Application of ANN and RSM techniques for modeling electrospinning process of polycaprolactone

Multi-variate multi-objective optimization of production conditions for electro-spun skin scaffold using RSM and investigation of gamma irradiation effects on the properties of the optimized sample

Developing electro-spun scaffolds with ideal mechanical properties for skin purposes can profit from using the Response Surface Methodology technique to define and optimize the outcome quality and required sterilization for use in vivo. This study investigated the effects of four main independent electrospinning variables for polycaprolactone nanofibers scaffold using multi-variable and multi-objective optimization. It was done to determine significant parameters on responses and find optimal conditions to reach the preferred properties. Young's modulus, elongation-at-break, and tensile strength were the responses. After obtaining appropriate models, the impact share of variables on the responses was determined using Sobol sensitivity analysis. The results showed that flow rate is the most significant parameter of elastic modulus and tensile strength responses, with 76.45 % and 41.27 % impact shares, respectively. The polymer concentration is the following significant parameter on elongation at break, tensile strength and, Young's modulus responses with 64.35 %, 39.485 and, 14.28 % impact share, respectively. Based on the optimized results, a skin scaffold with desired mechanical properties was achieved (under solution concentration of 10 % w/v, flow rate of 2 mL/h, nuzzle-collector distance of 15 cm, and applied voltage of 20 kV). Then it was sterilized with gamma radiation of various doses (25, 40, and 55 kGy) to use in vivo. The SEM analysis indicated no significant change in fibrous morphology due to gamma irradiation at any dosage. FTIR analysis demonstrated the breakup of ester bonds due to gamma irradiation. For samples irradiated by 25 kGy, the crystallinity percentage decreased and chains crosslinking without losing the mechanical stability was dominant. The studies demonstrated that 25 kGy of gamma irradiation could improve the mechanical properties of the optimized PCL skin scaffold, which is very promising for wound healing.

Artificial Neural Networks for Predicting the Diameter of Electrospun Nanofibers Synthesized from Solutions/Emulsions of Biopolymers and Oils

In the present work, different configurations of nt iartificial neural networks (ANNs) were analyzed in order to predict the experimental diameter of nanofibers produced by means of the electrospinning process and employing polyvinyl alcohol (PVA), PVA/chitosan (CS) and PVA/aloe vera (Av) solutions. In addition, gelatin type A (GT)/alpha-tocopherol (α-TOC), PVA/olive oil (OO), PVA/orange essential oil (OEO), and PVA/anise oil (AO) emulsions were used. The experimental diameters of the nanofibers electrospun from the different tested systems were obtained using scanning electron microscopy (SEM) and ranged from 93.52 nm to 352.1 nm. Of the three studied ANNs, the one that displayed the best prediction results was the one with three hidden layers with the flow rate, voltage, viscosity, and conductivity variables. The calculation error between the experimental and calculated diameters was 3.79%. Additionally, the correlation coefficient (R2) was identified as a function of the ANN configuration, obtaining values of 0.96, 0.98, and 0.98 for one, two, and three hidden layer(s), respectively. It was found that an ANN configuration having more than three hidden layers did not improve the prediction of the experimental diameter of synthesized nanofibers.

A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers

Nanofibers, which are formed by the electrospinning process, are used in a variety of applications. For this purpose, a specific diameter suited for each application is required, which is achieved by varying a set of parameters. This parameter adjustment process is empirical and works by trial and error, causing high input costs and wasting time and financial resources. In this work, an artificial neural network model is presented to predict the diameter of polyethylene nanofibers, based on the adjustment of 15 parameters. The model was trained from 105 records from data obtained from the literature and was then validated with nine nanofibers that were obtained and measured in the laboratory. The average error between the actual results was 2.29%. This result differs from those taken in an evaluation of the dataset. Therefore, the importance of increasing the dataset and the validation using independent data is highlighted.

Electrospun nanofiber membrane diameter prediction using a combined response surface methodology and machine learning approach

Despite the widespread interest in electrospinning technology, very few simulation studies have been conducted. Thus, the current research produced a system for providing a sustainable and effective electrospinning process by combining the design of experiments with machine learning prediction models. Specifically, in order to estimate the diameter of the electrospun nanofiber membrane, we developed a locally weighted kernel partial least squares regression (LW-KPLSR) model based on a response surface methodology (RSM). The accuracy of the model's predictions was evaluated based on its root mean square error (RMSE), its mean absolute error (MAE), and its coefficient of determination (R²). In addition to principal component regression (PCR), locally weighted partial least squares regression (LW-PLSR), partial least square regression (PLSR), and least square support vector regression model (LSSVR), some of the other types of regression models used to verify and compare the results were fuzzy modelling and least square support vector regression model (LSSVR). According to the results of our research, the LW-KPLSR model performed far better than other competing models when attempting to forecast the membrane's diameter. This is made clear by the much lower RMSE and MAE values of the LW-KPLSR model. In addition, it offered the highest R² values that could be achieved, reaching 0.9989.

