Process engineers use MATLAB® and Simulink® to analyze real-time sensor data, implement control strategies, and create predictive maintenance systems based on big data and machine learning. Integrate MATLAB within process simulators like Aspen Plus to add advanced analytics and custom unit operations.
MATLAB and Simulink help process engineers:
- Develop predictive maintenance systems by applying numerical techniques on high-speed sensor data
- Use machine learning with historical data to troubleshoot process problems
- Use data modeling to improve process performance
- Develop and implement advanced predictive control (APC) strategies
- Adopt digitization without depending on data scientists or IT personnel
As a manufacturing company we don’t have data scientists with machine learning expertise, but MathWorks provided the tools and technical knowhow that enabled us to develop a production preventative maintenance system in a matter of months.Dr. Michael Kohlert, Mondi Gronau
Watch an Example
Predictive Maintenance and Signal Processing to Optimize Assets
MATLAB can help you develop predictive maintenance algorithms customized to the specific operational and architectural profile of your equipment. Analyze data gathered from the equipment and determine which parameters have the strongest influence on wear and tear of the equipment.
Accurately determine the extent of corrosion and pitting in your pipelines through ultrasonic techniques using Audio System Toolbox™. You can also remotely detect the location and quantity of pipeline leaks through acoustic emissions with Signal Processing Toolbox™.
Read how Baker Hughes used MATLAB to implement a predictive maintenance platform for gas and oil extraction equipment and reduced overall costs by 30-40%.
Machine Learning and Big Data
Import and integrate structured data (e.g., from distributed control systems and data historians like OPC servers) and unstructured data (e.g., from operator log books and imaging sensors) using prebuilt tools. Interactive apps in Statistics and Machine Learning Toolbox™ let you apply machine learning techniques on historical process data without having to be an expert in data science. This enables you to perform fault detection and diagnosis and better monitor your processes.
Read how Ruukki engineers reduced their analysis times from several days to less than a minute by integrating various databases.
Process Improvement with Data Modeling
Use multivariate analysis tools in MATLAB to determine the independent driving variables affecting process performance. System Identification Toolbox™ lets you create and use models of dynamic systems that are not easily modeled from first principles or specifications. The toolbox also lets you interactively perform online parameter and state estimation.
Watch how Shell used MATLAB (3:35) to develop models and perform real-time optimization on a batch process.
Develop and Implement APC Strategies
Control engineers can embed process models from Aspen Plus and gPROMS into Simulink. This way you can redeploy existing models to design a control strategy in your preferred environment.
You can also use Simulink Control Design™ to design, implement, and monitor APC systems at your unit. Validate your design with hardware-in-the-loop testing and rapid prototyping.
Read how Genentech used MATLAB to prototype control algorithms on a pilot process before deploying to the plant.
MathWorks can help you adopt and implement big data strategies specific to the needs of your organization. MATLAB and Simulink enable your engineering team to complement their domain expertise with data science methods.
Watch how Shell embraced digitization (29:14) using MATLAB Production Server™. Shell engineers automated their processes for integrating data from different sources, building models, and deploying their analytics onto cloud and enterprise systems.