Order Model Research Articles

Sensor placement strategy based on reduced-order models for thermal error estimation in machine tools

To compensate for thermal errors in machine tools, strategic sensor placement and rapid error calculation are crucial. This study addresses these challenges using model order reduction. A transfer function matrix and a sensitivity function were defined to optimize the number and locations of sensors without physically attaching them to the machine tool. This methodology was validated through experiments on a 3-axis machining center. The number of sensors was reduced by 50 %, and the calculations were performed instantaneously on a laptop. These results demonstrate the approach's effectiveness in advancing sensor placement strategies and real-time thermal displacement estimation.

Fruit peel incorporated alginate based magnetic hydrogel bio-composite beads for removal of hexavalent chromium

High adsorption capacity, reusability and sustainability are the most important features sought in the adsorbent preferences to be used in wastewater treatment. In this research work, magnetic composite beads prepared from fruit peels (nectarine and orange) and alginate (ALG) as biopolymers (NAF and OAF) were synthesized by dropping and pH-precipitation method as alternative adsorbents. By encapsulating the adsorbent using alginate and imparting magnetic properties, the separation of the adsorbent from water after the adsorption process has been simplified. Fourier transform infrared spectroscopy analysis (FTIR), scanning electron microscopy (SEM) analysis, energy-dispersive X-ray (EDX)-mapping and X-ray diffraction (XRD) analyses were performed to examine the surface chemical structure and surface morphological structure of these new synthesized biosorbents. The calculated maximum adsorption capacities were 224.3 mg/g for OAF and 256.5 mg/g for NAF at 298 K and pH =2.0. It was observed that the adsorption process for both adsorbents was endothermic and spontaneous. Moreover, the adsorptions of Cr (VI) onto both adsorbents followed the pseudo-second order model and fit the Langmuir isotherm model better. OAF and NAF were found to be reusable with stable adsorption capacity for at least five cycles. Overall, this study demonstrates the performance of OAF and NAF in the removal of Cr (VI) ions from aqueous solutions, thus highlighting the promising potential of these magnetic bio-based adsorbents for sustainable water treatment.

A multi-field decomposed model order reduction approach for thermo-mechanically coupled gradient-extended damage simulations

Numerical simulations are crucial for comprehending how engineering structures behave under extreme conditions, particularly when dealing with thermo-mechanically coupled issues compounded by damage-induced material softening. However, such simulations often entail substantial computational expenses. To mitigate this, the focus has shifted towards employing model order reduction (MOR) techniques, which hold promise for accelerating computations. Yet, applying MOR to highly nonlinear, multi-physical problems influenced by material softening remains a relatively new area of research, with numerous unanswered questions. To bridge this gap, this study proposes and investigates a novel multi-field decomposed MOR technique, rooted in a snapshot-based Proper Orthogonal Decomposition-Galerkin (POD-G) projection approach. Utilizing a recently developed thermo-mechanically coupled gradient-extended damage-plasticity model as a case study, this work demonstrates that splitting snapshot vectors into distinct physical fields (displacements, damage, temperature) and projecting them onto separate lower-dimensional subspaces can yield more precise and stable outcomes compared to conventional methods. Through a series of numerical benchmark tests, our novel multi-field decomposed MOR technique demonstrates its capacity to significantly reduce computational expenses in simulations involving severe damage, while maintaining a high level of accuracy.

Challenges and Approaches to Extending the Safe Operational Life of Future Renewable Energy Infrastructure

