A comparative study of linear and non-linear regression analysis for ammonium exchange by clinoptilolite zeolite

Ammonium ion exchange from aqueous solution using clinoptilolite zeolite was investigated at laboratory scale. Batch experimental studies were conducted to evaluate the effect of various parameters such as pH, zeolite dosage, contact time, initial ammonium concentration and temperature. Freundlich and Langmuir isotherm models and pseudo-second-order model were fitted to experimental data. Linear and non-linear regression methods were compared to determine the best fitting of isotherm and kinetic model to experimental data. The rate limiting mechanism of ammonium uptake by zeolite was determined as chemical exchange. Non-linear regression has better performance for analyzing experimental data and Freundlich model was better than Langmuir to represent equilibrium data.     

Effect of Deep Cryogenic Treatment on Wear Resistance of AISI 52100 Bearing Steel

In this study, the effects of deep cryogenic treatment (DCT) on the wear resistance of AISI 52100 bearing steel were investigated. For this purpose, a number of bearing steel samples were held for different times (12, 24, 36, 48, 60 h) at deep cryogenic temperatures (?145 °C). The wear experiments were carried out in a ball–disk arrangement, by applying loads of 10 and 20 N and a sliding velocity of 0.15 m/s. After conducting the experimental studies, 36 h was found to be the optimal holding time. At this holding time, the wear rate and friction coefficient were decreased, while the hardness reached to maximum values. It was observed that DCT led to significant microstructural changes, which resulted in improved tribological properties.     

Nonlinear Short-term Prediction of Aluminum Foil Thickness via Global Regressor Combination

In this study, short-term prediction of aluminum foil thickness time-series data recorded during cold-rolling process was investigated. The locally projective nonlinear noise reduction was applied in order to improve the predictability of the time series. The higher-order statistics methods (bispectrum and bicoherence) were used to detect the nonlinearity. The embedding vectors with appropriate embedding dimension and time delay were obtained via the false nearest neighbors and mutual information methods, respectively. The maximum prediction horizon was determined depending on the maximal Lyapunov exponent. For various prediction horizons, the embedding vector and corresponding thickness value pairs were used as the dataset to assess the prediction performance of various machine learning algorithms (i.e., multilayer perceptron neural network, support vector machines with Pearson VII function-based kernel, and radial basis function network). The n-step ahead prediction outputs of the machine learning algorithms were globally combined with simple voting in favor of the one having minimum absolute error. The accuracy of our proposed method was compared with nonlinear autoregressive exogenous model for various thickness time-series data using mean absolute percentage error measure.     

Effect of Intercritical Annealing Parameters on Dual Phase Behavior of Commercial Low-Alloyed Steels

 It is known that dual phase (DP) heat treatments and alloying elements have a strong effect on martensitic transformations and mechanical properties. In the present work, the effects of some intercritical annealing parameters (heating rate, soaking temperature, soaking time, and quench media) on the microstructure and mechanical properties of cold rolled DP steel were studied. The microstructure of specimens quenched after each annealing stage, was analyzed using optical microscopy. The tensile properties, determined for specimens submitted to complete annealing cycles, are influenced by the volume fractions of multi phases (originated from martensite, bainite and retained austenite), which depend on annealing processing parameters. The results obtained showed that the yield strength (YS) and the ultimate tensile strength (UTS) increase with the increasing intercritical temperature and cooling rate. This can be explained by higher martensite volume ratio with the increased volume fraction of austenite formed at the higher temperatures and cooling rates. The experimental data also showed that, for the annealing cycles carried out, higher UTS values than ~ 800 MPa could be obtained with the S3 steel grade.