Advanced Information Management Systems

Project engineering and management consists of a number of types of individual activity, surrounded by a mass of information flow, which connects the activities according to certain procedures. The scope for enhancing project performance by utilizing advanced management information systems has been obvious since more powerful computers became generally available. Many such systems have been and continue to be developed. A few of the typical features and aspirations follow. 

The most fundamental aspect of integration is the datacentric system, which creates a single-entry database as the heart of the project s information. Documents are no longer data archives they are simply a focused extract from the database at a point in time. A general example of the major system components and their interaction is shown in Fig. 28.1. Software providers who have more or less developed such 

Some of the common reasons for unacceptable performance are the following. 

The classic example of the project organization hamstrung by such lack of flexibility is where every purchase order is delayed interminably, then issued in the form of a letter of intent (bypassing the computer system), because the input information is never quite right for the computer. Accounts are then unpaid because there is no purchase order, the suppliers cease to perform, and so on. The project team blame the computer system, the system designers blame the project team s inputs, and frustration continues. 

Unforeseen consequences of input error. Developing the previous theme, it is frequently experienced that certain types of input error have unforeseen results. For instance, a letter o instead of a zero may result in a whole item of input disappearing from the system, which may only be realized when an entire pipeline is found to be non-existent during the course of pre-commissioning checks. Ultimately, such unforeseen problems are only eliminated by experience of the application. 

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A laboratory information management system (LIMS) is a computer or computer network used to automate the acquisition and management of raw analytical data. In its simplest form, it tracks samples and test results through analytical laboratories and provides summaries of the status of these samples and tests. In its most advanced form, the system is interfaced to the laboratory s instmmentation and communication network to allow automation of data gathering, compilation, and reporting. 

Beyond simple data storage and instrument control, modern data systems provide extensive data analysis capabilities, including fitted baselines, peak start and stop tic marks, named components, retention times, timed events and baseline subtraction. Further, they provide advanced capabilities, such as multiple calibration techniques, user-customizable information and reports and collation of multiple reports. If a Laboratory Information Management System (LIMS) is available, the chromatographic data system should be able to directly transfer data files and reports to the LIMS without user intervention. The chapter by McDowall provides a terse but thorough description of the... 

Laboratory systems such as Laboratory Information Management Systems (LIMS) have also come under increased regulatory scrutiny as the complexity of the software they deploy has become more advanced. Currently, there are often two LIMS tiers of control, data acquisition and data processing, often implemented by two coupled LIMS applications. The data acquisition LIMS is likely, however, to disappear in the longer term as analytical... 

In conclusion, the scope of what can be done for the project and engineering organization by advanced computer systems is truly wonderful, but the prudent user needs to treat all such systems critically. Utilize only those which are rigorously specified, tested on real applications (by real project people), not over-ambitious, and manifestly will pay their way in terms of a conservative business plan. There are definite cut-off points in project size and complexity below which it is not economical to operate fully integrated systems. Systems application below this threshold should not be attempted unless there are other non-economic factors such as a client who requires them (usually, for interface into overall plant information management systems). 

Figure 6 illustrates the infrastructure required for computer control. APC and model-predictive control software usually runs on a separate process computer, which uses a data highway to communicate with the DCS, the laboratory information management system (LIMS), and a real-time database. Advanced applications receive process values from the DCS, calculate the sizes of MV moves, and send setpoints back to the DCS. 

There are some aspects of process design in which decisions are based primarily on past experience rather than on quantitative performance models. Problems of this type include the selection of constraction materials, the selection of appropriate models for evaluating the physical properties of homogeneous and heterogeneous mixtures of components, and the selection of safety systems. Advances in expert systems technology and information management will have a profound impact on expressing the solutions to these problems. 

With rapid advances in hardware, database management and information processing systems, efficient and competitive manufacturing has become an information-intensive activity. The amounts of data presently collected in the field on a routine basis are staggering, and it is not unusual to find plants where as many as 20,000 variables are continuously monitored and stored (Taylor, 1989). 

When the analytical laboratory is not responsible for sampling, the quality management system often does not even take these weak links in the analytical process into account. Furthermore, if sample preparation (extraction, cleanup, etc.) has not been carried out carefully, even the most advanced, quality-controlled analytical instruments and sophisticated computer techniques cannot prevent the results of the analysis from being called into question. Finally, unless the interpretation and evaluation of results are underpinned by solid statistical data, the significance of these results is unclear, which in turn greatly undermines their merit. We therefore believe that quality control and quality assurance should involve all the steps of chemical analysis as an integral process, of which the validation of the analytical methods is merely one step, albeit an important one. In laboratory practice, quality criteria should address the rationality of the sampling plan, validation of methods, instruments and laboratory procedures, the reliability of identifications, the accuracy and precision of measured concentrations, and the comparability of laboratory results with relevant information produced earlier or elsewhere. 

The Total Human Exposme Risk DataBase and Advanced Simulation Environment (THERdbASE) is a data/model management system which contains total human exposure information. THERdbASE is a USEPA-sponsored modeling platform which is being developed and upgraded by the Exposure Modeling and Software Engineering Division of InfoScientific, Inc. (Butler and Engelman, 1998). 

Frank A. Horrigan retired from the technical development staff for sensors and electronic systems at Raytheon Systems Company. Dr. Horrigan has a background in technologies relevant to military systems, in particular, radar and sensor technologies. A theoretical physicist, he has more than 37 years experience in advanced electronics, electro-optics, and advanced information systems. In addition, he has experience in planning and managing industry research and... 

Vassiliadis, P., Quix, C., Vassiliou, Y., Jarke, M. Data warehouse process management. Information Systems (Special Issue on Advanced Information Systems Engineering) 26(3), 205-236 (2001)... 

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