Isothermal Modelling of the Adsorption of Cibacron Blue onto Bean Peel

Biosorption is a sort of sorption technology in which the sorbent is a substance that is biologically sourced. In today's world, biosorption is seen as a simple, inexpensive, and ecologically friendly way for removing pollutants from the environment. One of the branches of bioremediation that is utilised to decrease environmental pollution in the context of minimising improper textile waste disposal is this method. The sorption isotherm of Cibacron Blue onto bean peel were analyzed using ten models—Henry, Langmuir, Dubinin-Radushkevich, Freundlich, BET, Toth, Sips, Fritz-Schlunder IV, Baudu and Fritz-Schlunder V, and fitted using non-linear regression. Statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), BIC and HQC showed that the Freundlich model was the best model in terms of overall best criteria. The calculated evidence ratio was 8 with an AICc probability value of 0.89 indicating that the best model was at least 8 times better than the nearest best model, which was Sips. The calculated Freundlich parameters KF (Freundlich isotherm constant) and nF (Freundlich exponent) were 5.369 (L/g) (95% confidence interval from 4.359 to 6.379) and 3.125 (95% confidence interval from 2.717 to 3.533). The Langmuir constant was utilized to calculate the maximum adsorption capacity QmL (mg/g) which gave a value of 27.83 mg/g (95% confidence interval from 23.69 to 31.98). The nonlinear regression method allows for the parameter values to be represented in the 95% confidence interval range which can better allow comparison with published results.

Isothermal Modelling of the Adsorption of Chromium onto Calcium Alginate Nanoparticles

Because of its extensive usage in industrial applications such as leather tanning, metallurgy, electroplating, and refractory materials, chromium is one of the most dangerous trace elements introduced into surface and ground waters. The sorption isotherm of chromium sorption onto calcium alginate nanoparticles were analyzed using ten models—Henry, Langmuir, Dubinin-Radushkevich, Freundlich, BET, Toth, Sips, Fritz-Schlunder IV, Baudu and Fritz-Schlunder V, and fitted using non-linear regression. Only the Toth and Fritz-Schlunder V models were unable to fit the data. Statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), BIC and HQC showed that the Langmuir model was the best model in terms of overall best criteria. The calculated evidence ratio was 7 with an AICc probability value of 0.87 indicating that the best model was at least 7 times better than the nearest best model, which was Freundlich. The calculated Langmuir parameters qmL value of 79.174 mg/g (95% confidence interval from 50.702 to 107.646) and bL value of 0.332 L/mg (95% confidence interval from 0.294 to 0.371) is not much different from the linearized published work for the qmL value of 145 mg/g but lower than the bL value of 0.34 L/mg. The nonlinear regression method allows for the parameter values to be represented in the 95% confidence interval range which can better allow comparison with published results.

Calibration of Automatic Sun Photometer with Temperature Correction in Field Environment

Aerosol optical depth (AOD) is an important atmospheric correction parameter in remote sensing. In order to obtain AOD accurately, the surface-based automatic sun photometer needs to carry out calibration regularly. The normally used Langley method can be effective only when the AOD and the calibration coefficients of the instrument remain unchanged throughout the day. However, when observing the AOD with CE318 sun photometer in field environment, it was found that the AOD of silicon (Si) detector at 1020 nm and indium gallium arsenide (InGaAs) detector at 1639 nm was strongly influenced by temperature due to the large temperature difference at the Dunhuang site. Based on the corresponding relationship between AOD and wavelength, the model of the calibration coefficients varying with temperature was established by nonlinear regression method in field environment. By comparing the AOD before and after temperature correction with the theoretical one, the ratio of data with relative error (RE) less than 5% increased from 0.195 and 0.14 to 0.894 and 0.355, respectively. By this method, calibration can be carried out without the limit of constant AOD. In addition, it is simpler, more convenient, and less costly to perform temperature correction in a field environment than in a laboratory.

High-Temperature Behavior and Constitutive Modeling of MS1180 High-Strength Steel

The flow behavior analysis of MS1180 steel at high-temperature was carried out by the stress–strain data which were obtained through isothermal uniaxial tensile tests with Gleeble-3800 thermal simulator. The temperature range is 600 °C~900 °C and the strain rate is varied from 0.005 s−1 to 0.02 s−1. A modified Johnson-Cook (JC) constitutive model of MS1180 steel considering the mutual effect of deformation parameters was proposed applying multiple nonlinear regression method. The calculated stress values obtained by the modified constitutive equations and the experimental ones were compared with each other. The average relative error is less than 5% and correlation coefficient is greater than 0.95. The results verified that the modified constitutive equations has good prediction accuracy for the flow stress of MS1180.

