Volume of data

Equilibrium Data Collection, DECHEMA, Frankfurt, Germany, 1979) have reported several volumes of data that have been correlated with activity-coefficient equations. 

When selecting in-process measures, try to use measures for which data are already available. For example, avoid using in-service failure data unless the maintenance systems can make this information readily available. These measures will be used to identify potential problems and correct them as early as possible. During the development of the integrated systems, data that will be available for in-process measurement should be identified and measures developed. These measures are most likely to be calculated annually as the volume of data required to provide useful data will be collected only over relatively long periods of time. 

The height of the transfer unit has not been satisfactorily correlated for application to a wide variety of systems. If pilot plant or other acceptable data are available to represent the system, then the height of packing can be safely scaled-up to commercial units. If such data are not available, rough approximations may be made by determining Hg and Hl as for absorption and combining to obtain an Hqg (Ref. 74, pg. 330). This is only very approximate. In fact it is because of the lack of any volume of data on commercial units that many potential applications of packed towers are designed as tray towers. 

Patents on Algorithms. Whereas until recently much of the analysis of data in pharmaceutical research and development was carried out essentially by manual processes, the volume of data that is currently being generated means that increasingly sophisticated algorithms are being used to order, sort, and analyze the data. 

Depending on the volume of data in support of the validation, it may be appropriate to list it here or provide reference to a separate hie. 

Scalability Can the system as designed and deployed be scaled up to handle greater usage demands (volume of data, numbers of users, rate of requests) Scalability is achieved primarily by repheating resources—processors, memory, storage media—and their software processing counterparts. Component-based designs in which the components can be deployed on separate processors, and where the overhead of cross-component coordination is proportionately small, enhances scalability. 

With the advance of computer techniques, especially implementation of distributed control systems (DCS) to chemical processes, a large set of on-line measurements are available at every sampling period. The rational use of this large volume of data requires the application of suitable techniques to improve their accuracy. This goal has triggered the focus on research and development, during the last ten years, in the area of plant data reconciliation. Complete reviews on the subject can be found in the works of Mah (1990), Madron (1992), and Crowe (1996). 

The volume of data acquired in modern laboratories is such that computer storage is most efficient and eliminates the need to provide the space for hard copies. 

Pressures on regulators to reduce the remaining significant risks will continue, as will pressures to force the collection of greater volumes of data on poorly tested but widespread chemicals. 

With the growing volume of data associated with PAT, systems are needed to integrate and store it. Platforms like SIPAT from Siemens, xPAT from ABB, Discoverant from Aegis or PAT Suite from Umetrics (MKS)... 

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