Sintering of WC-Ni nanocomposite powder: Experimental and artificial neural networks modeling study

The sintering behavior of WC-Ni nanocomposite powder was evaluated through experimental and statistical approaches to study the contribution of involving parameters of chemical composition (Ni wt. %) and sintering temperature on sinterability of system by assessing the resulted densification and microhardness. The experimental process was designed based on factorial experimental design for independent effective parameters of Ni percentage (12, 18 and 23 wt %), and sintering temperature (8 different values within 1350–1485 °C). The resulted products of experimental testing after compaction and sintering were analyzed by FESEM and EDX to image the microstructure and evaluate the chemical composition and elemental distribution. The density and microhardness were measured as well. An artificial neural network (ANN) was applied to describe the corresponding individual and mutual impacts on sintering. The ANN model was developed by feed-forward back propagation network including topology 2:5:2 and trainlm algorithm to model and predict density and microhardness. A great agreement was observed between the predicted values by the ANN model and the experimental data for density and microhardness (regression coefficients (R²) of 0.9983 and 0.9924 for target functions of relative density and microhardness, respectively). Results showed that the relative importance of operating parameters on target functions (relative density and microhardness) was found to be 62% and 38% for sintering temperature and Ni percentage, respectively. Also, ANN model exhibited relatively high predictive ability and accuracy in describing nonlinear behavior of the sintering of WC-Ni nanocomposite powder. The experimental results confirmed that the appropriate sintering temperature was influenced by Ni content. The optimum parameters were found to be 12 wt % Ni sintered at 1460 °C with the highest microhardness and relative density.     

Combination of ACO-artificial neural network method for modeling of manganese and cobalt extraction onto nanometer SiO2 from water samples

In this study, modeling based on ant-colony optimization – artificial neural network have been employed to develop the model for simulation and optimization of nanometer SiO₂ for the extraction of manganese and cobalt from water samples. The pH, time, amount of SiO₂ nanoparticles and concentration of 1-(2-pyridylazo)-2-naphthol (PAN) were the input variables, while the extraction% of analytes was the output. Under the optimum conditions, the detection limits were 0.52 and 0.7 μg L^?1, for manganese and cobalt, respectively. The method was applied to the extraction of manganese and cobalt from water samples and one certified reference material.     

Drag reduction phenomenon in viscous oil‐water dispersed pipe flow: Experimental investigation and phenomenological modeling

An experimental study on drag‐reduction phenomenon in dispersed oil‐water flow has been performed in a 26‐mm‐i.d. Twelve meter long horizontal glass pipe. The flow was characterized using a novel wire‐mesh sensor based on capacitance measurements and high‐speed video recording. New two‐phase pressure gradient, volume fraction, and phase distribution data have been used in the analysis. Drag reduction and slip ratio were detected at oil volume fractions between 10 and 45% and high mixture Reynolds numbers, and with water as the dominant phase. Phase‐fraction distribution diagrams and cross‐sectional imaging of the flow suggested the presence of a higher amount of water near to the pipe wall. Based on that, a phenomenology for explaining drag reduction in dispersed flow in a flow situation where slip ratio is significant is proposed. A simple phenomenological model is developed and the agreement between model predictions and data, including data from the literature, is encouraging. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Fluorescence‐based fouling prediction and optimization of a membrane filtration process for drinking water treatment

A novel fluorescence‐based approach is proposed for modeling, predicting, and optimizing different fouling dynamics in an ultrafiltration (UF) process for drinking water treatment. Principal component analysis (PCA) was used to extract information in terms of principal components (PCs), related to major membrane foulant groups, from fluorescence excitation–emission matrix measurements captured during UF of natural river water. The evolution of PC scores during the filtration process was then related to membrane fouling using dynamic balances of latent variable values (PC scores). This approach was found suitable for forecasting fouling behaviors with good accuracy based solely on fluorescence data collected 15 min from the start of the filtration. The proposed approach was tested experimentally through model‐based optimization of backwashing times with the objective of minimizing the energy consumption per unit amount of water produced during the filtration process. This approach was also useful for identifying fouling groups contributing to reversible and irreversible fouling. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Optimal design and operations of supply chain networks for water management in shale gas production: MILFP model and algorithms for the water‐energy nexus

