Best Advanced Process Control (APC) Systems for Microsoft Excel
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Compare the Top Advanced Process Control (APC) Systems that integrate with Microsoft Excel as of October 2025
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This a list of Advanced Process Control (APC) systems that integrate with Microsoft Excel. Use the filters on the left to add additional filters for products that have integrations with Microsoft Excel. View the products that work with Microsoft Excel in the table below.
What are Advanced Process Control (APC) Systems for Microsoft Excel?
Advanced Process Control (APC) systems are computer-based systems that use mathematical models and algorithms to optimize the performance of industrial processes. APC systems aim to maintain a steady state operation with tight control over process variables such as temperature, pressure, flow rate, and composition. It incorporates feedback from sensors in the process to monitor performance and take corrective action when needed, in order to keep the process operating in the desired range. APC systems can help improve efficiency, reduce energy usage, increase throughput, and reduce product variability. Compare and read user reviews of the best Advanced Process Control (APC) systems for Microsoft Excel currently available using the table below. This list is updated regularly.
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COLUMBO
PiControl Solutions
Closed-loop universal multivariable optimizer for Model Predictive Control (MPC) performance and Model Predictive Control (MPC) quality improvements. Use data in Excel files from DMC (Dynamic Matrix Control) from Aspen Tech, or from RMPCT (Robust Model Predictive Control Technology) from Honeywell, or Predict Pro from Emerson and use it to generate and improve correct models for the various MV-CV pairs. Amazing new optimization technology does not need step tests as required by Aspen tech, Honeywell, and others. It Works entirely in the time domain, is easy to use, compact, and practical. Model Predictive Controls (MPC) can have 10s or 100s of dynamic models. One or more could be wrong. Bad (wrong) Model Predictive Control (MPC) dynamic models produce a bias (model prediction error) between the predicted signal and the measured signal coming from the sensor. COLUMBO will help you to improve Model Predictive Control (MPC) models with either open-loop or completely closed-loop data.
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