Implement an Extended Kalman Filter in PCS 7 V9.0

For a supported PCS 7 V9.0 implementation, Siemens provides the PCS 7 Kalman-Filter Configurator and the APL KalFilt function block, plus a V9.0 unit-template example for a stirred-tank polymerization reactor. The example configures the process model and Kalman-filter equations in the configurator, then generates and integrates the corresponding blocks; it does not require MATLAB Runtime as described in the supplied example. Its model is application-specific and intended for batch or fed-batch operation, not a drop-in generic EKF.

What Siemens provides

The official unit template “Stirred tank reactor with Kalman filter” demonstrates an extended Kalman filter as a soft sensor for a polymerization reactor. It uses PCS 7 V9.0, APL V9.0 and the separately installed PCS 7 Kalman Configurator V9.0. Siemens describes measured reactor and jacket temperatures correcting internal estimated states such as component masses and heat transfer.

Configuration workflow

Obtain the Siemens unit-template documentation and project for PCS 7 V9.0, and review its stated assumptions and limits.
Install the Kalman Configurator version applicable to the PCS 7 V9.0 environment.
In the CFC project, open the KalFilt block and use Edit > Kalman Configure to define the model's initial values, bounds, state differential equations, output equations and algebraic equations.
Configure the noise covariance and other model parameters from process knowledge and measurements; do not reuse the template values blindly.
Generate the required model blocks and integrate the relevant charts and tags following the template's engineering steps.
Validate estimator behavior against measured data and an independently reviewed process model before using estimates in control.

Choosing between the example and custom code

The Siemens example supplies an implementation path using the Kalman Configurator and APL KalFilt. The source question asks about custom SCL or FB1925; the official example does not establish whether FB1925 is appropriate for another process or whether Matlab Runtime is used in a different implementation. Choose a custom estimator only with a validated model, numerical implementation, diagnostic strategy and version-compatible engineering method.

Verification

Compare estimated states with available measurements and independent mass/energy balances over the intended operating envelope. Monitor residuals and estimator validity, and validate batch/fed-batch transitions. Siemens explicitly limits the example to batch or fed-batch reactor use and states modeling assumptions, including omitted heat effects.

Limitations and Operational Precautions

The Siemens template is a non-binding application example, not a universal Kalman filter. It requires process-model knowledge and PCS 7/APL engineering competence. Do not use estimates as control inputs until the model, parameterization, fault response and operating envelope have been independently validated.

Technical References

Siemens, PCS 7 Unit Template “Stirred tank reactor with Kalman filter” using the example of the Chemical Industry, Entry ID 109756215, V1.0, 06/2018, especially sections 1.3, 4 and 5: https://cache.industry.siemens.com/dl/files/215/109756215/att_949796/v5/109756215_KalFilReactor_DOC_en.pdf
Siemens, PCS 7 Kalman-Filter Configurator Readme V9.0: https://support.industry.siemens.com/cs/attachments/109744324/KFConfig-Readme-Online_en-US.pdf

PCS 7 Kalman filter soft sensor
Extended Kalman filter process model validation

Related Tags

PCS 7, Kalman filter, soft sensor, APL

Engineer FAQ

Does the PCS 7 V9.0 example require MATLAB Runtime? The Siemens unit-template documentation identifies the PCS 7 Kalman Configurator V9.0 and APL KalFilt workflow; it does not list MATLAB Runtime as a requirement for that example.

What process does Siemens' example model? A stirred-tank polymerization reactor, using a calorimetric state observer to estimate variables that are not directly measured.

Can the example be used for continuous operation? No. Siemens says the template is intended for batch or fed-batch operation, not continuous operation.

Which block implements the estimator? The example uses the APL KalFilt function block with the PCS 7 Kalman-Filter Configurator.

Can I copy the example model unchanged to another reactor? No. Its equations, parameters and assumptions are specific to the example; adapt and validate them for the actual process.

What measurements correct the modeled internal states? In the example, reactor temperature and cooling-jacket outlet temperature are measured outputs used for state correction.

Which states can the template estimate? The documentation includes reactor and jacket temperatures, monomer and solvent mass, product mass, supplied monomer mass, reactor contents mass and heat transfer.

How should covariance values be selected? They must reflect the model and measurement uncertainties and be tuned using process evidence; the template's values are not universal settings.

Does Siemens' example prove the estimator is suitable for a plant? No. It is a non-binding application example and requires project-specific model and safety validation.

What should be monitored before using estimates in control? Validate residuals, state estimates and behavior across expected operating modes, and define handling for invalid estimates and sensor/model mismatch.

Implement an Extended Kalman Filter in PCS 7 V9.0