian, Jinchuan Song, Zhihuan Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Huzhou Univ Sch Engn Huzhou Key Lab Intelligent Sensing & Optimal Contr Huzhou 313000 Peoples R China Hangzhou Normal Univ Sch Math Hangzhou 311121 Peoples R China Qinting Data & Intelligence Co Ltd Hangzhou 311121 Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault D Maoming 525000 Peoples R China

Adaptive graph neural networks (AGNNs) have achieved remarkable success in industrial process soft sensing by incorporating explicit features that delineate the relationships between process variables. This article introduces a novel GNN framework, termed entropy-regularized ensemble adaptive graph (E(2)AG), aimed at enhancing the predictive accuracy of AGNNs. Specifically, this work pioneers a novel AGNN learning approach based on mirror descent, which is central to ensuring the efficiency of the training procedure and consequently guarantees that the learned graph naturally adheres to the row-normalization requirement intrinsic to the message-passing of GNNs. Subsequently, motivated by multi-head self-attention mechanism, the training of ensembled AGNNs is rigorously examined within this framework, incorporating an entropy regularization term in the learning objective to ensure the diversity of the learned graph. After that, the architecture and training algorithm of the model are then concisely summarized. Finally, to ascertain the efficacy of the proposed E(2)AG model, extensive experiments are conducted on real-world industrial datasets. The evaluation focuses on prediction accuracy, model efficacy, and sensitivity analysis, demonstrating the superiority of E(2)AG in industrial soft sensing applications.

关键词： Training Deep learning Adaptation models Analytical models Soft sensors Predictive models Graph neural networks Entropy Mathematical models Mirrors graph neural networks mirror descent reproduced kernel hilbert space soft sensor

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The process of curing epoxy-amine resins with a composition gradient in thick-walled cylindrical products

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JOURNAL OF MATERIALS SCIENCE 2025年第11期60卷 5267-5279页

作者： Khamidullin, O. L. Andrianova, K. A. Nikitin, V. S. Amirova, L. M. Kazan Natl Res Tech Univ 10 K Marx Str Kazan 420111 Russia

The idea of creating an epoxy-based gradient composition with hardeners of varying activities was proposed to control the exothermic effect during the curing process. Both resin and hardeners in powder form were selected to create a composition appropriate for the production of large-scale products. By modeling the thermal balance in Thermal Simulations software (Netzsch) based on data obtained from Thermokinetics 3 (Netzsch), a comparative analysis of temperature fields in thick-walled polymer samples of non-gradient and gradient compositions was carried out. A single-stage constant heating rate mode was developed to create a controlled gradient of the resin conversion from the inner layers of the part to the outer layers. The polymerization front is created by a certain ratio of hardeners with different reactivities in the layers of the matrix. The use of this method can lead to a reduction in the curing time, the prevention of overheating and a decrease in the residual stresses in the composite part.

关键词： Curing

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