公开(公告)号：US20220412318A1

公开(公告)日：2022-12-29

申请号：US17362681

申请日：2021-06-29

申请人： Aspen Technology, Inc.

发明人： Hong Zhao ,  Ashok Rao ,  Gaurav Rai ,  Mir Khan ,  Pedro Alejandro Castillo Castillo ,  Magiel J. Harmse

IPC分类号： F03D17/00 ,  G08B21/18

摘要： Systems and methods for building predictive and prescriptive analytics of wind turbines generate a historical operational dataset by loading historical operational SCADA data of one or more wind turbines. Each sensor measurement is associated with an engineering tag and at least one component of a wind turbine. The system creates one or more performance indicators corresponding to one or more sensor measurements, and applies at least one data clustering algorithm onto the dataset to identify and label normal operation data clusters. The system builds a normal operation model using normal operational data clusters with Efficiency of Wind-To-Power (EWTP) and defines a statistical confidence range around the normal operation model as criterion for monitoring wind turbine performance. As real-time SCADA data is received by the system, the system can detect an anomalous event, and issue an alert notification and prescriptive early-action recommendations to a user, such as a turbine operator, technician or manager.

公开(公告)号：US10990067B2

公开(公告)日：2021-04-27

申请号：US16308190

申请日：2017-07-05

申请人： Aspen Technology, Inc.

发明人： Ajay Modi ,  Ashok Rao ,  Thomas W. S. Lewis ,  Mikhail Noskov ,  Sheng Hua Zheng ,  Willie K. C. Chan

IPC分类号： G05B13/04 ,  G05B17/02

摘要： Computer-implemented methods and systems construct a calibrated operation-centric first-principles model suitable for online deployment to monitor, predict, and control real-time plant operations. The methods and systems identify a plant-wide first-principles model configured for offline use and select a modeled operating unit contained in the plant-wide model. The methods and systems convert the plant-wide model to an operation-centric first-principles model of the selected modeled operating unit. The methods and systems recalibrate the operation-centric model to function using real-time measurements collected by physical instruments of the operating unit at the plant. The recalibration may include reconciling flow and temperature, estimating feed compositions, and tuning liquid and vapor traffic flow in the model. The methods and systems deploy the operation-centric model to calculate KPIs (Key Performance Indicators) using real-time measurements. A processor employs the KPIs and automatically predicts and controls behavior of the physical operating unit at the plant.

公开(公告)号：US10921759B2

公开(公告)日：2021-02-16

申请号：US16307620

申请日：2017-07-07

申请人： Aspen Technology, Inc.

发明人： Jian Ma ,  Hong Zhao ,  Ashok Rao ,  Andrew L. Lui ,  Willie K. C. Chan

IPC分类号： G05B13/04 ,  G05B23/02

摘要： Embodiments are directed to computer methods and systems that build and deploy a pattern model to detect an operating event in an online plant process. To build the pattern model, the methods and systems define a signature of the operating event, such that the defined signature contains a time series pattern for a KPI associated with the operating event. The methods and systems deploy the pattern model to automatically monitor, during online execution of the plant process, trends in movement of the KPI as a time series. The methods and systems determine, in real-time, a distance score between a range of the monitored time series and the time series pattern contained in the defined signature. The methods and systems automatically detect the operating event in the online industrial process based on the determined distance score, and alter parameters of the process (e.g., valves, actuators, etc.) to prevent the operating event.

公开(公告)号：US20210034023A1

公开(公告)日：2021-02-04

申请号：US16530055

申请日：2019-08-02

申请人： Aspen Technology, Inc.

发明人： Michael R. Keenan ,  Qingsheng Quinn Zheng

IPC分类号： G05B13/04 ,  G05B13/02 ,  G06N3/04

摘要： Deep Learning is a candidate for advanced process control, but requires a significant amount of process data not normally available from regular plant operation data. Embodiments disclosed herein are directed to solving this issue. One example embodiment is a method for creating a Deep Learning based model predictive controller for an industrial process. The example method includes creating a linear dynamic model of the industrial process, and based on the linear dynamic model, creating a linear model predictive controller to control and perturb the industrial process. The linear model predictive controller is employed in the industrial process and data is collected during execution of the industrial process. The example method further includes training a Deep Learning model of the industrial process based on the data collected using the linear model predictive controller, and based on the Deep Learning model, creating a Deep Learning model predictive controller to control the industrial process.

