The Operation of Refinery Hydrogen Systems

The refinery hydrogen distribution system usually comprises a set of hydrogen main headers (pipelines) working at different pressures and hydrogen purities. Many makeup and recycle compressors drive the hydrogen through this complex network of consumer units, on-purpose production units, and platformers (see Figure 1a). On-purpose hydrogen plants generate high purity hydrogen at different costs while net production units are platformers generating low purity hydrogen as a byproduct. Hydrogen streams with different purities, pressures and flow rates coming from make-up hydrogen plants and platformers are supplied to multiple consumer units through the hydrogen main headers. Purge streams from hydrotreaters containing non-reacted hydrogen are partially recycled and mixed with fresh hydrogen streams from hydrogen headers before re-routing them to consuming units . 

The remaining off-gas stream is burnt as fuel gas. By controlling the fuel gas flow, the purity of the recycled hydrogen stream can be adjusted (Figure 1b). The major hydrotreater operating constraint is a minimum hydrogen/hydrocarbon ratio along the reactor in order to avoid carbon deposition over the catalyst and its premature deactivation. As the catalyst cost is very significant, an effective operation of the hydrogen network will help to increase the catalyst run length, thus boosting the refinery profitability. Moreover, some consuming units may have group of membranes that can be activated to separate and recycle higher-purity hydrogen streams to the hydrogen piping network (Figure 1b).

The MINLP mathematical model

The integrated management of the whole refinery hydrogen network is a very challenging task that requires effective computer-aided optimization tools. The key principle behind the hydrogen management is the fact that not all processes need hydrogen of the same purity. This section describes the proposed MINLP framework for the cost-effective management of refinery hydrogen systems. Main model decision variables and constraints permit to write accurate hydrogen mass balances in terms of purity and flowrate for every stream. The model aims at systematically improving the use of existing refinery hydrogen supplies as a network problem. Its main goal is to minimize the hydrogen production cost while satisfying predefined hydrocarbon production targets, actual  topological and operational restrictions as well as minimum utility hydrogen needs at desulphurization reactors. Problem constraints related to hydrogen production units, headers and consumer units are introduced below.

1. Hydrogen production unit constraints. As previously stated, a refinery system usually comprises several production units, i.e. H2-plants and catalytic reformers, that can simultaneously be supplying hydrogen streams with different levels of purity and pressure to the pipeline network. Therefore, if an existing production unit uÎPU is being operated in the refinery, i.e. Y_u = 1, equations (1) and (2) will enforce the corresponding lower and upper limits on hydrogen flowrate (Q_u) and purity (P_u), respectively. However, it is worth mentioning that hydrogen streams generated by platformers as a byproduct usually have a certain flowrate and purity, and consequently they become model parameters. Here, it should be noted that the optimization model will be able to choose the most convenient operating conditions for the alternative hydrogen sources in order to meet hydrogen demands at minimum cost. Equation (3) defines the amount of hydrogen feed that is being directly supplied from production units to alternative hydrogen headers hÎH and consumer units uÎCU.

2. Hydrogen pipeline constraints. The refinery pipeline network receives high-purity hydrogen streams coming from producer units and medium/low-purity streams from platformers and consumer unit recoveries. Different headers are usually operated at a given hydrogen purity and partial pressure. Equations (4) and (5) enforce a hydrogen mass balance between inlet and outlet streams in every header. Therefore, if at a given moment the hydrogen production exceeds the actual consumption, the balance is satisfied by supplying the surplus hydrogen to the refinery fuel gas system. In turn, equation (6) computes the header hydrogen purity (P_h) taking into account the total hydrogen flowrate in the header (Q_h), the flowrate of hydrogen inlet streams coming from alternative sources (q_uh) and their corresponding purities (P_u and P^out_u).

