Data-Based Prediction and Stochastic Analysis of Entrained Flow Coal Gasification under Uncertainty

Abstract

Entrained flow gasification is a commonly used method for conversion of coal into syngas. A stable and efficient operation of entrained flow coal gasification is always desired to reduce consumption of raw materials and utilities, and achieve higher productivity. However, uncertainty in the process hinders the stability and efficiency. In this work, a quantitative analysis of the effect of uncertainty on the conversion efficiency of the entrained flow gasification is performed. A data-driven, i.e., ensemble, model of the process was developed to predict conversion efficiency of the process. Then sensitivity analysis methods, i.e., Sobol and Fourier amplitude sensitivity test, were used to analyze the effect of each individual process variables on conversion efficiency. For analyzing the collective impact of uncertainty in process variables on conversion efficiency, a non-intrusive polynomial chaos expansion (PCE) method was used. The PCE predicts probability distribution of the conversion efficiency. Reliability of the process was determined on the basis of percentage of the probability distribution falling within control limits. Measured data is used to derive the control limits for off-line reliability analysis. For on-line reliability analysis of the process, measured data is not available so a just-in-time method, i.e., k–d tree, was used. The k–d tree searches the nearest neighbor sample from a database of historical data to determine the control limits.

An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

“…Chen et al [22] proposed a JIT-support vector regression model to achieve better silicon content prediction. Ahmad et al [23] applied the JIT strategy to develop an online model. Both moving window and JIT learning strategies need to reconstruct the model with completely reselected data, which has an influence on the computation time and the sensitivity of the model.…”

An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

“…One commonly used framework for the adaptive update is the moving window strategy, in which the model is constructed with the most recent data of the window with a certain width that moves over time [35][36][37][38][39]. For example, Shi et al [35] introduced a kernel function and the moving window algorithm to achieve real-time detection of rolling bearings faults; in Reference [36], the moving window was utilized to optimize the monitoring model based on the dynamic principal component analysis; and Sheriff et al [37] proposed a moving window generalized likelihood ratio test approach to reduce the missed fault detection rate.…”

“…Chen et al [38] proposed a JIT-support vector regression model to achieve better silicon content prediction. Ahmad et al [39] applied the JIT strategy to develop an online model. Both moving window and JIT learning strategies need to reconstruct the model with completely reselected data, and this process has an influence on the computation time and the sensitivity of the model.…”