Plant-wide Scheduling for Profitable Emission ... - ACS Publications

Subscriber access provided by TUFTS UNIV

Process Systems Engineering

Plant-wide Scheduling for Profitable Emission Reduction in Petroleum Refineries Jialin Xu, Jian Zhang, and Qiang Xu Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b00337 • Publication Date (Web): 28 Jun 2018 Downloaded from http://pubs.acs.org on July 1, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Industrial & Engineering Chemistry Research

Plant-wide Scheduling for Profitable Emission Reduction in Petroleum Refineries† Jialin Xu, Jian Zhang, and Qiang Xu* Dan F. Smith Department of Chemical Engineering Lamar University, Beaumont, Texas 77710, United States

Abstract Emission reduction becomes increasingly important for petroleum refineries nowadays. Cost-effective solution strategies require emission reductions to be addressed from the entire plant point of view, where emission source generations and reutilizations should be well balanced. This, however, presents a big challenge to refineries due to their large-scale complex manufacturing systems with hundreds of units and thousands of process streams. In this paper, a greedy concept of profitable emission reduction (PER) has been proposed, bearing merits of economically attractive, environmentally benign, and technologically viable for emission reduction and control in petroleum refineries.

To identify PER strategies, a methodology

framework and a general plant-wide scheduling model have been developed. It couples generic production activities and characterizations of major air emissions from refineries, such as CO2, VOCs, NOX, and PM. A case study has demonstrated the efficacy of the PER concept and the developed methodologies.

Keywords: Profitable emission reduction, refinery, production scheduling, optimization ______________________________________________________________________________ † For publication in Industrial & Engineering Chemistry Research. * All correspondence should be addressed to Prof. Qiang Xu (Phone: 409-880-7818; Fax: 409880-2197; E-mail: [email protected]). 1 ACS Paragon Plus Environment

Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1. Introduction

The petroleum refinery is a leading segment of the entire chemical process industry. It uses crude oils to produce various fuels such as gasoline, aviation kerosene, diesel, heavy fuel oil, and chemical raw materials such as naphtha and benzene. In 2016, 139 operable refineries in the U.S. processed 5.92 billion barrels of crude oil.1,2 It was reported that the value of the product shipments by petroleum refineries in the U.S. was $367.38 billion in 2016, and $437.32 billion in 2015.3 Because of the increasingly strict economic competitions and environmental regulations, refineries in one hand are driven to improve production solutions to leverage profitability margins in nowadays volatile market; on the other hand, they have to pursue cost-effective pollution prevention technologies to comply with more stringent environmental mandates, such as industrial emission requirements. Refineries annually emit large quantities of various pollutants such as volatile organic compounds (VOCs), greenhouse gases and particulate matter, from various parts of their operations.4 Due to an increasing concern, specific policies for refinery emission control have been enacted. In 1998, South Coast Area Air Quality Management District (SCAQMD) in California issued R1118 for control of emissions from refinery flares and amended it in 2005.5 In 2003 and 2006, Bay Area Air Quality Management District (BAAQMD) in California adopted flare control rules of R1212 and R1211 respectively to regulate the flares at petroleum refineries and flare monitoring at petroleum refineries.6,7 Recently, version 3 of the Emissions Estimation Protocol for Petroleum Refineries provides updates to certain emissions factors and methodologies developed using the additional test data collected as part of the 2011 Information Collection Request.8 Also California's Cap-and-Trade Program took effect in early 2012, and the enforceable compliance obligation began on January 1, 2013, for greenhouse gas (GHG) 2 ACS Paragon Plus Environment

Page 2 of 60

Page 3 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


emissions.9 Obviously, these emission control policies will heavily affect U.S. petroleum refineries. Air emission reduction in refineries is also a very challenging task. It involves a largescale complex manufacturing system, which consists of over 17 major facilities with more than 85 main operation units, and thousands of process streams. In such a large industrial system, air emissions could spatially scatter at many reactors, furnaces, and flaring facilities and temporally occur at any time. Thus, cost-effective emission-reduction strategies should neither depend on end-of-pipe approaches nor focus on solutions for some single unit or subsystem. The major thrusts should focus on the comprehensive study of material, energy, and information exchanges within the entire manufacturing system, so as to identify the best solution to the entire plant. From the system point of view, it is unnecessary to enforce emission sources from each individual facility to reach the minimum. As long as the generated emission sources from some facilities could be appropriately reused by other facilities to the maximum extent, the entire system emissions have better chances to approach the minimum. Therefore, to optimally balance emission source generations and utilizations among the entire system might be the smartest way to reduce refinery emissions. When an emission reduction strategy is implemented in a complicated refinery system with massive operational interactions, it will inevitably intervene the normal production activities. This most likely will cause more conservative design and operations, which require higher capital investment and manufacturing costs. Meanwhile, it is also known that emissions come from the loss of raw materials and energy which supposedly could generate much-needed products.