Intelligent optimization for modelling superhydrophobic ceramic membrane oil flux and oil-water separation efficiency: Evidence from wastewater treatment and experimental laboratory

Due to the significant energy and economic losses brought on by the global oil spill, there has been an increased interest in oil-water separation. This study presents strong non-linear machine learning models (support vector regression (SVR) and Gaussian process regression (GPR)) with the Response surface method (RSM) to predict the oil flux and oil-water separation efficiency of wastewater using ceramic membrane technology. For the model development and prediction of oil flux (OF) and oil-water separation efficiency (OSE), oil concentration (mg/L), feed flow rate (mL/min), and pH were considered as input variables. The input variables are combined in three combinations to study the most contributing input features to the models' performance. Mean square error (MSE) and Nash-Sutcliffe coefficient efficiency (NSE) were used to assess the prediction performances of the developed models with the different number of input combinations considered in the study. For the two target variables (OF and OSE), GPR and SVR models were used to separately predict them. For OF, the SVR-2 [Combo-2] model (MSE = 0.9255 and NSE = 2.7976) performed better with higher prediction accuracy compared to GPR-2 [Combo-2] model (MSE = 0.763 and NSE = 6.437). In addition, for OSE, the GPR-3 [Combo-3] model (MSE = 0.995 and NSE = 0.5544) performed slightly better than SVR-3 [Combo-3] model (MSE = 0.992 and NSE = 0.8066). The results showed that the SVR model with the combo-2 and GPR-3 models for OF and OSE variables are the proposed models with the best performance and accuracy. This machine learning study will aid in better evaluating the function of materials such as ceramic in membrane performance features such as oil flux and rejection prediction, separation efficiency, water recovery, membrane fouling, and so on. As for academics and manufacturers, this machine learning (ML) strategy will boost performance and allow a better understanding of system governance.

Impact of Pulse Parameters of a DC Power Generator on the Microstructural and Mechanical Properties of Sputtered AlN Film with In-Situ OES Data Analysis

In the present study, the sputtered aluminum nitride (AlN) films were processed in a reactive pulsed DC magnetron system. We applied a total of 15 different design of experiments (DOEs) on DC pulsed parameters (reverse voltage, pulse frequency, and duty cycle) with Box-Behnken experimental method and response surface method (RSM) to establish a mathematical model by experimental data for interpreting the relationship between independent and response variables. For the characterization of AlN films on the crystal quality, microstructure, thickness, and surface roughness, X-ray diffraction (XRD), atomic force microscopy (AFM), and field emission-scanning electron microscopy (FE-SEM) were utilized. AlN films have different microstructures and surface roughness under different pulse parameters. In addition, in-situ optical emission spectroscopy (OES) was employed to monitor the plasma in real-time, and its data were analyzed by principal component analysis (PCA) for dimensionality reduction and data preprocessing. Through the CatBoost modeling and analysis, we predicted results from XRD in full width at half maximum (FWHM) and SEM in grain size. This investigation identified the optimal pulse parameters for producing high-quality AlN films as a reverse voltage of 50 V, a pulse frequency of 250 kHz, and a duty cycle of 80.6061%. Additionally, a predictive CatBoost model for obtaining film FWHM and grain size was successfully trained.

Recent Progress in Protective Membranes Fabricated via Electrospinning: Advanced Materials, Biomimetic Structures, and Functional Applications

Electrospinning is a significant micro/nanofiber processing technology and has been rapidly developing in the past 2 decades. It has several applications, including advanced sensing, intelligent manufacturing, and high-efficiency catalysis. Here, multifunctional protective membranes fabricated via electrospinning in terms of novel material design, construction of novel structures, and various protection requirements in different environments are reviewed. To achieve excellent comprehensive properties, such as, high water vapor transmission, high hydrostatic pressure, optimal mechanical property, and air permeability, combinations of novel materials containing nondegradable/degradable materials and functional structures inspired by nature have been investigated for decades. Currently, research is mainly focused on conventional protective membranes with multifunctional properties, such as, anti-UV, antibacterial, and electromagnetic-shielding functions. However, important aspects, such as, the properties of electrospun monofilaments, development of "green electrospinning solutions" with high solid content, and approaches for enhancing adhesion between hydrophilic and hydrophobic layers are not considered. Based on this systematic review, the development of electrospinning for protective membranes is discussed, the existing gaps in research are discussed, and solutions for the development of technology are proposed. This review will assist in promoting the diversified development of protective membranes and is of great significance for fabricating advanced materials for intelligent protection.

