Energy Resources

MATLAB and Simulink for Process Optimization and Automation

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

Model Predictive Control Toolbox

Model Predictive Control Toolbox

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

PID制御

What is PID Control?

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

Control Systems - MATLAB & Simulink Solutions

Control Systems

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

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