Input Parameters Research Articles

A rainfall prediction model based on ERA5 and Elman neural network

The heightened frequency and intensity of heavy rainfall, brought about by global climate warming, significantly affect various regions. Rainfall prediction plays a pivotal role in ensuring societal security and fostering development. There has been limited exploration of the impact of input parameters on the accuracy of rainfall predictions. Despite the advantages of Elman neural network models in handling non-linear relationships and spatiotemporal data, their application in weather forecasting is restricted. This paper assesses the accuracy of the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) with a dataset from meteorological stations. It introduces a novel rainfall forecasting model based on the Elman neural network and ERA5 reanalysis data. The study also identifies optimal input parameters through factor correlation analysis. Experimental results showcase the precision and stability of ERA5 across various rainfall conditions. Meteorological parameters, such as Precipitable Water Vapor (PWV), exhibit noticeable correlations with temporal variables and precipitation volume. The seven-factor model, including PWV, Zenith Tropospheric Delay, temperature, relative humidity, day-of-year, and hour of day, outperforms in precision evaluation. It achieves a mean critical success index of 57.06 %, a correct forecast rate of 91.39 %, and a false alarm rate of 39.48 %. This rainfall forecasting model introduces a novel approach and empirical research to enhance predictive accuracy, holding significant implications for the amelioration of meteorological alert systems, risk mitigation, and the safeguarding of life and property.

Boosting Barlow Twins Reduced Order Modeling for Machine Learning‐Based Surrogate Models in Multiphase Flow Problems

AbstractWe present an innovative approach called boosting Barlow Twins reduced order modeling (BBT‐ROM) to enhance the reliability of machine learning surrogate models for multiphase flow problems. BBT‐ROM builds upon Barlow Twins reduced order modeling that leverages self‐supervised learning to effectively handle linear and nonlinear manifolds by constructing well‐structured latent spaces of input parameters and output quantities. To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT‐ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. To evaluate the performance of BBT‐ROM, we conduct three three‐dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. The results demonstrate that BBT‐ROM excels at predicting non‐wetting phase saturation (e.g., oil or saturation) and fluid pressure, with average relative errors ranging from 0.5% to 3%. Importantly, BBT‐ROM showcases robustness when faced with limited input parameter space during GCS testing.

Assessment of the Influence of Input Parameters in CFD Simulation of Ventilation in an Experimental Chamber

Abstract The aim of this paper is to evaluate the impact of selected input parameters on the accuracy and usability of CFD simulations. This paper compares the achieved results of Computational Fluid Dynamics (hereinafter CFD) simulations of air age with the ventilation results of a specific experimental box. A total of 16 variants were simulated for different simulation settings, including various computational domains and meshes in OpenFOAM, Ansys Fluent, DesignBuilder, and BlueCFD-AIR software. The paper primarily focuses on the importance of meeting the suitability criterion for the yPlus parameter used in the wall function of turbulent models k-ε and k-ω SST, and the resulting air age. Simulation with the best yPlus parameter shows the best agreement in airflow velocity and air age with the experiment. Overall, this led to an improvement in accuracy by 41 % compared to original simulation and 56 % compared to the previous our best OpenFOAM simulation.

Development of a novel energy efficient integrated system for concurrent waste water treatment, hydrogen production and carbon capture- A sustainable approach

Microbial Electrochemical Cells (MECs) technology offer a promising, integrated solution for wastewater treatment and hydrogen production. The present research aims to optimize the operation of a hybrid system for achieving high-efficiency wastewater treatment, carbon capture, and hydrogen production. Multi-criteria decision methods are used to identify optimal conditions for maximum performance. Wastewater Flow Rate (WFR), Energy Consumption (EC), Nanocomposites Concentration (NC), and Temperature (T) are chosen as input parameters. Utilizing the Entropy-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method, the optimal outputs were determined for specific input settings. The proposed system exhibited optimal input settings at 1100 m³/day (WFR), 95 kW (EC), 34 mg/L (NC), and 36 °C (T). NC emerged as the most influential parameter, holding a significance weightage of 32%, followed closely by temperature. Under these optimized conditions, the system demonstrated exceptional performance, achieving a Wastewater Treatment Efficiency of 98.1%, Carbon Capture Efficiency of 97%, and a Hydrogen production flow rate of 17.76 m³/hr. This research lays a foundation for sustainable and versatile wastewater management practices, emphasizing the potential of MEC systems in contributing to cleaner and resource-efficient industrial ecosystems.

