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Utilities and energy systems are often the major source of SOx, NOx and CO2 emissions, therefore, emissions control and the management of credits and quotas are tightly interrelated with energy management.
In the case of refineries, chemical and petrochemical plants, energy represents the main cost (second to feedstock) and therefore its reduction has become a bottom line business decision. The energy systems at these sites are inherently complex, with the emissions cost analysis and limits compliance introducing an additional factor to the complexity of the energy costs reduction challenge.
Process plants use different type of fuels, they often operate cogeneration units, their steam networks consist of several pressure levels, there are different types of energy consumers and there are emission limits to be observed. Import or export of electricity in deregulated markets, which could also be traded off with more or less CO2 and other contaminant gaseous emissions, increase the optimization problem complexity.
In the case of SOx emissions, they can be predicted based on each individual fuel composition.
On the other hand, NOx emissions depend not only on the individual fuel composition but also on the equipment in which the fuel is burned and on the use of burning additives; therefore, equipment-specific correlations need to be added to the model.
The SOx and NOx limits can be imposed as total emitted mass rate and concentration in flue (exhaust) gases. Additionally, an annual quota limiting the total mass emission of SOx and NOx can be enforced. Allowed emission limits can also change with respect to the liquid/gas fuels ratio used at a given boiler or heater (or a set of them associated to a given stack).
Several application examples and results corresponding to refineries around the world are presented and discussed.
How to integrate the emission costs and constraints within the overall energy system on line real time modeling and optimization is also explained.
Refineries face increasing governmental regulations and tax pressure to reduce emissions. They are challenged to optimize their energy systems costs with the additional goal of maintaining or reducing their SO2, NOx and CO2 emissions and, at the same time, increasing their competitiveness.
As the utility and energy systems are often the major source of SOx, NOx and CO2 emissions, the appropriate control of these emissions and management of credits and quotas are tightly related with energy management.
Refineries usually operate complex energy systems, SO2, NOx and CO2 emissions introduce an additional factor to the complexity of the energy costs reduction challenge. Moreover, since energy represents usually their main cost after feedstock, its reduction is more of a bottom line business decision than a challenge.
Refineries use different type of fuels, they often operate cogeneration units, their steam networks consists of several pressure levels, there are different types of energy consumers and there are emission limits to be observed. Import or export of electricity in deregulated markets, which could also be traded off with more or less SO2, NOx and CO2 and other contaminant gases emissions, increase the optimization problem complexity.
In order to successfully address the energy system and emissions management, the Visual MESA software is widely used (Ref. 1). It is a Real Time Optimization application that is saving refineries all over the world millions of dollars per year by advising on optimal operating conditions of their utilities systems, comprising steam, fuels, electricity, boiler feed water, condensates and emissions. Visual MESA has been adopted by the leading refiners worldwide and is the first choice in the segment of online energy optimization.
At the sites were Visual MESA is in use, operators always have a set of recommendations available to help them operate the energy system at the minimum cost under the current site production scenario and respecting economic, contractual and environmental constraints. The tool also acts as a "watch dog" since supervisors can evaluate how operators manage the energy system based on the Key Performance Indicators being generated. In the vast majority of cases we have seen that, before applying the optimizing recommendations (or when they are not taken into account), high variability in the way the energy system is operated and large potential benefits are frequently found. As soon as Visual MESA is commissioned and routinely in use, variability is noticeably reduced or eliminated, as it was reported by many implementations (see References 2, 3, 4, 5, 6 and 7).
Figure 1 shows an example of the savings found in a refinery during a shift period (savings are expressed as a % of the total energy cost). Each point in the plot corresponds to an automatic Visual MESA execution. Note the decrease in the potential savings when operators begin to apply the recommendations (last three hours of the shown shift) meaning the savings were truly captured.
Figures 2, 3 and 4 show respectively the corresponding potential reduction in CO2 emissions (t/h), SO2 and NOx emissions (in terms of concentration with data corresponding to one of the main stacks) found during the same operational shift period applying the optimization recommendations.
Energy costs reduction in the order of 3% on total energy costs were obtained in this particular example. They imply an associated reduction in CO2 emissions, in the order of 2 t/h. Additionally, and in this case also due to fuel management, a reduction in SO2 (200mg/Nm3 less) and in NOx (50 mg/Nm3 less) in one of the main stacks has been also obtained.
Some of the procedures used to calculate, constrain and optimize the emissions within the overall Site Energy Management strategy are discussed in the next paragraphs.
