Sunday, April 27, 2014


ON BOARD QUANTITY (OBQ): All the oil, water, sludge and sediment in the cargo and associated lines and pumps on a ship before loading a cargo commence. (this term may not apply to product movement).

QUANTITY REMAINING ON BOARD (ROB): All the oil, water, sludge and sediment in the cargo tanks and associated lines and pumps on a ship after discharging a cargo has been completed, excluding vapour but including clingage. (this term may not apply to product movements)

Determine the OBQ/ROB for Liquids as follows:
(1)    By measurement determine the depth of liquid in each tank. Measure-ments should be taken at as many points as possible to ascertain if the liquid covers the tank bottom.
(2)    Where there is a sufficient depth of liquid determine its temperature. If not assume the material to be at standard temperature.
(3)    Calculate and record corrected volumes using where appropriate :
(a)    Special dip tables or the wedge formula if the liquid does not cover the bottom of the tank.
(b)   Trim/list corrections if the liquid covers the bottom of the tank.
Note: When applicable, estimate the volumes of oil residues adhering to the surfaces of the tank walls and structure. Add this volume to the quantities determined above.
(4)    Where possible obtain a sample of the OBQ/ROB.

Slops which are to be loaded on top should be included in the OBQ/ROB report. Record on the report from the nature of the materiel and the method used to determine the volume in each compartment. Material in compartments not receiving cargo should also be measured and reported on an OBQ/ROB report from.

This report should be signed by the interested parties. If the vessels officer signed under protest a note shall be made as to whether the vessel chose to have a survey made by another company on its behalf .It is strongly recommended that Dry Tank Certificates are not signed by inspectors. Refer to specific instructions issued by interested parties concerning Dry Tank Certificates.

If there is an unresolved dispute between the vessels personnel and the inspector or other interested parties as to the quantity and character (liquid or non-liquid) of the ROB this shall be reported immediately by telephone or telex to all the parties concerned and noted on the OBQ/ROB report.
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Basic Concepts in Data Reconciliation

1.1 Process Measurements
Measured process data inevitably contain some inaccurate information, since measurements are obtained with imperfect instruments which have their own accuracy. In addition, signal transmission, power fluctuation, improper instrument installation and miscalibration are other sources of measurement errors.

It is assumed that any observation is composed of a true value plus some error value. This indicates that a measurement can be modeled as:
y = x + e           (1.1)
where y is the observed value of the raw measurement, x is the true value of the process variable, and e is the measurement error.

1.2 Measurement Error
The error term in Equation (1.1), e, can be divided into two subcomponents, random error and gross error, as shown in Figure 1.1.
Random error is caused by one or more factors that randomly affect measurement of a variable. It follows a Gaussian distribution.
The Gaussian noise is normally distributed with a mean value of zero and known variance. The probability density function (PDF) of a measurement with Gaussian noise is described by the formula:
where µ is the mean value of the measurements, and s is the standard deviation.

The important property of random error is that it adds variability to the data, but it does not affect average performance for the group.

Gross error (as depicted in Figure 1.3) can be caused by:
  • instrument systematic bias that is consistently erroneous, either higher or lower than the true value of the process variable, probably because of instrument miscalibration
  • measurement device failure
  • nonrandom events affecting process, such as process leak

Unlike random errors, gross errors tend to be consistently either positive or negative. Because of this, it is sometimes considered to be a bias in the measurement.
Generally, measurements with gross errors will lead to severely incorrect information about the process, much more so than those with random errors. Gross error detection is an important aspect in validation of process data.

Errors in measured data can lead to significant deterioration in plant operation. Small random and gross errors deteriorate the performance of control systems, whereas larger gross errors can nullify process optimization. It is important to estimate the true conditions of process states with the information provided by the raw measurements, in order to achieve optimal process monitoring, control, and optimization.