Evaluation of the Influence of the Combination of pH, Chloride, and Sulfate on the Corrosion Behavior of Pipeline Steel in Soil Using Response Surface Methodology

External damage to buried pipelines is mainly caused by corrosive components in soil solution. The reality that numerous agents are present in the corrosive environment simultaneously makes it troublesome to study. To solve that issue, this study aims to determine the influence of the combination of pH, chloride, and sulfate by using a statistical method according to the design of experiment (DOE). Response surface methodology (RSM) using the Box–Behnken design (BBD) was selected and applied to the design matrix for those three factors. The input corrosion current density was evaluated by electrochemical tests under variable conditions given in the design matrix. The output of this method is an equation that calculates the corrosion current density as a function of pH, chloride, and sulfate concentration. The level of influence of each factor on the corrosion current density was investigated and response surface plots, contour plots of each factor were created in this study.

Electrospinning Processing Techniques for the Manufacturing of Composite Dielectric Elastomer Fibers

Dielectric elastomers (DE) are novel composite architectures capable of large actuation strains and the ability to be formed into a variety of actuator configurations. However, the high voltage requirement of DE actuators limits their applications for a variety of applications. Fiber actuators composed of DE fibers are particularly attractive as they can be formed into artificial muscle architectures. The interest in manufacturing micro or nanoscale DE fibers is increasing due to the possible applications in tissue engineering, filtration, drug delivery, catalysis, protective textiles, and sensors. Drawing, self-assembly, template-direct synthesis, and electrospinning processing have been explored to manufacture these fibers. Electrospinning has been proposed because of its ability to produce sub-mm diameter size fibers. In this paper, we investigate the impact of electrospinning parameters on the production of composite dielectric elastomer fibers. In an electrospinning setup, an electrostatic field is applied to a viscous polymer solution at an electrode's tip. The polymer composite with carbon black and carbon nanotubes is expelled and accelerated towards a collector. Factors that are considered in this study include polymer concentration, solution viscosity, flow rate, electric field intensity, and the distance to the collector.

Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO₂ and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO₂ and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.

Preparation and modeling of electrospun polyhydroxybutyrate/polyaniline composite scaffold modified by plasma and printed by an inkjet method and its cellular study

The reconstruction of the nerve tissue engineering scaffold is always of particular interest due to the inability to recover and repair neural tissues after being damaged by diseases or physical injuries. The primary purpose of this study was obtaining a model used to predict the diameter of the fibers of electrospun polyhydroxybutyrate (PHB) scaffolds. Accordingly, the range of operating parameters, namely the applied voltage, the distance between the nozzle to the collector, and solution concentration, was designed for the electrospinning process at three different levels, giving seventeen experiments. These data were modeled utilizing response surface methodology and artificial neural network method using Design Expert and Matlab software.The effect of process parameters on the diameter, as well as their interactions were investigated in detail, and the corresponding models were suggested. Both the RSM and ANN models showed an excellent agreement between the experimental and predicted response values. In the second phase of the study, PHB natural polymer was electrospun into scaffolds with high biocompatibility, resulting in a 224-360 nm diameter range .To further modify the scaffold in order to improve the compatibility of PHB, the fibrous surface of scaffolds was exposed to oxygenated plasma gas radiation under controlled conditions. Next, polyaniline (PANI) nanoparticles were then synthesized and printed on the surface of scaffolds as parallel lines. Then samples were exposed to the electric field. Fourier-transform infrared spectroscopy, water contact angle, optical and electron microscopy, tensile test, and cell viability analysis were performed to study properties of resulting scaffolds. The results indicated the fact that modification of the scaffolds by oxygen plasma and printing PANI nanoparticles in particular patterns had a favorable impact on cell adhesion and direction of cell growth, showing the potential of resulting scaffolds for nerve tissue engineering applications.

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

Prediction of the diameter of a nanofiber is very difficult, owing to complexity of the interactions of the parameters which have an impact on the diameter and the fact that there is no comprehensive method to predict the diameter of a nanofiber. Therefore, the aim of this study was to compare the multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models to develop mathematical models for the diameter prediction of poly(ε-caprolactone) (PCL)/gelatin (Gt) nanofibers. Four parameters, namely, the weight ratio, applied voltage, injection rate, and distance, were considered as input data. Then, a prediction of the diameter for the nanofiber model (PDNFM) was developed using data mining techniques such as MLP, RBFNN, and SVM. The PDNFM_MLP is introduced as the most accurate model to predict the diameter of PCL/Gt nanofibers on the basis of costs and time-saving. According to the results of the sensitivity analysis, the value of the PCL/Gt weight ratio is the most significant input which influences PDNFM_MLP in PCL/Gt electrospinning. Therefore, the PDNFM model, using a decision support system (DSS) tool can easily predict the diameter of PCL/Gt nanofibers prior to electrospinning.

A new tool for prediction the diameter of nanofibers is presented: the use of adaptive modeling techniques to predict fiber diameter and study the impact of electrospinning process parameters on electrospinning fiber diameter.