On the Performance of Swarm Intelligence Optimization Algorithms for Phase Stability and Liquid-Liquid and Vapor-Liquid Equilibrium Calculations

This study introduces new soft computing optimization techniques for performing the phase stability analysis and phase equilibrium calculations in both reactive and non-reactive systems. In particular, the performance of the several swarm intelligence optimization methods is compared and discussed based on both reliability and computational efficiency using practical stopping criteria for these applied thermodynamic calculations.  These algorithms are: Intelligent Firefly Algorithm (IFA), Cuckoo Search (CS), Artificial Bee Algorithm (ABC) and Bat Algorithm (BA). It is important to note that no attempts have been reported in the literature to evaluate their performance in solving the phase and chemical equilibrium problems. Results indicated that CS was found to be the most reliable technique across different problems tried at the time that it requires similar computational effort to the other methods. In summary, this study provides new results and insights about the capabilities and limitations of bio-inspired optimization methods for performing applied thermodynamic calculations

On the Effectiveness of Nature-Inspired Metaheuristic Algorithms for Performing Phase Equilibrium Thermodynamic Calculations

The search for reliable and efficient global optimization algorithms for solving phase stability and phase equilibrium problems in applied thermodynamics is an ongoing area of research. In this study, we evaluated and compared the reliability and efficiency of eight selected nature-inspired metaheuristic algorithms for solving difficult phase stability and phase equilibrium problems. These algorithms are the cuckoo search (CS), intelligent firefly (IFA), bat (BA), artificial bee colony (ABC), MAKHA, a hybrid between monkey algorithm and krill herd algorithm, covariance matrix adaptation evolution strategy (CMAES), magnetic charged system search (MCSS), and bare bones particle swarm optimization (BBPSO). The results clearly showed that CS is the most reliable of all methods as it successfully solved all thermodynamic problems tested in this study. CS proved to be a promising nature-inspired optimization method to perform applied thermodynamic calculations for process design

Modeling of a microfluidic electrochemical cell for the electro-reduction of CO2 to CH3OH

This study focuses on developing a mathematical model for the electrochemical reduction of CO2 into CH3OH in a microfluidic flow cell. The present work is the first attempt to model the electro-reduction of CO2 to alcohols, which is a step forward toward the scale up of the process to industrial operation. The model features a simple geometry of a filter press cell in which the steady state isothermal reduction takes place. All significant physical phenomena occurring inside the cell are taken into account, including mass and charge balances and transport, fluid flow and electrode kinetics. The model is validated and fitted against experimental data and shows an average error of 20.2%. The model quantitatively demonstrated the dominance of the hydrogen evolution over the CH3OH production and the limitations imposed on the process due to the mass transfer of the reactants to the cathode, especially CO2. Also, the model shows that based on the flow pattern of CH3OH, more conductive membrane materials could be used to decrease the potential drop around the membrane in order to improve the process performance

Modeling and numerical investigation of the performance of gas diffusion electrodes for the electrochemical reduction of carbon dioxide to methanol

In this study, a model was built to investigate the role of Cu2O-/ZnO-based gas diffusion electrodes in enhancing the reduction of carbon dioxide into methanol inside an electrochemical cell. The model was simulated using COMSOL Multiphysics software and validated using experimental results. It showed reasonable agreement with an average error of 6%. The model demonstrated the dependence of the methanol production rate and faradic efficiency on process key variables: current density (j = 5-10 mA cm-2), gas flow rate (Qg/A = 10-20 mL min-1 cm-2), electrolyte flow rate, and CO2 gas feed concentration. The results showed a maximum methanol production rate of 50 -mol m-2 s-1 and faradic efficiency of 56% at -1.38 V vs Ag/AgCl. From the economic point of view, it is recommended to use a gas stream of 90% or slightly lower CO2 concentration and an electrolyte flow rate as low as 2 mL min-1 cm-2.The authors would like to convey special thanks to Prof. Mai Kamal El-Din for her willingness to share her knowledge and expertise that are of significant relevance to this work. J.A. gratefully acknowledges the financial support from the Spanish Ministry of Economy and Competitiveness (MINECO) under Ramon y Cajal program (RYC-2015-17080). The authors from ́ the Chemical Engineering Department, Cairo University, gratefully acknowledge the financial support provided by the Science and Technology Development Fund (STDF) of Egypt under project ID #11872. R.M.E.-M. acknowledges the support from the Oil and Green Chemistry research center and the Enhanced Oil Recovery Lab, Suez University, Egypt, and STDF (Science and Technology Development Fund) [Project ID 12395]

Magnetophoretic focusing on submicron particles dispersed in a polymer-stabilized magnetic fluid

