Reconstructing causal networks from data for the analysis, prediction, and optimization of complex industrial processes

Lacking the understanding of the first principles leads to the apparent black box attributes of complex industrial processes. How to understand complex industrial processes from data and guiding industrial decision-making has become an urgent problem to solve. However, the existing data-driven models are also black boxes, focusing only on the correlation relationships between data without reflecting causal relationships. Therefore, this study addresses the challenge of double black boxes in complex industrial decision-making, proposing a research framework of "causal analysis → performance prediction → process optimization". Firstly, nonparametric copula entropy, network deconvolution , and information geometric causal inference are integrated to construct the causal relations network. Also, the observability and controllability of complex industrial processes are analyzed to provide valuable insights for improving the dataset. Then, drawing inspiration from the transformational machine learning idea, an explainable predictive model is constructed for predicting key performance indicators . Lastly, taking this predictive model as the process surrogate model , the optimal process parameters are solved using the particle swarm optimization algorithm. Moreover, the dataset of 16600 samples from a real-world injection molding process is used for application validation. The research results show that by reconstructing the causal relations network from data, the proposed framework can support the analysis, prediction, and optimization of complex industrial processes, achieving the decision-making goals of safety, robustness, improving quality and efficiency.