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KNet Analytics

Enable Proactive Decision Making Through Actionable Knowledge and Insights

KNet Analytics is an out-of-the-box process data analytics software that collects and interprets operational data and information scattered across the plants, eliminating the need for gathering, analyzing, and reasoning over data and information from control systems, databases, plant applications, and operation procedures.

Using artificial intelligence and machine learning techniques, KNet Analytics can detect abnormal behavior of process and assets in real time and predict future performance. This shortens the decision-making process, preventing further performance deviation and safety issues while maximizing plant efficiency.

KNet Analytics


Machine Learning
Capture and mimic plant behavior to predict failures and recognize operating modes.
Identify the best values of an objective function to maximize productivity and optimize costs.
Exploration Tools
Explore data for cleaning, sampling, smoothing, correlation, and more.
Time Series
Transform time series data and analyze behavior for trends, seasons, and correlations.
Use embedded tools for efficient data visualization such as plots and charts.
Descriptive Modeling
Enhance process behavior understanding by identifying key variables and relationships.
Predictive Modeling
Predict system behavior using hybrid models and combining multiple techniques.
Automated Workflow
Build and automate sequential processes made of data analysis, learning, clustering, and modeling.
Graphic Environment
Use an intuitive and user-friendly environment with drag-and-drop graphics.
Pattern Recognition
Recognize patterns and detect regularities in data.


  • Automatic Identification of Plant Operating Modes & States - KNet Analytics combines clustering methods with classification and modeling to build expert rules. These expert rules can be deployed online to automatically identify plant operating modes or to detect and predict abnormal situations.
  • KPI Dynamic Targeting - KNet Analytics offers methods to set dynamic targets that consider process behavior in terms of operating modes, equipment availability, valves line-ups, special process scenarios, and others.
  • Sensors Validation - KNet Analytics includes a sensor validation module that allows flagging the faulty sensors and validating the values. In addition, KNet Analytics enables determining soft sensors for complex measurements such as analyzers or labs.
  • Optimization - Based on the system configuration, KNet Analytics provides users with appropriate configuration for an optimal system performance and reduced downtime.
  • Asset Performance Modeling - KNet Analytics allows modeling the performance of the asset as a function of key process inputs. This model might be used to identify the reference performance curve to be used to identify performance gaps.


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