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A machine learning method for soil conditioning automated decision-making of EPBM : hybrid GBDT and Random Forest Algorithm

Lin Lin, Guo Hao, Lv Yancheng, Liu Jie, Tong Changsheng, Yang Shuqin

Eksploatacja i Niezawodność

2022

Vol. 24, no. 2

237--247

There lacks an automated decision-making method for soil conditioning of EPBM with high accuracy and efficiency that is applicable to changeable geological conditions and takes drive parameters into consideration. A hybrid method of Gradient Boosting Decision Tree (GBDT) and random forest algorithm to make decisions on soil conditioning using foam is proposed in this paper to realize automated decision-making. Relevant parameters include decision parameters (geological parameters and drive parameters) and target parameters (dosage of foam). GBDT, an efficient algorithm based on decision tree, is used to determine the weights of geological parameters, forming 3 parameters sets. Then 3 decision-making models are established using random forest, an algorithm with high accuracy based on decision tree. The optimal model is obtained by Bayesian optimization. It proves that the model has obvious advantages in accuracy compared with other methods. The model can realize real-time decision-making with high accuracy under changeable geological conditions and reduce the experiment cost.

Autothermal Reforming of Diesel Oil for PdCeCrFeCu/Al2O3-Catalyzed Hydrogen Production

Lin Lin, Zhang Kai, Sun Chiyu, Zhang Ying

Polish Journal of Chemical Technology

2021

Vol. 23, nr 2

12--19

To make clear the feasibility and influence factors of diesel fuel autothermal reforming to hydrogen, PdCeCr-FeCu/Al2O3 catalyst was prepared by equivalent-volume impregnation method. Experimental facility based on an adiabatic tubular reactor with preheating section was designed and set up, the behaviors of diesel reforming to hydrogen with straight-run diesel as a raw material according to the analysis of the components were studied. Diesel oil reforming over a catalyst for hydrogen production was analyzed using an adiabatic tubular reactor with a preheating section that was designed and built in-house. The operating conditions were optimized. Under the suitable operating conditions, viz., catalyst bed inlet temperature of 700°C, diesel liquid space velocity of 0.24 h–1, water-carbon ratio of 20, and oxygen-carbon ratio of 0.6, the hydrogen yield reached 28.3 (mol/mol).