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Burlington, MA, United States

Aspen Technology, Inc. -- known as AspenTech -- is a provider of software and services for the process industries. Headquartered in Bedford, Massachusetts, USA, AspenTech has 30 offices around the world, spanning 6 continents. Wikipedia.


Watanasiri S.,Aspen Technology
Pure and Applied Chemistry | Year: 2011

Accurate thermophysical properties are essential to the development of high-quality process simulation models of chemical processes. Therefore, process-modeling software (simulator) must provide accurate, reliable, and easily accessible property data and models to enable efficient and robust process design. Property data and parameters for components of interest are generally available in the databases of the simulator. For components that are not in the databases, their property data must be supplied by the user. The number of components available in a typical simulator is about 1700. The number and types of components available in the simulator limit the scope and accuracy of process models that can be developed. In this paper, we review past practice in obtaining the necessary property data required in developing a process model and describe a new methodology that can be used to overcome the shortcomings of the current method. The new method is based on the dynamic data evaluation concept that combines the experimental data obtained from a comprehensive electronic database with structure-based property estimation system and data analysis and regression programs to generate critically evaluated property data. The concept and necessary software have been implemented in a process simulator, resulting in a new workflow that enables high-fidelity process models to be developed more easily and efficiently. © 2011 IUPAC, Publication date (Web): 5 May 2011. Source


Patent
Aspen Technology | Date: 2013-06-28

The ability to perform non-destructive editing of files and models requires the generation and persistence of input deltas that capture changes that are made to a base starting point. Reconstitution of saved state may be achieved through the application of deltas. This capability is useful for failover remediation in client/server environments since the client has access to the deltas, such that in the event that a stateful service becomes unresponsive (and therefore, no longer available), the service may be taken offline and a new resource may be assigned as a replacement. In such an event, the service is directed to load the baseline data and any changes may be reapplied, restoring the service state.


A system and method of model predictive control executes a model predictive control (MPC) controller of a subject dynamic process (e.g., processing plant) in a configuration mode, identification mode and model adaptation mode. Users input and specify model structure information in the configuration mode, including constraints. Using the specified model structure information in the identification mode, the MCP controller generates linear dynamic models of the subject process. The generated linear dynamic models collectively form a working master model. In model adaptation mode, the MPC controller uses the specified model structure information in a manner that forces control actions based on the formed working master model to closely match real-world behavior of the subject dynamic process. The MPC controller coordinates execution in identification mode and in model adaptation mode to provide adaptive modeling and preserve structural information of the model during a model update.


A computer-based method and system brings together data from two business domains: real-time actual plant status operation data and predictive process simulation data based upon a design specification. This method and system correlates the plant data and the simulation data, and displays the results side-by-side for the user. The results assist the user, to determine whether the plant is operating properly, and to make further improvements to both the plant assets and to the simulation models. The invention assists with monitoring, maintaining, trouble shooting, and problem solving of plant operation. The invention facilitates a progressive visual collaborative environment between plant operation and process engineering teams, where engineers from respective domains may socialize and trouble shoot problems. The Progressive Visual Collaboration helps professionals with searching, sharing, mapping, analyzing, framing problems, removing ambiguity and uncertainty by considering facts and figures, and providing a progressive workflow that solves plant problems.


A computer-based apparatus and method for automated data screening and selection in model identification and model adaptation in multivariable process control is disclosed. Data sample status information, PID control loop associations and internally built MISO (Multi-input, Single-output) predictive models are employed to automatically screen individual time-series of data, and based on various criteria bad data is automatically identified and marked for removal. The resulting plant step test/operational data is also repaired by interpolated replacement values substituted for certain removed bad data that satisfy some conditions. Computer implemented data point interconnection and adjustment techniques are provided to guarantee smooth/continuous replacement values.

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