Data Reconciliation Applied in a Transfer and Storage Terminal

2006 ◽  
Author(s):  
Rafael Noac Feldman ◽  
Elcio Cruz de Oliveira
Keyword(s):  
Diesel Oil ◽  
Data Reconciliation ◽  
Systematic Bias ◽  
Random Errors ◽  
Single Measure ◽  
Gross Error ◽  
Energy Balances ◽  
And Storage ◽  
Mass And Energy Balances

In a simply manner, data reconciliation is a mathematic treatment with propose of a better quality of the data in a process. Industrial processes typically have a large number of measured variables, which presents some degree of random errors and, less frequently, gross errors. In this text, in order to simplify the notation and terminology we classify all instrument and process errors in these two categories. Any significant systematic bias is included in the gross error category. Data reconciliation allows the measurements to be adjusted (“reconciled”) to satisfy process restrictions (mass and energy balances) and improve measurements quality. The results obtained by data reconciliation can also provide benefits in custody transfer issues. Custody transfer is the responsibility transfer during the storage and transportation of a measured refined product volume. Any loss or gain resulting in a non-trustful measurement is considered as the transportation company responsibility. Therefore, the work objective is to propose a data reconciliation methodology, in a process involving diesel oil custody transfer in a Transpetro’s terminal (Terminal of Sao Caetano do Sul), in order to evaluate and correct possible inconsistencies, besides to know a single measure that represents better the measurement system. In this study we will use data from static measurement in tanks, dynamic measurement in turbine and ultra-sonic device. A database will be obtained in two basic steps: process modeling and data reconciliation to consolidate the mass balance. The reconciled value shows us that there is a bias in the ultra-sonic meter and the turbine meter measurement is more reliable, as expected.

scholarly journals Antifreeze life cycle assessment, II: Mathematical modeling

2005 ◽  
Vol 11 (2) ◽  
pp. 85-92
Author(s):  
Jelena Kesic ◽  
Dejan Skala
Keyword(s):  
Mathematical Modeling ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Satisfactory Solution ◽  
Different Types ◽  
Two Parameters ◽  
Energy Processes ◽  
Energy Balances ◽  
Mass And Energy Balances

A mathematical model based on the mass and energy balances of all the processes included in antifreeze life cycle assessment (LCA) was defined in the first part of this study [1]. The part of energy that can be transformed into some other kind of energy is called exergy in all energy processes. The concept of exergy considers the quality of different types of energy and materials. It is also a connection between energy and mass transformations where the physical meaning of exergy loss is the loss of material and energy that must be used in the process. The results of the LCA calculation are very useful for analyzing the obtained products and used processes and for determining the conditions under which this analysis was conducted. The result of this study indicated that recycling is the most satisfactory solution for the treatment of used antifreeze taking into account two parameters: material and energy consumption. The reuse of antifreeze should not be neglected as a solution of this analysis.

Data Reconciliation of the Turbine Section: Evaluation of Estimation Uncertainty

2014 ◽  
Author(s):  
Olli Saarela ◽  
Emil Wingstedt
Keyword(s):  
First Principles ◽  
Nuclear Power ◽  
Process Model ◽  
A Priori ◽  
Data Reconciliation ◽  
Estimation Uncertainty ◽  
Data Driven Approach ◽  
Multiple Measurements ◽  
Energy Balances ◽  
Mass And Energy Balances

Data reconciliation is technique for reducing measurement uncertainty by adjusting measured data to comply with a first-principles process model, most importantly with mass and energy balances. It also provides estimates for modelled unmeasurable process variables and estimates for the uncertainties of the computed values. For computing these estimates the process model has to include estimates of measurement uncertainties defined a priori. A priori consideration of all potential sources of uncertainty is far from trivial. This paper discusses a data-driven approach of uncertainty evaluation, based on identifying and subtracting variability modes affecting multiple measurements. Possible bias in the measurements is not considered. The approach is applied to evaluate the uncertainties of estimates computed with a data reconciliation model of a turbine section of a nuclear power plant.

scholarly journals Industrial Processes: Data Reconciliation and Gross Error Detection

2009 ◽  
Vol 42 (7) ◽  
pp. 209-215 ◽  
Author(s):  
Yu Miao ◽  
Hongye Su ◽  
Rong Gang ◽  
Jian Chu
Keyword(s):  
Error Detection ◽  
Industrial Applications ◽  
Vital Role ◽  
Data Reconciliation ◽  
Industrial Processes ◽  
Random Errors ◽  
Process Data ◽  
Gross Error Detection ◽  
Business Decision ◽  
Gross Error

Process data plays a vital role in industrial processes, which are the basis for process control, monitoring, optimization and business decision making. However, it is inevitable that process data measurements will be corrupted by random errors. Therefore, data reconciliation has been developed to improve accuracy of process data by reducing the effect of random errors. Unfortunately, reconciled values would be deteriorated by gross errors, which may be present during measurement. Therefore, gross error detection is necessary to guarantee the efficiency of data reconciliation, which has been developed to identify and eliminate gross errors in process data. In this paper, a review of data reconciliation and gross error detection and relevant industrial applications are presented. As the efficiency of data reconciliation and gross error detection largely depends upon the locations of sensors, sensor networks design is also included in the review. Meanwhile, some achievements of the authors are also included.

