Abstract Technologies based on Artificial Intelligence (AI) and Machine Learning (ML)
concepts are advancing at a rapid pace. The new paradigms are challenging the status-quo …

The multi-parameter intricate process control of bioreactor systems poses an urgent
challenge to cell culture. It is feasible to simulate and analyze the implications of each …

The success of reinforcement learning (RL) combined with deep neural networks has led to
the development of numerous RL algorithms that have demonstrated remarkable …

This work proposes a control-informed reinforcement learning (CIRL) framework that
integrates proportional-integral-derivative (PID) control components into the architecture of …

While conducting large-depth vertical drilling, correcting well trajectory deviations is a critical
and challenging task. Designing a feasible deviation-correction trajectory becomes an …

Real-time predictions in fermentation processes are crucial because they enable continuous
monitoring and control of bioprocessing. However, the availability of online measurements is …

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model
named adaptive stacking approximate kernel based broad learning system is proposed in …

The adoption of reinforcement learning (RL) in chemical process industries is currently
hindered by the use of black-box models that cannot be easily visualized or interpreted as …

Predicting quality-relevant process variables is of paramount importance in optimizing and
controlling chemical processes. Probabilistic Slow Feature Analysis (PSFA), a potent data …

Data-driven modeling is a crucial technology for the real-time monitoring of large-scale
industrial systems. However, it often suffers from the redundancy of input variables, resulting …