To decarbonize our economy, a huge network of renewable energy infrastructure will need to be built both onshore and offshore, often at remote locations, to generate and transmit renewable energy, and to convert and store renewable energy. Corrosion and other forms of materials degradation will pose major challenges to critical components of renewable energy infrastructure that often operate in extreme and complex environmental conditions. Currently solar and wind farms are designed only for approximately 25 years due to the degradation of solar panels and wind turbines. Such a short design life is unsustainable, not only from lifecycle assessment point of view, but also for generating significant materials wastage. The lives of batteries, hydrogen electrolysers and fuel cells are also significantly affected by corrosion and materials degradation. Addressing these issues is critical for the feasibility and sustainability of future renewable energy-based economy. Corrosion engineering is expected to play an important role in the emerging renewable energy economy in order to address these issues and challenges. Unfortunately, although substantial progress has been made in corrosion science and engineering over the past decades, major weaknesses exist in the management and control of corrosion of engineering structures exposed to complex engineering environments. This problem becomes even more acute when complex forms of localized corrosion occur on ‘invisible’ and highly variable engineering structures such as underground energy pipelines and offshore wind farms. In most practical cases, the failure of engineering structures is due to unexpected and complex forms of localized damages. The safe service life of an engineering structure is often determined by the ‘worst case scenario’ localized corrosion such as corrosion under disbonded coatings on buried steel pipelines. Effective control and management of localized forms of corrosion in complex environments is probably the most significant issue that has not yet been sufficiently addressed by corrosion engineering. In particular, the prediction, detection and prevention of localized forms of corrosion in complex environments remain the most significant challenges to corrosion engineering.This paper critically reviews the challenges and issues in extending the safe operational life of renewable energy infrastructure in complex environments and discusses potential approaches to overcoming these challenges. It is shown that localized corrosion control is a critical issue that has to be addressed in corrosion science and engineering. Although various approaches to localized corrosion control can be taken in the future to help addressing these issues and challenges, the most practical approach to addressing major challenges of localized corrosion to future renewable energy infrastructure could probably be based on reliable and more effective localised corrosion prediction, detection and control. A prerequisite for achieving such effective and reliable corrosion management is timely knowledge about the initiation, propagation and seriousness of localized corrosion occurring over an engineering structure in order to take timely corrective actions. Currently accurate and reliable localized corrosion prediction in practical engineering structures is very difficult, if not impossible. Current industrial knowledge of localized corrosion is mostly from time based routine inspections using various condition assessment and in-line inspection tools. Although corrosion data from such inspection are useful for identifying longer-term corrosion trends, they often do not have sufficient temporal and spatial resolutions required for predicting dynamic changes in complex localized corrosion including these in renewable energy systems. Acquiring corrosion data more frequently and more reliably is necessary not only for early warning of unanticipated structural failure but also for evaluating the efficiency of corrosion control measures such as cathodic protection, corrosion inhibitors and protective coatings. It is concluded that future corrosion management of renewable energy infrastructure will need to incorporate advanced corrosion monitoring tools, data analytics, artificial intelligence and predictive modelling in order to achieve quantitative, accurate and reliable corrosion prediction and closed-loop smart corrosion control. More specifically, (i) the maintenance and management of future energy infrastructures will require more reliable and accurate corrosion forecast and prediction capabilities for more effective detection, monitoring and control of corrosion on infrastructure exposed to complex and variable environments. (ii) Future energy infrastructure will require more efficient corrosion protection technologies, especially those able to control localized forms of corrosion, in order to extend the service life of future renewable energy infrastructures. (iii) New eco-informed and environmentally friendly anti-corrosion materials and methods will be required to meet progressively strict regulations for environmental protection. Cases are presented from the author’s recent research and development over the past decade.Key references:Y Tan, Heterogeneous Electrode Processes and Localized Corrosion, Wiley (2012)Y M Tan, Localized Corrosion in Complex Environments, Wiley (2023)

Advancing Production of e-Fuels through Pressurized Co-Electrolysis in Solid Oxide Electrochemical Cells