Thus, emission reductions may possibly help plants to improve their profits.

Therefore, a greedy idea will come up: is it possible to identify some solution strategies for a

3 ACS Paragon Plus Environment


petroleum refinery that can help reduce its air emissions, and in the meantime, increase its profitability? This idea is summarized as profitable emission reduction (PER). Historically, Huang and his associates proposed the similar concept of profitable pollution prevention.10,11 The concept and associated methodologies were claimed as fundamental solutions for general industrial pollution prevention. However, their developments and applications exclusively focused on wastewater and chemical savings in electroplating and auto coating industries. For emission evaluations, Kassinis established a model to estimate individual facility’s pollution emissions and applied the model to estimate the air emissions of a petroleum refinery under two operating scenarios.12 Tehrani presented a linear programming model for CO2 emission allocation in oil refineries.13 A two-stage modeling methodology based the marginal contribution of oil products and the production elasticity of unit processes was proposed. However, refinery productions were treated by a black-box model. Moreno et al. analyzed FCC refinery atmospheric pollution events using lanthanoid- and vanadium-bearing aerosols.14 Xu et al. employed plant-wide dynamic simulation to minimize chemical plant flare emissions during plant startups.15,16,17 Their case studies were all for ethylene plants; meanwhile quantitative operation optimizations were not addressed. Also a new dynamic scheduling model for cracking furnace system operations has been developed by Zhang et al. considering the operational and environmental concerns.18 The obtained scheduling solution will be responsible for air-quality concerns by wisely allocating furnace decoking emission peaks into time windows. Overall, the PER concept has never been raised for petroleum refineries, and the associated methodologies have neither been systematically studied. To identify PER strategies for refinery plants, a plant-wide production scheduling model has to be involved. The key is to optimize the plant-wide material and energy flows to make the


Page 4 of 60

Page 5 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


plant net profit maximum, meanwhile to ensure emission source generations and utilizations to be wisely balanced.

Other manufacturing constraints, such as operation specifications and

inventory limits, should also be satisfied as well. Refinery scheduling addresses crude oil processing in the plant scale.

It is an important part of petroleum supply-chain

management.19,20,21 Its model includes discrete-time formulation and continuous-time formulation. Shah presented a discrete-time MILP model for crude oil scheduling.22 The model includes crude oil loading and unloading problems. Lee et al. also proposed a MILP model for crude oil scheduling using discrete time formulations.23 Ierapetritou and Floudas presented a novel continuous-time formulation for short-term scheduling problems, which decoupled the task events from the unit events.24,25 Jia et al. used a continuous-time method to build scheduling model, in which non-linear constraints were relaxed to generate a MILP model.26,27 Reddy et al. presented a continuous-time formulation for crude oil scheduling.28 An iterative algorithm was adopted to eliminate the crude composition discrepancy problem. Mendez et al. presented a simultaneous optimization approach for blending and scheduling of refinery plant.29 The model could be either discrete or continuous formulation. Meanwhile, Pinto et al. presented a general modeling framework for petroleum supply chain optimization.30,31 The model framework could integrate oilfield, crude oil supply, petroleum processing, and products distribution models into a large supply-chain model. But their work did not sufficiently characterize stream properties that are major concerns in refinery productions. Due to the complexity of normal scheduling models, to apply large-scale scheduling models effectively and efficiently in reality, Kelly and his associates proposed hierarchical decomposition methods based on heuristic rules.32,33

To

efficiently solve large-scale systems without compromising problem complexity, logic-based approaches,34 disjunctive programming35 and outer-approximation36 have been developed.



In this paper, a PER methodology framework and a general plant-wide scheduling model have been developed. The completed and structuralized scheduling model is a large-scale mixed integer nonlinear programming (MINLP) model and can be used for other chemical processes. Major air emissions from refineries: such as CO2, VOCs, nitrogen oxides (NOX), and particulate matters (PM) are characterized and quantified. It helps to identify the best PER strategies, which will be economically attractive, technologically viable, and environmentally benign for air emission reductions in petroleum refineries. The remainder of this article is organized in the following manner.