3. Hydrogen consumer unit constraints. Consumer units carry out different hydrotreating operations by utilizing the hydrogen streams available in the network. Equation (7) computes the total hydrogen feed (Qⁱⁿ_u) being supplied to consumer unit u from different sources while the bilinear equation (8) determines the actual purity (Pⁱⁿ_u) of the combined hydrogen inlet stream. In turn, equation (9) forces a minimum purity requirement for the combined inlet stream of every consumer unit. The minimum hydrogen need for processing the oil fraction (c_u) being treated in unit u is specified by equation (10) by enforcing a minimum hydrocarbon/hydrogen ratio. Equations (11) and (12) predict the flowrate (Q^out_u) and purity (P^out_u) of the non-reacted hydrogen stream from unit u. These estimations are obtained by using non-linear correlations fq and fp that are functions of the flowrate and purity of the inlet streams as well as the inherent features of the oil fraction being hydrotreated in the unit, i.e. density, sulphur and aromatics content, etc.  Finally, equation (13) determines the amount of off-gas that is being recycled and supplied to headers and other consumer units.

4. Objective function. The proposed objective function computes the total hydrogen cost required for hydrotreating pre-specified oil-fractions. The non-linear correlation fc calculates the total production cost as a function of the current hydrogen purity and flowrate in each producer unit u. This function may easily accommodate internal and/or external hydrogen suppliers with different cost and restrictions. Alternatively, the proposed model with minor changes could be used for maximizing the refinery profitability. In this case, the model may optimally select the oil-fractions to be hydroteated subject to minimum and maximum oil-fraction demands and a maximum hydrogen availability. This scenario seems to be particularly interesting for dealing with ultra low-sulphur targets and, consequently, future hydrogen shortfalls.

Case study

A case study of a H2 network comprising two on-purpose plants, two platformers and eight hydrotreating units with different needs of hydrogen purity and flowrates is depicted in Figure 2a. In turn, Figure 2b shows the optimal hydrogen balance when the HD3 hydrogen purity need decreased to 95.9%. The optimal balance generated by the MINLP model with modest CPU time obtained a 25% reduction in  H2 production cost.

Conclusions and future work

An MINLP-based approach has been presented to optimally manage complex hydrogen networks of refinery operations. The proposed model is able to systematically reduce utility cost by increasing hydrogen recovery in consumer units and reducing production cost in the alternative hydrogen suppliers. This project stage is mainly focused on a rigorous treatment of hydrogen mass balances. Future work will aim at extending the model to also consider actual compression costs and operational restrictions as well as the use of alternative separation units (membranes) to recycle higher-purity off-gas to consumer units. 

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Flare Gas Recovery (FGR) to Minimize Wastes and Economical Benefits

Abstract

One of the safe and pressure relieving systems in oil and gas refineries as well as petrochemical complexes, is the relief header with the flare stack being the last component. In this network all the excess gases are collected and sent to flare stack to be burnt. A great amount of these gas mixtures has a high heating value and in some cases it can even be used as the raw material forvarious units.

It is clear that burning this gas mixture in flare stack causes environmental problems like air and noise pollution and also is a financial waste. In this paper a step by step approach and calculations are given to calculate and discuss flare gas recovery benefits for refineries. The method contains data gathering of flare line’s composition and other conditions, simulating of data and calculate financial benefits for the case study by available equations. Therefore, Tabriz oil refinery flaring system and available equation for flare system are used as a case study.

Flare gas mixture in Tabriz refinery consists of a wide spectrum of gases. After studying the methods for flare gas recovery and economic analysis, a suitable method with a single stage compressor is selected. The financial gain of this method is around 105,000 $ per year. Considering the implementation of Kyoto protocol in Iran, flare gas recovery will be more economical.

Keyword: Flare gas recovery, FGR, FGR calculations, FGR simulation

Introduction:

Even in most advanced countries only a decade has passed from flare gas recovery technology, thus the method is a new methods for application in refineries wastes. Of such countries active in flare gas recovery are USA, Italy, the Netherlands, and Switzerland.

To recover flare gas, after collecting from header and flare knock-out drum, flare gas passes through a compressor. The compressor design and selection is the main part of the plan.

After gas compression based on refinery structure or related unit, the gases used as a feed or fuel. If required, to reach entrance gas temperature to flare gas recovery unit and external gas temperature from this unit to an optional temperature, heat exchangers are used.

To compress gases and to design flare gas recovery unit, in general, liquid ring compressors or reciprocating compressors are used. Advantage of first type is that gas is cooled during compression by heat transfer of gas through water inside compressor (usually water). It is possible to use amine instead of water in such compressor to separate hydrogen sulfide from flare gases.

Reciprocating compressors are purchased easily than the first type, also spare parts provision, repair and maintenance is much easier. If using reciprocating compressors, please note that it will explode if temperature exceeds over allowable limit.

