پیش بینی کشش سطحی مایعات یونی بر پایه ایمیدازولیوم با بکارگیری شبکه عصبی مصنوعی

نوع مقاله: مقاله شیمی

نویسنده

دانشکده مهندسی شیمی، آزمایشگاه ازدیادبرداشت نفت و فرآوری گاز، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران

چکیده

امروزه با پیشرفت تکنولوژی برای حل مسائلی که روابط دقیق ریاضی بین ورودی و خروجی برقرار نمی باشد از شبکه های عصبی مصنوعی استفاده می شود. در این پژوهش برای پیش‎بینی کشش سطحی مایعات یونی بر پایه ایمیدازولیوم دو شبکه عصبی پرسپترون چند لایه شامل شبکه عصبی مصنوعی پیشرو (FFANN) و شبکه عصبی آبشاری (CANN) پیشنهاد شد. برای بررسی صحت مدل ها، از 1251 داده آزمایشگاهی گردآوری شده از مقالات مختلف شامل کشش سطحی 40 مایع یونی در محدوده وسیع دمایی (از 61/263 الی 2/533 کلوین) استفاده شده است. نتایج نشان داد که مدل شبکه پرسپترون چند لایه CANN متشکل از چیدمانی با سه ورودی شامل جرم های مولکولی بخش های آنیونی و کاتیونی مایع یونی و دما و یک لایه مخفی حاوی 8 نرون با تابع فعال سازی تانژانت هیپربولیک که با استفاده از الگوریتم آموزشی لونبرگ-مارکوارت آموزش دیده بهترین دقت در پیش بینی کشش سطحی مایعات یونی داشته است. آنالیز خطا های داده‎های تست با درصد متوسط قدر مطلق خطاهای نسبی (AARD%) 07/1، بیانگر کارایی مدل غیرخطی CANN در برقراری ارتباط مابین ورودی های شبکه و کشش‎سطحی می باشد. علاوه بر آن مقایسه دقت مدل پیشنهادی با مدل های موجود از جمله قانون حالات متناظر، پاراچور، الگوریتم مدیریت داده ها به روش گروهی (GMDH) و مدل حداقل مربعات ماشین بردار پشتیبان (LSSVM) بیانگر برتری مدل پیشنهادی بوده است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Prediction of surface tension of ionic liquid based on imidazolium using artificial neural network

نویسنده [English]

  • Mostafa Lashkarbolooki
School of Chemical Engineering, Enhanced Oil Recovery and Gas Processing Lab, Babol Noshirvani University of Technology, Babol, Iran
چکیده [English]

Nowadays, with the progresses in technology to solve problems where there is no exact mathematical relationship between input and output, neural networks are efficiently proposed and used. In the shadow of its unique features, in this study, two multilayer perceptron neural networks including feedforward artificial neural network (FFANN) and cascade artificial neural network (CANN) were proposed to predict the surface tension of imidazolium-based ionic liquids. To verify the validity of the proposed models, 1251 experimental data points were collected from various previously published literature including the surface tension of 40 ionic liquids in a wide range of temperatures (from 263.61 to 533.2 K). The results showed that the proposed CANN consists of three inputs including molecular weights of anionic and cationic part of ionic liquid and temperature with a hidden layer containing 8 neurons with a hyperbolic tangent activation function and trained with Levenberg–Marquardt algorithm has the best correlative capability for surface tension of ionic liquids. In addition, error analysis of test data set with an average absolute relative deviation percent of 1.07 indicates the appropriate performance of the nonlinear CANN model in the linking between network inputs and surface tensions. Also, comparing the accuracy of the proposed model with existing models, including the corresponding states principle, Parachor, the group method of data handling (GMDH) and the model based on least-squared supported vector machine (LSSVM) indicate the superiority of the proposed model.

کلیدواژه‌ها [English]

  • Surface tension
  • Ionic liquid
  • modeling
  • Artificial network
  • Molecular weight
  • Temperature
 

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