Cloud-Based Predictive Control Strategy with Deep Learning Modeling for Efficient Zinc Rotary Kiln Process Control

This study aims to explore a strategy that combines cloud control technology and deep learning predictive control to enhance the efficiency of process control for zinc rotary kilns. Firstly, a deep learning model using state-space long short-term memory (SSLSTM) networks to capture the dynamic characteristics of the zinc rotary kiln system. This approach allows us to model complex nonlinearities and time dependencies that are difficult to capture using traditional modeling techniques. Subsequently, a predictive control algorithm is designed that utilizes the SSLSTM model to predict the future state of the system and generate optimal control inputs. Finally, by integrating cloud computing technology, the designed predictive control algorithm is deployed in the cloud for efficient computation of control inputs, which then fed back to the zinc rotary kiln system to optimize production efficiency and minimize energy consumption. The proposed cloud-based predictive control strategy was evaluated on a zinc rotary kiln system. The experiment results showed that our approach achieved more precise control than traditional control methods, resulting in a significant reduction in energy consumption while maintaining the desired production efficiency. Moreover, the cloud-based design enabled high scalability, flexibility, and adaptability, making the approach suitable for large-scale and complex industrial systems.