Excel spreadsheet interface

Figure 2.8 EXCEL spreadsheet interface for Beta function data fitting.

The procedure described in section 4 will now be applied to the case of Cimetidine, using the NRTL-SAC model of the full regression case presented in section 6.2. The following screening calculations were built into a Microsoft Excel spreadsheet, using the Aspen - Excel interface to calculate the solubility data. 

Since Excel is a powerful tool used widely for many different purposes with many options not all options can be discussed in this chapter. The focus of the chapter is on manually self-created spreadsheets for data calculation and checks against acceptance criteria (logical operations). Excel spreadsheets that are used with other electronic systems for automatic data or information entry, for further operations, or used as a view tool for databases are not within the scope of this chapter. Nevertheless, these types of spreadsheets are viewed as a normal spreadsheet with automatic entry, and validation, including validation of the interface, will be included to cover this item. In this chapter we provide guidance in validation and revalidation of Excel spreadsheets and information about managing validated spreadsheets. 

Linear and nonlinear programming solvers have been interfaced to spreadsheet software for desktop computers. The spreadsheet has become a popular user interface for entering and manipulating numeric data. Spreadsheet software increasingly incorporates analytic tools that are accessible from the spreadsheet interface and permit access to external databases. For example, Microsoft Excel incorporates an optimization-based routine called Solver that operates on the values and formulas of a spreadsheet model. Current versions (4.0 and later) include LP and NLP solvers and mixed integer programming (MIP) capability for both linear and nonlinear problems. The user specifies a set of cell addresses to be independently adjusted (the decision variables), a set of formula cells whose values are to be constrained (the constraints), and a formula cell designated as the optimization objective. 

Figure 19.8. With the ARCoDat module, the users can build themselves synthesis-specific data sets for end-stage compounds. Database interfaces permit substance data to be imported for stoichiometric calculations and plausibility/control. Search functions allow building block ensembles to be imported from, e.g., in-house central databases such as Oracle and MS-Access or files like SD-Files or Excel spreadsheets.

Once we click the Generate Derivatives button, the model runs several times at the base and perturbed values of the independent variables. The DELTA BASE values appear in the table shown in Figure 4.117. These values may be directly copied into an Excel spreadsheet for Aspen PIMS or exported for further study. We can export the table to a PIMS style interface by clicking the Export Data . The exported data appear as shown in Figure 4.118. 

The Quattro Pro Solver. The same team that packaged and developed the Excel Solver also interfaced the same NLP engine (GRG2) to the Quattro Pro spreadsheet. Solver operation and problem specification mechanisms are similar to those for Excel. 

A historical control database can take on many formats, from a simple spreadsheet (e.g., Microsoft Excel) to a fully searchable database that is interfaced or a part of the laboratory s computer data collection system. Most laboratories that conduct large numbers of studies according to GLP standards have a validated computer data collection system, and some of these systems automatically compile control data ftom studies so the user does not have to reenter the data into a separate historical control database. However, because of the inflexibility of the data acquisition systems, many laboratories still compile their historical control data by manually entering into a stand-alone database, such as customized spreadsheet format (e.g., Microsoft Excel). 

The examples herein are done using Excel98, on a Macintosh personal computer. It is easy to anticipate some changes as Excel progresses to newer releases, and there well may be some small differences between IBM-compatible PCs and the Macintosh implementations. While the syntax and communication interfaces will likely change in the future, the functional requirements for solving certain problems will not. The descriptions provided in this appendix are quite detailed nevertheless, some knowledge of spreadsheet operations is presumed. 

Traditionally data, properties, information etc has been stored in files on computer disks. More recently, it has become common practice on Macintosh computers, when using Microsoft software or some UNIX applications, to use either extensions to the file name or the first few bytes in the file (or another file) to indicate some aspects of the data, for example that it is suitable for Microsoft Excel. While this approach is practical to indicate something about files containing columns of data, it is not appropriate to store information about the values in cells in spreadsheet or how it relates to data in other columns. This requires a relational database such as ORACLE, and for performance reasons the values in the cells may only be accessed via the ORACLE API (Application Programming Interface) or SQL (Standard Query Language), in other words, it is suggested that relational databases such as ORACLE should be viewed as sophisticated file systems which allow the values to be organised, efficiently stored, rapidly retrieved etc. 

In all simulations of clay mineral systems we apply periodic boundary conditions at constant pressure and temperature (constant NPT), This allows the system volume to change freely at 100 kPa (1 bar) external pressure and 298 K. Furthermore we employ Ewald summation to compute both electrostatic potentials and dispersive van der Waals interactions, and the simulations are fully dynamic, using the Discover module and Insight II graphical user interface of the MSI molecular modeling suite (MSI, 1997). The free energy perturbation technique is not implemented in this software per se so that many of the aforementioned calculations have to be performed with spreadsheet software (e.g., Microsoft Excel). 

He gives a guideline to estimate these gas streams and corresponding compositions to adjust the atmospheric residue. We summarize the rules and automate the calculations by using MS Excel. Figure 3.5 represents the MS Excel interface we have developed to estimate these gas streams. The spreadsheet requires the atmospheric residue flow rate and flash zone temperature (Cell B2 and B3) to calculate the flow rate of gas streams. 

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