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Here is more information on underdamped, critically damped, and overdamped second order systems: apmonitor.com/pdc/index.php/Main/SecondOrderSystems

Great idea - it just adds an additional input parameter to the function, but makes the code more modular and reusable for other applications.

That's an interesting observation and likely another way to enforce a positive output constraint. Many of the machine learning packages also allow constraints to be specified now: www.tensorflow.org/api_docs/python/tf/keras/constraints

Sure, here is a complete series of tutorials with an Arduino device for real-time MPC. apmonitor.com/do/index.php/Main/AdvancedTemperatureControl Lab F is linear MPC, the easiest to implement.

You could estimate mass flow from the accumulation rate of the coolant. If you can put the condenser on a weight scale, it would give you a weight versus time that would translate into flow. It may not be practical if the condenser accumulator is built into an existing structure.

@apm oh thanks alot sir

The Teltonika FMB920 can transmit data using LTE or via a local gateway using a Raspberry Pi. For LTE, ensure the micro-SIM is active and configure the device with network and MQTT settings to send data directly to an MQTT broker over the internet. Alternatively, use a Raspberry Pi as a local gateway by enabling Bluetooth on both devices for local data transfer, and configure the Raspberry Pi to send this data to an MQTT broker using installed MQTT client software. This setup not only allows flexibility based on connectivity and mobility needs but also the possibility of integrating additional processing capabilities through the Raspberry Pi.

It is created when you train the model.

@@apm OK, got it, Thanku

The software is Python Gekko. Source code is available at apmonitor.com/me575/index.php/Main/LimestoneSlurry

@@apm thank you 🙏

Elementary Principles of Chemical Processes 3rd Edition by Richard M. Felder and Ronald W. Rousseau - unfortunately, I don't remember the problem number.

Elementary Principles of Chemical Processes 3rd Edition by Richard M. Felder and Ronald W. Rousseau

Time-Sensitive Networking (TSN) is important in many applications. I typically use MODBUS when speed is important. It is an older standard, but specializes in high-speed numerical data.

Great observation. Setting a bound or rearranging the equation to avoid divide-by-zero is often effective.

The EV_TYPE=2 option is a squared error objective. See the code at apmonitor.com/do/index.php/Main/EstimatorObjective It is also possible to specify a custom objective instead of using the build-in types using `m.Minimize()` function. Additional examples are shown at apmonitor.com/do/index.php/Main/DynamicEstimation

Here is an example with MIMO control. apmonitor.com/do/index.php/Main/TCLabF and a simple example: apmonitor.com/wiki/index.php/Apps/LinearStateSpace (see Gekko example at the end).

@@apm ok. Thanks a lot

Yes, no problem on deploying it as a web service. Just open the firewall on port 443 to not block the connection.

The easiest way to create a Neural Network model is to use scikit-learn or TensorFlow for fitting and then import the model into Gekko as shown here: gekko.readthedocs.io/en/latest/ml.html Gekko uses max2 functions for ReLU because they are more efficient to solve than the max3 or if3 functions with binary variables. Search for "relu" in the gekko ML.py file for more details: github.com/BYU-PRISM/GEKKO/blob/master/gekko/ML.py on how it translates scikit-learn and tensorflow models to Gekko.

Your method works for initialization. An alternative is to use STATUS=1 and .value=np.random.randn() without needing to use .FSTATUS and .MEAS

@@apm Thank you for your guidance! I In case I want to train my neural network with a constrained loss function, Can, using TensorFlow model for fitting and import it into Gekko, work??? If yes, how then should I use constrained optimization during model training?

@@apm Thank you very much !

@@user-su5eu5ts2z you can add constraints in TensorFlow: www.tensorflow.org/api_docs/python/tf/keras/constraints/Constraint and then import that fit model in Gekko. Gekko is more flexible with different types of constraints, but TensorFlow is built for larger datasets. Once the model is fit, those constraints are no longer required to make inferences.

The error message "The requested operation has no match to return." (BadNoMatch) from the Python OPC UA package generally indicates that the operation you attempted to perform did not find a valid target or match in the server's address space or data structures.

