Resolve Process Risks Using Autonomous Early Detection

Setting up DRA is quick and efficient. The installation process can be completed in just one day, and the system becomes fully functional within two weeks. This includes integrating DRA with existing data sources, completing initial data configuration, and performing historical back-calculations using its machine learning technology. This streamlined setup process allows customers to rapidly start benefiting from DRA's early risk detection capabilities.

Yes, our customers have made several presentations on the use of DRA at various conferences around the world. Here are some of the presentations: a) “Enhancing Proactive Organizational Culture and Improving Process Performance Using AI/Machine Learning: Celanese Experience,” Reuters Downstream USA Conference, 2024. b) “Dynamic Risk Analyzer and Fault Tree Analyzer as a Predictive Monitoring Tool,” IChemE Webinar, 2023. c) “Improved Process Performance and Safety via Autonomous AI: Dyno Waggaman Experience,” AIChE Ammonia Safety Conference, 2022. d) “A Study of Process Anomalies Leading to Plant Trips and the Effectiveness of a Software Tool in Trip Prevention,“ International Conference on Production, Energy and Reliability, 2021. e) “Process Anomaly Detection using Machine Learning Technology,” Asian Downstream Summit, 2019. f) “Sustaining Superior Performance through Influential Digital Transformation,” Asian Downstream Summit, 2019. g) “Autonomous Risk Detection for Improved Plant Performance: Lessons Learned and Case Studies,” Refining and Petrochemicals World, 2019. h) “Utilising Big Data Analytics for Optimal Performance and Cost Savings: PETRONAS Experience in the Implementation of DRA,” Asian Downstream Summit, 2018. i) “Early Detection of Process Risks: Improving OEE, Safety and Reliability,” Asian Downstream Summit 2017. j) “Early Detection of Process Risks for Improved Safety, Reliability and Operational Excellence,” AFPM Reliability and Maintenance Meeting, 2017. k) “Predicting Process Risks for Improved Safety and Operational Excellence: Breakthrough Technology and Case Studies,” AIChE Ammonia Safety Conference, 2016.

Our customers praise DRA for its user-friendly interface, comprehensive analytical capabilities, easy implementation, and cost-effectiveness. Their testimonials highlight DRA’s impact in preventing unplanned slowdowns and shutdowns, thereby saving significant costs, and maintaining high plant reliability. Users appreciate DRA's ability to produce essential insights without requiring ongoing maintenance, reflecting its effectiveness and utility in real-world settings.

Learning to use DRA is designed to be quick and straightforward, thanks to its user-friendly interface. Most new users can become proficient with just two hours of training. This efficient training schedule ensures that teams can start using the system effectively almost immediately after installation, enabling them to quickly take advantage of DRA's early risk detection capabilities.

DRA is a one-of-its-kind system with autonomous, self-optimizing machine learning algorithms that eliminate the need for model building and ongoing maintenance. It uniquely identifies risks at their inception, processes vast amounts of data rapidly, and delivers actionable insights. DRA is known for its rapid installation, seamless integration, and minimal training needs. Additionally, it operates securely within company firewalls, significantly reducing cybersecurity risks. Its scalability across various units and facilities without integration challenges further sets it apart, making it a highly effective solution for proactive risk detection in process industries.

DRA is designed for ease of integration. It can be installed in a day and becomes operational within two weeks without requiring extensive IT support or changes to existing infrastructure. Its compatibility to operate independently within company firewalls enhances its security and ease of deployment.

DRA differs significantly from self-service analytics primarily in its specialization and autonomy. DRA focuses on early risk detection using proprietary, self-optimizing unsupervised machine learning algorithms. It autonomously identifies anomalies and potential failures without requiring user intervention for model tuning or maintenance. In contrast, self-service analytics tools cater to a broad range of users and applications, requiring more input, customization, and manipulation from users to generate insights. DRA's deep operational analysis enables real-time, proactive risk management, which is crucial for preventing disruptions in plant operations.

By providing early detection of risks and analyzing thousands of tags rapidly, DRA allows engineers to focus on solving rather than finding problems, significantly boosting operational efficiency and reducing downtime.