Deep learning for pore-scale two-phase flow: Modelling drainage in realistic porous media

In order to predict phase distributions within complex pore structures during two-phase capillary-dominated drainage, we select subsamples from computerized tomography (CT) images of rocks and simulated porous media, and develop a pore morphology-based simulator (PMS) to create a diverse dataset of phase distributions. With pixel size, interfacial tension, contact angle, and pressure as input parameters, convolutional neural network (CNN), recurrent neural network (RNN) and vision transformer (ViT) are transformed, trained and evaluated to select the optimal model for predicting phase distribution. It is found that commonly used CNN and RNN have deficiencies in capturing phase connectivity. Subsequently, we develop a higher-dimensional vision transformer (HD-ViT) that drains pores solely based on their size, regardless of their spatial location, with phase connectivity enforced as a post-processing step. This approach enables inference for images of varying sizes and resolutions with inlet-outlet setup at any coordinate directions. We demonstrate that HD-ViT maintains its effectiveness, accuracy and speed advantage on larger sandstone and carbonate images, compared with the microfluidic-based displacement experiment. In the end, we train and validate a 3D version of the model.

Optimizing the performance parameters of vacuum evaporation technology for management of anaerobic digestate in a waste water treatment plant using fuzzy MCDM method

Evaporation technology in waste water treatment plants is used to treat excess liquid digestate, eventually reaping the essential nutrients from the process. In the present study, the input conditions have been optimized for implementing and achieving optimal performance of the digestate evaporation technology used in waste water treatment plants by employing fuzzy multi-criteria decision making method. Three input parameters, namely the Temperature, the Pressure, and the Mass Flow rate, each at three levels are considered. Experiments are conducted as per the Taguchi’s L9 standard orthogonal array. The performance of the evaporator is examined based on a few response characteristics, such as payback period, thermal energy consumption, power consumption and cost per unit of electricity. Temperature's impact on multi performance stands out significantly, contributing the most (70.52 %), followed by mass flow rate (18.95 %), and pressure (5.37 %). Result of the study reveals that within the investigated range of input parameters, temperature at 80 ℃, pressure at 0.3 bar and mass flow rate at 20 kg/h is the optimal combination. The results of the present study will help in enhancing the efficiency and effectiveness of the evaporation process, leading to improved digestate treatment that supports circular economy initiatives.

Influence of input parameters on the measurement accuracy of external clamp-on ultrasonic flowmeters: An experimental study

Abstract This study comprehensively evaluated the performance of clamp-on ultrasonic gas flowmeters at a natural gas field. The research focused on the influence of key input parameters on flow measurement accuracy during practical application of the instrument and proposed specific measures to improve measurement results. It was found that errors in outer diameter and wall thickness not only lead to flow calculation inaccuracies but also directly impact the propagation path of ultrasonic waves. Therefore, accurate input parameters are crucial to ensure precise flow results. Additionally, the study revealed that changes in installation distance have minimal effects on flow measurement results and adjustments can be made as needed to obtain better signal parameters. Variations in input temperature and pressure mainly affect the conversion coefficient from operating conditions to standard conditions, with minor impacts on flow velocity and operating flow rate. Thus, the velocity ratio method can be considered during on-site calibration to mitigate the effects of temperature and pressure changes.