First, a general refinery process with more emphasis on air emission

characterization and quantification will be introduced. Then, a PER methodology framework and a general plant-wide scheduling model for refinery plants will be presented. A detailed case study will be followed to demonstrate the efficacy of the developed methodologies. Finally, the advantages of this study will be summarized.

2. Process Description and Emission Characterization

2.1. Refinery Process Description A typical refinery process includes crude distillation, reforming, cracking, hydrotreating, blending, gas processing, and sulfur recovering facilities, which are shown in Figure 1. Introductions of each facility in terms of its general functionality and emissions are given below37. Crude distillation. Compounds in crude oil can be separated according to the principle that the longer the carbon chain, the higher the temperature at which the compounds will boil. A crude distillation facility is used to heat the crude oil into gas mixtures and split them into gas,


Page 6 of 60

Page 7 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


naphtha, kerosene, diesel, vacuum gas oil (VGO), and vacuum residues by atmospheric and vacuum distillation columns. All these intermediate products will be further processed by downstream facilities. Air emissions mainly occur at the furnace to pre-heat crude oils. Reforming. A reforming facility is used to convert low-octane-number naphtha to highoctane-number gasoline. Its naphtha input comes from crude distillation, hydrocracking, and hydrotreating facilities. After reforming operation, some hydrocarbon molecules from naphtha are restructured to more complex molecular shapes; and some others are broken into smaller molecules, which make the overall product having higher octane values. It also generates byproducts of low-purity (about 80%) hydrogen and dry gas, which are partially used as heating utilities by other facilities. Reforming operation itself consumes lots of heavy oils as heating utilities, which causes lots of air emissions. Cracking. Cracking facilities include fluid catalytic cracking (FCC), hydrocracking, and coking operations. With the help of catalysts, FCC facility converts VGO and vacuum residue oil into gas, liquefied petroleum gas (LPG), gasoline, and diesel. Air emissions occur at the catalyst regenerator of FCC, where carbon films deposited on catalyst surface need to be burnt out for catalyst regeneration. A hydrocracking facility employs hydrogen to crack light VGO into gas, naphtha, and diesel. For the vacuum residue, the heaviest intermediate product from crude distillation columns, a coking facility has to be employed to crack it into gas, LPG, naphtha, and diesel. Both hydrocracking and coking operations will use fuel gas (could be dry gas, surplus hydrogen, or LPG) as heating utilities, which will cause emissions. Hydrotreating.

Hydrotreating

facilities

include

kerosene,

diesel,

and

VGO

hydrotreating. They are designed to use hydrogen to remove contaminants such as sulfur, nitrogen, condensed ring aromatics, or metals from feedstock.


The kerosene hydrotreating


facility refines kerosene feedstock to produce aviation kerosene; while the diesel hydrotreating facility refines diesel and coking naphtha. The VGO hydrotreating facility refines VGO to reduce its sulfur content and make VGO suitable for FCC processing. Hydrotreating facilities use fuel gas as heating utilities, which cause emissions. Blending. Blending facilities are used for crude oil blending and product blending. In this paper, we only consider product blending, such as naphtha, gasoline, kerosene, diesel, and fuel oil blending operations. Inflows of blenders come from upstream facilities. Thus, product blending always belongs to the final processing facilities of a refinery plant. Blending operations should satisfy both product quantity and quality requirements. Gas processing.

Gas processing facilities include gas fractionation, alkylation, and

hydrogen plant. The gas fractionation separates LPG from the catalytic cracking facility into propane, propylene, butane, and butylene. The alkylation facility uses LPG from catalytic cracking, which mainly contains isobutene, to produce high-octane and branched-chain paraffins. The hydrogen plant facility uses dry gas from other facilities to produce high-purity (99%) hydrogen used for hydrocracking and hydrotreating facilities. For safety considerations, surplus hydrogen, dry gas, or LPG should be sent to flare system for destruction. Sulfur recovery. Sulfur recovery facilities include sulfurated hydrogen (H2S) removing and sulfur recovering facilities. The H2S removing facility scrubs H2S from dry gas and LPG streams to reduce air pollutions before they are burned. The sulfur recovering facility converts H2S into sulfur products. Usually, emissions of H2S and its oxides (SOX) are very small during refinery normal productions.

