A major strategy in process industries is to run industrial processes at the highest economic performance through involvement of hardware and software solutions. Traditionally petroleum industries are using controllers ranging from proportional controllers to advanced predictive based controllers such as Model Predictive Controllers (MPC). The most of these systems were developed 20-30 years ago and requires careful analysis of the process dynamics, development of abstract mathematical models and derivation of a control law that meets certain design criteria. The drawbacks of this well-proven technology are mainly related to complexity of dynamic models and their continuous maintenance requirements to accommodate feedstock changes, process improvements and deviations in product demands as a result of the changing global economy.
Just like internet companies that can create high value by optimizing the commercial assets in the consumer market by data analytics, petroleum industry can also generate high value by optimizing their assets from analytics of machine and processes data they have acquired from the operations and productions. Modern machine and deep learning technologies enables simply interacting with the process and incrementally improving control behavior.
The purpose of ANACON-AI package is to provide process engineers with set of modern AI tools, which enables connectivity, validation and prediction of main KPIs, which enables to take the correct decisions to maintain and improve effective industrial processes management. The software calculates and predicts physical properties and chemical compositions for different process streams, and proposes required process set points, that will accomplish the calculated predictions.
Process analyzers provides online analytic data, which is verified and validated against the laboratory results and predicted products quality. Integration of these three technologies will afford a tool that allows the simulated process’ “digital twin” to continuously be updated to allow highest possible efficiency of the process at lowest cost.
ANACON-AI package provides an entire overview of the operation of the process units based on streams quality data and safety/security/environmental considerations. Using the modern AI tools powered by accurate KPIs measurement, forms a basic tool from managers to operators to take the correct decisions to maintain and improve effective industrial processes management.
Core functions of ANACON-AI are as follows:
- Connectivity to industrial data bases using ODBC, OPC and Modbus TCP/IP
- Interactive HMI for data acquisition and monitoring
- Automatic verification, validation and correction of measurements results according to the international standards and proprietary Freetune software
- Statistical evaluation and reporting of validation results
- Prediction of main process KPIs, such as physical properties and chemical compositions for different process streams, using process data from industrial data bases and machine learning technologies
- Big data analysis functionality, including multidimensional fusion and distribution of incoming data, abnormality of novel events detection, clustering, decision trees, linear, polynomial, logistic regression, escalation of novelty real-time analysis, etc. – using deep learning technologies.
The starting point for choosing which KPIs are key to a particular process, should be to be focused on those, that can characterize the given inputs against target outputs. ANACON-AI is using a well-proven technology of setting network input and output parameters, input preprocessing settings and output postprocessing settings, using weight initialization techniques to match input/target data.
The obvious way for this exercise in downstream industry is to apply KPIs, which are directly related to the quality of process streams, i.e. physical properties and chemical composition of the incoming material and outgoing products in each process unit. This enables to establish a simplified process’ digital twin, which describes process objectives and includes in decision tree only those KPIs, which are more relevant for the process efficiency.
Additional KPIs to be considered are related to safety, security and environmental requirements, which shall be applied as a constraints. This method enables overall process optimization through integration of the network input and target KPIs, using linear programming techniques to maximize the overall profit.
Main KPIs, which shall be considered here are as follows:
- Physical properties and chemical compositions for different process streams – using pressure, temperature, flow, level and other measurements by field instrumentation. These predictions shall be validated against process analyzers and laboratory results, including correction by Freetune
- Early leak detection – using pressure, flow, acoustic, seismic, electromagnetic, mechanical, chemical, thermal and other pipeline measurements. The system analyses the calculated pipeline state, searches for anomalies that suggest a leak and determines their location
- Emission sources localization – real-time operational implementation of dynamic model and monitoring system for detailed post-event analysis that allows to pinpoints the source of a chemical release using meteorological data and concentration measurements from the analyzers/sensors
- Advanced corrosion analytics – using deep learning technology powered by process analyzers and high temperature hydrogen attack, partial pressure and length of exposure limits methodologies
- Improving process units yield and safety – leverage existing OT data infrastructure to enable the use of machine learning and deep learning technologies.
ANACON-AI connectivity to industrial data bases is provided using the standard ODBC, OPC and Modbus TCP/IP communication platforms. Process data is extracted from existing PI real-time data management system, which is normally secured by unidirectional information flow and can be easily reached by external devices. Additional connectivity to LIMS, IMS and other data bases enables to get data which isn’t readily available in PI.
Using the information from the process, ANACON-AI performs data acquisition, monitoring, verification, validation, statistical evaluation, correction and reporting of measurements results. Prediction of main process KPIs, such as physical properties and chemical compositions for different process streams is provided by multivariable data analysis algorithms. Deep learning and big data analytics functionality is provided by neural network learning tools and can be as option installed offline, in order to eliminate a need in continuous cloud computing.
By combining process knowledge, remote analysis technologies and big data analytics power, ANACON-AI solution driving unprecedented levels of efficiency, productivity, and performance.