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For green field implementation, Reliability based Modeling would help plant owners to select the most optimal design with clearly laid out CAPEX and OPEX required for the entire life of the plant. There is a clear view of plant availability along with known risks in operations which needs to be mitigated by meticulous planning. In brown field implementation, KRTI 4.0 can help re-access the design and identify areas to optimize operations to reduce costs and increase asset reliability.

KRTI 4.0 collects real time information from plant assets using pervasive and secure connectivity. The machine learning environment identifies patterns of behavior from the collected data to forecast and predict anomalies. The impact of the predicted anomaly is correlated with the RAMS framework to identify the impact and risks associated with assets and their failure. This simplifies decision making with detailed insights.

Powered by a knowledge platform, KRTI 4.0 learns from technical manuals, operator knowledge, maintenance logs, etc., and converts this data into knowledge ontologies. This allows enterprises to manage and enhance learnings and reduce Mean Time To Repair by quickly identifying root causes and use standardized approaches for resolution.

Energy consumption patterns help identify variations in asset health. This not only forecasts energy needs but also optimizes energy consumption across every plant, thereby reducing the carbon footprint.

Machine learning and deep learning-based forecasting models help operations teams to identify risks related to environmental and human safety, and adopt the right strategy to eliminate risk and failures.