توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Evaluatoin of the efficiency of isolated microorganisms from crude oil for removing floating crude oil
نویسنده(ها): ، ، ،
اطلاعات انتشار: دوازدهمین کنگره ملی مهندسی شیمی ایران، سال
تعداد صفحات: ۸
In this study, two strains of bacteria were isolated from the crude oil feed of Isfahan oil refinery in Iran These bacteria which caled A–1 and A–2 , were gram negatives and cocci . The mixture of A–2 were inoculated to a semi – batch bioreactor to decrease the concentration of floating crude oil water . The removal efficiency was reached to 89% after a 7.5 days operation of bioreactor in the best conditions .Durig 91 days mixed liquid suspended solid (MLSS) in the bioreactor and sludge volume index (SVI) evaluated . Three concentration of crude oil applied for this study and the effect were investigated . During of study , SVI in reactor was very low and was between 20 to 90 mg\I . Microbial flocks in bioreactor were scarce and microorganisms had dispersed growth. These low SVI and disperse growth were due to toxic components that exist in crude oil. Maximum MLSS was about 8000 mg\I and in the first addition of crude oil (17150mg\I), it decreased to about 4000 mg\I . but in second increase of substrate , the changer of MLSS was low . A–1 and A–2 could produce biosurfactant and the emulsification index of A–1 , A–2 and mixed them were 20% , 25% and 21% respectively . Our data showed that isolated microorganisms dont need to any synthetic surfactants for startup of crude oil biodegradation.<\div>

۲A Statistical Analyzing Approach for Quantum Evolutionary Algorithms
نویسنده(ها): ، ، ،
اطلاعات انتشار: نوزدهمین کنفرانس مهندسی برق ایران، سال
تعداد صفحات: ۶
This paper proposes a novel reinitialization \guidance operator for q–individuals in Quantum Evolutionary Algorithms (QEA) called Statistical Analyzing Reinitialization Quantum Evolutionary Algorithm (SARQEA). Evolutionary algorithms suffer from trapping in local optima and QEA is not an exception. In QEA, after convergence, the q–bits in qindividuals converge to true states of [0 1] or [1 0]. Trapping in local optima, the q–individuals have no chance to explore the search space. In order to improve the exploration ability of QEA and help the algorithm to escape from local optima, this paper proposes a novel reinitialization operator. In SARQEA algorithm, at first the convergence of the population is examined. If the population is converged, in the second step, using the statistical information gathered from previous searches the q–individuals are reinitialized. The new values of reinitialized q–individuals are based on the gathered information from previous searches. Several experimental results on Knapsack, Trap and some numerical function optimization algorithms are performed and the results show better performance for the proposed algorithm.<\div>
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