A novel modeling approach to optimize oxygen–steam ratios in coal gasification process

Arabloo, Milad; Bahadori, Alireza; Ghiasi, Mohammad M.; Lee, Moonyong; Abbas, Ali; Zendehboudi, Sohrab

doi:10.1016/j.fuel.2015.02.083

Cited by 62 publications

(17 citation statements)

References 34 publications

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“…Figure 12 presents the RMSE values for the study. Arabloo et al (2015) [47] introduced a novel methodology for the optimization of the oxygensteam ratio in the gasification process of coal. A methodology utilizing support vector machine algorithm was presented for estimation of proper steam-oxygen ratio to balance the heat requirement and released heat in the coal gasification process.…”

Section: Svmmentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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Section: Svmmentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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“…After the successful ignition, oxygen enriched air was first introduced to quickly heat up the surrounding coal seam till temperatures around the ignition site above 1000°C, then maintained for some time to accumulate sufficient thermal energy necessary for the subsequent steam gasification process. With steam provided into the gasification zone, the temperature dropped because of the endothermic reactions . Once the temperature declined below 700°C , oxygen enriched air was supplied again to rebuild the needed high temperature field.…”

Section: Experimental Methodsmentioning

confidence: 99%

The environmental effect of underground coal gasification semi‐coke on confined groundwater

Chen

Xing

et al. 2016

Env Prog and Sustain Energy

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From the health and environmental point of view, the control of soluble metal elements and organic compounds in underground coal gasification (UCG) by-product is of great importance for groundwater protection. Unlike the leaching of gasification ash as well as leaching in normal pressure, the leaching of trace elements and organics from UCG semicoke into confined groundwater has received little concern. In this research, UCG model test of Inner Mongolia lignite was performed, the obtained UCG semi-coke was then soaked by applying stainless steel leaching column at 0.5, 1.0, 1.5 and 2.0 MPa with nitrogen as pressurizing agent, the concentrations of trace element and total organic carbons (TOC values) in lixiviums were finally determined by applying ICP-MS and TOC analyzer to investigate the slight leaching in confined groundwater. The results indicate that Cd, Co, Cr, Ni, Pb and V are undetected in all lixivium samples, and compared with soaking under normal pressure, the concentrations of As, Ba, Mn and Sr are lower in confined water. TOC values in lixivium samples approximately approach to the background value of groundwater, indicating that no extra organic compounds could be leached and TOC values also show the similar changing trend as As, Ba, Mn and Sr behave under normal and high pressures.

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“…Arabloo et al applies the support vector machine algorithm to estimate oxygen-steam ratios in coal gasification process. The average absolute error between their modeling outputs and real data is reported lower than 1.0% [12]. Sidaoui predicted the optimum injection rate of carbonate acidizing [13].…”

Section: Introductionmentioning

confidence: 93%

Predicting the Surveillance Data in a Low-Permeability Carbonate Reservoir with the Machine-Learning Tree Boosting Method and the Time-Segmented Feature Extraction

Zhao

Xiao

2020

Energies

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Predictive analysis of the reservoir surveillance data is crucial for the high-efficiency management of oil and gas reservoirs. Here we introduce a new approach to reservoir surveillance that uses the machine learning tree boosting method to forecast production data. In this method, the prediction target is the decline rate of oil production at a given time for one well in the low-permeability carbonate reservoir. The input data to train the model includes reservoir production data (e.g., oil rate, water cut, gas oil ratio (GOR)) and reservoir operation data (e.g., history of choke size and shut-down activity) of 91 producers in this reservoir for the last 20 years. The tree boosting algorithm aims to quantitatively uncover the complicated hidden patterns between the target prediction parameter and other monitored data of a high variety, through state-of-the-art automatic classification and multiple linear regression algorithms. We also introduce a segmentation technique that divides the multivariate time-series production and operation data into a sequence of discrete segments. This feature extraction technique can transfer key features, based on expert knowledge derived from the in-reservoir surveillance, into a data form that is suitable for the machine learning algorithm. Compared with traditional methods, the approach proposed in this article can handle surveillance data in a multivariate time-series form with different strengths of internal correlation. It also provides capabilities for data obtained in multiple wells, measured from multiple sources, as well as of multiple attributes. Our application results indicate that this approach is quite promising in capturing the complicated patterns between the target variable and several other explanatory variables, and thus in predicting the daily oil production rate.

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A novel modeling approach to optimize oxygen–steam ratios in coal gasification process

Cited by 62 publications

References 34 publications

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

The environmental effect of underground coal gasification semi‐coke on confined groundwater

Predicting the Surveillance Data in a Low-Permeability Carbonate Reservoir with the Machine-Learning Tree Boosting Method and the Time-Segmented Feature Extraction

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