Erosion-corrosion of a stainless steel distillation column in food industry

Unusual extended corrosion phenomena were detected in a distillation column made in AISI type 316 stainless steel (UNS S31600) of a plant for natural pectins extraction from citrus. The column was the first of a series of two distillation columns representing the unit core. Corrosion problems were observed only in that column and mainly along the surface of the trays located in the lower section of the column. The phenomenon was observed subsequently to a modification of the original plant layout that caused an increase of the operating temperatures and turbulence of the process stream inside the column.     

On the evaluation of the global heat transfer coefficient in cutting

The use of numerical simulations for investigating machining processes is remarkably increasing because of the simulation cost is lower than the experiments and the possibility to analyze local variables such as pressures, strains, and temperatures is allowable. Process simulation is very hard from a computational point of view, since it frequently requires remeshing phases and very small time steps. As a consequence, the simulated cutting time is usually of the order of few milliseconds and no steady cutting conditions are generally achieved, at least as far as thermal conditions are concerned. Therefore, nowadays numerical prediction of cutting temperatures cannot be considered fully reliable. In the paper this issue was taken into account: a mixed Lagrangian-Eulerian numerical approach was utilized and the global heat transfer (film) coefficient at the tool-chip interface was derived through an inverse approach. Finally, the dependence of the film coefficient on pressure and temperature on the rake face was investigated.     

A critical analysis on the friction modelling in orthogonal machining

Despite the development of high performance finite element-based codes, the simulation of machining still represents a very hard task due to the geometric complexity of the real chip-tool systems and the high cutting speed that requires very long simulation times. For these reasons, many aspects related to machining are not very clear and so easy to simulate. In this paper a rigorous investigation on the role played by the implemented friction model within a 2D simulation of orthogonal cutting was carried out, taking into account different models proposed by the researchers in the last years. The main simulation results were compared with experimental measurements in order to verify if it is possible to identify the best model. Once the comparison with mechanical variables was completed, a subsequent study on temperature predictions utilizing the above friction models was executed as well. The results of this integrated numerical and experimental work are carefully reported in the paper.     

Diffusional kinetics of metalliding zinc into solid copper

The process of incorporation of zinc into a copper cathode has been experimentally studied in a molten salt system at 381±2° C and at various current densities. The process is shown to be kinetically controlled by the diffusion of Zn into the solid matrix. A galvanostatic pulse titration technique has been used to determine the chemical diffusion coefficient at various alloy compositions, and an exponential relationship has been found between the diffusivity and the third power of the zinc concentration in the alloy. This relationship was then used in the diffusion equation within the solid matrix and a numerical integration was performed. Very good agreement was found between the calculated and experimental data for Zn interfacial concentration versus time. The same calculation procedure was used to determine zinc concentration profiles in the alloys.Nomenclature c _A concentration of the diffusing metal (mol cm^–3) - D _AB chemical diffusion coefficient of A into B (cm² s^–1) - E cell e.m.f. (mV) - F Faraday number - i current density (A cm^–2) - t time (s) - V _M alloy molar volume (cm³ mol^–1) - x linear dimension in the diffusion direction (cm) - X zinc mass fraction in the alloy - z ionic valence of Zn - stoichiometric ratio Zn/Cu     

The pH dependence of the ozone absorption kinetics in aqueous phenol solutions

Using a wetted wall laboratory absorber, the rate of ozone absorption in aqueous phenol solutions has been measured in the pH range 1.75–12.The phenol concentration was varied in a wide range (7–800 ppM) and the ozone partial pressure over a six-fold range. The temperature and the liquidThe results were interpreted by assuming that the rate determining step of a reaction sequence is different in acid and basic solutions.     

Bioactivity of Phenanthrenes from Juncus acutus on Selenastrum capricornutum

Twenty-five 9,10-dihydrophenanthrenes, four phenanthrenes, a dihydrodibenzoxepin, and a pyrene, isolated from the wetland plant Juncus acutus, were tested to detect their effects on the green alga Selenastrum capricornutum. Nine of the compounds were isolated and identified for the first time. Most of the compounds caused inhibition of algal growth. The 9,10-dihydrophenanthrenes 1, 5, 21, and 22 were the most active.     

A new contact model for the discrete element method simulation of $$\hbox {TiO}_2$$ nanoparticle films under mechanical load

We develop a novel coarse-grained contact model for Discrete Element Method simulations of \(\hbox {TiO}_2\) nanoparticle films subjected to mechanical stress. All model elements and parameters are derived in a self-consistent and physically sound way from all-atom Molecular Dynamics simulations of interacting particles and surfaces. In particular, the nature of atomic-scale friction and dissipation effects is taken into account by explicit modelling of the surface features and water adsorbate layers that strongly mediate the particle-particle interactions. The quantitative accuracy of the coarse-grained model is validated against all-atom simulations of \(\hbox {TiO}_2\) nanoparticle agglomerates under tensile stress. Moreover, its predictive power is demonstrated with calculations of force-displacement curves of entire nanoparticle films probed with force spectroscopy. The simulation results are compared with Atomic Force Microscopy and Transmission Electron Microscopy experiments.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.