2.2. Emission Characterization for Refinery


Page 8 of 60

Page 9 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


To address air emission reductions in a refinery, spatial emission allocations in the plant need to be first identified.

Then, emission species at each allocation and their emission

quantities under process operation conditions have to be identified. Note that such emission characterization and quantification are based on refinery normal operation conditions. Accidental or emergency emissions due to equipment leakages, process significant upsets, and plant turnarounds are not included in this paper. Emission from heating furnaces. Most refinery facilities employ furnaces for heating inflow streams. These utility furnaces may use dry gas, heavy oils, hydrogen, or even LPG as fuels. Most heating fuels are actually by-products from other refinery facilities. The identified emission sources for furnace heating utilities and their major components are summarized in the table S1 in the Supporting Information. Theoretically, a utility furnace may select one of several listed fuel sources at a time. In reality, however, fuel source selections are subject to two important constraints: i) provide adequate heating duty for unit processing requirements; ii) balance fuel source generations and consumptions. It means if a fuel source is generated too much from some facilities, the plant will try to consume it as a heating utility as much as possible, such that the production and consumption could be balanced. When fuel gas or heavy oil utilities are burned, a large quantity of air emissions will be generated, such as CO2, VOCs, NOX, and PM. Since sulfur removal equipments usually work efficiently, the sulfur dioxide content in the final vent gas can be neglected. Similarly, carbon monoxide (CO) content in vent gas comparably is also small. Thus, the concerned emission species in this paper include CO2, VOCs, NOX, and PM. Emission from processing units. Emissions may come from two types of processing facility/unit directly instead of heating furnaces: i) The hydrogen plant generates high-purity



hydrogen for hydrocracking and hydrotreating facilities. Meanwhile, it also emits waste gas, which is mainly CO2. ii) In the FCC unit, coke is generated and deposited onto the catalyst surface. It has to be burnt immediately in a catalyst regenerator to recover catalyst functionality. This burning process will generate large amounts of waste gas containing CO2, NOX, PM, VOCs, and SOX. Although FCC PM emission is controlled by cyclones and/or electrostatic precipitators with efficiencies as high as 85%, it is still much more significant than PM emissions from other facilities because of the fast catalyst regeneration rate. For VOCs emissions, the final emitted amount from FCC is usually small due to waste gas post processing in waste-heat boilers. Again, SOX can be neglected because of built-in water or caustic scrubber.12 Emission from flaring system. Flaring system is designed to burn out waste gas under normal conditions, or large amounts of off-spec product streams for air pollution mitigation. In some emergency situations, it is used to relieve abnormally high system pressures for equipment and personnel safeties. In this paper, we only consider normal operation conditions of refineries. Meanwhile, since plant scheduling model can optimize refinery operations in such a way that all waste gases at least can be burned for heat recovery, one could expect the flaring emission amount is near zero based on optimized scheduling solutions. Note that flaring is specifically talking about the gas sent to the flaring system for destruction without any heat recovery. It is different from the combustion of waste gases in the heating furnaces. The emission from the flaring system is assumed near zero. However, emissions from the heating furnaces in our scheduling model are still allowable. Emission factor calculation. Overall, major emission sources under normal operation conditions include dry gas, LPG, fuel oil, and burning coke. As reported, every emission source would have some emission factors, which can characterize its emission distributions with respect


Page 10 of 60

Page 11 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


to per unit weight of the emission source.12 Table 1 has summarized the emission characterizations under various emission sources. The unit of the original formulas is pound per thousand barrels, which should be transferred to SI unit as T/kT. The transfer transformation is:

φ SI = φ EN

0.45359 × 10 −3 1000 × 0.15899 × 10 −6 ρ

(1)

where φ SI is the emission factor in SI unit as T/kT, φ EN is the emission factor in pound per thousand barrels from Kassinis.12 0.45359 × 10 −3 is the conversion factor from pound to ton. 0.15899 is the conversion factor from barrel to m3. ρ is the density of emission source and the unit of ρ is kg/m3. The transferred emission factors are listed in the 4th column in Table 1. It should be noted that the CO2 emission is estimated through mass balance. Since CO2 comes from burning of hydrocarbon (CxHy), the general reaction formula is:

y y C x H y + ( x + )O 2 = xCO 2 + H 2 O 4 2

(2)

