Reconciliation Programming

To achieve its goals, a PSM must be calibrated on plant data collected during special organised test-runs . Additional data reconciliation programs may be used to increase the reliability of the plant data, by minimising the errors in measurements and supplying estimations for non-measured variables. 

In Computer Aided Operation we can mention the real time monitoring of material and energy balance, managed nowadays by means of data reconciliation programs. The plant operation can be adapted and optimised in real time by means of computerised tools based on dynamic flowsheeting. Other advanced applications are simulators for safety studies and operator training. 

To determine if a process unit is at steady state, a program monitors key plant measurements (e.g., compositions, product rates, feed rates, and so on) and determines if the plant is steady enough to start the sequence. Only when all of the key measurements are within the allowable tolerances is the plant considered steady and the optimization sequence started. Tolerances for each measurement can be tuned separately. Measured data are then collec ted by the optimization computer. The optimization system runs a program to screen the measurements for unreasonable data (gross error detection). This validity checkiug automatically modifies tne model updating calculation to reflec t any bad data or when equipment is taken out of service. Data vahdation and reconciliation (on-line or off-line) is an extremely critical part of any optimization system. 

The program must require the vendors to measure a number of reference samples and/or duplicates submitted in a planned sequence. It should require prompt measurement and reporting of these data and should maintain the results in a control chart format. Prompt feedback and follow-up of any apparent data discrepancies and reconciliation of the results with control charts maintained by the vendors are required to minimize the length of uncertain performance. The quality assurance plan should include random sampling of the vendors data for their validity and conformance with quality assurance requirements. If quality assurance is properly practiced at all levels, an inspection of 5 percent of the total data output should be adequate. 

Several researchers [e.g., Tjoa and Biegler (1992) and Robertson et al. (1996)] have demonstrated advantages of using nonlinear programming (NLP) techniques over such traditional data reconciliation methods as successive linearization for steady-state or dynamic processes. Through the inclusion of variable bounds and a more robust treatment of the nonlinear algebraic constraints, improved reconciliation performance can be realized. 

Liebman, M. J. T. F. Edgar and L. S. Lasdon. Efficient Data Reconciliation and Estimation for Dynamic Processes Using Nonlinear Programming Techniques. Comput Chem Eng 16(10/11) 963-986 (1992). 

Dynamic Data Reconciliation Using Nonlinear Programming Techniques 148... 

In this sense, the application of Q-R factorizations constitutes an efficient alternative for solving bilinear data reconciliation. Successive linearizations and nonlinear programming are required for more complex models. These techniques are more reliable and accurate for most problems, and thus require more computation time. 

In this chapter, the data reconciliation problem for dynamic/quasi-steady-state evolving processes is considered. The problem of measurement bias is extended to consider dynamic situations. Finally in this chapter, an alternative approach for nonlinear dynamic data reconciliation using nonlinear programming techniques will be discussed. 

DYNAMIC DATA RECONCILIATION USING NONLINEAR PROGRAMMING TECHNIQUES... 

Finally, an approach for nonlinear dynamic data reconciliation using nonlinear programming techniques was discussed. This formulation involves the optimization of an objective function through the adjustment of estimate functions constrained by differential and algebraic equalities and inequalities. 

Jang, S. S., Josepth, B and Mukai, H. (1986). Comparison of two approaches to on-line parameter and state estimation problem of non-linear systems. Ind. Eng. Chem. Process Des. Dev. 25, 809-814. Jazwinski, A. H. (1970). Stochastic Processes and Filtering Theory. Academic Press, New York. Liebman, M. J., Edgar, T. F., and Lasdon, L. S. (1992). Efficient data reconciliation and estimation for dynamic process using non-linear programming techniques. Comput. Chem. Eng. 16, 963-986. McBrayer, K. F., and Edgar, T. F. (1995). Bias detection and estimation on dynamic data reconciliation. J Proc. Control 15, 285-289. 

The most straightforward approach for solving nonlinear EVM problems is to use nonlinear programming to estimate zy and 6 simultaneously. In the traditional weighted least squares parameter estimation formulation there are only n optimization variables corresponding to the number of unknown parameters. In contrast, the simultaneous parameter estimation and data reconciliation formulation has (pM + n)... 

In this section the extension of the use of nonlinear programming techniques to solve the dynamic joint data reconciliation and parameter estimation problem is briefly discussed. As shown in Chapter 8, the general nonlinear dynamic data reconciliation (NDDR) formulation can be written as ... 

In this chapter, the general problem of joint parameter estimation and data reconciliation was discussed. First, the typical parameter estimation problem was analyzed, in which the independent variables are error-free, and aspects related to the sequential processing of the information were considered. Later, the more general formulation in terms of the error-in-variable method (EVM), where measurement errors in all variables are considered in the parameter estimation problem, was stated. Alternative solution techniques were briefly discussed. Finally, joint parameter-state estimation in dynamic processes was considered and two different approaches, based on filtering techniques and nonlinear programming techniques, were discussed. 

Liebman, M. J., Edgar, T. F., and Lasdon, L. S. (1992). Efficient data reconciliation and estimation for dynamic process using non-linear programming techniques. Comput. Chem. Eng. 16,963-986. 

In more practical terms there will be SOPs to describe sample preparation, equipment set-up including programming, and an SOP for operation of the equipment. The documentation does not stop here since there will be defined instructions for fraction analysis, fraction reconciliation and recovery of product from the fractions. 

Health policy and professional responsibilities have changed during the years since DUR was introduced into the health care system. To maintain accreditation, hospitals and health care systems are bmmd to conduct MUE/ DUE programs. In the retail setting, the Omnibus Reconciliation Act of 1990... 

To enhance the quality, appropriateness, and effectiveness of health care services, and access to these services the federal government in the Omnibus Budget Reconciliation Act of 1989 (Public Law 101-239) established the AHCPR. The act, sometimes referred to as the Patient Outcome Research Act, called for the establishment of a broad-based, patient-centered outcomes research program. In addition to the traditional measures of survival, clinical endpoints and disease- and treatment-specific symptoms and problems, the law mandated measures of functional status and well-being and patient satisfaction. In 1999, then President Clinton signed the Healthcare Research and Quality Act, reauthorizing AHCPR as the AHRQ until the end of fiscal year 2005. Presently, its mission is to improve the outcomes and quality of health care, reduce its costs, address patient safety and medical errors, broaden access to effective services, and improve the quality of health care services. 

The hazard evaluation program requires the expertise of a number of different disciplines as well as the coordination and reconciliation of the project schedule with factors such as equipment suitability, personnel, training and effluent considerations. Obviously, to take into account all of the difficulties associated with starting and running an unfamiliar process in addition to examining the potential hazards of the process is a complicated task. The format described here will work for most manufacturing operations. 

On December 11,1980, Congress enacted the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) to create the Superfund hazardous substance cleanup program. The Superfund Amendments and Reauthorization Act of 1986 (SARA) made numerous changes to CERCLA to expand the program s scope. The Omnibus Budget Reconciliation Act of 1990 extended the law s taxing authority, which had expired at the end of 1991 under SARA, through December 31, 1995. 

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