Abstract
Fluid Catalytic Cracking (FCC), a key unit for secondary processing of heavy oil, is one of the main pollutant emissions of NOx in refineries which can be harmful for the human health. Owing to its complex behaviour in reaction, product separation, and regeneration, it is difficult to accurately predict NOx emission during FCC process. In this paper, a novel deep learning architecture formed by integrating Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) for nitrogen oxide emission prediction is proposed and validated. CNN is used to extract features among multidimensional data. LSTM is employed to identify the relationships between different time steps. The data from the Distributed Control System (DCS) in one refinery was used to evaluate the performance of the proposed architecture. The results indicate the effectiveness of CNN-LSTM in handling multidimensional time series datasets with the RMSE of 23.7098, and the R2 of 0.8237. Compared with previous methods (CNN and LSTM), CNN-LSTM overcomes the limitation of high-quality feature dependence and handles large amounts of high-dimensional data with better efficiency and accuracy. The proposed CNN-LSTM scheme would be a beneficial contribution to the accurate and stable prediction of irregular trends for NOx emission from refining industry, providing more reliable information for NOx risk assessment and management.
Highlights
Fluid Catalytic Cracking (FCC) is one of the most important technologies for secondary processing of heavy oil in refining and chemical enterprises [1]
In the catalytic cracking reaction, crude oil is transformed into gasoline and diesel under catalysis during which 40%–50% of nitrogen in feedstock is transferred to coke and deposited on the catalyst [2,3,4]. en, coke-covered spent catalysts are burned in the reaction regenerator for catalyst active regeneration, heat balance, energy recovery, and stable operation
RMSE and R2 were considered as objective function to optimize the size and number of convolution kernel, the number of batch size, the number of convolution layers, and the probability of dropout. e results shown in Figure 6 indicate the process of optimizing hyperparameters for the proposed method
Summary
Fluid Catalytic Cracking (FCC) is one of the most important technologies for secondary processing of heavy oil in refining and chemical enterprises [1]. Catalytic cracking reaction and catalyst regeneration are the main chemical processes of FCC. In the catalytic cracking reaction, crude oil is transformed into gasoline and diesel under catalysis during which 40%–50% of nitrogen in feedstock is transferred to coke and deposited on the catalyst [2,3,4]. About 90% of the nitrogen in coke is converted into N2 and the rest into NOx and other reduced nitrogen compounds (NH3, HCN, etc.). With the development of refining chemical technology, especially catalytic technique, more heavy oil with high percentage of nitrogen (such as residual oil and wax oil) were utilized
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