Also noted that the CO emission amount can be neglected based on the reference data.12 Meanwhile, a part of hydrocarbon will be transferred to VOCs or PM. The left amount is assumed to be totally transformed to CO2. To calculate the CO2 emission, the average molecular weights of all emission sources (dry gas, LPG, fuel oil, and burning coke), VOCs, and PM should be known. They can be estimated based on the plant data. Thus, the CO2 emission factor can be obtained as below:

φ SI ,CO = 1000 2

44 xVOCs 44 x PM 44 x − φ SI ,VOCs − φ SI , PM 12 x + y 12 xVOCs + yVOCs 12 x PM + y PM

(3)

where φSI ,CO 2 is the emission factor of CO2 in SI unit as T/kT. x and y are respectively C and H ratios based on the emission source formula. φ SI ,VOCs and φ SI , PM are emission factors of VOCs



and PM in SI unit. xVOCs , yVOCs , xPM and y PM are the average molecular formula of C and H of VOCs and PM. In summary, the listed emission factors in Table 1 will be used in the emissionconsidered production scheduling model.

3. PER Methodology Framework

Figure 2 gives the general PER methodology framework. Since this paper addresses PER opportunities from the system point of view to optimally balance the emission source generation and utilization, the emission-considered plant-wide scheduling model is the major focus. According to the scheduling model development and improvement, the framework includes two stages. Generally, the modeling starts from the development of individual facility models followed by the validation, where the plant design and operation data are needed. Then, to address the product quality, operation costs, and air emissions simultaneously, the characterization formulas for product properties, utility flow, and emission should be coupled, which help generate emission-considered plant-wide scheduling model. In the second stage, the scheduling model can be solved for the current plant design and operations. Comprehensive analysis based on the scheduling results from the first stage will be conducted. Since the plant profit and emission can be quantitatively evaluated, alternative plant design and operations for PER opportunities might be raised, which may come from fundamental/theoretical analysis or industrial expertise.

One motivation to improve the

manufacturing is that a hydrogen purification facility could be added, such that it can upgrade the low-purity hydrogen from reforming to high-purity hydrogen that could be reused by hydrocracking and VGO hydrotreating facilities directly. Based on the alternative design and


Page 12 of 60

Page 13 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


operations, the scheduling model will be modified and solved again to examine whether the proposed PER opportunity is feasible.

A feasible PER opportunity means the plant can

experience higher profit and lower emissions simultaneously during this range. If it is not virtually accomplished, the proposed new design or operations should be refined. Through such a model improvement iteratively, a feasible PER design or operation strategy may finally be generated. Note that to accommodate various design and operation modifications, the scheduling model should be developed in general and well structuralized, such that the expense for the model modifications in terms of remodeling time and efforts can be saved to the maximum extent. Also note that the proposed methodology framework provides a way to help identify and accomplish PER opportunities, the driving force behind is the fundamental understanding of the refinery process and the industrial expertise.

4. Plant-Wide Production Scheduling Model Development

The refinery crude processing model is mainly borrowed from a previous study37 with modest modifications, which addresses the detailed processing strategy based on the given input of crude blends. Property mixing functions, mixing unit model, reactor model, separator model, plant feedstock and output model, inventory unit model, as well as plant utility balance constraints, are included in this model. Because the crude scheduling model is very complicated, for conciseness, only those newly developed model equations from this study are discussed in detail in the following section. The detailed refinery crude processing model constraints similar to the previous study are summarized in the Supporting Information.



Page 14 of 60

A typical production unit has the general input-output relation as shown in Figure 3. Plant feedstock model is a virtual model, which does not exist in reality. However, by assuming all the plant inputs (include raw materials and purchased utilities) must pass through the feedstock model, we could easily model and account all plant feedstock costs. As shown in Figure 4, feedstock unit is designated as ui . Plant output model is another virtual model, which assumes all the plant output (e.g., products, wastes, or sold utilities) has to pass through an output unit of uo (See, Figure 5). Since emission streams have to go through the plant output unit, the emission identification and quantification need only to focus on this virtual unit. For every concerned emission species, m , the final emission amount is the summation of every stream containing m . It is formulated as Equation (4). FE m = ∑ t

∑ ∑ (F

v , s ,uo , t

φ vm, s ,uo ) , ∀ m ∈ ME

(4)

v∈Vuo s∈S v , uo

where FE m is the amount of emission species m. Fv , s ,uo , t is the flow rate of the emission source, and φ vm, s ,uo is the emission factor. The general objective of the refinery scheduling model is to maximize total plant profit, which is defined in Equation (5). Note that due to the introduced plant feedstock and output models, profit calculation for the entire plant only needs to focus on these two virtual units, plus inventory units accounting for inventory value changes38.