1.3 Data reconciliation
The estimation of a process state involves the processing of the raw data and their transformation into reliable information.
For example
a cooling-water station provides water for four plants as shown in Figure 1.4. All the flow rates for the circulation water arem easured in this network. At steady-state, the raw measurements and their standard deviations are listed in Table 1.1.
If we make mass balances around each plant in the network using the raw measurements, we will find that all the flow measurements contain errors. This is because the true values of the flow rates must satisfy mass balances at steady state.

For example, the measurement of stream 1, coming into Plant 1, is 110.5 kt/h. However, the sum of the measured flows for streams 2 and 3 leaving Plant 1 is 60.8 + 35.0 = 95.8 kt/h. Now the question is, how many tons of cooling water does each plant use? For Plant 1, is it 110.5 kt/h or 95.8 kt/h? The estimation of the true values for the flows in this network can be solved by Date Reconciliation (DR).

Data reconciliation is the estimation of process variables based on information contained in the process measurements and models. The process models used in the data reconciliation are usually mass and energy conservation equations.

The DR technique allows the adjustment of the measurements so that the corrected measurements are consistent with the corresponding balances. This information from the reconciled data can be used by the company for different purposes such as:

This is especially true with the implementation of a Distributed Control System (DCS), as shown in Figure 1.5.
  • Monitoring
  • Management
  • Optimization
  • Modeling
  • Simulation
  • Control
  • Instrument maintenance
  • Equipment analysis

The interest in applying DR techniques started in the 1980’s when plant management realized the benefits of having access to more reliable estimates of process data. Nowadays, data reconciliation techniques have been widely applied to various processing industries, such as:
  • Refinery
  • Petrochemical
  • Metal/Mineral
  • Chemical
  • Pulp/Paper

Commercial software specializing in data reconciliation is available. A demo-version of one commercial software can be downloaded at:

Research and development during the past 30 years have led to two major types of applications:
  • Mass and heat balance reconciliation. The simplest example is the off-line reconciling of flow rates around process units. The reconciled flow rates satisfy the overall mass balance of the units.
  • Model parameter estimation. Accurate, precise estimates of model parameters are required in order to obtain reliable model predictions for process simulation, design and optimization. One approach to the parameter estimation is to solve the estimation problem simultaneously with the data reconciliation problem. The reconciled model parameters are expected to be more accurate and can be used with greater confidence.
In general, the optimal estimates for process variables by DR are solutions to a constrained least-squares or maximum likelihood objective function, where the measurement errors are minimized with process model constraints.

With the assumption of normally distributed measurements, a least-squares objective function is conventionally formulated for the data reconciliation problem. At process steady state, the reconciled data are obtained by:
Minimizing subject to
J(yˆ,zˆ ) = (y - yˆ )TV-1(y - yˆ )       (1.3)
f (yˆ,zˆ ) = 0
g (yˆ,zˆ ) ≥ 0
y is an M×1 vector of raw measurements for M process variables,
ˆyis an M×1 vector of estimates (reconciled values) for the M process variables,
ˆz is an N×1 vector of estimates for unmeasured process variables, z,
V is an M ×M covariance matrix of the measurements,
f is a C×1 vector describing the functional form of model equality constraints,
g is a D×1 vector describing the functional form of model inequality constraints which include simple upper and lower bounds.

The models employed in DR represent variable  relationships of the physical system of the process. The reconciled data takes information from both the measurements and the models. In reconciling steady-state measurements, the model constraints are algebraic equations. On the other hand, when dealing with dynamic processes, dynamic models that are differential equations have to be used.

Based on the type of model constraints, the data reconciliation problem can be divided into several subproblems as shown in Figure 1.6. Each sub-problem will be discussed respectively in this module.
The algorithm of the DR formulated by Equation (1.3)  indicates that the data reconciliation techniques not only reconcile the raw measurements, but also estimate unmeasured process variables or model parameters, provided that they are observable.

