فصلنامه سیستم های انرژی پایدار

فصلنامه سیستم های انرژی پایدار

بهینه‌سازی تصفیۀ بیولوژیکی فاضلاب و تولید زیست‌توده توسط ریزجلبک Desmodesmus sp.: مقایسۀ روش سطح پاسخ (RSM) و شبکۀ عصبی مصنوعی (ANN)

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشکدۀ مهندسی انرژی و منابع پایدار، دانشکدگان علوم‌ و فناوری‌های میان‌رشته‌ای، دانشگاه تهران، تهران، ایران
2 دانشیار دانشکدۀ مهندسی انرژی و منابع پایدار، دانشکدگان علوم‌ و فناوری‌های میان‌رشته‌ای، دانشگاه تهران، تهران، ایران
10.22059/ses.2026.416097.1258
چکیده
این پژوهش به بررسی و بهینه‌سازی تصفیۀ بیولوژیکی فاضلاب و تولید زیست‌توده توسط ریزجلبک Desmodesmus sp. می‌پردازد و عملکرد دو روش مدل‌سازی روش سطح پاسخ (RSM) و شبکۀ عصبی مصنوعی (ANN) را مقایسه می‌کند. در این مطالعه، نمونه‌ها از تالاب انزلی جمع‌آوری شده و پس از پیش‌تیمار و غنی‌سازی، در شرایط کنترل‌شده کشت شدند. متغیرهای عملیاتی شامل شدت نور، نرخ هوادهی، pH و زمان ماند بودند که برای یافتن شرایط بهینه مورد بررسی قرار گرفتند. نتایج نشان داد مدل‌ ANN نسبت به RSM دقت پیش‌بینی بالاتری دارد و توانست روابط غیرخطی بین متغیرهای فرایند و پاسخ‌ها را بهتر شبیه‌سازی کند. شرایط بهینۀ به‌دست‌آمده شامل شدت نور 5/352 µmolphotons.m⁻².s⁻¹، نرخ هوادهی 29/1 mg.L-1، pH برابر 79/7 و زمان ماند 95/7 روز بود. در این شرایط، بیشترین راندمان‌ها شامل 2/90 درصد حذف COD، 61/80 درصد حذف TN، 14/91 درصد حذف TP و تولید 27/1 mg.L⁻¹ زیست‌توده پیش‌بینی شد. نتایج پژوهش نشان می‌دهد استفاده از ریزجلبک Desmodesmus sp. می‌تواند رویکردی مؤثر برای تصفیۀ فاضلاب همراه با تولید زیست‌توده ارزشمند باشد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Optimization of Biological Wastewater Treatment and Biomass Production by Microalgae Desmodesmus sp.: A Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

نویسندگان English

Sina Labbafi 1
Fatemeh Razi Astaraei 2
1 School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran
2 School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran
چکیده English

This study investigated the optimization of biological wastewater treatment and biomass production by the microalga Desmodesmus sp. while comparing two modeling approaches: Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Samples were collected from Anzali Wetland, pretreated, enriched, and cultivated under controlled laboratory conditions. The operational variables included light intensity, aeration rate, pH, and retention time, which were optimized to achieve the best treatment performance. The results indicated that ANN provided more accurate predictions than RSM and was better able to capture the nonlinear relationships between process variables and responses. The optimal conditions were determined as a light intensity of 352.5 , aeration rate of 1.29 , pH 7.79, and retention time of 7.95 days. Under these conditions, the highest predicted efficiencies were 90.2% COD removal, 80.61% TN removal, 91.14% TP removal, and biomass production of 1.27 mg.L⁻¹. Overall, the findings suggest that Desmodesmus sp. can serve as an effective system for simultaneous wastewater treatment and valuable biomass production, with ANN offering superior predictive and optimization performance compared with RSM.

کلیدواژه‌ها English

Optimization
Wastewater Treatment
Microalgae
Biomass
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