Abstract: A catheter pump is disclosed herein. The catheter pump can include a catheter assembly that comprises a drive shaft and an impeller coupled to a distal end of the drive shaft. A driven assembly can be coupled to a proximal end of the drive shaft within a driven assembly housing. The catheter pump can also include a drive system that comprises a motor and a drive magnet coupled to an output shaft of the motor. The drive system can include a drive assembly housing having at least one magnet therein. Further, a securement device can be configured to prevent disengagement of the driven assembly housing from the drive assembly housing during operation of the pump.

Abstract: A catheter pump is disclosed herein. The catheter pump can include a catheter assembly that comprises a drive shaft and an impeller coupled to a distal end of the drive shaft. A driven assembly can be coupled to a proximal end of the drive shaft within a driven assembly housing. The catheter pump can also include a drive system that comprises a motor and a drive magnet coupled to an output shaft of the motor. The drive system can include a drive assembly housing having at least one magnet therein. Further, a securement device can be configured to prevent disengagement of the driven assembly housing from the drive assembly housing during operation of the pump.

Abstract: A catheter pump is disclosed herein. The catheter pump can include a catheter assembly that comprises a drive shaft and an impeller coupled to a distal end of the drive shaft. A driven assembly can be coupled to a proximal end of the drive shaft within a driven assembly housing. The catheter pump can also include a drive system that comprises a motor and a drive magnet coupled to an output shaft of the motor. The drive system can include a drive assembly housing having at least one magnet therein. Further, a securement device can be configured to prevent disengagement of the driven assembly housing from the drive assembly housing during operation of the pump.

Abstract: A catheter pump is disclosed herein. The catheter pump can include a catheter assembly that comprises a drive shaft and an impeller coupled to a distal end of the drive shaft. A driven assembly can be coupled to a proximal end of the drive shaft within a driven assembly housing. The catheter pump can also include a drive system that comprises a motor and a drive magnet coupled to an output shaft of the motor. The drive system can include a drive assembly housing having at least one magnet therein. Further, a securement device can be configured to prevent disengagement of the driven assembly housing from the drive assembly housing during operation of the pump.

Abstract: 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.

Abstract: Deep learning models and other complex models provide accurate representations of complex industrial processes. However, these models often fail to satisfy properties needed for their use in closed loop systems such as Advanced Process Control. In particular, models need to satisfy gain-constraints. Methods and systems embodying the present invention create complex closed-loop compatible models. In one embodiment, a method creates a controller for an industrial process. The method includes accessing a model of an industrial process and receiving indication of at least one constraint. The method further includes constructing and solving an objective function based on at least one constraint and the model of the industrial process. The solution of the objective function defines a modified model of the industrial process that satisfies the received constraint and can be used to create a closed-loop controller to control the industrial process.

Abstract: Deep learning models and other complex models provide accurate representations of complex industrial processes. However, these models often fail to satisfy properties needed for their use in closed loop systems such as Advanced Process Control. In particular, models need to satisfy gain-constraints. Methods and systems embodying the present invention create complex closed-loop compatible models. In one embodiment, a method creates a controller for an industrial process. The method includes accessing a model of an industrial process and receiving indication of at least one constraint. The method further includes constructing and solving an objective function based on at least one constraint and the model of the industrial process. The solution of the objective function defines a modified model of the industrial process that satisfies the received constraint and can be used to create a closed-loop controller to control the industrial process.

Abstract: 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.

Abstract: 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.

Abstract: 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 MPC 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The method of the present invention provides a fast, robust, and automated multivariate statistical analysis of gas chromatography/mass spectroscopy (GC/MS) data sets. The method can involve systematic elimination of undesired, saturated peak masses to yield data that follow a linear, additive model. The cleaned data can then be subjected to a combination of PCA and orthogonal factor rotation followed by refinement with MCR-ALS to yield highly interpretable results.

Abstract: Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights.

Abstract: Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights.

Abstract: Bias plays an important role in factor analysis and is often implicitly made use of, for example, to constrain solutions to factors that conform to physical reality. However, when components are collinear, a large range of solutions may exist that satisfy the basic constraints and fit the data equally well. In such cases, the introduction of mathematical bias through the application of constraints may select solutions that are less than optimal. The biased alternating least squares algorithm of the present invention can offset mathematical bias introduced by constraints in the standard alternating least squares analysis to achieve factor solutions that are most consistent with physical reality. In addition, these methods can be used to explicitly exploit bias to provide alternative views and provide additional insights into spectral data sets.

Abstract: A fast combinatorial algorithm can significantly reduce the computational burden when solving general equality and inequality constrained least squares problems with large numbers of observation vectors. The combinatorial algorithm provides a mathematically rigorous solution and operates at great speed by reorganizing the calculations to take advantage of the combinatorial nature of the problems to be solved. The combinatorial algorithm exploits the structure that exists in large-scale problems in order to minimize the number of arithmetic operations required to obtain a solution.

Abstract: A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.