Artificial neural network model of co-gasification of petroleum coke with coal or biomass in bubbling fluidized bed

Co-gasification of petroleum coke (petcoke) with coal or biomass in fluidized bed is a promising way to avoid environmentally problems caused by its discharge. To give more accurately prediction in this process, feed-forward back propagation neural network (FFBPNN) with three optimization algorithms were conducted, including Levenberge Marquardt (LM), genetic algorithm (GA) and particle swarm optimization (PSO). The input parameters in the ANN model were petcoke ratio (W), equivalence ratio (ER), steam flow rate (S), particle diameter (Dp), volatiles (V), and fixed carbon (FC). And the output data were carbon conversion (X), ratio of H-2/CO, LHV of syngas and gas yield (Q). The predicted data showed a good agreement with the experimental results. The PSO showed much better performances than those of LM and GA. With ER increased, the predicted X increased and the ratio of H-2/CO decreased. But they were almost no changed with Dp increased. The contributive ratio of W was the largest (0.37) at petcoke ratio of 20%. The contributive ratio of ER increased not the same ratio as ER increased. The contributive ratio of particle size (Dp) almost not changed with Dp increased. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

The co-gasification of petroleum coke with coal or biomass in a fluidized bed is a promising solution to avoid environmental issues. This study utilized a feed-forward back propagation neural network (FFBPNN) with three optimization algorithms to predict the process outcomes accurately.