Impact of inhibition mechanisms, automation, and computational models on the discovery of organic corrosion inhibitors

Progress in Materials Science, Journal Year: 2024, Volume and Issue: unknown, P. 101392 - 101392

Published: Oct. 1, 2024

Language: Английский

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Enhancing accuracy in Equivalent In-Service-Time assessment for homogeneous solid propellants: A novel temperature-independent predictive model utilizing PCA of FTIR Data

FirePhysChem, Journal Year: 2024, Volume and Issue: unknown

Published: June 1, 2024

The present study was devoted to setting a universal T-independent predictive model of equivalent in-service-time (EIST) for homogenous solid propellant (HSP) surpass the limits van't Hoff law particularly when high aging temperatures and/or extended durations are employed in artificial plans. To achieve this objective, four double base rocket propellants (DBRP) underwent 4 months at 323.65 K, 338.65 353.65 and 368.65 with sampling conducted every 20 days. Fourier Transform Infrared spectrometry (FTIR) showed that homolytic scission O-NO2 bonds hydrocarbon chains nitrate esters main processes occurring during chemical decomposition. With heating temperature increase, decomposition becomes more predominant. Furthermore, scatter plot from Principal Component Analysis (PCA) FTIR spectra obtained each showed, respectively, over than 88.9%, 94.3%, 97.4%, 98.6 variances were described by first principal component. This latter value found 97.6% PCA applied all spectra. Using PCA/FTIR approach recently developed, EIST assessed investigated samples. Subsequently, an individual set temperature, which used establish model. final computed relative deviation 5.3% compared those experimental way. Moreover, two similar DBRPs aged different have been validate model, associated mean absolute percentage error (MAPE) 4.6%. comprehensive statistical analysis highlighted excellent goodness-of-fit metrics decrease increase natural temperature.

Language: Английский

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A Comparison Between Allied Ordnance Procedure‐48 and Various Machine Learning Models

Propellants Explosives Pyrotechnics, Journal Year: 2025, Volume and Issue: unknown

Published: April 21, 2025

ABSTRACT This study examines the stabilizer depletion data of seven distinct gun propellants (single base and double base, stabilized with DPA or Arkadite II) using allied ordnance procedure (AOP)‐48 kinetic approach three machine learning algorithms: random forest, extreme gradient boosting, neural network. The efficacy various methodologies is evaluated in relation to quantity training data. AOP‐48 model demonstrates optimal performance when trained on sufficiently sized datasets, accurately predicting content a mean absolute error 0.03%. errors achieved by algorithms were between 0.06% 0.15% for content. Nevertheless, models can be enhanced incorporating propellant composition into their architecture, thereby reducing range 0.05%–0.075% impact varying testing partitions has been subjected comprehensive analysis, requisite points developing yielding accurate predictions (below 0.05% concentration) determined approximately 30 per formulation, while 15 are sufficient procedure.

Language: Английский

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Adsorption Capacity Prediction and Optimization of Electrospun Nanofiber Membranes for Estrogenic Hormone Removal Using Machine Learning Algorithms

Polymers for Advanced Technologies, Journal Year: 2024, Volume and Issue: 35(11)

Published: Nov. 1, 2024

ABSTRACT This study focuses on developing four machine learning (ML) models (Gaussian process regression (GPR), support vector (SVM), decision tree (DT), and ensemble (ELT)) optimized hyperparameters tuned via genetic algorithm (GA) particle swarm optimization (PSO) to analyze predict the adsorption capacity of estrogenic hormones. These hormones are a serious cause fish femininity various forms cancer in humans. Their electrospun nanofibers offers sustainable relatively environmentally friendly solution compared nanoparticle adsorbents, which require secondary treatment. The intricate task is find relationship between input parameters obtain optimum conditions, requires an efficient ML model. GPR integrated GA hybrid model performed most accurate precise results with R 2 = 0.999 RMSE 2.4052e −06 , followed by ELT (0.9976 4.3458e −17 ), DT (0.9586 2.4673e −16 SVM (0.7110 0.0639). 2D 3D partial dependence plots showed temperature, dosage, initial concentration, contact time, pH as vital parameters. Additionally, Shapley's analysis further revealed time dosage sensitive Finally, user‐friendly graphical user interface (GUI) was developed predictor utilizing (GPR‐GA), were experimentally validated maximum error < 3.3% for all tests. Thus, GUI can legitimately work any desired material given conditions efficiently monitor removal concentration simultaneously at wastewater treatment plants.

Language: Английский

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Industrial Automation Through AI-Powered Intelligent Machines—Enabling Real-Time Decision-Making

Published: Jan. 1, 2024

Language: Английский

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Research on performance degradation of force sensors based on improved error back propagation algorithm

Journal of Physics Conference Series, Journal Year: 2024, Volume and Issue: 2849(1), P. 012025 - 012025

Published: Sept. 1, 2024

Abstract Studying the performance degradation of force sensors, a core component aircraft control stick measurement devices, is essential. The key to investigating equipment lies in constructing model. When dealing with data from specific relying solely on fitting methods may not effectively describe equipment. This study introduces an error backpropagation neural network model for and optimization improvements are made by using genetic algorithm. Experimental results demonstrate 99% reduction Root Mean Square Error proposed modeling approach.

Language: Английский

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