SO2 emissions can be predicted based on each individual fuel composition.
SO2 flow= %S x 64/32 x Fuel flow
In terms of concentration:
SO2 flow / flue gases flow (e.g. mg / Nm3)
Flue gases flows are calculated based on fuel composition and excess air, since each component has a factor in Nm3/kg of burned fuel.
The concentration limits are usually standardised to a certain level of %O2 (e.g. 3%O2 dry basis).
The concentration emission limit also can be function of the % of liquid or gas fuel that is burnt (variable constraint). For example:
SO2 Conc. limit per stack (mg/Nm3) = 1700 x (%L) + 35 x (%G), where %L is the mass percentage of liquid fuel burnt and %G is the percentage of gas fuel burnt in equipment discharging to the same stack. In this example, if ratio is 50/50, the limit is 867 mg/Nm3. Other emission limit usually imposed by the legislation is related to the total annual amount of SO2 emitted (e.g. ton/year).
All these aspects have to be taken into account by the on-line optimization model. Figure 4a shows an example of the representation in the model of the SO2 mass flow calculation, which is based on sulphur balance for two boilers, with different burners, discharging to one stack.
The stoichiometric flue gas production (in dry basis and normalized to 3% O2) is calculated for each boiler, based on the fuel compositions and the stoichiometric flue gas factors. In case such flue gas is also measured, the difference (imbalance) is also calculated. The follow-up of such imbalance is useful to check measurements quality and/or adjust the flue gas production calculation. Figure 4b shows an example.
Then, the corresponding SO2 concentration is calculated based on SO2 and flue gas flows (SO2 flow divided by flue gas flow).
In case there is an on-line measurement of the concentration, the model calculates the bias between calculated and measured value (correction factor). Figure 4c shows an example:
When optimizing, the model predicts the emissions in the optimized situation using the calculated deviation (bias).
When the concentration limit depends on the fuel liquid/gas ratio, such ratio is calculated in current situation and it is taken into account as a constraint variable during optimization, as it is shown in Figure 4d.
Fuels choices and fuels consumption reduction as part of energy system optimization are the typical reported ways for day to day SO2 emissions reduction.
NOx emissions depend not only on the individual fuels composition but also on the equipment where they are burnt. Specific correlations need to be considered.
NOx Conc = Sum (Fueltypes Ei * Fi * Vi) / Sum(Fueltypes Fi * Vi) + A* Esteam *(Fsteam - B)
NOx Conc NOx Concentration in the stack
Ei NOx Emission factor per fuel type (mg/Nm3)
Fi Quantity of fuel burned (t/h)
Vi Stack gases volume produced, per fuel type (Nm3/t)
Esteam Correction factor for the feed (mg/Nm3/(t/h))
Fsteam Production rate of steam at the boiler (t/h)
A and B Correction factors
The concentration limits are usually standardised to a certain level of %O2 (e.g. 3%O2 dry basis). The concentration emission limit also can be function of the % of liquid or gas fuel that is burnt (variable constraint). For example:
NOx Conc limit per stack (mg/Nm3) = 450x (%L) + 225 x (%G), where %L is the mass percentage of liquid fuel burnt and %G is the percentage of gas fuel burnt in equipment discharging to the same stack. In this example, if ratio is 50/50, the limit is 337 mg/Nm3. Other emission limit usually imposed by the legislation is related to the total annual amount of NOx emitted (e.g. tons/year).
All these aspects have to be taken into account by the on-line optimization model.
Figure 5 shows an example of the representation in the model of the NOx mass flow calculation, which is based on correlations for each boiler, with different burners, discharging to a single stack.
The rest of the model is done in similar way to the SO2 modelling already explained.
Fuels choice, fuels consumption reduction and equipment operating parameters (boiler steam production) as part of energy system optimization are the typical reported ways for daily NOx emissions reduction.
Each fuel has associated an emission factor that can be calculated based on its % of Carbon:
Emission factor (ton of CO2 per ton of fuel) = 44/12 x %C in fuel/100
For example, a fuel gas with 65% of C has an emission factor of 2.4 ton of CO2 per ton of fuel gas.
Figure 6 shows an example of the calculation of emissions for a whole Site, including CO2
Among the different ways of reducing CO2 emissions, efficiency improvements, fuels substitution and crude substitution are the most commonly put in practice.
