Ahmed Mohamed Elsayed Khalil

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Nanoscale zero-valent iron (nZVI) supported on heat-modified activated charcoal (nFe(0)/AC) can improve nitrate reduction and phosphate adsorption regarding reaction conversion, adsorption capacity, and kinetics. It is more effective in terms of mobility and stability than bare nZVI. Both nZVI and its supported type were synthesized, characterized, and examined in interference studies for applications in environmental remediation technologies. Solutions of 45.18 mg nitrate–N/L, 50 mg phosphorus/L, and a mixture of them were treated using nZVI and nFe(0)/AC in municipal wastewater body and also deionized water body in the coexistence of anions (phosphate and sulfate), cations (cuprous and cupric), organic matters (humic acid), and hardness (calcium carbonates) at different concentrations. Results showed the significant impact of interference on nitrate reduction and superiority of nFe(0)/AC by ca. 27{%} to ca. 183{%} increase in treatment efficiency over nZVI. Anions were easily attracted to the surface of nano-iron particles resulting in a negative intervening effect. Hardness and contaminants of municipal wastewater provided a negative impact and significantly interfered with nitrate removal, while organic matters had a lower negative interference compared to others. On the contrary, copper cations could improve removal efficiency until complete elimination of nitrate. The experimental data were best-fitted to a kinetic rate model that combined the pseudo-first-order rate with the deactivation rate arising from the passivation of interfering substances on the surface of nZVI-based materials. The value of parameters of this rate equation could estimate the degree and type of interference occurring during nitrate decontamination.

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Phase equilibrium calculations (PECs) and phase stability (PS) analysis of reactive and nonreactive systems problems are important for the simulation and design of chemical engineering processes. These problems, which are challenging, multi-variable, and non-convex, require optimization techniques that are both efficient and effective in finding the solution. Stochastic global optimization algorithms, especially swarm algorithms, are promising tools for such problems. In this study, monkey algorithm (MA), gravitational search algorithm (GSA), and Krill Herd algorithm (KHA) were used to solve PS, phase equilibrium, and chemical equilibrium problems. We have also studied the effect of adding a local optimizer at the end of the stochastic optimizer run. The results were compared to determine the strengths and weaknesses of each algorithm. When a local optimizer was used, MA was found to be a reliable algorithm in solving the problems. GSA had relatively the least numerical effort for all problems among the three algorithms but with low reliability. KHA was more reliable than other two algorithms without the use of a local optimizer. The performance of GSA, MA, and KHA was compared with firefly algorithm and cuckoo search (CS). In summary, this study found that CS algorithm was more reliable than the newly tested algorithms. Nevertheless, MA and GSA algorithms, when combined with a local optimizer, solve the thermodynamic problems as reliably and efficiently as CS.

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.