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Application of Artificial Neural Network Modeling in Prediction of The Extraction Yield of Copper-Morin Complex from Aqueous Media Utilizing a Molecularly Imprinted Polymer Coated Stir Bar | ||
| Iranian Journal of Analytical Chemistry | ||
| مقاله 11، دوره 7، شماره 1 - شماره پیاپی 13، خرداد 2020، صفحه 78-87 اصل مقاله (1.23 M) | ||
| نوع مقاله: Full research article | ||
| شناسه دیجیتال (DOI): 10.30473/ijac.2020.51710.1167 | ||
| نویسندگان | ||
| Sayyed Hossein Hashemi* 1؛ Massoud Kaykhaii2؛ Mohamad Shakeri3 | ||
| 1Department of Marine Chemistry, Faculty of Marine Science, Chabahar Maritime University, P.O. Box 98617-85553, Chabahar, Iran | ||
| 2Department of Chemistry, Faculty of Sciences, University of Sistan and Baluchestan, Zahedan, Iran | ||
| 3Department of Chemistry, University of Zabol, Zabol, Iran | ||
| چکیده | ||
| In this research, a new modeling method based on three-layer artificial neural network (ANN) technique was applied to predict the extraction yield of copper-morin complex from aqueous samples by means of molecularly imprinted stir bar sorptive extraction. Input variables of the model were pH of the solution, absorption and desorption times, stirring rate, temperature, and amount of morin ligand; while the output was extraction yield of copper ions. It was found that a network with 12 hidden neurons is highly accurate in predicting extraction recovery of copper-morin complex. The mean squared error and correlation coefficient between the experimental data and the ANN predictions were achieved as 0.0009 and 0.9999 for training, 0.0032 and 0.976 for validation and 0.0030 and 0.96666 for testing data sets. Under the optimum conditions, the linear range found to be in the range of 5-1000 μg L-1 with the detection limit of 0.38 μg L-1. The relative standard deviation was obtained to be below 5.3%. The method was successfully applied for preconcentration and determination of Cu in a few real samples. | ||
| کلیدواژهها | ||
| Artificial Neural Network؛ Copper Extraction؛ Molecularly Imprinted Stir Bar Sorptive Extraction؛ Water Analysis | ||
| عنوان مقاله [English] | ||
| کاربرد شبکه عصبی مصنوعی در پیشگویی بازده استخراج کمپلکس مس-مورین از محیط های آبی با بکارگیری مولکول نگاری پلیمری پوشش دار شده روی میله همزن | ||
| نویسندگان [English] | ||
| سید حسین هاشمی1؛ مسعود کیخوائی2؛ محمد شاکری3 | ||
| 1گروه شیمی دریا، دانشکده علوم دریایی، دانشگاه دریانوردی و علوم دریایی، چابهار، ایران | ||
| 2گروه شیمی، دانشکدة علوم، دانشگاه سیستان و بلوچستان، زاهدان | ||
| 3گروه شیمی، دانشکدة علوم، دانشگاه زابل، ایران | ||
| چکیده [English] | ||
| در این تحقیق، یک روش جدید مدل سازی با شبکه عصبی مصنوعی سه لایهای برای پیشگویی بازده استخراج کمپلکس مس- مورین از نمونههای آبی توسط استخراج بر روی میلة همزن پوششدار شده با مولکول نگاری پلیمری بکار رفت. دادههای ورودی مدل شبکه عصبی مصنوعی، pH، زمان جذب و واجذب، سرعت همزدن، دما و مقدار لیگاند بودند و خروجی آن بازده استخراج یونهای مس بود. نتایج نشان داد که شبکة با 12 نرون مخفی صحت بالائی در پیشگویی بازده استخراج کمپلکس مس- مورین در نمونههای آبی دارد. میانگین خطای مربعات و ضریب همبستگی بین دادههای تجربی و پیشگوییهای 0009/0 و 9999/0 برای آموزش، 0032/0 و 976/0 برای ارزیابی و 0030/0 و 96666/0 برای دادههای آزمایش تعیین شد. در شرایط بهینه، گسترة خطی دینامیکی 0/5 تا 0/1000 میکروگرم بر لیتر با حدّ تشخیص 38/0 میکروگرم بر لیتر به دست آمد و انحراف استاندارد نسبی کمتر از 3/5% بود. این روش با موفقیّت برای پیش تغلیظ و تعیین مس در چند نمونة حقیقی بکار گرفته شد. | ||
| کلیدواژهها [English] | ||
| شبکه عصبی مصنوعی, استخراج مس, میلة همزن پوشش دار شده با مولکول نگاری پلیمری, تجزیة آب | ||
| مراجع | ||
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