ASSESSMENT OF THE ECONOMIC EFFICIENCY OF REAL-TIME DATA PROCESSING SYSTEMS

This project lays out a step‑by‑step method for judging how economically efficient and practical real‑time data processing systems are in industrial settings, especially for downstream recovery lines in bioprocessing. By using sensor networks, IoT setups, and automated monitoring tools, we build a model that shows how real‑time data collection can cut equipment downtime and make better use of resources during the critical product‑recovery stage. The evaluation framework looks at several important factors: – System scalability – Integration overhead – Data quality – Organizational readiness We compare the traditional batch‑based analysis with continuous real‑time monitoring in bioprocess operations. This comparison provides a clear research path for measuring cost savings, higher yields,

and overall efficiency gains. The proposed model helps decision makers weigh the upfront cost of installing the system against the long‑term savings it can generate. In short, the project serves as a technical guide for setting up reliable, real‑time, data‑driven systems that improve flexibility, data quality, and the economic performance of modern bioprocessing plants.

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