By integrating CO2 emissions in an energy costs optimization model, the cost of CO2 emissions is taken into account by the Visual MESA model together with all the other existing purchase and supply contracts of fuels, steam, water and electricity. The CO2 emissions modeling and economics must be configured according to each site's specific needs.
For example, the CO2 emission cost and total quota constraint can be added to the optimization economical Objective Function (OF) so that, when an optimizer minimizes the OF, CO2 cost is taken into account together with all the other costs (fuels, electricity, demineralized water, etc.). In this way the optimum fuel feeds to boilers and gas turbines are recommended. Of course, also the limits and/or quotas imposed to other emission gases can be taken into account at the same time.
In general, since the energy cost savings are mainly achieved by a reduction in fuels consumption, the optimization will always imply a reduction in CO2 emission, except in those scenarios where the optimizer finds the use of a cheaper fuel that generates more CO2 instead of using a more expensive fuel that generates less CO2. This could be the case when replacing Natural Gas with a heavy liquid fuel. This challenging tradeoff is affected directly by both the CO2 allowance price and the annual emission quota.
The following sections explain the importance of including the cost of CO2 emissions and how it should be taken into account when managing and optimizing the energy systems.
Furthermore, it is shown how an optimization tool like Visual MESA helps to perform case studies to evaluate energy system modifications taking into account this aspect.
The consideration of CO2 emissions in the energy system model for everyday usage, to perform the energy system Real Time, On Line, Optimization, is also explained.
In many countries, a given industrial complex has an assigned quota for total CO2 emissions. They periodically report the total generated CO2 related to fuels consumptions and operating processes. At the end of the year, if the quota is exceeded, each ton of CO2 emitted above the quota has to be paid at a given market price. For instance, the price may be referred to the European Union Allowance (EUA), equivalent to one metric ton of CO2 emissions (see http://pointcarbon.com).
In some countries, there is an additional tax, sometimes much more expensive than the allowance price, as a penalty for having exceeded the quota.
Also, if emissions are below the quota, the tons of CO2 saved can be sold at the market price of the emissions allowance.
The cost of CO2 emissions in the OF can be incorporated in several different ways depending on whether the quota has been exceeded or the accumulated emissions are below the quota, at a given point of time and over a given accounting period (generally one year):
a) For each ton of CO2 emitted a price equal to the emission allowance price is assigned (plus the applicable taxes). This approach could not be fully realistic from the accounting perspective, unless the plant has exceeded the CO2 emissions quota. However, it assures that the optimization will be always focused on minimizing CO2 emissions. This approach may influence the optimization results in those cases that a compromise between using a more expensive fuel with less CO2 emissions and a cheaper fuel with more CO2 emissions exists. It will, in fact, penalize the cheaper fuel.
b) No cost is assigned to the emitted CO2 until the quota is achieved. In this option, if there were compromise solutions between the use of a more expensive fuel with less CO2 emissions or a cheaper one with more emissions, the optimization would advise the second. Consequently, the quota will be achieved early in time. This approach should be only applied in those plants where, due to its particular operating conditions, the annual quota is unlikely to be achieved.
c) The CO2 emissions have always an associated cost, however it will depend on the emissions projection for the rest of the period (typically one year). If this projection of emissions estimates that at the end of the period the quota will not be reached, each ton of CO2 below the quota will have a negative cost (-) equal to the price of sale of the emissions rights, which for optimization purposes will correspond to a credit (assuming this emissions rights not used will be able to be sold). If the projection foresees the quota will be reached, the price will be equal to the cost of emission (plus the applicable taxes).
d) In all cases a constraint can be imposed on the current CO2 emissions. Such a constraint should be to be equal to the projection of the future emissions calculated in such a way that the quota would be met at the end of the considered period (i.e., end of the year). This approach would help manage the fuels consumption so that the site is always below the emissions quota in order and therefore take the maximum advantage of the quota at the end of the considered period.If the quota eventually is exceeded before the end or the period, the additional CO2 emissions cost will be included in the Objective Function to be minimized. Under this scenario the price of each ton of emitted CO2 will be equal to the CO2 emissions allowance (plus the applicable taxes).
All the examples shown in this section correspond to refineries.
This first example shows the effect of the optimization by using an online model which includes the emissions when the limit depends on fuels liquid/gas ratio.
In this case, an increase in the % of liquid burnt, as result of the total energy cost system optimization, implies an increase in the corresponding emissions. However, in the case of SO2 emission, the system will work even further from the emission limit (the new emission limit is higher). In the case of NOx, the system will operate at its new emission limit (while the simulation case was 6 mg/Nm3 far from this limit.