Solid oxide electrolyzers (SOE) offer a great advantage over low-temperature electrolyzers, which makte them great candidates for intregration with downstream processes for synthesis of e-fuels. Moreover, SOE can be operated in co-electrolysis mode which makes it possible to simultaneously reduce H2O and CO2 in an electrochemical process. Such a mode of operation results in mixture of H2, CO, H2O and CO2 which can be directly fed to Fischer-Tropsch synthesis. Additionally, co-SOE can be pressurized in order to increase activation resistance and elevate partial pressures of reactants at the triple phase boundary.Despite the great potential, up to date SOE were pressurized in only a few laboratories worldwide, therefore literature data are limited.In the recent study, two batches of 5 cm x 5 cm Ni-8YSZ supported cells with LSC oxygen electrode were investigated. The commercial cells and cells produced in house at the Institute of Power Engineering - National Research Institute. Experimental studies included operation at the ambient pressure and measurements under pressurized conditions. The experimental campaing was constrained by two parameters (whichever occurs first): voltage equal 1.4 V or reaching limiting concentration of reactants at the outlet which correspond to carbon desposition isotherm under given working conditions.Upon completion of the in operando analysis, area specific resistance (ASR) was calculated and analyzed (see Fig. 1). Post-mortem analysis of cross-sections and central location in the electrode-support was investigated using SEM-EDS analysis. Finally, results of the experimental studies were used for the development of model of co-electrolysis in order to determine the performances of same cells operated under ambient and pressurized conditions beyond standard conditions. Application of a dedicated model based on artificial neural included implementation of Adam ‘Adaptive Moment Estimation’ which was chosen as it introduces an adaptive learning rate for each parameter. With such an approach the optimization became more precise. The design of the network targeted minimization of the number of hidden layers and neurons to mitigate the risk of overfitting. Model was found to precisely predict the current-voltage characteristics of co-SOE under ambient and pressurized conditions (see Fig. 2). Pressurization of co-SOE is qualitatively and quantitatively assessed as a promising process for generating substrates for reaction in which liquid e-fuels are produced.The work integrates experimental tools and numerical modeling in order to predict performance and composition of gases exiting co-SOE which are feed Fischer-Tropsch synthesis. The general requirements of the downstream process are presented and discussed. Figure 1

Mitigating Localized Corrosion in Complex Environments through Corrosion Probe Regulated Cathodic Protection

Although progresses in corrosion science and engineering made over the past decades have facilitated the effective control of uniform and general corrosion in many industrial structures, the management and control of localized corrosion in complex engineering environments remains a significant challenge. Localized forms of corrosion remain critical and tenacious threats to the integrity and safety of the huge network of civil and industrial infrastructure assets especially those exposed to complex and varying environmental conditions. An example is localized corrosion of buried steel pipelines that are affected not only by seasonal changes in soil moisture and oxygen levels, inhomogeneous coating defects and coating disbondment, but also by fluctuating stray currents and oscillating mechanical stresses. Another example is localized corrosion on offshore structures such as wind turbines and oil platforms that are affected by multi-zone and dynamically changing marine environmental conditions. Variable and complex environmental conditions can not only lead to changes in corrosion rates, but also in corrosion patterns and mechanisms. Unexpected changes in environment and mechanism could also cause suddenly accelerated localized corrosion damages that are not predictable by conventional laboratory testing and models. This paper reports a new approach to localized corrosion mitigation through the use of localized corrosion probe regulated closed-loop controlled cathodic protection.A new method based on the use of an electrochemically integrated multielectrode array and closed-loop cathodic protection control technologies have been developed over the recent years. This paper will present typical cases of using the method to mitigate localized corrosion of steel pipeline under disbonded coatings - a serious and difficult issue in the energy pipelines industry. Discussion is extended to future perspectives of using this approach to addressing critical issues in corrosion science and engineering, to preventing the pre-mature failure of engineering materials and to extending the safe operational life of engineering structures that that are vital for the provision of the world’s essential services and the maintenance of its economic activities. It is shown that localized corrosion control will play a critical role in the emerging renewable energy economy because localized corrosion is expected to significantly affect the safety, durability, and sustainability of essential infrastructure required for the production, delivery, storage and utilisation of renewable energy such as wind, solar, hydrogen, geothermal, hydropower, ocean and bioenergy. Future corrosion management of renewable energy infrastructure will need to effectively mitigate localized forms of corrosion by incorporating advanced corrosion monitoring tools, data analytics, artificial intelligence and predictive modelling in order to achieve quantitative, accurate and reliable localized corrosion prediction and closed-loop smart localized corrosion control.Key references:Y Tan, Heterogeneous Electrode Processes and Localized Corrosion, Wiley (2012)Y M Tan, Localized Corrosion in Complex Environments, Wiley (2023)

