Document Type : Research Paper
Authors
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
Abstract
With the increasing demand for renewable energy sources, the optimization of existing technologies in this field has become inevitable. Among the renewable sources that have attracted a lot of attention in research, we can refer to biomass sources. 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). 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.
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