Topic: Artificial Intelligence Aided Modeling, Control, and Optimization of Industrial Processes

For advanced performance of industrial processes, data-driven modeling, supervision, control, and online optimization techniques have received growing attention over the past five decades. Indeed, these topics have formed a foundation for further applications within industrial processes, such as performance evaluation and intelligent maintenance. However, with the significantly growing level of automation and intelligence, industrial processes have become more complicated and large-scale, making system supervision and control increasingly challenging and face considerable barriers. By discovering quantitative or qualitative information hidden in measurement data of automation systems, artificial intelligence (AI) methods can execute these tasks in a manner akin to human capabilities and, therefore, become future-oriented. In industrial intelligence, AI now plays a vital role in, for instance, performing the lifecycle management of digital twins. It naturally leads to the wave of popularity in improving safety and advanced control performance for large-scale industrial processes using AI methods.

While significant progress has been made in the field of AI-aided methods for modeling, control, and optimization, it is important to note that these developments are still in their embryonic forms. One of the main reasons is that AI-aided methods should be elaborated based on the physical working principle of industrial processes rather than a “simple” classification or fitting problem. Besides, the effectiveness of AI-aided modeling, control, and optimization approaches should be ensured and evaluated through explainable theory analysis in all design phases. These facts suggest that AI-aided developments deserve more attention in the system modeling, supervision, and control issues of industrial processes, firmly based on the improved interpretability.

The goal of the Special Issue is to collect new ideas and contributions at the frontier for modeling, control, and optimization of industrial processes. We truly believe that these AI-aided attempts will provide engineers and researchers with some instructive and valuable guidance.

Topics of interest to this Special Issue include, but are not limited to:

● AI-aided modeling and control;

● Data-driven modeling, supervision, and control;

● Performance evaluation using machine learning techniques;

● Prognostics and health management using AI methods;

● Adaptive learning for supervision and real-time control;

● AI-aided supervision and control for distributed and interconnected systems;

● Intelligent lifecycle management of digital twins.

30 Nov 2024