What are the best ways to test and validate your process control and optimization models?

1 Offline testing

Offline testing enables you to evaluate your process models before implementing them in the real plant, helping you identify potential errors and inconsistencies in your models, as well as optimize the parameters and settings. You can use various tools and software, such as MATLAB, Aspen Plus, or Excel, to perform offline testing. Some of the common methods include sensitivity analysis, which involves changing one or more input variables and observing the effect on the output variables in order to assess the robustness and stability of your models; using a validation data set to avoid overfitting or underfitting your models; and cross-validation to reduce variance and bias of your models and estimate their generalization ability.

2 Online testing

Online testing involves using real-time or near-real-time data to evaluate your process models while they are running in the plant. This can help you monitor and update your models, as well as detect and correct any deviations or anomalies. By employing various sensors, controllers, and actuators, you can also communicate with the plant operators and managers. Common online testing methods include feedback control, feedforward control, and model predictive control. Feedback control involves using a feedback loop to compare the actual output of the process with the desired output, and adjusting the input accordingly. Feedforward control involves using a feedforward loop to predict the output of the process based on the input, and adjusting the input accordingly. Model predictive control involves using a model to forecast the future output of the process based on the current and past input, and optimizing the input accordingly. All these methods can help you maintain optimal conditions in your process, prevent deviations or errors from occurring, maximize or minimize output, as well as handle any constraints or trade-offs.

4 Testing challenges

Testing and validating process models can be difficult due to the many factors and uncertainties that can affect results and decisions. Data quality must be considered, as it's essential to have reliable, consistent, and complete data that is accurate, relevant, and representative of the process. Additionally, model complexity should be taken into account; one must find a balance between simplicity and complexity, as well as between empirical and mechanistic models depending on goals and resources. Finally, model adaptation is important to ensure the model is flexible, dynamic, and adaptable. It should be updated regularly to account for any changes or variations in the process such as aging, wear, or degradation.

5 Testing best practices

To achieve the best results, you need to follow certain best practices when testing and validating your process models. It's important to define objectives and requirements clearly and communicate them with all stakeholders. Additionally, you should select the most suitable testing methods and tools for your process and models, as well as validate them before use. You should also collect and analyze data systematically and rigorously, document findings, and report recommendations. Furthermore, it's essential to test and validate models periodically and continuously, comparing them with other models or standards.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?