Thus, exchanges of all the internal

material and utility are considered free. Also noted that this is gross profit, where the costs such as facility depreciation, labor costs, and taxes are not considered in the model. The gross profit includes product and utility sales income, raw material and utility consumption cost, and inventory value additions.

  max profit = ∑  Sale _ POt + Sale _ UOt − Cos t _ FDt − Cos t _ UDt + ∑ ∆IVu ,t  t  u∈UINV  14 ACS Paragon Plus Environment

(5)

Page 15 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Various sales income and costs listed in the objective function are formulated as below: Sale _ POt = ∑ PPOk ,t FPk ,t

(6)

k∈K

out Sale_ UOt = PETt UETuoout,t + PARt UARuoout,t + PWATt UWAuo ,t out T out + PSWtT USWuoout,t + PFGTt UFGuo ,t + PRCt URCuo,t

Cost _ FDt = ∑ PFD j ,t FD j ,t

(7)

(8)

j∈J

Cost _ UDt = PETt UETuiin,t + PARt UARuiin,t + PWATt UWAuiin,t + PSWtT USWuiin,t + PFGTt UFGuiin,t + PRCTt URCuiin,t

∆IVu , t = PVu , t ( IVu ,t − IVu , t −1 ) , u ∈ UINV

(9)

(10)

where PPOk ,t is unit price of the product k; FPk ,t is the flow rate of the product k; PETt , PARt , out PWA Tt , PSWtT , PFG Tt , and PRC Tt are unit price of utilities; UETuoout,t , UARuoout,t , UWA uo, t ,

out out out USWuo, t , UFG uo,t , and URC uo,t are quantities of utilities; PFD j ,t is unit price of feedstock j;

FD j ,t is the flow rate of feedstock j; PVu , t is the price of inventory u; IVu , t −1 and IVu , t are

inventories at the time t-1 and t. Equations (6) and (7) give the product and utility sale incomes. out The superscript T means vector transposition. For instance, PWA Tt UWA uo ,t represents the dot

product of two vectors. Similarly, Equations (8) and (9) give purchased raw material and utility costs. Equation (10) accounts for the inventory value changes for inventory units. In summary, Equations (4) through (10) and equations in Supporting Information represents a general scheduling model framework for an entire refinery plant, which can also be used for modeling other continuous chemical processes at large time scale. It is a MINLP model. The manipulated variables include various plant feedstock and product flowrates, internal stream flow rates, and unit operation strategies. Case studies based on the scheduling model have been



formulated with GAMS.39 Through comparison, the solver used in this paper is BARON, where CPLEX and MINOS are used for solving MILP master problem and NLP subproblem, respectively.40,41,42

5. Model Validation

As shown in the methodology framework (see, Figure 2), a plant-wide scheduling model starts from the modeling and validation of each individual facility model. In the developed refinery model, it includes 23 different units, which covers the processing of crude distillation, cracking, coking, reforming, hydrotreating, product blending and component recovery. They all should be validated before PER applications. If all model validation activities are presented, the paper would be extremely lengthy and distract its major merits. Therefore, for the sake of this paper, an FCC model, one of the most important facility models for a refinery, is used as an example to demonstrate the modeling and validation efforts during the plant-wide scheduling model development. Figure 6 shows the flowsheet of an FCC facility. An FCC facility includes reactorregenerator, main fractionator, separation columns, and treaters. Feed stream and recycled slurry are mixed and sent to the reactor. The mixed feed is cracked to gas, LPG, gasoline, and diesel at about 500 °C with catalyst. At the same time, part of the feed forms carbon deposited to the catalyst surface. The coke is burned at the regenerator to reactive the catalyst. The cracked stream is sent to the main fractionator and is separated to cracked gas, light naphtha, diesel, and slurry. The diesel is sent out of the FCC facility for further treatment. Part of the slurry is recycled as the feed, and the rest part is sent out of the facility. The cracking gas is compressed