1.4 Process Variable Classification
It is also important to clarify some concepts in DR techniques Measured variables are classified as redundant and nonredundant, whereas unmeasured variables are classified as observable and nonobservable. The classification of  process variables is shown in Figure 1.7.
  • A redundant variable is a measured variable that can be estimated by other measured variables via process models, in addition to its measurement.
  • A nonredundant variable is a measured variable that cannot be estimated other than by its own measurement.
  • An observable variable is an unmeasured variable that can be estimated from measured variables through physical models.
  • A nonobservable variable is a variable for which no information is available

To demonstrate these concepts, we take the cooling water network as the example:
In Figure 1.4, all six flows are measured, and any one of them can be estimated by mass balances using other measured flows, so they are all redundant variables.

However, if the measurements of flows 2, 4, and 6 wereeliminated as shown in Figure 1.8, flow 1 becomes a measured nonredundant variable, but the measurements of flows 3 and 5 are redundant. The unmeasured flows 2, 4, and 6, in this case, are observable, because their values can be estimated by mass balances around the plants, using the measured flows..

1.5 Redundancy
A measurement is spatially redundant if there are more thanenough data to completely define the process at any instant intime. Referring to Figure 1.4, all the measurements arespatially redundant. For example, we don’t need the value ofthe measurement for flow stream 1, we can still completely
define the process. This is because flow stream 1 can becalculated by other spatial measurements via mass balances.

A measurement is temporally redundant if its pastmeasurements can be used to estimate the current state. A typical case for a temporally redundant measurement is that, at the current sampling time, t, the true value of the process variable can be predicted by dynamic models, in addition to the raw measurement.

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Friday, April 25, 2014

Trading Strategies for Crude Futures

Crude oil futures traders can match their trading strategy with their risk tolerance.
Comstock Images/Comstock/Getty Images

Crude oil futures are known for their high volatility and wide price swings. It’s not unusual for crude oil futures to trade down in the morning but close at a new high when the trading day ends. Traders use several popular strategies that take advantage of crude oil’s unpredictable nature. By analyzing the crude oil futures market, traders select the tactics they believe will result in a profit before the crude oil futures contract expires.
Buy and Hold Trading Strategy
Buy and hold is probably the best known and most widely used trading strategy. Traders analyze fundamentals such as supply and demand and the geopolitical climate, and buy a crude oil futures contract in anticipation of a price increase or sell a crude oil futures contract if expecting the price to fall. The price must make a big enough move to give the trader a profit before the futures contract expires. If the trader’s prediction about the market direction or price behavior is wrong, the trade ends in a loss.

Technical Analysis Trading Strategy
Crude oil traders formulate their investment decisions by applying technical indicators to crude oil price charts over different time periods. Candlesticks, bar charts and volume indicators help traders predict crude oil’s next price move. By using the same technical indicators on a two-minute chart, five-minute chart, one-hour chart and a day chart, traders decide whether to buy or sell a crude oil future. Technical traders often hold their positions open a week or longer to give the trade time to develop.

Swing Trading Strategy
Swing trading involves buying a security and holding it for a short time period that ranges from a few minutes up to four days. Crude oil swing traders rely on short-term changes in supply and demand and technical analysis to determine the market’s trend. Swing traders buy a futures contract if the market is trending up and sell if the market trends down. Crude oil futures swing traders benefit from crude oil’s volatility and will close out a trade when it makes a small profit. Swing trading is very risky, and traders can lose money quickly if the market unexpectedly moves against them.

Spread Trading Strategy
Spread trading involves buying one crude oil futures contract in one month and selling another crude oil futures contract in a farther out month. The goal is to profit from the expected change between the purchase and selling price of both contracts. For example, a trader could sell the March crude oil futures contact trading at $94.50 and buy the June contract for $95.80, for a difference of $1.30. If the trade widens more than the $1.30, the trader has a profit. The trader would buy a March contract and sell a June contract to close out the trade. But if the spread contracts, the trader will realize a loss.
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