Study of the adsorption of a dye on activated carbon from plant waste

The increasing demand for adsorbents used in environmental protection processes has made their cost increasingly high. To meet this demand, research is focused on the use of low-cost, locally available, biodegradable adsorbents, made from natural sources such as agricultural waste (olive pits, apple peelings, coffee grounds, etc.). This makes it possible to recover these wastes while contributing to environmental protection. The results obtained allowed us, on the one hand, to refine our understanding of the adsorption mechanism of walnut shells and activated carbon, and on the other hand to verify via the comparative study of walnut shells and activated carbon. Our work aimed to apply a process for treating water contaminated with methyl violet dye using adsorbents prepared from walnut shells (biomass, activated biomass, activated carbon and functionalized activated carbon).In this study, many effects were discussed, such as: the effect of the adsorbent, the mass of the activated carbon, the pH, the initial concentration, the functionalized activated carbon.The comparative study of adsorbents shows that the best adsorbent is activated biomass with removal efficiency equal to 77.71%.According to the results obtained, the pseudo second order model was the best, which describes the adsorption modeling, and for the isotherm, it was the Freundlich model.

A Reduced Order Modeling Approach to Capture Transport within a Three-Electrode System Towards Fast Charge Applications

In support of GM's traction battery efforts, we derive and implement a reduced order modeling method to describe the electrochemical performance of a battery cell through the combination of a modified Newman Pseudo-2-Dimensional model and a three-electrode experimental apparatus. To assess the capability of the method, we compare the modeling results with experimental data for a lithiated graphite electrode and a lithiated nickel rich electrode system. The model is applied to simulate the electrochemical and transport processes within the battery cell to predict the negative electrode potential and positive electrode potential with respect to a lithium iron phosphate reference electrode, as well as the terminal voltage.The manufacture and set up of the three-electrode system, model development and reduction approaches, as well as a discussion of applicability to fast charge considerations to avoid lithium plating at the anode during a charging event will be presented.

Novel Reduced-Order Electrochemical Models for Lithium-Ion Batteries Integrating Comprehensive Degradation Mechanisms across Various Operational Scenarios

It is crucial to enhance the computational efficiency while ensuring accuracy in battery simulations, particularly given the presence of degradation factors. We propose reduced order electrochemical models, including a revised single-particle model (RSPM) and fast-calculating P2D (FCP2D) model. They use shape functions to model the lithium-ion concentration and electrolyte potential distribution, and apply the weak form to construct equations for solving the time-dependent polynomial parameters. These two reduced-order electrochemical models have incorporated comprehensive side-reactions, including the anode SEI formation and evolution, anode transitional metal reduction, cathode SEI formation and evolution, cathode film formation and evolution, cathode transitional metal dissolution, and electrolyte oxidation. The performances of the reduced order models are evaluated by comparing the simulation results with those generated by the P2D benchmark model during constant current charging/discharging single cycle, random C-rate operation mode, and dynamic charging/discharging cycling. Results show that the RSPM and FCP2D models can predict the battery performance (e.g., terminal voltage, salt concentration, electrolyte potential) and degradation (e.g., the current density of each side-reaction, the concentration of side-reaction species, cell capacity degradation) accurately and efficiently. The relative error is below 10% while the RSPM and FCP2D demonstrate a much higher calculation efficiency than the P2D benchmark model, with a calculation speed up to ten times faster.

POD-Galerkin reduced order model coupled with neural networks to solve flow in porous media

This paper deals with the numerical modeling of flow around and through a porous obstacle by a reduced order model (ROM) obtained by Galerkin projection of the Navier–Stokes equations onto a Proper Orthogonal Decomposition (POD) reduced basis. In the few existing works dealing with model reduction techniques applied to flows in porous media, flows were described by Darcy’s law and the non linear Forchheimer term was neglected. This last term cannot be expressed in reduced form during the Galerkin projection phase. Indeed, at each new time step, the norm of the velocity needs to be recalculated and projected, which significantly increases the computational cost, rendering the reduced model inefficient. To overcome this difficulty, we propose to model the projected Forchheimer term with artificial neural networks. Moreover in order to build a stable ROM, the influence of unresolved modes and pressure variations are also modeled using a neural network. Instead of separately modeling each term, these terms were combined into a single term, which was modeled using the multilayer perceptron method (MLP). The validation of this approach was carried out for laminar flow past a porous obstacle in an unconfined channel. The proposed ROM coupled with MLP approach is able to accurately predict the dynamics of the flow while the standard ROM yields wrong results. Moreover, the ROM MLP method improves the prediction of flow for Reynolds numbers that are not included in the sampling and for times longer than sampling times. In the final part of the paper, the ROM MLP method was compared with purely data driven methods. It was shown that the MLP method is superior to the purely data driven methods.