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Coriolis gas flowmeters

Recent advances in the development and performances of Coriolis meters have meant that the measurement of the mass flow rate of gases, such as natural gas for custody transfer applications, is now a reality.

International standards

This has been reflected by the large acceptance of this technology within the natural gas industry. As an example, Micormotion has supplied 5,000 Coriolis meters for natural gas applications in the last 3 years. This industrial acceptance motivated ISO to develop a standard through the ISO Technical Committee—ISO Standard TC30/SC12. In addition to this ISO standard, there is also an engineering technical report prepared by AGA entitled Coriolis Flow Measurement for Natural Gas Applications.

For additional information on Coriolis meters and their use in liquid service, see Inference liquid meters.

Although there is no ISO standard for natural gas measurement using Coriolis measurement, some countries have issued type-approval certificates for natural gas measurement using Coriolis meters. These countries include: The Netherlands (Netherlands Inst. for Metrology and Technology), Germany (Physickalisch-Technische Burdessarstalt), Canada (Measurement Canada), and Russia (Gosstandard).

Coriolis meter overview

A Coriolis meter comprises two main parts:

§  A sensor (primary element)

§  A transmitter (secondary element)

See Fig. 1.

Fig. 1—Coriolis flowmeter (Courtesy of Daniel Industries).

With this design, the gas flows through a U-shaped tube. The tube is made to vibrate in a perpendicular direction to the flow. Gas flow through the tube generates a Coriolis force, which interacts with the vibration, causing the tube to twist. The greater the angle is twisted, the more the flow increases. The sensing coils, located on the inlet and outlet, oscillate in proportion to the sinusoidal vibration. During the flow, the vibrating tubes and gas mass flow couple together because of the Coriolis force, causing a phase shift between the vibrating sensing coils. The phase shift, which is measured by the Coriolis meter transmitter, is directly proportional to the mass flow rate. The vibration frequency is proportional to the flowing density of the flow. However, the density measurement from the Coriolis meter is not normally used as part of the gas measurement station. Like other meters, the Coriolis is usually mounted in a meter tube. Because the device is insensitive to flow disturbances, there is no requirement for any form of flow conditioning, straight lengths, or meter tube.

Theory of operation

Coriolis meters operate on the principle that, if a particle inside a rotating body moves in a direction toward or away from the center of rotation, the particle generates inertial forces that act on the body. Coriolis meters create a rotating motion by vibrating a tube or tubes carrying the flow, and the inertial force (Coriolis force) that results is proportional to the mass flow rate. By measuring the amount of inertial force or deflection, it is possible to infer the mass flow rate. It is this phenomenon that is harnessed within the Coriolis flowmeter.

It is also important to consider any additional uncertainty associated with the through-life stability of the Coriolis meter. There are two main influencing factors: the change in flow-tube structural characteristics caused by erosion of the tube wall by abrasive particles and the coating of the flow tube by debris. Abrasion of the flow tubes by abrasive particles can directly affect the flow calibration of the meter. Coating of the flow tubes by debris is only a concern at low fluid flow velocities when the meter is not self-cleaning. This influence does not affect the meter’s calibration and only affects the meter’s zero. It can be corrected by regular zero checks for drift and zeroing, if required. Both of these influences can be identified as occurring under flowing conditions by monitoring the drift in flowing density over time.

Advantages and disadvantages

The advantages and disadvantages for Coriolis meters are shown in Table 1.

Table 1

Sizing

Gas Coriolis meters, like all Coriolis meters, are mass devices. The sensitivity of the meter to measure small amounts of mass flow determines the low end of the metering range. The upper end of the measurement range is most often determined by the largest acceptable pressure loss. The pressure loss across the meter increases with flow rate and the corresponding velocity through the meter. Velocities through the meter can be a substantial fraction of the speed of sound but clearly should not exceed about 0.5 Mach.

References

1.    ISO Standard TC30/SC12, Measurement of Fluid Flow in Closed Conduits—Mass Methods. 2005. Geneva, Switzerland: ISO Technical Committee.

2.    Coriolis Flow Measurement for Natural Gas Applications, technical report. 2001. Washington, DC: AGA.

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OBQ and ROB

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.

Note:

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: http://www.simsci.com/products/datacon.stm.

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

where

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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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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