استفاده از یادگیری ماشین برای پیش‌بینی تولید و کیفیت روغن زیستی از زیست‌توده به روش پیرولیز

نوع مقاله : مقاله مروری

نویسندگان

1 گروه مهندسی مکانیک ماشین‌های کشاورزی، دانشکده فنی و مهندسی کشاورزی، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران

2 گروه مهندسی مکانیک ماشین‌های کشاورزی، دانشکده فنی و مهندسی کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

3 پژوهشکده بیوتکنولوژی کشاورزی ایران

4 استادیار، 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

موضوعات

عنوان مقاله [English]

Using machine learning to predict the production and quality of bio-oil from pyrolysis biomass

نویسندگان [English]

  • Alireza Shafizadeh 1
  • Mortaza Aghbashlo 2
  • Meisam Tabatabaei 3
  • Hossein Mobli 1
  • Mohammad Hossein Nadian 4
1 Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
2 Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
3 Agricultural Biotechnology Research Institute of Iran
4 Brain 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

Using machine learning to predict the production and quality of bio-oil from pyrolysis biomass

EXTENDED ABSTRACT

Introduction

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.

Biomass pyrolysis

Biomass pyrolysis is a process that involves heating organic matter in the absence of oxygen to produce bio-oil. The biofuel produced can be refined into transportation fuels or used as feedstock for chemical production. There are several different types of pyrolysis. Flash pyrolysis involves rapidly heating biomass to high temperatures without oxygen, forming a high-quality bio-oil. Slow pyrolysis, on the other hand, involves heating biomass at lower temperatures for longer periods, producing more biochar and less bio-oil. The pyrolysis process is complex and influenced by numerous variables, such as feedstock properties, heating rate, temperature, and residence time. Modeling can be a promising strategy to optimize the process parameters and improve product yield and quality.

Machine learning modeling

Machine learning is a field of artificial intelligence that uses algorithms to enable computers to learn from data and make predictions or decisions without being explicitly programmed. The process involves feeding data into a machine learning model, which then uses statistical analysis to identify patterns and relationships in the data. These patterns are used to make predictions or decisions about new, unseen data.

Supervised machine learning

Supervised machine learning is a type of machine learning where the algorithm is trained on labeled data, meaning the data has already been classified or grouped into different categories. The goal is to create a model to accurately predict the correct category or label for new, unseen data. There are two main subcategories of supervised machine learning: classification and regression. Classification involves predicting categorical labels, such as whether an email is spam or not. Regression, on the other hand, involves predicting a continuous numerical value. Several popular algorithms are used in supervised machine learning, including multi-linear regression, bagging regression decision trees, and random forest logistic regression.

Application of machine learning in the prediction of bio yield and quality prediction

Various studies have considered several independent inputs for modeling the pyrolysis process with machine learning methods. Regarding biomass composition, parameters such as the carbon content, hydrogen, oxygen, nitrogen, and sulfur of biomass, moisture content, ash content, and the amount of fixed carbon are considered effective parameters on the pyrolysis process in independent inputs. Operational input features include operating temperature, heating rate, reaction time, particle size, and input carrier fluid rate. Bio-oil yield, calorific value, carbon-to-hydrogen or carbon-to-oxygen ratio, sulfur and nitrogen in bio-oil, acidity, acid content, and various amounts of organic compounds have been considered dependence output parameters.

Application of parameter importance analysis in machine learning for predicting the production process and quality of bio-oil.

Although machine learning techniques can model complex phenomena, the nature of machine learning techniques, like a black box, makes interpreting results and identifying influential factors on the model very challenging. Therefore, the results of each machine learning model should be explained using advanced tools. These methods, such as Shapley Additive Explanations, provide a way to measure the impact of each independent input parameter of the pyrolysis process on the dependent response in the output of the machine learning models.

Results and Future Trends

Developing and expanding machine learning models for more complex processes and considering other influential parameters, such as catalyst properties on the biomass catalytic pyrolysis process, is possible. For instance, catalyst type, the amount used, and its structural and chemical properties greatly affect the product performance and quality improvement of bio-oil. Therefore, providing solutions in the future that can quantitatively incorporate important catalyst features as independent parameters during the modeling process and accurately model the effects of these factors on the outputs would be highly significant. Additionally, the use of machine learning modeling in semi-industrial and industrial systems for biomass pyrolysis can be investigated to improve the performance of these units.

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مهندسی بیوسیستم ایران
دوره 54، شماره 1
(فصل بهار)
فروردین 1402
صفحه 87-113

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  • تاریخ دریافت: 04 خرداد 1402
  • تاریخ بازنگری: 02 آبان 1402
  • تاریخ پذیرش: 07 آبان 1402

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