In this example, a set of manual operating recommendations given by the optimizer during a shift have been:
• Pump swaps
• Fuels to boilers (i.e., FG and FO)
As a result of the manual actions, the changes performed by the control system have been:
• Steam production at boilers
• Letdown and vents rates
• Around 1 t/h less of FO consumed
• Approx. 7 t/h less of high pressure steam produced
• Approx. 2 t/h less of CO2 emitted
• Approx. 200 kW more of electricity imported
The following figures show the impact on steam production, fuel use and CO2 emissions reduction.
When considering the CO2 emission cost, the following is an example of the recommendation for operators:
• Fuels to boilers
As a result of the manual change in the boilers fuels diet (increase FG and decrease FO in the overall), the Fuel Gas header pressure control system made the necessary adjustments which resulted in a Natural Gas net import.
The following figure shows the fuel gas network model representation highlighting the differences between current and optimized situation (delta view, duty flows).
On the left, fuel gas suppliers are represented while on the right Fuel Gas consumers are displayed. The values indicate the corresponding change, expressed in MW, after the application of the recommendations (zero value means no change).
As a result of replacing FO by FG (with the need to import more Natural Gas), CO2 emissions are reduced in 4.7 t/h. This is important to be considered when there is a trade-off between cheaper fuels that produce more CO2 and more expensive fuels that produce less CO2. This is very important when the CO2 emissions quota is expected to be exceeded by the end of the year.
This fourth example corresponds to a Middle East refinery where the Visual MESA based
Real Time Energy Management System was recently commissioned. In addition to the energy cost savings a reduction in CO2 emissions reduction was achieved.
Figure 17 shows the reported results from one of the refineries (KNPC Mina Al Ahmadi). The base case was established when the model was completed in May 2012 with energy cost savings opportunity of about 680 $/hr. By the end of December'12 the energy cost savings potential were reduced to about 180 $/hr reflecting obtained savings of about 500 $/hr (4.4 MM$/year).
The cost savings and CO2 emission reductions came from two main sources:
• Excess steam vent / condensing reduction (i.e., less fuel gas used for high pressure steam production because of the steam waste reduction)
• Letdown reduction (i.e., less power purchase for driving the pumps and compressors because of the use of turbines instead of motors)
Refining examples have been presented in which, with the existing equipment and utilities infrastructure, NOx, SO2 and CO2 emissions reduction were achieved while optimizing the energy system costs using a real time on line software tool.
Optimization is configured to provide recommendations to operational personnel on a routine basis.
1. "An Energy Management System", O. Santollani, D. Ruiz, C. Ruiz, Hydrocarbon Engineering, September 2008, Pages 51-58.
2. "Online energy management", S. Benedicto, B. Garrote, D. Ruiz, J. Mamprin and C. Ruiz, Petroleum Technology Quarterly, Q1 2007, pages 131-138.
3. "The Use of an On-line model for Energy Site-Wide Costs Minimisation", García Casas, J.M., Kihn, M., Ruiz D. and Ruiz C., European Refining Technology Conference (ERTC) Asset Maximisation Conference, May 21-23, 2007, Rome, Italy.
4. "Energy System Real Time Optimization", D. Uztürk, H. D. Franklin, J. M. Righi, A. T. Georgiou, NPRA Plant Automation and Decision Support Conference, 2006, Phoenix, USA
5. "Site-wide Energy Costs Reduction at TOTAL Feyzin Refinery", Département Procédés - Energie, Logistique, Utilités, TOTAL - Raffinerie de Feyzin, D. Ruiz, J. Mamprin and C. Ruiz, European Refining Technology Conference (ERTC) 12th Annual Meeting, 19-21 November 2007, Barcelona.
6. "Real Time Online Energy Management at KNPC Refineries", M. Ershaid, A. Al-Tarkeet, J. Al-Mutairi, S. Al-Anezi, C. Ruiz, D. Ruiz, S. Cúneo, D. Periyasamy, (2013), Society of Petroleum Engineers (SPE) Kuwait Oil and Gas Show and Conference, October 2013, Mishref, Kuwait.
7. "Online Energy Management System Implementation in KNPC Mina Al-Ahmadi Refinery", M. Ershaid, A. Al-Tarkeet, D. Periyasamy, N. Visuara, C. Ruiz, S. Cúneo, Middle East Process Engineering (MEPEC), Bahrain, September 2013.
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