Page 16 of 60

Page 17 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


by a two-stage compressor and separated to vapor and liquid phases by flash drums. The vapor phase is mainly composed of methane and ethane. It is treated to remove acidic components such as hydrogen sulfide and carbon dioxide and is sent out of the facility as a fuel gas product. The liquid phase is sent to a stripper column to remove methane, ethane, and other light components. The light naphtha from the main fractionator is also sent to the stripper column. The liquid product of the stripper column is sent to a debutanizer column. The vapor product of the debutanizer contains C3 and C4, which is treated to remove acidic components and sent out of the facility as LPG product. The liquid product of the debutanizer is gasoline. The gasoline is treated to remove acidic gas and mercaptan and is sent out of the facility as the gasoline product. Products yields of the FCC facility are affected by many factors. Key factors are K factor and carbon residue of the feed, cracking characteristic of catalyst, reaction temperature, and separation factors of the main fractionator. The K factor is an indication of feed crackability, which is defined as: 9   5 (V ABP + 273.15)  K= SG

1/ 3

(11)

where S G is specific gravity at 15.56 °C, which is dimensionless. V ABP is the volumetric average boiling point in unit of °C, which is defined as: V ABP =

T10% + T30% + T50% + T70% + T90% 5

(12)

where T10% , T30% , T50% , T70% , and T90% are temperature (in unit of °C) based on ASTM D1160 Standard Testing Method for Distillation. The carbon residue, a measured value, is the tendency to form carbon deposits under the high temperature. Reaction temperature is an important factor. Higher reaction temperature will 17 ACS Paragon Plus Environment


Page 18 of 60

generate more gas, LPG, and gasoline. However, it will also deposit more carbon on the catalyst surface, and the diesel yield will reduce. Cracking characteristic of catalyst affects the yields of all products. But because the catalyst is usually unchanged during normal conditions, the factor of catalyst characteristic could be omitted. Thus, the equations and constraints of the reactor sub-model for an FCC facility are listed below:

F8,1,5,t + F10,1,5,t + F13,1,5,t + F4,1,5,t + F1,1,5,t + F1, 2,5,t + F1,3,5,t + F2,1,5,t + F2, 2,5,t + F2,3,5,t = FGAS ,5,t + FLPG ,5,t + FREG ,5,t + FDSL ,5,t + FSLR ,5,t + FCOK ,5,t

(13)

f GAS ,5, t = Pam1,1 + Pam 2 ,1 (K in − K 0 ) + Pam3,1 (CRin − CR0 ) + Pam 4 ,1 (TR − T0 )

(14)

f LPG ,5,t = Pam1, 2 + Pam 2 , 2 (K in − K 0 ) + Pam3, 2 (CRin − CR0 ) + Pam 4 , 2 (TR − T0 )

(15)

f GSO ,5 ,t = Pam1, 3 + Pam 2 ,3 (K in − K 0 ) + Pam 3,3 (CR in − CR 0 ) + Pam 4 ,3 (TR − T0 )

(16)

f DSL , 5,t = Pam1, 4 + Pam 2 , 4 (K in − K 0 ) + Pam 3, 4 (CR in − CR 0 ) + Pam 4 , 4 (TR − T0 )

(17)

f SLR ,5,t = Pam1,5 + Pam 2 ,5 (K in − K 0 ) + Pam 3,5 (CR in − CR 0 ) + Pam 4 ,5 (TR − T0 )

(18)

f COK ,5, t = Pam1,6 + Pam2,6 (K in − K 0 ) + Pam3,6 (CRin − CR0 ) + Pam4,6 (TR − T0 )

(19)

Crea,5,t = F8,1,5,t + F10,1,5,t + F13,1,5,t + F4,1,5,t + F1,1,5,t + F1,2,5,t + F1,3,5,t + F2,1,5,t + F2,2,5,t + F2,3,5,t

(20)

min max yrea ,5, t Crea , 5, t ≤ C rea ,5, t ≤ y rea , 5, t C rea ,5, t

(21)