Modelling of wall-bounded cavitating flow and spray mixing in multi-component environments using the PC-SAFT equation of state

This work introduces a numerical multiphase model for multi-component mixtures, utilizing tabulated data for physical and transport properties across a spectrum of conditions from near-vacuum pressures to supercritical states. The property data are derived using Perturbed Chain Statistical Associating Fluid Theory (PC-SAFT), vapor-liquid equilibrium (VLE) calculations, entropy scaling methodologies, and Group Contribution (GC) methods. These techniques accurately reflect the thermodynamic behaviors of real fluids, avoiding the empirical estimation of Equation of State (EoS) input parameters. Implemented in OpenFOAM, the fluid dynamics solver is designed to address the three-dimensional Navier-Stokes equations for multi-component mixtures. The methodology integrates operator splitting to manage hyperbolic and parabolic steps distinctively. Hyperbolic terms are solved using the HLLC (Harten-Lax-van Leer-Contact) solver with temporal integration performed via a third-order Strong-Stability-Preserving Runge–Kutta (SSP-RK3) method. Viscous stress tensor contributions in the momentum equation are handled through an implicit velocity correction equation, while parabolic terms in the energy equation are explicitly solved. The simulation efficiency is further enhanced by adaptive Local Time Stepping and the Immersed Boundary (IB) method, which addresses interactions between the fluid and solid boundaries. Turbulence is resolved using the Wall Adaptive Large Eddy (WALE) model. Applied to high-pressure diesel fuel spray injections into non-reacting (nitrogen) gas environments, the model has been validated against Engine Combustion Network (ECN) data for the Spray-C configuration, featuring a fully cavitating multi-hole orifice. Results demonstrate that the model achieves accurate predictions across a broad range of tested conditions without the need for tuning or calibration parameters.

Simulation of a System to Simultaneously Recover CO2 and Sweet Carbon-Neutral Natural Gas from Wet Natural Gas: A Delve into Process Inputs and Units Performances

The growing need for carbon-neutral energy solutions necessitates the development of efficient systems for carbon dioxide (CO2) recovery and the production of sweet carbon-neutral natural gas (CNNG) from wet natural gas. Despite existing approaches, limitations in process optimization, solvent efficiency, and output purity persist. This study aims to address these gaps by simulating a system for simultaneous recovery of CO2 and CNNG using an integrated three-stage process, modeled in Aspen Plus V8.8. The unique aspect of this work lies in employing the ENRTL-RK base model, coupled with sensitivity analyses to optimize input parameters across 13 interconnected process units, including compressors, heat exchangers, and extraction columns. Key innovations include the novel configuration of units, yielding a recovery efficiency of 95.94% for CNNG and a CO2 purity of 93.185% at optimal conditions, surpassing conventional methods. The performance of the monoethanolamine (MEA) solvent was enhanced by careful adjustment of input parameters, improving its absorption efficiency by 12% compared to standard operational settings. Sensitivity analysis revealed critical parameters such as feed pressure and solvent flow rate as primary drivers for maximizing output efficiency. This study also provides a detailed quantitative assessment of power requirements, with a compressor brake horsepower (BHP) of 18,2605 watts at 110 bar discharge pressure. It addresses the existing research gap by introducing a systematic approach to process optimization, significantly improving the purity and recovery of CNNG and CO2 while minimizing energy consumption. The results not only demonstrate the viability of this process but also provide a foundation for further refinement in sustainable gas processing technologies.

A novel FDEM-GSA method with applications in deformation and damage analysis of surrounding rock in deep-buried tunnels

In underground hydraulic tunnel engineering, particularlydeep-buried projects, the deformations and stability of the surrounding rock are critical to the construction process. Due to the complex depositional environment of such tunnels, multiple factors influence the stability of the surrounding rock during construction. This paper proposes a novel hybrid method combining Finite-Discrete Element Method (FDEM) with Global Sensitivity Analysis (GSA) and machine learning. The FDEM model is used to establish an approximate relationship between input and output parameters, and its accuracy is validated through comparison with monitoring data. On the basis of data simulated by the FDEM model, a meta-model is developed by Particle Swarm Optimization-Extreme Gradient Boosting (PSO-XGBoost). 10,000 data sets are generated using the meta-model for Sobol sensitivity analysis. The proposed analysis framework is applied to study the deep-buried water diversion tunnel in the Central Yunnan Water Diversion Project (CYWDP). The results indicate that in deep-buried tunnel environments, as the tunnel depth increases, the dominant factor influencing the deformation of the surrounding rock transitions from tunnel depth to the mechanical properties of the rock itself. These findings provide valuable insights for optimizing construction plans in similar tunnel projects and offer a new perspective on sensitivity analysis frameworks based on numerical computations.

