توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Adaptive Differential Evolution (ADE) for optimization of Non–linear chemical processes
نویسنده(ها): ،
اطلاعات انتشار: ششمین کنگره بین المللی مهندسی شیمی، سال
تعداد صفحات: ۶
Differential Evolution algorithm (DE), one of the evolutionary algorithms, is a novel optimization method capable of handling non–differentiable, non–linear and multimodal objective functions. DE takes large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This paper introduces a modification on original DE that enhances the convergence rate. Our Adaptive Differential Evolution algorithm (ADE) uses variable scaling parameter (F) as against constant scaling parameter in original DE at any iteration. Some functions such as logarithmic, exponential, inverse and square for changing F with iteration are examined, and Numerical results suggest that square function has a best performance to reduce solution vectors dispersal and results in faster convergence. The proposed ADE is applied to optimize three non–linear chemical engineering problems. Results obtained are compared with those obtained using DE by considering the convergence history (CPU time and the number of runs converged to global optimum) and error in any iteration. As compared to DE, ADE is found to perform better in locating the global optimal solution, reduces the memory and computational efforts by reducing the number of iteration to reach a global optimal solution for all the considered problems.<\div>

۲Phase equilibria modeling of binary systems containing Ethanol
اطلاعات انتشار: چهاردهمین کنگره ملی مهندسی شیمی ایران، سال
تعداد صفحات: ۴
Understanding vapor liquid equilibrium (VLE) is one of the most important information for designing of process equipments. In this work, artificial neural network (ANN) was used to modelthe bubble point pressure and vapor phase composition of binary ethanol (C2H5OH) mixtures. The proposed ANN model has been constructed with VLE experimental data of nine different binary systems containing C2H5OH collected from various literatures. Optimal configuration of the ANNmodel has been determined using minimizing %AARD, MSE and suitable R2. By using thisprocedure a two–layer ANN model with twenty–three hidden neuron has been found as an optimal topology. The accuracy of our optimal two layers ANN model has been compared with the Peng–Robinson cubic equation of state. Comparison with available literatures data and Peng–Robinsonequation of state confirm that the present ANN model is more accurate than the other published works. The sensitivity errors analysis clarify that our ANN model could predict vapor phase composition and bubble point pressure with %AARD of 1.52% and 2.59% respectively<\div>
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