Prediction in Catalytic Cracking Process Based on Swarm Intelligence Algorithm Optimization of LSTM

Deep learning can realize the approximation of complex functions by learning deep nonlinear network structures, characterizing the distributed representation of input data, and demonstrating the powerful ability to learn the essential features of data sets from a small number of sample sets. A long short-term memory network (LSTM) is a deep learning neural network often used in research, which can effectively extract the dependency relationship between time series data. The LSTM model has many problems such as excessive reliance on empirical settings for network parameters, as well as low model accuracy and weak generalization ability caused by human parameter settings. Optimizing LSTM through swarm intelligence algorithms (SIA-LSTM) can effectively solve these problems. Group behavior has complex behavioral patterns, which makes swarm intelligence algorithms exhibit strong information exchange capabilities. The particle swarm optimization algorithm (PSO) and cuckoo search (CS) algorithm are two excellent algorithms in swarm intelligent optimization. The PSO algorithm has the advantage of being a simple algorithm with fast convergence speed, fewer requirements on optimization function, and easy implementation. The CS algorithm also has these advantages, using the simulation of the parasitic reproduction behavior of cuckoo birds during their breeding period. The SIM-LSTM model is constructed in this paper, and some hyperparameters of LSTM are optimized by using the PSO algorithm and CS algorithm with a wide search range and fast convergence speed. The optimal parameter set of an LSTM is found. The SIM-LSTM model achieves high prediction accuracy. In the prediction of the main control variables in the catalytic cracking process, the predictive performance of the SIM-LSTM model is greatly improved.