A model of task-level human stepping regulation yields semistable walking.

A simple lateral dynamic walker, with swing leg dynamics and three adjustable input parameters, is used to study how motor regulation affects frontal-plane stepping. Motivated by experimental observations and phenomenological models, we imposed task-level multi-objective regulation targeting the walker's optimal lateral foot placement at each step. The regulator prioritizes achieving step width and lateral body position goals to varying degrees by choosing a mixture parameter. Our model thus integrates a lateral mechanical template, which captures the fundamental mechanics of frontal-plane walking, with a lateral motor regulation template, an empirically verified model of how humans manipulate lateral foot placements in a goal-directed manner. The model captures experimentally observed stepping fluctuation statistics and demonstrates how linear empirical models of stepping dynamics can emerge from first-principles nonlinear mechanics. We find that task-level regulation gives rise to a goal-equivalent manifold in the system's extended state space of mechanical states and inputs, a subset of which contains a continuum of period-1 gaits forming a semistable set: perturbations off of any of its gaits result in transients that return to the set, though typically to different gaits.

Effects of new nonlinear curvature models and non-local elasticity on buckling investigation of FG tapered nano-beams locating on elastic foundations

New nonlinear curvature models are proposed to show their effects on the compressive critical buckling load on functionally graded (FG) tapered nano-beams locating on elastic foundations in the presence of non-local elasticity. Fourth and fifth orders finite difference methods (bvp4c and bvp5c) are applied on the governing differential equation which is a highly non-linear, due to curvature and has variable coefficients, due to properties. Dimensional analyses have been applied on the governing system together with the problem conditions to produce their dimensionless forms. For different considered parameters, the critical buckling load is computed using the eigenvalue theorem. The effects of nonlinear curvature, variable Young modulus, variable moment inertia and foundation parameters are studied and displayed in tables and figures. The stability and convergence of the present solution are tested using more than one strategy as: comparison with available results, comparison between bvp4c and bvp5c with different mesh intervals and tolerances of truncation errors. These comparisons show that, the new curvature model affects the critical buckling with different ratios depending on variability of properties and foundation parameters. The new results show that the nonlinear curvature model reduces the critical buckling load in comparison with the classical linear model, which, leads to decreasing in factor of safety in design of structures. The new results show that the design of structures will be influenced with the new reduction of the compressive critical buckling load, since it may affect the factor of safety in design of structures. The new results can lead, generally, to more reduction of critical buckling load for large or small input parameters and it will affect other critical values such that frequencies. The introduced Tables and Figures of the present problem with comparisons of previously exact, analytical and numerical works establish the higher accuracy and stability of current findings using bvp4c or bvp5c with preferable of the later. This study is related to some practical applications such as aeronautical and structural engineering.

Flow and sensitivity analysis of MHD radiative hybrid nanofluid flow across a permeable wedge with slip condition: Response surface methodology

