پیش‌بینی خروجی های تکنولوژی گازی‌سازی منابع زیست توده با بهره‌گیری از یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی ماشین های کشاورزی، دانشگدکان کشاورزی و منابع طبیعی، دانشگاه تهران، ایران

2 گروه مهندسی ماشین های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

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

چکیده

با افزایش روز‌افزون تقاضا برای منابع انرژی تجدیدپذیر، بهینه‌سازی فناوری‌های موجود در این حوزه بدل به امری اجتناب‌ناپذیر شده است. از جمله منابع تجدیدپذیر که در تحقیقات توجهات بسیاری را به خود جلب کرده است می‌توان به منابع زیست‌توده اشاره داشت. در این پژوهش تلاش شده است که یکی از فناوری‌های استحصال انرژی از منابع زیست‌توده - گازی‌سازی - مورد بررسی قرار گیرد و به‌منظور بهینه‌سازی و کنترل هر چه بیشتر این فناوری، پس از تشکیل پایگاه داده مستخرج از بررسی جامع مقالات مرتبط، خروجی‌های آن با بهره‌گیری از چندین تکنیک در حوزه هوش مصنوعی و یادگیری ماشین پیش‌بینی شده‌اند. روش‌های هوش مصنوعی آماری استفاده شده در این پژوهش پس از بررسی مقالات مشابه انتخاب شدند و عبارت‌بودند از رگرسیون خطی ، رگرسیون تقویت‌کننده گرادیان ، رگرسیون درخت تصمیم‌گیری ، رگرسیون جنگل رندوم ، رگرسیون بردار ساپورت  و رگرسیون کرنل ریج . در نهایت این پژوهش منتج به چندین مدل پیش‌‌بینی بر پایه هوش مصنوعی بادقت‌های پیش‌بینی مختلف شد که با پارامترهای آماری مربوطه، دقت پیش‌بینی مدل‌های مختلف مورد ارزیابی قرار گرفت. در میان تکنیک‌های یادگیری ماشین مذکور و با درنظرگرفتن پارامترهای مختلف ارزیابی دقت مدل‌ها که از جمله مهم‌ترین آن‌ها می‌توان به مربع خطا درداده های تست اشاره کرد، روش‌های رگرسیون خطی، رگرسیون تقویت‌کننده گرادیان و رگرسیون جنگل تصادفی، که میزان مربع خطا در هر یک از مدل‌ها به ترتیب برابر 909/0، 829/0 و 818/0 بود، عملکرد بهتری از خود نسبت به سایر فناوری‌های پیشنهاد شده نشان دادند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Predicting biomass Gasification outputs with the aid of machine-learning

نویسندگان [English]

  • Pouria Mohammad Javaheri 1
  • shahin rafiee 2
  • Mortaza Aghbashlo 3
1 Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Iran
2 Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran,
3 Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
چکیده [English]

Optimizing existing technologies in this field has become inevitable with the increasing demand for renewable energy sources. Among the renewable resources that have attracted much attention in research, biomass resources can be mentioned. In this study, an attempt has been made to examine one of the technologies for extracting energy from biomass resources - gasification - and to optimize and control this technology as much as possible, after forming a database extracted from a comprehensive review of related articles, its outputs have been predicted using several techniques in the field of artificial intelligence and machine learning. The statistical artificial intelligence methods used in this study were selected after reviewing similar articles. They included linear regression, gradient boosting regression, decision tree regression, random forest regression, support vector regression, and kernel ridge regression. Finally, this research resulted in several AI-based forecasting models with different forecast accuracies, which were evaluated with the relevant statistical parameters. Among the aforementioned machine learning techniques and considering the various parameters for evaluating the accuracy of the models, the most important of which is the squared error in the test data, the linear regression, gradient boosting regression, and random forest regression methods, whose squared error rates in each of the models were 0.909, 0.829, and 0.818, respectively, showed better performance than other proposed technologies.

کلیدواژه‌ها [English]

  • Artificial Inteligence
  • Biomass
  • Gasification
  • Machine-Learning

EXTENDED ABSTRACT

 

Introduction

With the increasing demand for renewable energy sources, the optimization of existing technologies in this field has become inevitable. Among different renewable energy sources, biomass showed great potential possessing features such as being abundant, environmental-friendly (e.g. lower environmental impact, being carbon-neutral, etc.), low-cost, and producing a wide variety of desirable products. The mentioned advantageous characteristics resulted in its importance in the current global energy supply and accountability for above 70 percent of green energy production.

Materials and Methods

In this research, an attempt has been made to examine one of the technologies for extracting energy from biomass sources - gasification - and in order to optimize and control this technology as much as possible, after gathering data and making the database, its outputs by using several techniques in the field of artificial intelligence and learning Machines are predicted. The statistical artificial intelligence methods used in this research were selected after reviewing similar articles and include linear regression (LR), gradient boosting regression (GBR), decision tree regression (DTR), random forest regression (RFR), Support vector regression (SVR) and kernel Ridge Regression (KRR).

Results and Discussion

Machine learning (ML) shows great potential for being a fast, relatively inexpensive, and accurate approach to predicting gasification outputs. Although some efforts have been made to predict the effect of control parameters on the gasification output, this paper offers a comprehensive approach to evaluate the relationship between different biomass precursor characteristics and the energy output of the gasification process with the aid of different data-driven ML techniques. Finally, this research resulted in several prediction models based on artificial intelligence with different prediction accuracies. Among the mentioned machine learning techniques and taking into account various parameters for assessing the accuracy of models, among the most important of which we can mention the square error in the test data, linear regression (LR), gradient boosting regression (GBR) and Random Forest Regression (RFR) with r-squared of 0.909, 0/829 and 0/818  performed better than the rest of the proposed technologies.

Conclusion

This research resulted in several prediction models based on artificial intelligence with different prediction accuracies. Among the mentioned machine learning techniques and taking into account various parameters for assessing the accuracy of models, among the most important of which we can mention the square error in the test data, linear regression (LR), gradient boosting regression (GBR) and Random Forest Regression (RFR) with r-squared of 0.909, 0.829 and 0.818  performed better than the rest of the proposed technologies.

Author Contributions

All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.

Data Availability  Statement

Data available on request from the authors.

Ethical considerations

The study was approved by the Ethics Committee of the University of ABCD (Ethical code: IR.UT.RES.2024.500). The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Conflict of interest

The author declares no conflict of interest.

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مهندسی بیوسیستم ایران
دوره 55، شماره 3
مهر 1403
صفحه 73-85

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سابقه مقاله

  • تاریخ دریافت: 03 تیر 1403
  • تاریخ بازنگری: 11 دی 1403
  • تاریخ پذیرش: 14 بهمن 1403

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