Data-Driven Modeling for Multiphysics Parametrized Problems-Application to Induction Hardening Process

Data-driven modeling provides an efficient approach to compute approximate solutions for complex multiphysics parametrized problems such as induction hardening (IH) process. Basically, some physical quantities of interest (QoI) related to the IH process will be evaluated under real-time constraint, without any explicit knowledge of the physical behavior of the system. Hence, computationally expensive finite element models will be replaced by a parametric solution, called metamodel. Two data-driven models for temporal evolution of temperature and austenite phase transformation, during induction heating, were first developed by using the proper orthogonal decomposition based reduced-order model followed by a nonlinear regression method for temperature field and a classification combined with regression for austenite evolution. Then, data-driven and hybrid models were created to predict hardness, after quenching. It is shown that the results of artificial intelligence models are promising and provide good approximations in the low-data limit case.

Stochastic optimization and modeling of high-velocity impact tests on high-temperature carbon–carbon composites

In this study, it is intended to optimize a high-velocity impact case of a composite plate. The case selected from literature focused on the failure response of advanced carbon–carbon (C/C) composites under high-velocity impacts. Based on the stochastic optimization method, three unique models are introduced within the present study's scope as dimensionless damage areas of front and back sides and the composite impact energy response. The difference between the equations found in the present study and the base study is the number of variables. Obtained prediction models consist of only the tests' input variables; thus, these models can be considered the essential prediction functions of high-velocity impact response of C/C composites under high temperatures. Multiple nonlinear regression method is used for objective functions of the optimization problem. Since the determination coefficient values have been found quite similar to the ones in the literature, the presented models can be considered successful in predicting the results. By utilizing the novel regression functions presented in this study, the damaged areas are minimized. Without the necessity of experimental research, further predictions can be made by operating the models found in the present study.

Thermoelectric Efficiency of Silicon–Germanium Alloys in Finite-Time Thermodynamics

We analyze the efficiency in terms of a thermoelectric system of a one-dimensional Silicon–Germanium alloy. The dependency of thermal conductivity on the stoichiometry is pointed out, and the best fit of the experimental data is determined by a nonlinear regression method (NLRM). The thermoelectric efficiency of that system as function of the composition and of the effective temperature gradient is calculated as well. For three different temperatures (T=300 K, T=400 K, T=500 K), we determine the values of composition and thermal conductivity corresponding to the optimal thermoelectric energy conversion. The relationship of our approach with Finite-Time Thermodynamics is pointed out.

Grain Growth Kinetics of 0.65Ca0.61La0.26TiO3-0.35Sm(Mg0.5Ti0.5)O3 Dielectric Ceramic

The 0.65Ca0.61La0.26TiO3-0.35Sm(Mg0.5Ti0.5)O3[0.65CLT-0.35SMT] ceramic was prepared by the solid-state reaction method. The effects of sintering process on its microstructure and grain growth behavior were investigated. The Hillert model and a simplified Sellars model were established by linear regression, and the Sellars-Anelli model with a time index was established by using a nonlinear regression method. The results show that the grain size gradually increases with the increase of sintering temperature and holding time. Meanwhile, the sintering temperature has a more significant effect on the grain growth. The grain sizes of 0.65CLT-0.35SMT ceramic were predicted by the three models and compared with the experimentally measured grain size. The results indicate that for the 0.65CLT-0.35SMT ceramic, the Hillert model has the lowest prediction accuracy and the Sellars-Anelli model, the highest prediction accuracy. In this work, the Sellars-Anelli model can effectively predict the grain growth process of 0.65CLT-0.35SMT ceramic.

Investigation of lamb fat quality attributes during deodorization

Edible lamb fat is an illustrious frying fat due to its good flavor and stability to oxidation. Fat deodorization is a vacuum–steam distillation process that is accomplished for removing the unwanted components such as free fatty acids and volatile compounds. The present work has studied the kinetics of lamb fat deodorization under different temperatures in a batch deodorization system. Variations of acid value (AV), peroxide value, p-Anisidine value, TOTOX value and total color difference were measured during the deodorization process. The Logarithmic, Wang and Singh, Hénon et al., and linear models were fitted with obtained data using nonlinear regression method. Results indicated that the Logarithmic and Henon et al. models gave the best fitness respectively with AV and p-Anisidine value, based on the statistical criteria of correlation coefficient (R2), reduced chi-square (χ2) and root mean square error (RMSE). Furthermore, the linear model was selected as the best model to describe the variations of TOTOX value and peroxide value during the deodorization process.