Methods and systems for evaluating and/or monitoring chromatography column performance are provided. Embodiments apply multivariate analysis (MVA) methods to process data as well as transition analysis data to provide a comprehensive evaluation of chromatography column performance. In embodiments, transition analysis data generated over extended periods of time can be analyzed together with process data to evaluate column performance. Further, embodiments enable a compact and robust tool for combining and presenting performance evaluation results, which allows for time-efficient performance examination. According to embodiments, MVA methods applied on transition analysis and process data provide (1) near real-time ability to comprehensively monitor column packing quality; (2) sensitive detection of column integrity breaches; (3) sensitive detection of subtle changes in column packing; (4) sensitive detection of different types of changes in column packing; (5) sensitive detection of fronting/tailing; and (6) sensitive detection of changes in process performance.

Systems and methods for evaluating chromatography column performance

The invention relates to a recursive ridge ELM (Extreme Learning Machine) based predication method of a gas velocity of a loading point for a packed column. The method comprises the following steps of: collecting data related to the gas velocity of the loading point in a random packing packed column experiment as a sample set required for modeling, analyzing data characteristics and impact factors on the gas velocity of the loading point, and determining the input variable and the output variable of a loading point gas velocity model; due to the larger gap between the data, in order to ensure the reliability of modeling, firstly preprocessing the data, wherein normalization is a preprocessing method used by neural networks and other modeling methods, i.e., data values are converted to numbers in an interval [0,1], and a logarithmic normalization method is used; according to the data characteristics after the loading point gas velocity is normalized, for different types of random packing, quickly establishing an RRELM (recursive ridge ELM) model for loading point gas velocity prediction; and applying the established RRELM model to predict, anti-normalizing a predicted value, and finally obtaining the predicated value of the loading point air velocity by calculation.

Recursive ridge ELM (Extreme Learning Machine) based predication method of gas velocity of loading point for packed column

The disclosure is directed to a system and method for control of at least one bioreactor, other cell cultivation-related equipment, and systems containing any combination of these in a plant. For example, a Plant-Wide Control System (PWCS) or a Process Control System (PCS) may be divided into three main components: hardware (including operating systems, such as a controller communicating with one or more servers of a network associated with the PWCS, (2) software (such as a control module) for performing control, and (3) one or more instrument control loops, which may be used by the software to maintain certain process values. Moreover, a Height Equivalent of a Theoretical Plate (HETP) value and an asymmetry factor may be determined based on real-time analysis on a chromatography column, using PWCS components.

System and method for regulating cell culture based production of biologics

Embodiments of the present disclosure are directed to methods and systems for assessing integrity of chromatography columns, systems, and processes. The methods and systems can comprise one or more of extracting a block and signal combination for analysis, performing a transition analysis, performing one or more statistical process controls, and/or implementing in-process controls based on the statistical process controls.

Methods and systems for chromatography data analysis

Methods for evaluating and/or monitoring chromatography column performance are provided. Embodiments apply multivariate analysis (MVA) methods to process data as well as transition analysis data to provide a comprehensive evaluation of chromatography column performance. In embodiments, transition analysis data generated over extended periods of time can be analyzed together with process data to evaluate column performance. Further, embodiments enable a compact and robust tool for combining and presenting performance evaluation results, which allows for time-efficient performance examination. According to embodiments, MVA methods applied on transition analysis and process data provide (1) near real-time ability to comprehensively monitor column packing quality; (2) sensitive detection of column integrity breaches; (3) sensitive detection of subtle changes in column packing; (4) sensitive detection of different types of changes in column packing; (5) sensitive detection of fronting/tailing; and (6) sensitive detection of changes in process performance.

Methods for evaluating chromatography column performance

The present invention provides automated systems and methods for monitoring column performance in process chromatography, and applications thereof. In an embodiment, column performance is monitored by generating a plurality of process values such as, for example, conductivity values or pH values with a detector during a chromatography step transition between a first mobile phase liquid and a second mobile phase liquid. The process values are transformed to form transformed process values in which noise present in the process values is suppressed. Column performance parameters are calculated based on the transformed process values and displayed during movement of the second mobile phase liquid through the chromatography column. The displayed performance parameters enable an operator to make a determination, for example, regarding the quality of the chromatography column packing and whether to continue the chromatography process or stop the chromatography process until the chromatography column can be repacked or replaced.

Automated System and Method for Monitoring Chromatography Column Performance, and Applications Thereof

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Automated System and Method for Monitoring Chromatography Column Performance, and Applications Thereof