AbstractThis current paper has been written to investigate the effect of a hybrid Tiwari and Das nanofluid model along with optimizing the flow using sensitivity analysis. Heat transfer rates and skin friction assessments were examined with the supplementation of external effects like heat source, thermal radiation, buoyancy forces, velocity slip as well as magnetic effect with spherical‐shaped nanoparticles. The mathematical modeling was formulated using a system of nonlinear PDE. Similarity transformations were implemented in the governing equations for obtaining the final set of nondimensional equations which were then solved using bvp4c code in MATLAB. The results obtained were in close agreement with published results. Some of the significant results obtained included an increase in Nusselt number by 2.26% for an increase in velocity ratio parameter () from to 0.3 along with an increase in skin friction coefficient by 0.77% with magnetic () parameter to . The heat transfer profile was augmented by 0.79% with increasing radiation parameter to while an increase of 18.84% was observed for skin friction profile values recorded for the hybrid model as compared to the base fluid model. Nusselt number reduced by 0.51% for a transition to nanofluid model from regular fluid and by 1.76% for the hybrid model compared to the nanofluid model The unblocked face‐centered central composite design (CCD) was used for analyzing the sensitivity analysis of independent, continuous input parameters on the response parameters, skin friction coefficient, and Nusselt number. The high values for , and , for Nusselt number and skin friction coefficient values, respectively, validate the ANOVA results obtained using response surface methodology (RSM). Only shows positive sensitivity for both the Nusselt number and skin friction responses. The present hybrid nanofluid model finds extensive applications in bio‐toxicity studies, manufacturing of water heaters, and forging of hot exhaust valve heads.

Harnessing centrifugal and Euler forces for tunable buckling of a rotating elastica

We investigate the geometrically nonlinear deformation and buckling of a slender elastic beam subject to time-dependent ‘fictitious’ (non-inertial) forces arising from unsteady rotation. Using a rotary apparatus that accurately imposes an angular acceleration around a fixed axis, we demonstrate that dynamically coupled centrifugal and Euler forces can produce tunable structural deformations. Specifically, by systematically varying the acceleration ramp in a highly automated experimental setup, we show how the buckling onset of a cantilevered beam can be precisely tuned and its deformation direction selected. In a second configuration, we demonstrate that Euler forces can cause a pre-arched beam to snap-through, on demand, between its two stable states. We also formulate a theoretical model rooted in Euler’s elastica that rationalizes the problem and provides predictions in excellent quantitative agreement with the experimental data. Our findings demonstrate an innovative approach to the programmable actuation of slender rotating structures, where complex loading fields can be produced by controlling a single input parameter, the angular position of a rotating system. The ability to predict and control the buckling behaviors under such non-trivial loading conditions opens avenues for designing devices based on rotational fictitious forces.

Effects of interval treadmill training on spatiotemporal parameters in children with cerebral palsy: A machine learning approach

Quantifying individualized rehabilitation responses and optimizing therapy for each person is challenging. For interventions like treadmill training, there are multiple parameters, such as speed or incline, that can be adjusted throughout sessions. This study evaluates if causal modeling and Bayesian Additive Regression Trees (BART) can be used to accurately track the direct effects of treadmill training on gait. We developed a Directed Acyclic Graph (DAG) to specify the assumed relationship between training input parameters and spatiotemporal outcomes during Short Burst Locomotor Treadmill Training (SBLTT), a therapy designed specifically for children with cerebral palsy (CP). We evaluated outcomes after 24 sessions of SBLTT for simulated datasets of 150 virtual participants and experimental data from four children with CP, ages 4–13 years old. Individual BART models were created from treadmill data of each step. Simulated datasets demonstrated that BART could accurately identify specified responses to training, including strong correlations for step length progression (R2 = 0.73) and plateaus (R2 = 0.87). Model fit was stronger for participants with less step-to-step variability but did not impact model accuracy. For experimental data, participants’ step lengths increased by 26 ± 13 % after 24 sessions. Using BART to control for speed or incline, we found that step length increased for three participants (direct effect: 13.5 ± 4.5 %), while one participant decreased step length (−11.6 %). SBLTT had minimal effects on step length asymmetry and step width. Tools such as BART can leverage step-by-step data collected during training for researchers and clinicians to monitor progression, optimize rehabilitation protocols, and inform the causal mechanisms driving individual responses.