公开(公告)号：US10755214B2

公开(公告)日：2020-08-25

申请号：US15133701

申请日：2016-04-20

申请人： Aspen Technology, Inc.

发明人： Robert M. Apap ,  Dimitrios Varvarezos

IPC分类号： G06Q10/06 ,  G06Q50/04 ,  G06F17/11 ,  G06F17/18 ,  G06N7/00 ,  G06Q10/04 ,  G06Q30/02 ,  G06Q50/06

摘要： A computer system and method optimize feedstock selection planning for an industrial process by evaluating first and second stages at separate intervals throughout the planning process. Evaluating the first stage determines a set of robust feedstocks to procure on long-term contracts. The computer system and method solve, in parallel, multiple simulation cases of a non-linear model generated with different expectation values for uncertain input parameters related to selecting feedstocks to procure on long-term contracts. Probabilistic analyses on the solutions from the simulation cases, including the application of chance-constraints, determine the set of robust feedstocks to procure on long-term contracts. Evaluating the second stage determines a set of robust feedstocks to procure in the spot market, using the information from the first stage. Specifically, the computer system and method solve each of multiple new simulation cases of the non-linear model, generated with different expectation values for uncertain input parameters related to selecting feedstocks to procure in the spot market. Each simulation case is solved to determine breakeven prices for one or more available spot feedstocks, and probabilistic analyses are performed on the breakeven prices for these spot feedstocks to determine a set of robust feedstocks to procure in the spot market.

公开(公告)号：US10698372B2

公开(公告)日：2020-06-30

申请号：US15995753

申请日：2018-06-01

申请人： Aspen Technology, Inc.

发明人： Hong Zhao ,  Ashok Rao ,  Lucas L. G. Reis ,  Magiel J. Harmse

IPC分类号： G05B13/04 ,  G05B17/02 ,  G06F3/0486 ,  G06N5/02 ,  G06N5/04 ,  G06Q10/06

摘要： Embodiments are directed to systems that build and deploy inferential models for generating predictions of a plant process. The systems select input variables and an output variable for the plant process. The systems load continuous measurements for the selected input variables. For the selected output variable, the systems load measurements of type: continuous from the subject plant process, intermittent from an online analyzer, or intermittent from lab data. If continuous or analyzer measurements are loaded, the systems build a FIR model with a subspace ID technique using continuous output measurements. From intermittent analyzer measurements, the systems generate continuous output measurements using interpolation. If lab data is loaded, the systems build a hybrid FIR model with subspace ID and PLS techniques, using continuous measurements of a reference variable correlated to the selected output variable. The systems deploy the built model to generate continuous key performance indicators for predicting the plant process.

公开(公告)号：US08660831B2

公开(公告)日：2014-02-25

申请号：US13271455

申请日：2011-10-12

申请人： Shu Wang ,  Yuhua Song ,  Chau-Chyun Chen

发明人： Shu Wang ,  Yuhua Song ,  Chau-Chyun Chen

IPC分类号： G06G7/58

CPC分类号： G06F19/704

摘要： An extension of COSMO-SAC to electrolytes (eCOSMO-SAC) combines the COSMO-SAC term for short range molecule-molecule, molecule-ion and ion-ion interactions with the extended symmetric Pitzer-Debye-Hückel term for long range ion-ion interactions. The extension recognizes that like-ion repulsion and local electroneutrality govern the surface segment contacts, and introduces a dual sigma profile concept for electrolyte systems. The eCOSMO-SAC formulation predicts activity coefficients of several representative electrolyte systems.

摘要翻译： COSMO-SAC对电解质（eCOSMO-SAC）的扩展将COSMO-SAC术语用于短程分子分子，分子离子和离子相互作用与扩展对称的Pitzer-Debye-Hückel术语的长距离离子 互动 延伸认识到类似离子排斥和局部电中性控制表面段接触，并引入电解质系统的双重西格玛分布概念。 eCOSMO-SAC制剂预测了几种代表性电解质体系的活度系数。

公开(公告)号：US20130246316A1

公开(公告)日：2013-09-19

申请号：US13890818

申请日：2013-05-09

申请人： ASPEN TECHNOLOGY, INC.