Real-time update of data-driven reduced and full order models with applications

We consider a dynamic mode decomposition (DMD) based technique to identify data-driven reduced-order and full-order models and propose two approaches to update them in real-time. These updates are crucial for the models to adapt to the evolving process. The proposed approaches function by calculating the update of the singular value decomposition (SVD), which is the core operation in DMD. In particular, two approaches involving temporal updates and additive modifications are used to update the SVDs. Further, the equivalence of both approaches is proved under special rank conditions. Also, the computational costs involved in these approaches are discussed. The technique is well suited for adaptive process modeling that can be exploited for real-time process monitoring, estimation, control, and optimization. The efficacy of the proposed approach is demonstrated using a large-scale benchmark wastewater treatment process.

Exploring Phosphate-Based Mixed Polyanionic Materials for Enhanced Metal-Ion Rechargeable Batteries

Affordable and high-performance energy storage solutions are essential to meet the growing energy demands of society. Rechargeable batteries employing cost-effective and readily scalable battery materials offer promising prospects for fulfilling these needs. Mixed polyanionic insertion materials offer a rich treasure house for designing robust cathode materials for rechargeable batteries, leveraging properties such as chemical/thermal stability, tunable redox voltage, and excellent electrochemical performance.1,2 Among these, PO4-based materials stand out as exceptional systems, offering scalable synthesis, safe storage/handling, and high energy density. Here, we delve into three distinct classes of mixed polyanionic materials, elucidating their electrochemical behavior and mechanism.(1) Mixed Phosphates: Nanostructured carbon-coated Na4Co3(PO4)2P2O7 and Na4Ni3(PO4)2P2O7 were explored as bifunctional electrocatalysts for oxygen evolution and reduction reactions (OER and ORR).3 Both materials showed bifunctional behavior, suggesting potential use in sodium-air batteries. For practical applications, nanoscale in-situ carbon-coated Na4Fe3(PO4)2P2O7 (NFPP) material was utilized for the fabrication of sodium ion full cells using hard carbon (HC) as the anode.4 Reduced order modeling (ROM) was employed to accurately monitor cycling-related degradation. A stable NFPP/HC full cell exhibited a robust specific capacity for over 200 cycles with minimum degradation was achieved.(2) NASICON-type Phosphate-sulfate: Mixed polyanionic NaFe2PO4(SO4)2 was explored for Li- and Na-ion batteries, leveraging the Fe+3/+2 redox couple within a voltage range of 2.0 V to 4.5 V with excellent capacity and cycling stability. Spray-drying synthesized material with uniform spherical particles was studied using powder X-ray diffraction data followed by various physical characterizations (SEM, TEM, FTIR, Raman, UV vis and calorimetry, etc.).5,6 A single particle-based model (SPM) was employed to analyze the system for predictions of alterations associated with different geometric and physical parameters.(3) Fluorophosphates: The electrochemical properties of Na2FePO4F fluorophosphate (3 V vs Na) were also investigated using a single particle model, validated using the half-cell data and various geometric and physical parameters. The magnetic structure and properties of fluorophosphate Na2MnPO4F sodium insertion material were explored using magnetic susceptibility measurements and neutron powder diffraction.7 A long-range antiferromagnetic ordering was exhibited below the Néel temperature (TN) ~10.4 K. Reference s : [1] B. Senthilkumar, C. Murugesan, L. Sharma, S. Lochab, P. Barpanda, Small Methods 3 (2019) 1800253[2] S. Lochab, S. Singh, S. P. Vanam, M. Fichtner, P. Barpanda, Elsevier Publications (2023) 00714[3] S. Lochab, B. Senthilkumar, D. Singh, R. Ahuja, R.K. Rai, H.N. Alshareef, P. Barpanda, Nanoletters (Manuscript submitted)[4] S. Lochab, S. Bharathraj, K. S. Mayya, P. Barpanda, S. P. Adiga (under review)[5] S. Lochab, K. Jayanthi, A. Navrotsky, P. Barpanda (to be submitted)[6] S. Lochab, B. Senthilkumar, P. Barpanda, Patent, CS-MRC-2023-153[7] S. Lochab, S. Rayaprol, M. Avdeev, L. Sharma, P. Barpanda, J. Solid State Chem 308 (2022) 122926

