Development of a natural gas Methane Number prediction model

Roy, Partho Sarothi; Ryu, Christopher; Dong, Sang Keun; Park, Chan Seung

doi:10.1016/j.fuel.2019.02.116

Cited by 17 publications

(6 citation statements)

References 6 publications

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“…The dataset used was based on the UCI (University of California Irvine) dataset [25], which consists of the responses of methane, ethylene, air, and their mixtures in arrays of 16 sensors (TGS2600, TGS2602, TGS2610, and TGS2620; four units of each type) with a continuous measurement time of 10,486 s. The gas-sensing material of this type of gas sensor is a metal oxide which is adsorbed on the surface of the metal oxide when it is heated to a certain high temperature in the air. When a reducing gas occurs, the surface concentration of the negatively charged oxygen decreases, causing the resistance of the sensor to decrease.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In addition, the structure of a neural network is generally determined by an empirical method, which leads to a certain degree of gas identification accuracy decline. In many previous works, SVR has been shown to outperform other competing methods in regression tasks for gas quantification [24,25]. However, the hyperparameters of this algorithm are determined using the grid search method [6], which traverses the subspace of the specified value parameter to select the optimal value.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array

Fan

Xia

et al. 2019

Sensors

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The gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for gas sensor array applications. The features extracted from the raw data utilizing principal component analysis (PCA) were used to complete random forest (RF) modeling, which enabled qualitative identification. Support vector regression (SVR), optimized by the particle swarm optimization (PSO) algorithm, was used to select hyperparameters C and γ to establish the optimal regression model for the purpose of quantitative analysis. Utilizing the dataset, we evaluated the effectiveness of our approach. Compared with logistic regression (LR) and support vector machine (SVM), the average recognition rate of PCA combined with RF was the highest (97%). The fitting effect of SVR optimized by PSO for gas concentration was better than that of SVR and solved the problem of hyperparameters selection.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array

Fan

Xia

et al. 2019

Sensors

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“…Sun et al [34] used SVR to model catalytic cracking product, and found a series of optimized conditions that can improve the yield. Roy et al [35] used multiple regression and SVR separately to predict methane content in natural gas.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

“…On the one hand, a linear model can be transformed into a nonlinear model by introducing a kernel function. Roy et al [35] introduced a polynomial kernel into SVR, which makes the prediction accuracy increase from 40% to 52%. And after using Gaussian kernel, the accuracy reaches 98%.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

“…And after using Gaussian kernel, the accuracy reaches 98%. The use of kernel functions based on dimension reduction, such as kernel PCA [35] and kernel Partial Least Squares (PLS) [36] , can bring significant performance improvement in chemical process modeling. On the other hand, nonlinear machine learning models can be directly used to fit the relationship between various influencing factors and production results, such as polynomial regression [36] , C4.5 decision tree [37] , random forest [38] , and Gradient Boosted Decision Tree (GBDT) [39] , which have been applied in the chemical process modeling.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

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Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Yang

Lei

et al. 2023

Big Data Min. Anal.

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Fluidic Catalytic Cracking (FCC) is a complex petrochemical process affected by many highly non-linear and interrelated factors. Product yield analysis, flue gas desulfurization prediction, and abnormal condition warning are several key research directions in FCC. This paper will sort out the relevant research results of the existing Artificial Intelligence (AI) algorithms applied to the analysis and optimization of catalytic cracking processes, with a view to providing help for the follow-up research. Compared with the traditional mathematical mechanism method, the AI method can effectively solve the difficulties in FCC process modeling, such as high-dimensional, nonlinear, strong correlation, and large delay. AI methods applied in product yield analysis build models based on massive data. By fitting the functional relationship between operating variables and products, the excessive simplification of mechanism model can be avoided, resulting in high model accuracy. AI methods applied in flue gas desulfurization can be usually divided into two stages: modeling and optimization. In the modeling stage, data-driven methods are often used to build the system model or rule base; In the optimization stage, heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base. AI methods, including data-driven and knowledge-driven algorithms, are widely used in the abnormal condition warning. Knowledge-driven methods have advantages in interpretability and generalization, but disadvantages in construction difficulty and prediction recall. While the data-driven methods are just the opposite. Thus, some studies combine these two methods to obtain better results.

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