The optimal design and operations of water supply chain networks for shale gas production is addressed. A mixed‐integer linear fractional programming (MILFP) model is developed with the objective to maximize profit per unit freshwater consumption, such that both economic performance and water‐use efficiency are optimized. The model simultaneously accounts for the design and operational decisions for freshwater source selection, multiple transportation modes, and water management options. Water management options include disposal, commercial centralized wastewater treatment, and onsite treatment (filtration, lime softening, thermal distillation). To globally optimize the resulting MILFP problem efficiently, three tailored solution algorithms are presented: a parametric approach, a reformulation‐linearization method, and a novel Branch‐and‐Bound and Charnes–Cooper transformation method. The proposed models and algorithms are illustrated through two case studies based on Marcellus shale play, in which onsite treatment shows its superiority in improving freshwater conservancy, maintaining a stable water flow, and reducing transportation burden. © 2014 American Institute of Chemical Engineers AIChE J, 61: 1184–1208, 2015     

循环冷却水系统高压静电离子棒抑垢缓蚀的试验研究

循环冷却水系统的结垢与腐蚀一直是火电厂需要解决的主要问题.针对这一现状,简要介绍了高压静电离子棒的工作原理,通过试验对高压静电离子棒的抑垢、缓蚀性能进行研究.试验结果表明:高压静电离子棒具有较好的抑垢与缓蚀作用,在循环冷却水流量较低时,静电离子棒的抑垢效果相对较好.     

In silico theoretical molecular modeling for Alzheimer's disease: the nicotine-curcumin paradigm in neuroprotection and neurotherapy

The aggregation of the amyloid-β-peptide (AβP) into well-ordered fibrils has been considered as the key pathological marker of Alzheimer's disease. Molecular attributes related to the specific binding interactions, covalently and non-covalently, of a library of compounds targeting of conformational scaffolds were computed employing static lattice atomistic simulations and array constructions. A combinatorial approach using isobolographic analysis was stochastically modeled employing Artificial Neural Networks and a Design of Experiments approach, namely an orthogonal Face-Centered Central Composite Design for small molecules, such as curcumin and glycosylated nornicotine exhibiting concentration-dependent behavior on modulating AβP aggregation and oligomerization. This work provides a mathematical and in silico approach that constitutes a new frontier in providing neuroscientists with a template for in vitro and in vivo experimentation. In future this could potentially allow neuroscientists to adopt this in silico approach for the development of novel therapeutic interventions in the neuroprotection and neurotherapy of Alzheimer's disease. In addition, the neuroprotective entities identified in this study may also be valuable in this regard.     

Influence of unsteady mass transfer on dynamics of rising and sinking droplet in water: Experimental and CFD study

Experimental and numerical investigations were conducted to study the effect of unsteady mass transfer on the dynamics of an organic droplet released in quiescent water. The situation is important and relevant to deep sea oil spill scenario. The droplet contains two components, one is heavier (immiscible) than water and other is lighter (miscible). When released, with an initial mixture density (890–975 kg/m³) lower than that of surrounding water, droplet rises in the column. The mass transfer of lighter solute component into water causes the droplet density to increase and droplet sinks when the density exceeds that of water. A mass‐transfer correlation accounting for the loss of the solute, based on Reynolds, Grashoff, and Schmidt numbers was developed. A two‐dimensional axisymmetric Computational Fluid Dynamics (CFD) model accounting for species transport was developed to emulate the experimental observations. The study also helped in identifying dominant mass‐transfer mechanisms during different stages of droplet motion. © 2014 American Institute of Chemical Engineers AIChE J, 61: 342–354, 2015     

Experimental evaluation of a kinetic method for the study of non-catalysed heterogeneous reactions in solid–liquid systems: Leaching of apatite by dilute nitric acid

A method for evaluating the formal reaction order n of a chemical reaction is proposed, based on a kinetic equation describing chemical dissolution of solid particles in a batch reactor. The evaluation is based on the fact that the function expressing the dependence of the conversion, X_B, on the term c_A0ⁿt has the same course for different initial concentrations of the liquid reactant, c_A0 (t denotes time), at constant temperature and ratio of reactants. The measured dependences of the conversion on the time give a function, f_s(X_B), characterizing changes of the solid surface area during the reaction. Minimization of deviations of this function permits the influence of mass transfer between the bulk of the liquid and the solid surface on the measured formal reaction order to be corrected. Knowledge of the value of n and the function f_s(X_B) enables the derived kinetic equation to be integrated numerically and the dependence of the conversion on time to be calculated. A set of experimental data measured during dissolution of natural apatite in dilute nitric acid (0.1–1.5 mol dm⁻³) at 20°C was used for demonstration of the proposed method. © 1998 SCI