An Ensemble-Learning Approach To Predict the Coke Yield of Commercial FCC Unit

This work proposes an ensemble learning-based catalytic cracking coke yield prediction model called the harmonic-ensembled extreme learning machine (HEELM). The model integrates extreme learning machine (ELM) base learners with different activation functions to improve the overall prediction effect. An overfitting index is proposed, and the optimal number of hidden layer nodes of ELM-base learners is determined with it. By examining the influence of different activation functions on the prediction results, the best activation function structure of the ELM-base learner has been determined. Besides, a harmonic layer is established to determine the weight of each base learner in real-time. The proposed model is validated using 1.5 years of historical data from China’s commercial fluidized catalytic cracking (FCC) plant. The results show that the proposed model has outperformed most other ELM-base learners. The relative prediction error is further reduced by 10.97% after introducing the harmonic layer. The proposed model exhibits stable performance with good generalization in three segments of industrial data, and it has guiding significance for stable operation and CO2 emission reduction of the FCC plant.