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Article Citation - WoS: 4Citation - Scopus: 4Heavy Metal Inhibition on an Alternating Activated Sludge System and Its Comparison To Conventional Methods: Case Study of Cu2+(Iwa Publishing, 2021) Buaisha, Magdi; Balku, Saziye; Ozalp-Yaman, SenizIn order to understand the behaviour of wastewater treatment plants (WWTPs) with heavy metal presence, the present study evaluates the treatment process in the presence of heavy metals (Cu2+ as a case study) and compares it with the absence of heavy metals. An activated sludge model is improved by means of incorporating other novel inhibitory kinetic and settler models for this evaluation. To achieve this goal, a simulation algorithm is developed using the MATLAB code to detect any heavy metal influence on the aerobic and anoxic growth of heterotrophic and autotrophic biomass. The code also allows for a comparison of treatment plant performance with and without Cu2+ in both conventional and alternating systems. The results reveal that the presence of heavy metals, in case of the present study for Cu2+ at 0.5 mg/L, in a biological treatment system, has an inhibitory effect on the heterotrophic bacteria but more so on the autotrophic bacteria growth and it prevents nitrification and denitrification, thus negatively effecting on the nitrogen removal in the alternating systems.Article Citation - WoS: 15Citation - Scopus: 18Control Vector Parameterization Approach in Optimization of Alternating Aerobic-Anoxic Systems(Wiley, 2009) Balku, Saziye; Yuceer, Mehmet; Berber, RidvanDetermination of the optimal aeration profile for an activated sludge system in which nitrification and denitrification take place sequentially in a single reactor (alternating aerobic-anoxic) is an attractive optimization problem because of complexities involved in, and high computational times required for solution. The rigorous dynamic modeling and start-up simulation of such a system, together with aeration profile optimization by an evolutionary algorithm (EA), were tackled in a previous study. In this paper an easy-to-implement dynamic optimization technique based on sequential quadratic programming method and control vector parameterization approach is provided. In comparison with EA, the proposed algorithm gives better results in shorter computation times. Copyright (C) 2009 John Wiley & Sons, Ltd.
