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استفاده از یادگیری ماشین برای پیشبینی تولید و کیفیت روغن زیستی از زیستتوده به روش پیرولیز | ||
مهندسی بیوسیستم ایران | ||
دوره 54، شماره 1، فروردین 1402، صفحه 87-113 اصل مقاله (1.87 M) | ||
نوع مقاله: مقاله مروری | ||
شناسه دیجیتال (DOI): 10.22059/ijbse.2023.359872.665510 | ||
نویسندگان | ||
علیرضا شفیع زاده1؛ مرتضی آغباشلو* 2؛ میثم طباطبائی3؛ حسین مبلی1؛ محمدحسین نادیان4 | ||
1گروه مهندسی مکانیک ماشینهای کشاورزی، دانشکده فنی و مهندسی کشاورزی، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران | ||
2گروه مهندسی مکانیک ماشینهای کشاورزی، دانشکده فنی و مهندسی کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
3پژوهشکده بیوتکنولوژی کشاورزی ایران | ||
4استادیار، پژوهشکده علومشناختی، پژوهشگاه دانشهای بنیادی، تهران، ایران | ||
چکیده | ||
کاهش ذخیره منابع انرژیهای فسیلی یک زنگ خطر برای بشر است. از طرف دیگر، مصرف روبهرشد سوختهای فسیلی مشکلهای زیستمحیطی بسیاری مانند گرمایش زمین را با خود به همراه داشته است. این موارد جایگزینی انرژیهای تجدیدپذیر را اجتنابناپذیر ساخته است. در میان انواع انرژیهای تجدیدپذیر زیستتوده یکی از منابع قابلاطمینان و پایدار است. تبدیلهای حرارتی - شیمیایی زیستتوده بهعنوان یک روش امیدوارکننده جهت تبدیل زیستتوده خام به سوخت در حالتهای مایع (روغن زیستی)، جامد (کربن زیستی) و گاز (گاز زیستی) در نظر گرفته شده است. پیرولیز بهعنوان یکی از مهمترین تبدیلهای حرارتی - شیمیایی برای تولید مؤثر روغن زیستی موردتوجه گسترده قرار گرفته است. بااینحال، باتوجهبه پیچیدگی و نیاز به تجهیزات پیشرفته این فرایندها، اندازهگیری مقدار محصولهای تولید شده و کیفیت آنها به دلیل زمان و هزینهبربودن بسیار چالشبرانگیز است؛ بنابراین مدلسازی بهعنوان یک شیوه مؤثر برای به حداکثر رساندن عملکرد و بهرهوری پیرولیز موردتوجه گسترده قرار گرفته است. در میان روشهای مختلف مدلسازی، یادگیری ماشین در سالهای اخیر بخصوص برای بهینهسازی فرایند پیرولیز پیشبینی بازده، پایش بلادرنگ و کنترل فرایند توجه زیادی را به خود جلب کرده است. برایناساس، علاوه بر روشهای پایه همچون شبکههای عصبی مصنوعی (یادگیری همبستگیهای غیرخطی بین مقادیر ورودی و خروجی)، مدلهای هم آمیخته یادگیری ماشین که از مدلهای رایج برای مدلسازی و بهینهسازی مسائل پیچیده بسیار بهتر عمل میکنند موردتوجه خاص قرار گرفتهاند. این مطالعه به طور جامع به تحقیقهای صورتگرفته در مورد کاربردهای یادگیری ماشین در مدلسازی فرایند پیرولیز و چشمانداز پیشروی این فناوری میپردازد. این مدلهای ماشین یادگیری برای پیشبینی تولید روغن زیستی ضریب تعیین بین 26/0 در ضعیفترین حالت و 99/0 را در بهترین حالت ارائه دادهاند. این مدلها مقادیر بین 6/0 و 93/0 را برای پیشبینی ارتقای کیفیت روغن زیستی ارائه نمودهاند. | ||
کلیدواژهها | ||
پیرولیز؛ تبدیل حرارتی &ndash؛ شیمیایی؛ زیستتوده؛ مدلسازی؛ یادگیری ماشین | ||
عنوان مقاله [English] | ||
Using machine learning to predict the production and quality of bio-oil from pyrolysis biomass | ||
نویسندگان [English] | ||
Alireza Shafizadeh1؛ Mortaza Aghbashlo2؛ Meisam Tabatabaei3؛ Hossein Mobli1؛ Mohammad Hossein Nadian4 | ||
1Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran | ||
2Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran | ||
3Agricultural Biotechnology Research Institute of Iran | ||
4Brain Engineering Research Center, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5531, Tehran, Iran | ||
چکیده [English] | ||
Reducing the reserves of fossil energy sources serves as a warning sign for humanity. On the other hand, the increasing consumption of fossil fuels has led to significant environmental problems, such as global warming. These issues make the replacement of renewable energy sources with fossil fuels inevitable. Among various renewable energy sources, biomass is a reliable and sustainable resource. Thermochemical conversions of biomass are a promising method for converting raw biomass into liquid (bio-oil), solid (bio-char), and gas (biogas) fuels suitable for modern life. As one of the most important thermochemical conversions for efficient bio-oil production, pyrolysis has received significant attention. However, pyrolysis requires advanced equipment, precise product quantity, and quality measurement, which can be challenging and costly. Therefore, modeling has been extensively researched to enhance the performance and efficiency of pyrolysis. In recent years, machine learning has gained considerable attention in pyrolysis modeling, particularly for yield optimization, real-time monitoring, and process control. In addition to conventional techniques like artificial neural networks that capture nonlinear correlations between input and output values, combined machine learning models have been of particular interest for modeling and optimizing complex problems more effectively. This study provides a comprehensive overview of the research conducted on the application of machine learning in pyrolysis process modeling and assesses the prospects of this technology. These machine learning models have provided R2 between 0.26 in the weakest case and 0.99 in the best case for predicting bio-oil production. These values have been presented between 0.6 and 0.93 to predict the improvement of bio-oil quality modeling. | ||
کلیدواژهها [English] | ||
Biomass, Thermochemical conversion, Pyrolysis, Modeling, Machine learning | ||
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