Uncertainty and sensitivity analysis of the fission product behaviour in the Phébus FPT1 test with the system code AC2

The Phèbus FP (Fission Product) research program, conducted by IRSN and CEA, aimed to improve the understanding of the phenomena governing core melt down, fission product release and transport, as well as the fission product behaviour in the containment. The test facility represents a 900 MWe PWR at a 1/5000 scale. The second test, FPT1, was carried out in 1996 and utilised an Ag-In-Cd alloy control rod. There is a growing interest in Best Estimate Plus Uncertainty analysis both in the research, as well as in the licensing. It has a long-standing history in case of thermal–hydraulic system codes focusing on design basis accidents, but its application is limited on the severe accident domain. In recent times however an increasing attention has been paid to evaluating severe accident codes’ uncertainties. This study presents an uncertainty and sensitivity analysis (UaSA) of the FPT1 test, focusing on fission product behavior modeling within the severe accident code system AC2. The detailed application of the coupled codes ATHLET-CD and COCOSYS in this context is novelty. Detailed description of the methodological and practical approach is provided. The uncertain input parameters chosen and quantified are directly related to the phenomena describing the fission product transport and retention phenomena both in the primary circuit, as well as in the containment, while thermal–hydraulic conditions within the primary circuit were kept as much as possible constant. Finally, the study has been complemented by a sensitivity analysis deriving the Spearman's rank correlation coefficient for each uncertain input parameter.

Incorporating Crumb Rubber in Slag-Based Geopolymer: Experimental Work and Predictive Modelling

Crumb rubber (CR), a prevalent hazardous waste material, poses significant environmental challenges that necessitate innovative management strategies. Recent studies have focused on incorporating CR and ground granulated blast furnace slag (GGBS) into geopolymer concrete (GC) to address this challenge. This research presents a novel approach that combines experimental analysis with machine learning (ML) techniques to investigate the chemical effects of these materials on GC. Initially, compressive strength (CS) tests are conducted on rubberized geopolymer (RG) concrete, and the resulting data are further analyzed through ML algorithms to enhance the understanding of material behavior. Despite numerous attempts by researchers to integrate CR into geopolymer-based materials, the challenge of mitigating strength degradation persists. Therefore, it becomes imperative to construct a predictive model that relies on new variables influencing the strength characteristics. This research develops a predictive model to assess concrete compressive strength (CS). Key variables such as CR grade (particle size), incorporation percentage, and oxide ratio were analyzed for their impact on geopolymer strength. Employing ensemble techniques, namely Bagging (BA) and Gradient boosting (GB) with five learners (M5P, random forest (RF), random tree (RT), reduced error pruning tree (REPT), and support vector machine (SVM), the study found that M5P-GB models offered the most accurate CS predictions with the highest accuracy and lowest errors. Moreover, the dataset was checked via the k-fold cross-validation technique and confirmed the developed models' robustness, reliability, and effectiveness. Furthermore, uncertainty analysis revealed that GB-M5P-unpruned models-maintained uncertainty percentages of 14.769% at 7 days and 11.277% at 28 days, which was under the 35% threshold. Additionally, the sensitivity analysis identified the CR grade and replacement percentage by volume of crusher dust (CD) as the most critical factors affecting CS. These findings underscore the importance of considering predictive model development in decision-making processes related to RG concrete properties, providing valuable insights into optimizing the model's robustness and reliability for practical applications. However, the study's reliance on specific input parameters and controlled laboratory conditions may limit the generalizability of the predictive models, necessitating further validation under diverse real-world conditions and variations in CR source and environmental factors.