发明人： Hong Zhao ,  Magiel J. Harmse

IPC分类号： G06N5/02

CPC分类号： G06N5/02 ,  G05B13/048

摘要： 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.

摘要翻译： 公开了一种用于多变量过程控制中的模型识别和模型适应中的自动数据筛选和选择的基于计算机的装置和方法。 采用数据采样状态信息，PID控制回路关联和内部建立的MISO（多输入，单输出）预测模型，自动筛选各个时间序列的数据，并根据各种准则自动识别不良数据并标记清除 。 所得到的工厂步骤测试/操作数据也通过替代满足某些条件的某些去除的坏数据的内插替换值进行修复。 提供计算机实现的数据点互联和调整技术，以保证平滑/连续的替换值。

公开(公告)号：US20130204403A1

公开(公告)日：2013-08-08

申请号：US13760949

申请日：2013-02-06

申请人： Aspen Technology, Inc.

发明人： Qingsheng Quinn Zheng ,  Magiel J. Harmse

IPC分类号： G05B13/02

CPC分类号： G05B13/022 ,  G05B13/048

摘要： 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.

摘要翻译： 公开了一种综合多变量预测控制器（MPC）和测试仪。 本发明系统提供了用于模型识别目的的小幅度步长的多变量动态过程的最佳控制和步长测试，而不会偏离最佳控制目标。 可调参数指定了最佳过程操作与过程变量的最小移动之间的权衡，建立了在最小成本设置上运行MPC和最小移动设置之间的中间位置。 利用这个中间点，实施例在最优稳态解决方案附近进行低幅度步长测试，使得数据适合建模目的。 新系统决定MPC何时运行在优化模式下，以及何时可以在受限步骤测试模式下运行。 本发明系统确定叠加的步骤测试信号的何时以及可能有多大，使得临时优化赠送被限制在可接受的范围内。

公开(公告)号：US08449727B2

公开(公告)日：2013-05-28

申请号：US12765611

申请日：2010-04-22

申请人： Brian Hanley

发明人： Brian Hanley

IPC分类号： B01D3/42

CPC分类号： B01D3/4211 ,  B01J19/0033 ,  B01J19/30 ,  B01J19/32 ,  B01J2219/319 ,  B01J2219/326 ,  B01J2219/3325

摘要： A flood point for a packed column is determined by providing a data set of gas pressure drop values as a function of gas flow rate values at several liquid flow rates through a packed column, known flood point value for one liquid flow rate, setting flood point values for higher liquid flow rates at values lower than the known flood point value, and setting flood point values for lower liquid flow rates at values higher than the known flood point value, followed by expressing gas flow rates for liquid flow rates as fractions of the flood point value for each respective liquid flow rate. At a constant gas pressure drop, the method then includes calculating an average fractional flood point value for the liquid flow rates and minimizing the standard deviation between the fractional flood point value at different liquid flow rates and the calculated average fractional flood point value by iteratively resetting fractional flood point values and recalculating the average fractional flood point value for the liquid flow rates, thus resulting in determining a flood point for the packed column at any liquid flow rate, and thereby producing a plot of pressure drop as a function of fraction of flood point at any liquid flow rate, or a mathematical expression thereof that can be used in a computer-implemented column design and process modeling.

摘要翻译： 通过提供气体压降值的数据集作为气体流速值的数据集，通过填充柱的几种液体流速，已知的一种液体流量的洪水点值，设定洪水点来确定填充塔的洪水点 在低于已知洪水点值的值下提高液体流速的值，以及在高于已知洪水点值的值下设定较低液体流速的洪峰值，然后将液体流速的气体流速表示为 每个液体流量的泛洪点值。 在恒定的气体压降下，该方法包括计算液体流速的平均分数泛洪点值，并通过迭代重置来最小化不同液体流量下的分数泛洪点值与计算的平均分数洪峰值之间的标准偏差 重新计算液体流量的平​​均分数泛点值，从而确定任何液体流速下填充塔的洪水点，从而产生作为洪水分数的函数的压降图 指向任何液体流速，或其数学表达式，可用于计算机实现的列设计和过程建模。