Deep Learning Methods for Tasks of Creating Digital Twins for Technological Processes

The digital twin of a technological process is a complex of mathematical models that allow the determination of qualitative and quantitative dependencies between process parameters. It can predict the values of controlled and observed variables dynamically, depending on the process state and control actions. Additionally, it can identify hidden dependencies, states, and factors affecting the technological process and implement the selection of optimal control actions based on goals, technological limitations, or financial constraints. When building such models using data-driven methods, the input data consist of multidimensional time series from the production system’s sensor readings. The aim of this work is to develop, implement, and evaluate a subset of the digital twin functionality as applied to the oil cracking process. The key components of the proposed methods include data preprocessing, which encompasses the challenge of selecting stable operational periods for the plant, and feature selection, represented by a gradient boosting approach. We also focus on the construction of differentiable predictive models, which use modern deep learning methods to predict controlled parameter values based on dynamic system states and control. Moreover, we apply differentiable neural network models as constraints, objective functions, and state equations to solve the optimal control problem using a classical optimal control approach.