Data Reconciliation and Suspect Measurement Identification for Gas Turbine Cogeneration Systems

2013 ◽  
Vol 135 (9) ◽  
Author(s):  
Mohammed S. Syed ◽  
Kerry M. Dooley ◽  
F. Carl Knopf ◽  
Michael R. Erbes ◽  
Frantisek Madron
Keyword(s):  
Power Systems ◽  
Error Detection ◽  
Monte Carlo Study ◽  
Data Reconciliation ◽  
Random Errors ◽  
Process Data ◽  
Gross Error Detection ◽  
Gross Error ◽  
Cogeneration System ◽  
Model Linearization

Data reconciliation is widely used in the chemical process industry to suppress the influence of random errors in process data and help detect gross errors. Data reconciliation is currently seeing increased use in the power industry. Here, we use data from a recently constructed cogeneration system to show the data reconciliation process and the difficulties associated with gross error detection and suspect measurement identification. Problems in gross error detection and suspect measurement identification are often traced to weak variable redundancy, which can be characterized by variable adjustability and threshold value. Proper suspect measurement identification is accomplished using a variable measurement test coupled with the variable adjustability. Cogeneration and power systems provide a unique opportunity to include performance equations in the problem formulation. Gross error detection and suspect measurement identification can be significantly enhanced by increasing variable redundancy through the use of performance equations. Cogeneration system models are nonlinear, but a detailed analysis of gross error detection and suspect measurement identification is based on model linearization. A Monte Carlo study was used to verify results from the linearized models.

Exergy-Based Study of a Binary Rankine Cycle

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Mathias Hofmann ◽  
George Tsatsaronis
Keyword(s):  
Flue Gas ◽  
Rankine Cycle ◽  
Specific Enthalpy ◽  
Base Case ◽  
Gas Streams ◽  
Sensitivity Studies ◽  
Energy Balances ◽  
Exergy Analyses ◽  
Mass And Energy Balances

The aim of this work is to study a binary Rankine process with a significantly higher efficiency compared to a conventional coal-fired power plant. This paper focuses on the design of the process and especially on an efficient combination of flue gas, potassium, and water streams in the components of the steam generator, such as economizers, evaporators, and superheaters, to decrease the overall exergy destruction. Based on a literature review, a base case for a coal-fired binary Rankine cycle with potassium and water as working fluids was developed and, in order to evaluate the thermodynamic quality of several variants, comparative exergy analyses were conducted. A simulation of the process and calculation of the values for the streams were carried out by using the flow-sheeting program CycleTempo, which simultaneously solves the mass and energy balances and contains property functions for the specific enthalpy and entropy of all the substances used. Necessary assumptions are predominantly based on literature data or they are discussed in the paper. We present the exergy analysis of the overall process that includes the flue gas streams as well as the potassium and water cycles. A design analysis and sensitivity studies show the effects of stream combinations and key parameters on the net efficiency, which is higher than 50%.

STUDY OF INACCURACY IN THE OPERATION OF THE MAIN CONTROLLER OF STB MACHINE

2019 ◽  
Keyword(s):  
Normal Distribution ◽  
Stable Process ◽  
Random Errors ◽  
Actual Length ◽  
Experimental Data Processing ◽  
Set Up ◽  
Main Regulator ◽  
Transmission Gear ◽  
Selection Of

At production of fabrics, including fabrics for agricultural purpose, an important role is played by the cor-rect adjustment of operation of machine main regulator. The quality of setup of machine main controller is determined by the proper selection of rotation angle of warp beam weaving per one filling thread. In the pro-cess of using the regulator as a result of mistakes in adjustment, wear of transmission gear and backlashes in connections of details there are random changes in threads length. The purpose of the article is the research of property of random errors of basis giving by STB machine regulator. Mistakes can be both negative, and positive. In case of emergence only negative or only positive mistakes operation of the machine becomes im-possible as there will be a consecutive accumulation of mistakes. As a result of experimental data processing for stable process of weaving and the invariable diameter of basis threads winding of threads it is revealed that the random error of giving is set up as linear function of the accidental length having normal distribution. Measurements of accidental deviations in giving of a basis by the main regulator allowed to construct a curve of normal distribution of its actual length for one pass of weft thread. The presented curve of distribution of random errors in giving of a basis is the displaced curve of normal distribution of the accidental sizes. Also we define the density of probability of normal distribution of basis giving errors connected with a margin er-ror operation of the main regulator knowing of which allows to plan ways of their decrease that is important for improvement of quality of the produced fabrics.

EFEK PENAMBAHAN EKSTRAK AIR JAHE (Zingiber officinale Roscoe) DAN PENYIMPANAN DINGIN TERHADAP MUTU SENSORI IKAN TUNA (Thunnus albacores)

2010 ◽  
Vol 6 (1) ◽  
pp. 36
Author(s):  
Silvana Dinaintang Harikedua
Keyword(s):  
Sensory Quality ◽  
Zingiber Officinale ◽  
The Other ◽  
Ginger Extract ◽  
Other Hand ◽  
Storage Treatment ◽  
Zingiber Officinale Roscoe ◽  
Hedonic Scale ◽  
And Storage

The objective of this study was to investigate the effect of ginger extract addition and refrigerate storage on sensory quality of Tuna through panelist’s perception. Panelists (n=30) evaluated samples for overall appearance and flavor attribute using hedonic scale 1–7. The sample which is more acceptable by panelists on flavor attributes having 3% gingers extract and storage for 3 days. The less acceptable sample on flavor attribute having 0% ginger extract and storage for 9 days. On the other hand, the sample which is more acceptable by panelists on overall appearance having 0% ginger extract without storage treatment. The less acceptable sample on overall appearance having 3% ginger extract and storage for 9 days.

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