(Invited) Reduced Order Modeling of Li-Ion Battery Systems - from Fundaments to Real-Life Applications

The development of efficient and safe battery packs is crucial for advancing energy storage technologies. This presentation offers a comprehensive overview of the transition from full-order physics-based modeling to reduced order modeling (ROM) in the context of single battery cells and battery packs, with a specific emphasis on managing and understanding thermal runaway. A detailed exploration of the methodologies used in full-order physics-based models, including ROM and multiphysics coupling, will provide a granular view of electrochemical, thermal, and electrical behaviors in battery systems. To address these challenges, the presentation explores the development and application of reduced order models that approximate the behavior of these complex systems with significantly reduced computational demands. The focus is on simplifying the models while retaining essential dynamics, particularly in the context of thermal management to prevent thermal runaway. The session will also cover how ROMs can accurately predict temperature and voltage within battery packs, enhancing predictive capabilities and operational safety. The electrical modeling of bus-plates and the electromagnetic modeling of cylindrical single cells will be discussed, highlighting their importance in the overall performance and safety of battery systems. The discussion extends to the application of these ROMs in real-world scenarios, illustrating how they can be integrated into battery management systems to enhance performance and safety while ensuring efficiency. Case studies where ROMs have been successfully implemented to predict and mitigate thermal runaway in battery packs will be highlighted, showcasing the transition from fundamental research to practical applications.

Predictive model and optimization of micromixers geometry using Gaussian process with uncertainty quantification and genetic algorithm

Abstract Microfluidic devices are gaining attention for their small size and ability to handle tiny fluid volumes. Mixing fluids efficiently at this scale, known as micromixing, is crucial. This article builds upon previous research by introducing a novel optimization approach in microfluidics, combining Computational Fluid Dynamics (CFD) with Machine Learning (ML) techniques. Our research focuses on enhancing global optimization techniques while minimizing computational costs. It draws inspiration from a Y-type micromixer, initially featuring cylindrical grooves on the main channel's surface and internal obstructions. Simulations, conducted using OpenFOAM software, evaluate the impact of circular obstructions on mixing percentage and pressure drop, considering variations in obstruction diameter and offset. A Gaussian Process (GP) was utilized to model the data, providing model uncertainty. This study optimizes geometries by using genetic algorithm (GA) based on the reduced order model provided by GP. Results align with previous research, showing that medium-sized obstructions (137 mm diameter, 10 mm offset) near the channel wall are optimal. This approach not only provides efficient microfluidic optimization with uncertainty quantification but also highlights the effectiveness of combining CFD and ML techniques in achieving desired outcomes.

Parthenium hysterophorus invasive weed valorization into biochar for removal of pharmaceuticals and personal care products: Competitive adsorption analysis via batch and fixed–bed column systems

The proliferation of invasive weeds and emergence of new contaminants have become significant global concern, threatening human–health and environment. This investigation assesses performance of Parthenium hysterophorus weed–derived biochar (HPC and KPC) for removing acetaminophen (ACT) and metronidazole (MET) in single (ACT/MET) and multi–component (ACT + MET) systems in batch and column modes. Synthesized HPC and KPC were characterized through SEM, EDX, XRD, porosimetry, TGA and FTIR analyses. HPC displayed micropores, featuring surface area of 166.62 m2/g, while KPC exhibited mesopores with 146.16 m2/g surface area. The investigation in batch study includes exploring operating conditions like contact time (0–180 min), concentration (0.5–20 mg/L), biochar dose (0.25–4 g/L) and pH (2–12). At optimal conditions (1 h, pH 6.5 and 1 g/L dose), HPC achieved maximum sorption capacity 48.72 mg/g for MET and 32.10 mg/g for ACT, and KPC exhibited 18.41 mg/g for MET and 16.54 mg/g for ACT in batch mode. According to findings, pseudo–second–order and Langmuir models provided accurate–fit for batch experimental data. Furthermore, study also investigated influence of column operating conditions such as bed depth (3–9 cm), flow rate (0.25–1.00 L/h) and concentration (5–20 mg/L). In column, HPC outperformed KPC in removing ACT and MET, with breakthrough data fitting Thomas model. Both systems exhibit synergistic effect in simultaneously removing ACT and MET from multi–pollutant mixture. In summary, converting invasive weeds into biochar and employing it to remove emerging contaminants provides sustainable approach to protecting aquatic environment and invasive weeds management.