Calibration of the UBC3D-PLM soil model from Dilatometer Marchetti Test (DMT) for the liquefaction behaviour and cyclic resistance ratio (CRR) of sandy soils

Seismic liquefaction is one of the most devastating natural hazards that can cause significant damage to structures and infrastructure. The liquefaction behaviour is simulated in the finite element code PLAXIS by the UBC3D-PLM constitutive model that is 3-D generalized formulation of the 2-D UBCSAND model developed at the University of British Colombia. The UBC3D-PLM model used in this work was successfully employed in many recent studies, e.g. to evaluate the liquefaction effects on the seismic soil–structure interaction, to assess the dynamic behaviour of earthen embankments built on liquefiable soil and to investigate the seismic performance of offshore foundations. Moreover, UBC3D-PLM model involves many input parameters to model the onset of the liquefaction phenomenon. Therefore, their determination becomes a crucial concern. Previous studies elaborated a specific formulation that requires the corrected Standard Penetration Test (SPT) blow counts as input. However, the Dilatometer Marchetti Test (DMT), compared to the SPT, is more sensitive to several factors that affect the liquefaction resistance such as aging, stress history, overconsolidation and horizontal earth pressure. For this reason, a new parameter selection procedure, which uses the horizontal stress index derived from DMT, was developed in this study.The new relationships were applied for determining the initial parameters of the UBC3D-PLM model to describe the behavior of several liquefiable deposits located in eastern Sicily (Italy) that experienced destructive earthquakes in the past. For each site, the model was calibrated to the DMT-based liquefaction triggering curve, developed by combining DMT correlations with the current method based on SPT test, by the simulation of cyclic direct simple shear tests (CDSS). Finally, CDSS tests were performed by means of the CDSS device at the Soil Dynamics and Geotechnical Engineering Laboratory of the University “Kore” of Enna (Italy). This allowed to validate the applicability of the proposed procedure in simulating the liquefaction behavior of sandy soils.

Establishment of a semi-continuous nano-production line using the Microfluidizer® technology for the fabrication of lipid-based nanoparticles part 1: Screening of critical parameters and Design of Experiment optimization studies

A variety of strategies for producing high-quality nanoparticles have been reported in recent years. Batch-based bottom-up and top-down technologies are generally the most efficient methods, but present a number of challenges, particularly in terms of variability, safety, sustainability and large-scale production. In this study, a scalable, semi-continuous production line was built by connecting individual processing units, including a high shear mixing device, the Microfluidizer® technology and a cooling system. Each unit was equipped with an adequate temperature control to allow solvent-free production of solid lipid nanoparticles (consisting of Precirol® ATO 5 or Gelucire® 43/01) and nanostructured lipid carriers (additionally comprising Labrafac™ lipophile WL 1349). Subsequently, critical formulation parameters and critical process parameters (CPPs) of the individual processing units and their effects on particle size (i.e., critical quality attribute (CQA)) were investigated to identify appropriate input parameters for the subsequent Design of Experiment (DoE) studies conducted after linking the process units to a semi-continuous production line. For particle size monitoring, spatially resolved dynamic light scattering (SR-DLS) measurements were conducted and compared to standard DLS measurements to evaluate the applicability of SR-DLS as an inline monitoring tool. It was found that matrix composition, emulsifier concentration, pressure and number of cycles when processing through Microfluidizer® processor were the most influencing parameters. By optimizing these parameters, five-times higher throughputs could be achieved by the semi-continuous manufacturing line. In addition, the particle size measurements with SR-DLS confirmed the feasibility of implementing this technology for real-time particle size monitoring as an important safety factor in quality control.

An active learning framework assisted development of corrosion risk assessment strategies for offshore pipelines

Aggressive fluids and prolonged service life result in an increasing internal corrosion risk of offshore pipelines, especially for perforation. A framework was constructed by active learning (AL) aimed at assessing the future corrosion risk of offshore pipelines and rapidly developing in-line inspection (ILI) strategies. Compared with Support Vector Regression (SVR), K-Nearest Neighbors (KNN) and Random Forest (RF), the Gradient Boosting Regression (GBR) model exhibits greater robustness and stability under both 10-fold cross-validation (CV) and bootstrap method. The RMSE and R2 are 1.48 and 0.83, respectively. Through the double iterative operation of input parameters and models, the AL framework can significantly reduce the demand for training samples, while maintaining accurate predictions. This study contributes to promoting the application of active learning methods in pipeline integrity management and planning, and providing assistance for the construction of intelligent and digital oilfields.