A combinatorial approach to evaluation of monomer conversion and particle size distribution in vinyl chloride emulsion polymerization

This work is targeted to study emulsion polymerization of vinyl chloride monomer (VCM) using experimental and mathematical methods. To fulfill this goal, a computer code was developed on the basis of zero–one population balance by which the effects of initiator and emulsifier concentration on the evolution of VCM conversion were investigated in the course of polymerization. The model was also trained to capture the coagulation of the particles. This enabled to adopt a reliable way of evaluating the particle size distribution (PSD). In particular, the rates of homogeneous and micellar nucleation mechanisms were simulated and reasonably predicted alterations in the PSD and the number of polymer particles under the influence of aforementioned parameters. The results from modeling were satisfactorily consistent with the experimental outputs and obviously visualized the impact of initiator and surfactant concentration on the PSD of the prepared PVC latexes.     

Reductive removal of hexavalent chromium from aqueous solution using sepiolite-stabilized zero-valent iron nanoparticles: Process optimization and kinetic studies

We studied the optimization of hexavalent chromium (Cr(VI)) removal from aqueous solution using the synthesized zero-valent iron nanoparticles stabilized with sepiolite clay (S-ZVIN), under various parameters such as reaction time (min), initial solution pH and concentration of S-ZVIN (g·L^?1) using response surface methodology (RSM). The kinetic study of Cr(VI) was conducted using three types of the most commonly used kinetic models including pseudo zero-order, pseudo first-order, and pseudo second-order models. The rate of reduction reaction showed the best fit with the pseudo first-order kinetic model. The process optimization results revealed a high agreement between the experimental and the predicted data (R²=0.945, Adj-R²=0.890). The results of statistical analyses showed that reaction time was the most impressive factor influencing the efficiency of removal process. The optimum conditions for maximum response (98.15%) were achieved at the initial pH of 4.7, S-ZVIN concentration of 1.3 g·L^?1 and the reaction time of 75 min.     

Role of nanoparticles on nanofluid boiling phenomenon: Nanoparticle deposition

Suspended nanoparticles inside the nanofluids can modify the characteristics of heated surfaces and the physical properties of the base liquids, offering a great opportunity to optimize boiling heat transfer. This paper reviews the mechanisms of nanoparticle deposition and the effects induced by deposited nanoparticles on surface roughness, force balance at the triple line, surface wettability, active nucleation site density, receding and advancing contact angles, boiling heat transfer coefficient and critical heat flux. Both enhancement and deterioration effects on boiling heat transfer coefficient and critical heat flux have been discussed. Most of the existing experimental data confirms the enhancement of critical heat flux using alumina nanofluid, however there is no consistency about its boiling heat transfer coefficient.     

Performance analysis of three advanced controllers for polymerization batch reactor: An experimental investigation

The performances of three advanced non-linear controllers are analyzed for the optimal set point tracking of styrene free radical polymerization (FRP) in batch reactors. The three controllers are the artificial neural network-based MPC (NN-MPC), the artificial fuzzy logic controller (FLC) as well as the generic model controller (GMC). A recently developed hybrid model (Hosen et al., 2011a. Asia-Pac. J. Chem. Eng. 6(2), 274) is utilized in the control study to design and tune the proposed controllers. The optimal minimum temperature profiles are determined using the Hamiltonian maximum principle. Different types of disturbances are introduced and applied to examine the stability of controller performance. The experimental studies revealed that the performance of the NN-MPC is superior to that of FLC and GMC.     

A thermodynamically consistent framework for non-isothermal modelling of local heat generation and electromotive force in SOFC single electrodes

Mathematical models for single electrode reversible heat and non-isothermal electromotive force (EMF) of a solid oxide fuel cell (SOFC) are developed. These models estimate the volumetric reversible heat generation and EMF of electrochemical reactions, within each electrode at local conditions of temperature and pressure, based on entropy change of half reactions. The resulting equations are thermodynamically consistent. They inherently obey the conservation of energy law as the electrochemical energy released added to the heat of reactions at each electrode equate the enthalpy change of the reacted species. The equations are implemented to model electrodes in a tubular micro- solid oxide fuel cell (TμSOFC). The thermodynamic consistency of the model is numerically confirmed as the enthalpy of the reactants equates the electric energy released by the cell plus the sum of electrode heats plus electrolyte Ohmic heat. The effect of thermal gradients on the cell's overall EMF is found to be negligible. The reversible and irreversible heat generation of each electrode are distinguished. Overall, the anode is found to be endothermic, and the cathode exothermic.     