Design of some ferrite-based nanoparticles for uranium removal from Sella leach liquor

ABSTRACT Uranium is a non-biodegradable, toxic and radioactive inorganic pollutant that can cause serious human health impacts. This work aims to dip magnesium and lanthanum in ferrite nanoparticles and study their ability to bind uranium ions from uranium leach liquor. MgFe2O4, LaFe2O4 and CeFe2O4 nano-adsorbents were synthesised and characterised. Comparative studies of uranium sorption on the synthesised nano-adsorbents were achieved through a series of experiments at different adsorption conditions. The results show fast uranium sorption on the nanoparticles (with a high efficiency of 86.4 to 91.6), which reflects their higher affinities towards uranium ions. There is a clear improvement in the adsorption capacities of the nanoparticles for uranium binding in adsorption systems at higher temperatures. The maximum adsorption capacity of the MgFe2O4, LaFe2O4 and CeFe2O4 nano-adsorbents was obtained at pH 5, 0.5 g/L (S/L), 400 mg/L (Ci), 333 K and 90 min. The maximum adsorption capacities of uranium on MgFe2O4, LaFe2O4 and CeFe2O4 nanoparticles are 160.4, 118.8 and 129.2 mg/g, respectively. The assessment of the kinetic, isotherm and thermodynamic models was studied to evaluate the reliability and accuracy of these models in predicting the behaviour of uranium adsorption onto the used nanoparticles under various conditions. The outcome of these models confirms the hypothesis of the Elovich, pseudo-second order, Bangham, Langmuir, Dubinin and Radushkevich models to describe the adsorption process. Additionally, it suggests that uranium removal by the nanoparticles takes place through endothermic and spontaneous physio-chemical adsorption reactions. The nanoparticles show high capacity (115.7–142.3 mg/g) and efficiency (78.1–81.7%) for the uranium ions from different uranium-containing samples. These results confirm the high affinity of the synthesised nanoparticles for uranium adsorption in different media.

Fractional order models of viscoelastic polymeric solids undergoing large deformations

We present a fractional order model for nonlinear visco-hyperelastic solids taking into account large deformations. A three-field form of the Hu-Washizu principle is introduced to create a stable finite element method in the context of nearly incompressible dynamics. The β-method (a generalized midpoint rule) for time discretization is implemented into a variational finite element framework for efficient computing of numerical approximations to the initial boundary-value problem for hyperbolic equation of motion. Finally, a 2-D cantilever beam problem with a step end load is considered in order to demonstrate the algorithm.

Non-intrusive parametric hyper-reduction for nonlinear structural finite element formulations

Model Order Reduction (MOR) is a core technology for the creation of comprehensive executable Digital Twins, since it efficiently reduces the computational burden of high-fidelity models. When dealing with nonlinear structural Finite Element analyses, several Hyper-Reduction (HR) approaches have been developed to reduce the computational cost. Nonetheless, HR approaches are typically intrusive in nature, posing challenges when it comes to integration into existing (commercial) software. Recently, data driven Non-Intrusive MOR methodologies have been proposed. However, these techniques often suffer from overfitting and violate key physics properties, leading to unstable behavior. This work proposes to use Scientific Machine Learning to reintegrate critical stability-preserving physics properties. It introduces a data-driven, physics-augmented, parametric approach that combines Proper Orthogonal Decomposition (POD) with a Partially Input Convex Neural Network (PICNN) architecture. The proposed method effectively reduces the computational burden associated with parametric static nonlinear elastic structural problems while retaining material consistency, hyper-elasticity, and material stability properties in the Reduced Order Model. Numerical validation on several structural models subjected to geometrical and material nonlinearities under static loading conditions demonstrates the effectiveness of the POD-PICNN approach. Additionally, three different sampling strategies have been compared to assess their impact on the method performance. The results emphasize that physics-augmentation is required, as it inherently embeds essential physical constraints into the neural network architecture, ensuring stable and consistent behavior, while highlighting its potential for dynamic and multiphysics applications.

