Intelligent Hybrid Modeling of Complex Leaching System Based on LSTM Neural Network

Abstract

In order to improve the leaching efficiency of gold ore and reduce the environmental treatment cost of residual sodium cyanide, continuous stirred tank reactors are often connected in a cascade manner. A gold leaching system is a multiphase chemical reaction system, and its kinetic reaction mechanism is complex and affected by random factors. Using intelligent modeling technology to establish a hybrid prediction model of the leaching system, the dynamic performance of the process can be easily analyzed. According to the reaction principle and the theory of substance conservation, a mechanism model is established to reflect the main dynamic performance of the leaching system. In order to improve the global convergence of the optimization target, a particle swarm optimization (PSO) algorithm based on simulated annealing is used to optimize the adjustment parameters in the kinetic reaction velocity model. The multilayer long short-term memory (LSTM) neural network approach is used to compensate for the prediction errors caused by the unmodeled dynamics, and a hybrid model is established. The hybrid prediction model can accurately predict the leaching rate, which provides a reliable basis for guiding production, and also provides a model basis for process optimization, controller design, and operation monitoring. Finally, the superiority and practicability of the hybrid model are verified by a practical leaching industrial system test. The prediction model of key variables in the leaching process is established for the first time using the latest time series prediction technology and intelligent optimization technology. The research results of this paper can provide a good reference and guidance for other research on complex system hybrid modeling.

Multi-layer CNN-LSTM network with self-attention mechanism for robust estimation of nonlinear uncertain systems

“…The test results for the four groups of health testers are shown in Figure 7 . In order to highlight the superiority, the existing prediction LSTM algorithm in Dong et al (2023) and the algorithm in Zhou et al (2022) are also tested for prediction healthy subject 1 in Figure 8 . Comparison and verification results show that the proposed prediction algorithm has better prediction accuracy and robustness.…”

Long Short-Term Memory Optimization Using Hybrid Sparrow Search Algorithm and Particle Swarm Optimization in Prediction of Water Level at Sluice Gates

“…Long Short-Term Memory Optimization Using Hybrid Sparrow Search Algorithm and Particle Swarm Optimization in Prediction of Water Level at Sluice Gates Previous research on hyperparameter optimization in LSTM includes using Particle Swarm Optimization (Wang et al, 2020;Tang et al, 2021;Teng et al, 2023;Gao et al, 2023;Pei & Yang, 2023;Yang, 2023;Zheng & Li, 2023;Jia et al, 2023;Dong et al, 2023;Chen & Long, 2023;Geng et al, 2023). Other research uses algorithms such as the Genetic Algorithm (Dai et al, 2021;Kim & Choi, 2021;Liu and Liu, 2021;Alhussan et al, 2023;Kumar Pandey et al, 2023;Liu et al, 2023) and the Sparrow Search Algorithm (Madiniyeti et al, 2023;PengJun and GuiLin, 2023;Zhang et al, 2023).…”

Short-Term Demand Prediction for On-Demand Food Delivery with Attention-Based Convolutional LSTM

“…The challenges identified in the introduction is not limited to ODFD. Deep-learning models have been adopted to capture spatial and temporal correlations for many systems [30][31][32]. Existing short-term predictions in the transportation field, such as crowd flow prediction [33], traffic flow prediction [34], and ride-hailing demand prediction [7], have achieved higher prediction accuracies than traditional and ML methods using such deep-learning-based approaches.…”