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Comparative Analysis of Computational Prediction Methods for Liquid-Liquid Equilibria in Ternary Water-Ethanol-Benzene Mixtures | ||
| Iranian Journal of Analytical Chemistry | ||
| دوره 11، شماره 1، خرداد 2024، صفحه 1-11 اصل مقاله (516.31 K) | ||
| نوع مقاله: Full research article | ||
| شناسه دیجیتال (DOI): 10.30473/ijac.2024.71186.1296 | ||
| نویسندگان | ||
| pouya Es'haghi1؛ Alireza Mohammadi2؛ Keivan Shayesteh* 1؛ Hassan Seddighi1 | ||
| 1Department of Chemical Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, Ardebil, Iran | ||
| 2Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran | ||
| چکیده | ||
| Ethanol (EtOH) purification is a pivotal research pursuit, with liquid-liquid extraction emerging as a significant purification methodology. This study focuses on utilizing benzene solvent for EtOH purification and investigates the liquid-liquid equilibrium (LLE) within three-component systems comprising EtOH, water, and benzene. Thermodynamic modeling of EtOH-benzene-water systems at temperatures of 20 °C, 30 °C, 40 °C, and 55 °C was conducted. In this paper, the equations used for predicting mole fraction include Non-Random Two-Liquid (NRTL), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multilayer Perceptron Artificial Neural Network (MLP-ANN). First, the equation parameters were optimized using the particle swarm optimization (PSO) algorithm to employ the NRTL equation Experimental data was used to train the MLP-ANN and ANFIS methods, and the same experimental datasets were used for all models. These models estimated integral components across both phases, revealing effective system control across all methodologies. However, the comparative analysis indicated the superior performance of the MLP-ANN and ANFIS methods over the NRTL model. The Root Mean Square Deviation (RMSD) errors for the NRTL, MLP-ANN, and ANFIS models were 0.0253, 0.0035, and 0.0017, respectively. These results indicate that despite the low prediction error of all three methods, the NRTL equation has the highest error, and the ANFIS method has the lowest mole fraction prediction error. | ||
| کلیدواژهها | ||
| Liquid-liquid equilibria؛ Adaptive neuro-fuzzy inference system؛ Artificial neural network؛ NRTL model؛ Partial Swarm Optimization Algorithm | ||
| عنوان مقاله [English] | ||
| تحلیل مقایسهای روشهای پیشبینی محاسباتی برای تعادل مایع-مایع در مخلوطهای سهجزئی آب- اتانول- بنزن | ||
| نویسندگان [English] | ||
| پویا اسحقی1؛ علیرضا محمدی2؛ کیوان شایسته1؛ حسن صدیقی1 | ||
| 1گروه مهندسی شیمی، دانشکده فنی، دانشگاه محقق اردبیلی، اردبیل، ایران | ||
| 2گروه مهندسی شیمی و نفت، دانشگاه صنعتی شریف، تهران، ایران | ||
| چکیده [English] | ||
| خالصسازی اتانول یک تحقیق محوری است که استخراج مایع-مایع بهعنوان یک روش خالصسازی قابل توجه در حال ظهور است. این مطالعه بر روی استفاده از حلال بنزن برای خالصسازی اتانول تمرکز دارد و تعادل مایع-مایع (LLE) را در سیستمهای سهجزئی شامل اتانول، آب و بنزن بررسی میکند. مدلسازی ترمودینامیکی سیستمهای اتانول-بنزن-آب در دماهای 20، 30، 40 و 55 درجه سانتیگراد انجام شد. در این مقاله، معادلات مورد استفاده برای پیشبینی کسر مولی عبارتند از: دو مایع غیر تصادفی (NRTL)، سیستم استنتاج تطبیقی عصبی-فازی (ANFIS) و شبکه عصبی مصنوعی پرسپترون چند لایه (MLP-ANN). البته ابتدا پارامترهای معادله NRTL با استفاده از الگوریتم بهینهسازی ازدحام ذرات (PSO) بهینه شدند. دادههای تجربی برای آموزش روشهای MLP-ANN و ANFIS، با مجموعه دادههای تجربی یکسان برای همه مدلها مورد استفاده قرار گرفت. این مدلها اجزای مولی را در هر دو فاز تخمین زدند. با این حال، تجزیه و تحلیل مقایسهای نشان داد که روشهای MLP-ANN و ANFIS نسبت به مدل NRTL عملکرد بهتری دارند. خطاهای ریشه میانگین مربعات انحراف (RMSD) به دستآمده برای مدلهای NRTL، MLP-ANN و ANFIS بهترتیب 0253/0، 0035/0 و 0017/0 بود. نتایج نشان میدهد که با وجود خطای کم پیشبینی هر سه روش، معادله NRTL بیشترین خطا و روش ANFIS کمترین خطای پیشبینی کسر مولی را دارد. | ||
| کلیدواژهها [English] | ||
| تعادل مایع-مایع, مدل دو مایع غیر تصادفی, سیستم استنتاج تطبیقی عصبی-فازی, شبکه عصبی مصنوعی پرسپترون چند لایه, الگوریتم بهینهسازی ازدحام ذرات | ||
| مراجع | ||
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