Generalized SmartScan: An Intelligent LPBF Scan Sequence Optimization Approach for Reduced Residual Stress and Distortion in Three-Dimensional Part Geometries

Abstract Laser powder bed fusion (LPBF) is an additive manufacturing technique that is gaining popularity for producing metallic parts in various industries. However, parts produced by LPBF are prone to residual stress, deformation, cracks, and other quality defects due to uneven temperature distribution during the LPBF process. To address this issue, in prior work, the authors have proposed SmartScan, a method for determining laser scan sequence in LPBF using an intelligent (i.e., model-based and optimization-driven) approach, rather than using heuristics, and applied it to simple 2D geometries. This paper presents a generalized SmartScan methodology that is applicable to arbitrary 3D geometries. This is achieved by (1) expanding the thermal model and optimization approach used in SmartScan to multiple layers, (2) enabling SmartScan to process shapes with arbitrary contours and infill patterns within each layer, (3) providing the optimization in SmartScan with a balance of exploration and exploitation to make it less myopic, and (4) improving SmartScan’s computational efficiency via model order reduction using singular value decomposition. Sample 3D test artifacts are simulated and printed using SmartScan in comparison with common heuristic scan sequences. Reductions of up to 92% in temperature inhomogeneity, 86% in residual stress, 24% in maximum deformation, and 50% in geometric inaccuracy were observed using SmartScan, without significantly sacrificing print speed. An approach for using SmartScan for printing complex 3D parts in practice, by integrating it as a plug-in to a commercial slicing software, was also demonstrated experimentally, along with its benefits in significantly improving printed part quality.

Innovative preparation of multi‐dentate Schiff base adsorbent for the adsorption of silver and its application on nano silver particles preparation from liquid photographic wastes

AbstractBACKGROUNDA novel silver adsorbent, (2Z,2′ E)‐2,2′‐((1,10‐phenanthroline‐2,9‐diyl)bis(methaneylylidene))bis(N‐phenylhydrazine‐1‐carboxamide) (PHMC), was synthesized for efficient Ag+ adsorption. PHMC was prepared by refluxing 1,10‐phenanthroline‐2,9‐dicarboxaldehyde with N‐phenylhydrazinecarboxamide at 120 °C. The structural and morphological properties of PHMC were characterized using various analytical techniques including FTIR, SEM, BET surface area analysis, 13CNMR, 1HNMR, and GC–MS, to confirm the formation of PHMC adsorbent.RESULTSThe results demonstrated that PHMC possesses an exceptional silver sorption capacity of 440.67 mg g−1 in aqueous solutions. Additionally, PHMC exhibited excellent reusability, with effective desorption of Ag(I) using a 1 M thiourea solution, enabling adsorbent regeneration. The adsorption of Ag+ onto PHMC was well described by Langmuir and D‐R isotherms, as well as pseudo‐second order and intraparticle diffusion (IPD) kinetic models. PHMC showed remarkable selectivity for silver, even in the presence of competing ions, and demonstrated significant stability, retaining over 95% of its initial capacity after four regeneration cycles and more than 80% after seven cycles. Thermodynamic investigations revealed that the sorption process was endothermic.CONCLUSIONPHMC proved to be a highly effective adsorbent for silver removal, with superior reusability compared to other materials. Optimal adsorption conditions were established for the removal of silver from liquid photographic waste. The recovered silver nitrate was successfully used to synthesize Ag nanoparticles using trisodium citrate (TSC) as a reducing agent, and the resulting nanoparticles were characterized using various analytical techniques to confirm the formation of silver nanoparticles. © 2024 Society of Chemical Industry (SCI).