In MQTT (Message Queuing Telemetry Transport), topics are used as the routing information for messages, and clients subscribe to these topics to receive messages that interest them. However, MQTT does not have a built-in feature to discover topics because it is designed to be lightweight and simple. Topics are typically determined by the application design or the configuration of the MQTT broker and its clients. If you're working with an MQTT broker and you don't know the topics, here are a few approaches you can take to discover or manage topics: Broker Configuration and Logs: Check Broker Configuration: If you have access to the MQTT broker, you can check its configuration files. Some brokers might list the topics that have been configured or have defaults. Broker Logs: Some MQTT brokers log details about connected clients and their subscriptions. By examining these logs, you might be able to identify active topics. Contact System Administrator or Developer: If you're working in a managed environment, the system administrator or the developer who set up the MQTT broker might have a list of topics or documentation about the topic structure.

Yes, no problem on Spyder. Here is a way to display real-time plots: In the Spyder IDE menu bar select Tools, Preferences, IPython Console, Graphics, Backend=Automatic. This will allow real-time plots to display as a new window instead of inline in the IPython console. Here is additional help: apmonitor.com/pdc/index.php/Main/ArduinoSetup

@@apm Thank you so much for providing great content! I appreciate your work!

Sure, the file location is shown at 0:48 in the video

Set `stream=True`. Here is example code: github.com/ollama/ollama-python

If you don't know the non-linear equation, try using regression with Machine Learning: apmonitor.com/pds/index.php/Main/RegressionOverview The lazypredict package in Python helps evaluate the regression on multiple models. Otherwise, try different functional forms that you design.

When working with OPC servers, identifying and managing stale or outdated data values is important in industrial applications where data integrity is vital for operation safety and efficiency. Here are strategies to detect stale values with OPC UA: 1. Timestamp Checking Each value read from an OPC UA server includes a timestamp that can be used to detect when data becomes stale if the timestamp remains unchanged over an extended period. 2. Subscription and Monitored Items Instead of polling data (repeatedly reading values), OPC UA supports subscriptions where the server notifies the client of changes. You can specify a publishing interval and a "deadband" to filter out unnecessary updates. Monitored items can be set to notify only on significant changes or after a certain time of inactivity. 3. Setting Data Quality Flags OPC UA servers can be configured to include data quality flags with each value. These flags can indicate issues such as communication errors, sensor malfunctions, or configuration errors. By checking these flags, you can determine if the data should be considered reliable. 4. Implement Heartbeat Signals For critical variables that must be updated regularly, implementing a "heartbeat" signal can be effective. This is a write operation performed by the data source at a regular interval to a specific variable, indicating that the source is still operational. The absence of recent updates to this heartbeat variable can signal an issue with the data source. 5. Analyzing Historical Data Patterns In some cases, analyzing the historical patterns of data updates can help identify anomalies in data freshness. If certain variables typically update at regular intervals, deviations from this pattern can be flagged for further investigation.

These tutorials may help: apmonitor.com/me575/index.php/Main/KuhnTucker

Here is a series of tutorials that may help: apmonitor.com/me575/index.php/Main/KuhnTucker

@@apm Thanks for reply. However. Isn't what I wrote correct ?

@@Anonimka338 that works for systems with 2 constraints. For problems with hundreds or thousands of constraints, such as solved with Gekko (gekko.readthedocs.io/en/latest/), interior point or active set methods are two popular methods to solve constrained optimization problems. Here is additional information on using barrier methods with Interior Point Methods: apmonitor.com/me575/index.php/Main/InteriorPointMethod Active set methods use Lagrange multiplier information and heuristics to determine when to add or remove constraints from the active set (or constraining) set.

@@apm Thank you very much )

Sure, it is all available here with a Jupyter Notebook and a link to open the Notebook on Google Colab: apmonitor.com/dde/index.php/Main/RAGLargeLanguageModel

You need to switch to wss instead of ws for https protocol.

@@apm managed to do it! The clients needed to do a handshake cause the server was secured hosted as https and so the ws endpoint also switched to secured wss.

That's part of the general form for Interior Point solvers. You can translate any general optimization problem into that form with slack variables and rearrangement.