Big Data Stream Processing Models for Building a Predictive System for Controlling Biological Risks in CIP Processes

With the rapid growth of technological and biological data, the development of intelligent predictive control systems has become a pressing task to enhance the reliability and safety of industrial processes. This article explores streaming data processing models in the context of building a predictive control system for biological risks in Clean-in-Place (CIP) processes. Architectural approaches are examined for integrating streaming analytics with machine learning and predictive modeling tools that enable real-time identification of potential microbiological contamination risks, assessment of their dynamics, and forecasting of critical situations. Special attention is given to the selection and adaptation of streaming technologies such as Apache Kafka, Apache Flink, and Spark Streaming, considering the specific characteristics of biotechnological processes, high data variability, and the necessity of compliance with regulatory requirements in pharmaceutical and food production. It is demonstrated that hybrid models combining statistical methods, machine learning algorithms, and digital twins of CIP processes not only improve prediction accuracy but also allow proactive risk management within automated systems. The article also addresses the integration of predictive control with existing Quality Management Systems and Manufacturing Execution Systems platforms, thereby creating a unified digital monitoring ecosystem. The results of experimental modeling are presented, confirming the effectiveness of streaming models in minimizing the probability of biological risks and optimizing operational costs.