Burlington, MA, United States
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.

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The present invention is directed to computer-based methods and system to perform root-cause analysis on an industrial process. The methods and system load process data for an industrial process from a historian database and build a hybrid first-principles and inferential model. The methods and system then executes the hybrid model to generate KPIs for the industrial process using the loaded process variables. The methods and system then selects a subset of the KPIs to represent an event occurring in the industrial process, and divides the data for the subset into multiple subset of time series. The system and methods select time intervals from the time series based on the data variability in the selected time intervals and perform a cross-correlation between the loaded process variables and the selected time interval, resulting in a cross-correlation score for each loaded process variable. The methods and system then select precursor candidates from the loaded process variables based on the cross-correlation scores and execute a parametric model for performing quantitative analysis of the selected precursor candidates, resulting in a strength of correlation score for each precursor candidate. The methods and system select root-cause variables from the selected precursor candidates based on the strength of correlation scores for analyzing the root-cause of the event.

Computer system and methods for optimally controlling the behavior of an industrial process, in accordance with plant operating goals, without requiring a complicated trial and error process. The system and methods enable configuring optimization preference and optimization priority for key manipulated variables (MVs) of the industrial process. The system and methods translate the configured optimization preference and optimization priority for each key MV into prioritized economic objective functions. The system and methods calculate a set of normalized cost factors for use in a given prioritized economic functions based on a model gain matrix of manipulated variables and controlled variables of the industrial process. The system and methods automatically determine best achievable targets for the MVs by solving each prioritized economic objective functions in sequence of priority within the constraints of: (1) the determined CV best achievable steady-state targets, and (2) the determined MV best achievable steady-state targets from higher prioritized economic objective functions.

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.

Aspen Technology | Date: 2014-11-24

A method, apparatus, and computer program product for increasing efficiency in a plant by creating a planning model for said plant comprising a plurality of runtime models stored in a database. Each runtime model corresponds to a reactor in the plant and mimics real world behavior of the reactor by identifying the mathematical relationships of the inputs and outputs of the reactor. Each runtime model further comprises a set of tuning factors, which allows the user to adjust the runtime model to more closely align with the users desired output or otherwise account for real-life plant activity. By properly creating and utilizing a plurality of runtime models and implementing them into a planning model, a user can increase efficiency of the plant by optimizing product output, forcing the plant to balance materials-in and materials-out, or forcing the plant to stoichiometrically balance elements going in, and coming out of the plant or reactor.

An integrated multivariable predictive controller (MPC) and tester is disclosed. The invention system provides optimal control and step testing of a multivariable dynamic process using a small amplitude step for model identification purposes, without moving too far from optimal control targets. A tunable parameter specifies the trade-off between optimal process operation and minimum movement of process variables, establishing a middle ground between running a MPC on the Minimum Cost setting and the Minimum Move setting. Exploiting this middle ground, embodiments carry out low amplitude step testing near the optimal steady state solution, such that the data is suitable for modeling purposes. The new system decides when the MPC should run in optimization mode and when it can run in constrained step testing mode. The invention system determines when and how big the superimposed step testing signals can be, such that the temporary optimization give-away is constrained to an acceptable range.

Aspen Technology | Date: 2013-04-30

A product output rate for a packed column is optimized by setting a desired product output rate from the distillation column, calculating a fraction of flood point of the distillation column at a reflux ratio, and determining a pressure drop value within the distillation column at the fraction of flood point. The step of determining the pressure drop employs the method of producing a plot of pressure drop as a function of fraction of flood point at any liquid flow rate, or producing a mathematical expression thereof. The method of optimizing a product output rate from a distillation column then includes calculating a pressure at a point in the distillation column for a pressure at a different point in the distillation column, calculating the pressure drop within the distillation column of a given length, calculating a temperature corresponding to the calculated pressure at a point in the distillation column, and adjusting the desired product output rate or the reflux ratio or the pressure at a different point in the distillation column.

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.

A method, apparatus, and computer program product for increasing closed-loop stability in a MPC controller controlling a process where there are significant uncertainties in the model used by the controller. This invention focuses on the improvement of the robustness of the steady-state target calculation. This is achieved through the use of a user defined robustness factor, which is then used to calculate an economic objective function giveaway tolerance and controlled variable constraint violation tolerance. The calculation engine uses these tolerances to find a solution that minimize the target changes between control cycles and prevent weak direction moves caused by near collinearity in the model. If the controller continues to exhibit large variations in the process, it can slow down the manipulated variable movement to stabilize the process.

A computer-implemented method of characterizing metal content and chemical composition of crude oil, including determining at least one respective organometallic class and subclass derived from physical and chemical property data for each organometallic class and crude oil physical and chemical property data and at least one segment type and segment number range of the segment type bound to each organometallic subclass. The method determines a relative ratio of each organometallic class and subclass that forms a chemical composition representative of the given crude oil, such that the determined relative ratio and the determined respective organometallic class and subclass, segment type, and segment number range form a characterization of the metal content and the chemical composition of the given crude oil, resulting in a display, as output to an end-user, of the formed characterization of the metal content and the chemical composition of the given crude oil.

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