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2002.Includes bibliographical references.Magnetophoresis is the migration of particles upon the application of an inhomogeneous magnetic field. The overall goal of this work was to investigate the magnetophoretic focusing of non-magnetic particles suspended in magnetic fluids, which are colloidal suspensions of nano-sized magnetic particles. With the magnetic fluid as the solvent, dispersed non-magnetic particles behave as if they were diamagnetic due to the difference in magnetic susceptibility between them and the surrounding magnetic continuum. When an inhomogeneous magnetic force is applied, a magnetic force acts on the colloidal particles, the magnitude of which is linearly proportional to the volume of the particles, the difference in the magnetic susceptibilities of the particles and the surrounding magnetic fluid, and the gradient of the square of the magnetic field. One potential application for this phenomenon is in the separation of submicron biological particles such as viruses, cell fragments, DNA and inclusion bodies. Magnetic fluids have several characteristics that make them attractive for use in separation. For example, they can be tailored to the separation needs at hand, manipulated using external magnetic fields, and completely removed through magnetic filtration. Since the scope of the work was to use physical forces for attaining the desired separations, the magnetic particles were designed and synthesized without any chemical affinity to the solute to be separated. They were prepared by coprecipitation of iron (II) and (III) ions to form magnetite, which is coated by a comb copolymer that serves two purposes: to limit growth of magnetite to about 10 nm and to stabilize the particles against aggregation.(cont.) The polymer was prepared by a reaction between amine-terminated polyethylene oxide and polyacrylic acid. Characterization of the particles was done experimentally and theoretically. Dynamic light scattering was used to measure the diffusion coefficient and the hydrodynamic diameter of the particles, while transmission electron microscopy was used to measure the diameter of the magnetic core. Since the structure of the magnetic fluid is an important parameter in its application in any magnetophoretic separation, we characterized the aggregation behavior of the magnetic fluids using different theoretical techniques. Monte Carlo simulation was used to understand the clustering in sterically-stabilized magnetic fluids. Simulation results agree favorably with the scattering experiments with regards to the cluster sizes and fractal dimensions. The characterization of a closely related system, a charge stabilized magnetic fluid, was also performed to explain the finite cluster size observed experimentally. Next, we investigated magnetophoretic focusing in the synthesized magnetic fluid, as a means to separate submicron colloidal particles based on size. The magnetophoresis concepts were validated experimentally by monitoring the dynamic evolution of the concentration profile of fluorescently-tagged polymer beads of various sizes in a magnetic fluid upon the application of an inhomogeneous magnetic field. Polymer beads larger than 0.2 /um focused at the point of zero force, and the effect of the magnetic field on the particles was correlated with their size...by Seif-Eddeen K. Fateen.Ph.D

Gradient-Based Cuckoo Search for Global Optimization

One of the major advantages of stochastic global optimization methods is the lack of the need of the gradient of the objective function. However, in some cases, this gradient is readily available and can be used to improve the numerical performance of stochastic optimization methods specially the quality and precision of global optimal solution. In this study, we proposed a gradient-based modification to the cuckoo search algorithm, which is a nature-inspired swarm-based stochastic global optimization method. We introduced the gradient-based cuckoo search (GBCS) and evaluated its performance vis-à-vis the original algorithm in solving twenty-four benchmark functions. The use of GBCS improved reliability and effectiveness of the algorithm in all but four of the tested benchmark problems. GBCS proved to be a strong candidate for solving difficult optimization problems, for which the gradient of the objective function is readily available

Semi-empirical correlation for binary interaction parameters of the Peng–Robinson equation of state with the van der Waals mixing rules for the prediction of high-pressure vapor–liquid equilibrium

Peng–Robinson equation of state is widely used with the classical van der Waals mixing rules to predict vapor liquid equilibria for systems containing hydrocarbons and related compounds. This model requires good values of the binary interaction parameter kij. In this work, we developed a semi-empirical correlation for kij partly based on the Huron–Vidal mixing rules. We obtained values for the adjustable parameters of the developed formula for over 60 binary systems and over 10 categories of components. The predictions of the new equation system were slightly better than the constant-kij model in most cases, except for 10 systems whose predictions were considerably improved with the new correlation

MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm

The search for efficient and reliable bio-inspired optimization methods continues to be an active topic of research due to the wide application of the developed methods. In this study, we developed a reliable and efficient optimization method via the hybridization of two bio-inspired swarm intelligence optimization algorithms, namely, the Monkey Algorithm (MA) and the Krill Herd Algorithm (KHA). The hybridization made use of the efficient steps in each of the two original algorithms and provided a better balance between the exploration/diversification steps and the exploitation/intensification steps. The new hybrid algorithm, MAKHA, was rigorously tested with 27 benchmark problems and its results were compared with the results of the two original algorithms. MAKHA proved to be considerably more reliable and more efficient in tested problems