Intelligent Methods for Predicting Technological Risks Based on Simulation Modeling

This article explores modern approaches to forecasting technological risks using simulation modeling and machine learning algorithms. It analyzes typical failure scenarios, methods for constructing digital twins, and the architecture of interaction between simulation environments and intelligent modules. The effectiveness of hybrid systems is emphasized in the context of unstable technological regimes, including aggressive environments and equipment failure prevention. The study presents simulation results of a manufacturing process using the SimPy environment and embedded ML classifiers, which demonstrated a reduction in emergency shutdowns and production losses. The findings confirm the practical applicability of intelligent systems for adaptive control and enhanced resilience of industrial processes under uncertainty.