DogwoodAI applies the most suitable machine learning technologies tailored to each client’s needs and plant conditions, developing AI solutions through a range of foundational approaches described below, that support the realization of autonomous manufacturing.
Tailor-made AI Technology
About
Our tailor-made AI technology is specifically developed to address the unique needs of clients under the domain understandings of client data in the industrial sector. It is optimized to utilize real-time data from a wide range of sensors (RTDB) installed in chemical plants.
Features
- Processes and analyzes large volumes of field-generated data
- Predicts multiple product qualities simultaneously
- Customizable for different industrial applications
Benefits
- Enables precise predictions tailored to the specific production environments of manufacturing, refining, and chemical plants
- Improves operational efficiency by utilizing actual plant data
- Enhances product quality and consistency
Case
Korean oil & fine chemical companies successfully completed a 3-6 months field test, validating the solution’s reliability in a real operational environment.
UI Platform for Tailor-made ML for HCR
Real data-based AI Technology
About
This technology focuses on analyzing real-time operational data collected directly from manufacturing plants to provide immediate, actionable insights.
Features
- Monitors and analyzes real-time sensor data
- Predicts key process variables such as product yield and quality
- Outperforms traditional platform-based AI in industrial environments
Benefits
- Provides higher prediction accuracy using actual data
- Supports real-time process control and optimization
- Enhances safety and productivity in manufacturing settings
Model-driven AI Technology
About
Model-based AI leverages first-principle mathematical equations to simulate industrial processes and generate high-quality data for training AI models.
Features
- Creates dynamic models based on governing equations
- Generates synthetic data and conducts sensitivity analyses
- Validates models using real-world measurements
Benefits
- Enables AI development in data-scarce environments
- Reduces dependency on large-scale historical datasets
- Offers scalable modeling for complex integrated processes
Case
Successfully developed and validated a dynamic AI model for an integrated hydrogen production process, enabling efficient hydrogen recovery and CO₂ capture from steam methane reforming tail gas.
Physics-driven AI Technology
About
Physics-based AI technology combines scientific theory with AI, especially in cases where field data is insufficient for traditional data- based approaches.
Features
- Integrates physicochemical theory with big data AI algorithms
- Predicts beyond actual operating ranges
- Generates consistent, theoretically grounded data
Benefits
- Enables solution development in data-limited industrial environments
- Supports future performance forecasting with enhanced reliability
- Reduces risks associated with data insufficiency
Prediction of solubility in electrochemical solutions through Physics-based ML Solution
