Retraction Note to: Secondary treated wastewater of latex processing: reusing for irrigation or treatment by membrane filtration

Research on Chemical Intermediates -     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

On Necessary Optimality Conditions with Higher-Order Complementarity Slackness for Set-Valued Optimization Problems

Set-Valued and Variational Analysis - We aim to establish Karush-Kuhn-Tucker multiplier rules involving higher-order complementarity slackness under Hölder metric subregularity. These rules...     

Enhanced magnetocaloric effect in Eu-doped La0.7Ca0.3MnO3 compounds

Orthorhombic La_0.7-xEu_xCa_0.3MnO₃ samples (x = 0.04–0.12) with apparent density of ρ = 3.9–4.1 g/cm³ prepared by solid-state reactions have been studied. The analysis of temperature-dependent magnetization for an applied field H = 500 Oe indicated a decrease of the Curie temperature (T_C) from about 225 K for x = 0.04 through 189 K for x = 0.08–146 K for x = 0.12. The magnetocaloric (MC) study upon analyzing M(H, T) data has revealed that the magnetic entropy change around T_C reaches the maximum (|ΔS_max|), which is dependent on both x and H. For an applied field interval of ΔH = 60 kOe, |ΔS_max| values are about 5.88, 4.93, and 4.71 J/kg⋅K for x = 0.04, 0.08, and 0.12, respectively. Though |ΔS_max| decreases with increasing x, relative cooling power (RCP) increases remarkably from 383 J/kg for x = 0.04 to about 428 J/kg for x = 0.08 and 0.12. This is related to the widening of the ferromagnetic-paramagnetic transition region when x increases. Particularly, if combining two compounds with x = 0.04 and 0.08 (or 0.12) as refrigerant blocks for MC applications, a cooling device can work in a large temperature range of 145–270 K, corresponding to RCP ≈ 640 J/kg for H = 60 kOe. M(H) analyses around T_C have proved x = 0.04 exhibiting the mixture of first- and second-order phase transitions while x = 0.08 and 0.12 exhibit a second-order nature. The obtained results show potential applications of Eu-doped La_0.7Ca_0.3MnO₃ materials for magnetic refrigeration below room temperature.     

Optimal Sensor Placement Method Considering the Importance of Structural Performance Degradation for the Allowable Loadings for Damage Identification

Considering the importance of damage for the structural performance and for decreasing the identification error, this paper proposes an optimal sensor placement method based on a weighted standard deviation norm (WSDN) index. The standard deviation of the identified damage parameters is solved using the series expansion theory and probabilistic method to quantify the effect of a measurement error on damage identification. The damage estimation weight (DEW) index, which can reflect the importance of each element in the structural capabilities, is established based on a performance-damage curve. A significant DEW for a specified element indicates that the element is important for the structure and that its identification error should be small. The WSDN index is obtained from the Hadamard product of the standard deviations (SDs) and DEWs. Thus, the identification error of the entire structure is measured using the weighting coefficient. The optimal sensor placement (OSP) procedure is performed by minimizing the WSDN index. The proposed method can clearly decrease the uncertainties of the identification results for the important elements. Other OSP criteria, including the condition number, information entropy, and standard deviation norm, which aim to decrease the identification error, are discussed in this paper for comparison with the proposed method. Two numerical examples and an experiment, which pertain to the deformation performance, buckling features, and dynamic characteristics, are discussed to verify the advantages of the proposed method.