Solid-phase electrochemical reduction of graphene oxide films in alkaline solution

Graphene oxide (GO) film was evaporated onto graphite and used as an electrode to produce electrochemically reduced graphene oxide (ERGO) films by electrochemical reduction in 6 M KOH solution through voltammetric cycling. Fourier transformed infrared and Raman spectroscopy confirmed the presence of ERGO. Electrochemical impedance spectroscopy characterization of ERGO and GO films in ferrocyanide/ferricyanide redox couple with 0.1 M KCl supporting electrolyte gave results that are in accordance with previous reports. Based on the EIS results, ERGO shows higher capacitance and lower charge transfer resistance compared to GO.     

Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks

In recent years, as a result of climate change as well as rainfall reduction in arid and semi‐arid regions, modelling qualitative and quantitative parameters belonging to aquifers has become crucially important. In Iran, as aquifers are treated as the most commonly used drinking water resources, modelling their qualitative and quantitative parameters is enormously important. In this paper, for the first time, values of salinity, total dissolved solids (TDS), groundwater level (GWL) and electrical conductivity (EC) of the Arak Plain, located in Markazi Province, Iran, are simulated by means of four modern artificial intelligence models including extreme learning machine (ELM), wavelet extreme learning machine (WELM), online sequential extreme learning machine (OSELM) and wavelet online sequential extreme learning machine (WOSELM) as well as the MODFLOW software for a 15‐year period monthly. To develop the hybrid artificial intelligence models, the wavelet is employed. First, the effective lags in estimating the qualitative and quantitative parameters of the groundwater are identified using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analysis. After that, four different models are developed by the selected input combinations and also the ACF and the PACF in the form of different lags for each of ELM, WAELM, OSELM and WOSELM methods. Then, the superior models in simulating the groundwater qualitative and qualitative parameters are detected by conducting a sensitivity analysis. To forecast the electrical conductivity (EC) by the best WOSELM model, the values of the Nash–Sutcliffe efficiency coefficient (NSC), Mean Absolute Error (MAE) and the scatter index (SI) are obtained to be 0.991, 18.005 and 4.28E‐03, respectively. In addition, the most effective lags in estimating these parameters are introduced. Subsequently, the results found by the MODFLOW model are compared with those of the artificial intelligence models and it is concluded that the latter are more accurate. For instance, the scatter index and Nash–Sutcliffe efficiency coefficient values calculated by WOSELM for TDS, respectively, are 5.34E‐03 and 0.991. Finally, an uncertainty analysis is conducted to evaluate the performance of different numerical models. For example, MODFLOW has an underestimated performance in simulating the salinity parameter.     

Classification of Geothermal Energy Resources in Japan Applying Exergy Concept

Higher demand for energy consumption and importance of environmental issues has encouraged researchers and policy makers to consider renewable energies more seriously. Geothermal resources are a green energy source that can make a considerable contribution in some countries. Japan has the third ranking geothermal energy potential, and its geothermal electricity production is currently eighth in the world. Since the nature of geothermal resources dictates its method of utilization, it is important to categorize available resources. There is no consensus on classification of geothermal resources. Most scientists, from geologist to engineers, agree on the term temperature. However, temperature or enthalpy alone cannot describe the nature of fluids; they can have same temperature with different phases, such as saturated water or saturated steam. Using exergy for resource classification benefits their comparison, according to their ability to do work. In this paper, exergetic classification of geothermal resources was applied to 18 under‐operating geothermal power plants in Japan. Six geothermal fields have high exergy resources according to their SExI values in excess of 0.5. The remaining geothermal fields in Japan are classified in the medium resources zone. Classification results can be used by decision makers as a reference for future geothermal development. Copyright © 2012 John Wiley & Sons, Ltd.     

Dielectrophoretically assembled particles: feasibility for optofluidic systems

This work presents the dielectrophoretic manipulation of sub-micron particles suspended in water and the investigation of their optical responses using a microfluidic system. The particles are made of silica and have different diameters of 600, 450, and 250 nm. Experiments show a very interesting feature of the curved microelectrodes, in which the particles are pushed toward or away from the microchannel centerline depending on their levitation heights, which is further analyzed by numerical simulations. In doing so, applying an AC signal of 12 V_p–p and 5 MHz across the microelectrodes along with a flow rate of 1 μl/min within the microchannel leads to the formation of a tunable band of particles along the centerline. Experiments show that the 250 nm particles guide the longitudinal light along the microchannel due to their small scattering. This arrangement is employed to study the feasibility of developing an optofluidic system, which can be potentially used for the formation of particles-core/liquid-cladding optical waveguides.