MATLAB and Simulink enable rapid testing and assessment of technical and operational processes at subsurface, surface, field, plant, or asset level to optimize production performance, minimize operating costs and downtime, and maximize return on investment.
With MATLAB and Simulink, you can:
- Customize and scale up the design, modeling, and simulation of dynamic, multi-physics systems at component, equipment, production line, or asset level
- Speed-up big data analysis with computer vision (image and signal processing), data science (AI, machine learning, and deep learning), and high-power computing (HPC) capabilities
- Interconnect with external software applications, create your own applications, or let MATLAB code generator and compiler products do the work for you
Upstream and Downstream Energy Processes
Process optimization is fully customizable with MATLAB and Simulink. These platforms can help you digitize, integrate, and automate your upstream and downstream processes using:
- Digital twins for new energy (such as Hydrogen) processes
- Digital twins and interactive workflows of multi-physics processes using Simulink, Simscape, and Stateflow
- Optimized predictive analytics and code generation using predictive maintenance, Optimization Toolbox, and MATLAB Coder
- Big data, real-time analytics, and deployment using deep learning, parallel computing, App Designer, and Industrial Communication Toolbox
Resources
Digital Twins in MATLAB and Simulink
Process Optimization Applications
Model Predictive Control
- Economic MPC of Ethylene Oxide production - Example
- Nonlinear MPC of an Ethylene Oxidation plant - Example
- Design a Model Predictive Controller for a CSTR in Simulink - Example
- Design a Model Predictive Controller by linearizing a nonlinear plant in Simulink - Example
Perform adaptive MPC of a nonlinear chemical reactor:
- Using Online Model Estimation - Example
- Using Successive Linearization - Example
- Using Linear Parameter Varying System - Example
System Identification
- Fault Detection Using Data Based Models - Example
- Fault Detection Using an Extended Kalman Filter - Example
- Condition Monitoring and Prognostics Using Vibration Signals - Example
- Estimate Linear and Nonlinear Models | System Identification and Control System Design (4:12)
- Estimating Transfer Function Models for a Heat Exchanger - Example
- Nonlinear State Estimation Using an Unscented Kalman Filter - Example
- Fault Detection and Diagnosis in Chemical and Petrochemical Processes - Video Series
Process Control
- Process Control with Reinforcement Learning (15:34) - Video
- Gain-Scheduled Control of a Chemical Reactor - Example
- Optimize Multi-Loop Controller Parameters of a Distillation Column Producing Methanol - Example
- Independently Control Feedback Loops in a Distillation Column - Example
- Temperature Control in a Heat Exchanger - Example
- Distillation Controller Tuning - Example
Other Resources
- Optimizing Scheduling and Blending Operations in the Process Industries (32:00) - Video
- Optimize the Dimensions of a CSTR to Minimize Cost and Variation in Concentration with Changing Feedstock - Example
- Chemical Kinetics with MATLAB - File Exchange
- Regression Strategies for Large Datasets - Article
- MATLAB for Chemical Reaction Engineering - Community
- Cybersecurity of Chemical Plants - Community
- Linearization of a Pulp Paper Process - Example
- Oil and Gas Production Data Analysis with MATLAB - Whitepaper
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