On the evaluation of the carbon dioxide solubility in polymers using gene expression programming.

Evaluation, prediction, and measurement of carbon dioxide (CO2) solubility in different polymers are crucial for engineers in various chemical applications, such as extraction and generation of novel materials. In this paper, correlations based on gene expression programming (GEP) were generated to predict the value of carbon dioxide solubility in three polymers. Results showed that the generated correlations could represent an outstanding efficiency and provide predictions for carbon dioxide solubility with satisfactory average absolute relative errors of 9.71%, 5.87%, and 1.63% for polystyrene (PS), polybutylene succinate-co-adipate (PBSA), and polybutylene succinate (PBS), respectively. Trend analysis based on Henry's law illustrated that increasing pressure and decreasing temperature lead to an increase in carbon dioxide solubility. Finally, outlier discovery was applied using the leverage approach to detect the suspected data points. The outlier detection demonstrated the statistical validity of the developed correlations. William's plot of three generated correlations showed that all of the data points are located in the valid zone except one point for PBS polymer and three points for PS polymer.

Modeling of the Sintered Density in Cu-Al Alloy Using Machine Learning Approaches.

In powder metallurgy materials, sintered density in Cu-Al alloy plays a critical role in detecting mechanical properties. Experimental measurement of this property is costly and time-consuming. In this study, adaptive boosting decision tree, support vector regression, k-nearest neighbors, extreme gradient boosting, and four multilayer perceptron (MLP) models tuned by resilient backpropagation, Levenberg-Marquardt (LM), scaled conjugate gradient, and Bayesian regularization were employed for predicting powder densification through sintering. Yield strength, Young's modulus, volume variation caused by the phase transformation, hardness, liquid volume, liquidus temperature, the solubility ratio among the liquid phase and the solid phase, sintered temperature, solidus temperature, sintered atmosphere, holding time, compaction pressure, particle size, and specific shape factor were regarded as the input parameters of the suggested models. The cross plot, error distribution curve, and cumulative frequency diagram as graphical tools and average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), standard deviation (SD), and coefficient of correlation (R) as the statistical evaluations were utilized to estimate the models' accuracy. All of the developed models were compared with preexisting approaches, and the results exhibited that the developed models in the present work are more precise and valid than the existing ones. The designed MLP-LM model was found to be the most precise approach with AAPRE = 1.292%, APRE = -0.032%, SD = 0.020, RMSE = 0.016, and R = 0.989. Lately, outlier detection was applied performing the leverage technique to detect the suspected data points. The outlier detection discovered that few points are located out of the applicability domain of the proposed MLP-LM model.

Modelling rate of penetration in drilling operations using RBF, MLP, LSSVM, and DT models.

One of the most important problems that the drilling industry faces is drilling cost. Many factors affect the cost of drilling. Increasing drilling time has a significant role in increasing drilling costs. One of the solutions to reduce drilling time is to optimize the drilling rate. Drilling wells at the optimum time will reduce the time and thus reduce the cost of drilling. The drilling rate depends on different factors, some of which are controllable and some are uncontrollable. In this study, several smart models and a correlation were proposed to predict the rate of penetration (ROP) which is very important for planning a drilling operation. 5040 real data points from a field in the South of Iran have been used. The ROP was modelled using Radial Basis Function, Decision Tree (DT), Least Square Vector Machine (LSSVM), and Multilayer Perceptron (MLP). Bayesian Regularization Algorithm (BRA), Scaled Conjugate Gradient Algorithm and Levenberg-Marquardt Algorithm were employed to train MLP and Gradient Boosting (GB) was used for DT. To evaluate the accuracy of the developed models, both graphical and statistical techniques were used. The results showed that DT-GB model with an R2 of 0.977, has the best performance, followed by LSSVM and MLP-BRA with R2 of 0.971 and 0.969, respectively. Aside from that, the proposed empirical correlation has an acceptable accuracy in spite of simplicity. Moreover, sensitivity analysis illustrated that depth and pump pressure have the highest effects on ROP. In addition, the leverage approach approved that the developed DT-GB model is valid statistically and about 1% of the data are suspected or out of the applicability domain of the model.

Acoustic Emission Signal Entropy as a Means to Estimate Loads in Fiber Reinforced Polymer Rods.

Fibre reinforced polymer (FRP) rods are widely used as corrosion-resistant reinforcing in civil structures. However, developing a method to determine the loads on in-service FRP rods remains a challenge. In this study, the entropy of acoustic emission (AE) emanating from FRP rods is used to estimate the applied loads. As loads increased, the fraction of AE hits with higher entropy also increased. High entropy AE hits are defined using the one-sided Chebyshev's inequality with parameter k = 2 where the histogram of AE entropy up to 10-15% of ultimate load was used as a baseline. According to the one-sided Chebyshev's inequality, when more than 20% (k = 2) of AE hits that fall further than two standard deviations away from the mean are classified as high entropy events, a new distribution of high entropy AE hits is assumed to exist. We have found that the fraction of high AE hits. In glass FRP and carbon FRP rods, a high entropy AE hit fraction of 20% was exceeded at approximately 40% and 50% of the ultimate load, respectively. This work demonstrates that monitoring high entropy AE hits may provide a useful means to estimate the loads on FRP rods.

Comparative Analysis of Machine Learning Models for Nanofluids Viscosity Assessment.

The process of selecting a nanofluid for a particular application requires determining the thermophysical properties of nanofluid, such as viscosity. However, the experimental measurement of nanofluid viscosity is expensive. Several closed-form formulas for calculating the viscosity have been proposed by scientists based on theoretical and empirical methods, but these methods produce inaccurate results. Recently, a machine learning model based on the combination of seven baselines, which is called the committee machine intelligent system (CMIS), was proposed to predict the viscosity of nanofluids. CMIS was applied on 3144 experimental data of relative viscosity of 42 different nanofluid systems based on five features (temperature, the viscosity of the base fluid, nanoparticle volume fraction, size, and density) and returned an average absolute relative error (AARE) of 4.036% on the test. In this work, eight models (on the same dataset as the one used in CMIS), including two multilayer perceptron (MLP), each with Nesterov accelerated adaptive moment (Nadam) optimizer; two MLP, each with three hidden layers and Adamax optimizer; a support vector regression (SVR) with radial basis function (RBF) kernel; a decision tree (DT); tree-based ensemble models, including random forest (RF) and extra tree (ET), were proposed. The performance of these models at different ranges of input variables was assessed and compared with the ones presented in the literature. Based on our result, all the eight suggested models outperformed the baselines used in the literature, and five of our presented models outperformed the CMIS, where two of them returned an AARE less than 3% on the test data. Besides, the physical validity of models was studied by examining the physically expected trends of nanofluid viscosity due to changing volume fraction.