UI 1,5,t = Pam1, 7 ( F8,1,5,t + F10,1,5,t + F13,1,5,t + F4,1,5,t + F1,1,5,t + F1, 2,5,t + F1,3,5,t + F2,1,5,t + F2, 2,5,t + F2,3,5,t ) UI 2 ,5 ,t = Pam1,8 ( F8,1, 5, t + F10 ,1,5 ,t + F13,1, 5,t + F4 ,1, 5,t + F1,1,5 ,t + F1, 2 ,5 ,t + F1,3, 5, t + F2 ,1,5, t + F2 , 2 ,5 ,t + F2 , 3,5 ,t ) UI 3, 5,t = Pam1, 9 ( F8,1, 5, t + F10 ,1,5 ,t + F13,1, 5,t + F4 ,1, 5, t + F1,1,5 ,t + F1, 2 , 5,t + F1,3,5, t + F2 ,1,5, t + F2 , 2 ,5 ,t + F2 , 3,5 ,t ) UI 4,5,t = Pam1,10 ( F8,1,5,t + F10,1,5,t + F13,1,5,t + F4,1,5,t + F1,1,5,t + F1, 2,5,t + F1,3,5,t + F2,1,5,t + F2, 2,5,t + F2,3,5,t )


(22)

(23)

(24)

(25)

Page 19 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


UI 5,5,t = Pam1,11 FCOK ,5,t

(26)

where F8,1,5,t , F10,1,5,t , F13,1,5,t , F4,1,5,t , F1,1,5,t , F1, 2,5,t , F1,3,5,t , F2,1,5,t , F2, 2,5,t , and F2,3,5, t are the inlet streams of the FCC unit; FGAS ,5,t , FLPG ,5,t , FREG ,5,t , FDSL ,5,t , FSLR,5,t , and FCOK ,5, t are the outlet streams; Pam1,1 , Pam2,1 , ..., Pam4,6 are parameters obtained from the regressions. K in and K 0 are the K factor of mixed inlet streams and base value; CRin and CR0 are carbon residue indexes of the mixed inlet streams and their base values, respectively; TR and T0 are the reaction temperature and its base value, respectively; Crea ,5,t is the processing capacity of the reactor; yrea ,5,t is a binary variable that denotes whether the reactor exists or not; UI1,5,t through UI 5,5,t are

quantities of the utility consumed by the reactor. Pam1,7 through Pam1,11 are parameters related to utility consumptions. Besides the reaction sub-model, a separator sub-model is also included in an FCC model. The operating conditions of the main fractionator affect the yields of gasoline, diesel, and slurry. The parameters identified as the major impacts to the product yields include T10% and T90% of gasoline and diesel. The equations and constraints of a separator sub-model are listed below:

FGAS,5,t + FLPG,5,t + FREG,5,t + FDSL,5,t + FSLR,5,t + FCOK,5,t = F5,1,11,t + F5,1,12,t + F5,1,9,t + F5,1,15,t + F5,1,7,t + F5,1,8,t + F5,1,out,t

(27)

F5,1,11,t = FGAS ,5,t Pam1,12

(28)

F5,1,12,t = FGAS ,5,t (1 − Pam1,12 )

(29)

REG , 0 ) F5,1,9,t = FLPG ,5,t (T10REG % − T10%

(30)

REG , 0 ) F5,1,15,t = FREG ,5,t (T90REG % − T90%

(31)

DSL , 0 ) F5,1, 7 ,t = FDSL,5,t (T10DSL % − T10%

(32)



Page 20 of 60

DSL , 0 ) F5,1,8,t = FSLR ,5,t (T90DSL % − T90%

(33)

F5,1,out ,t = FCOK ,5,t

(34)

where FGAS ,5,t , FLPG ,5,t , FREG ,5,t , FDSL,5,t , FSLR,5,t , and FCOK ,5, t are the inlet streams of the separator, which are also the outlet streams of the reactor; F5,1,11, t , F5,1,12, t , F5,1,9,t , F5,1,15,t , F5,1, 7 ,t , F5,1,8,t , and F5,1,out ,t are the outlet streams of the separator, which are also the outlet streams of the FCC ,0 facility; Pam1,12 is the sulfur content of the dry gas. T10REG and T10REG are 10% distillation % %

temperature of gasoline and its base value, which are used in Equation (30) to determine the flow DSL DSL rate of outlet LPG flow; Similarly, T90REG % , T10% , and T90% are used to determine the flow rate of

outlet gasoline, diesel, and slurry of the FCC facility. Based on the developed FCC model, real plant historical data is employed to validate the modeling quality.

Figure 7 shows the comparison of calculated yield data with the real

measurements. It shows the yields of fuel gas, gasoline, LPG, and diesel are in good agreement with the real measurements in a continuous time horizon of one month.

Thus, it fully

demonstrates the modeling quality of the FCC facility model.

6. Case Study

6.1. Comprehensive Analysis based on Current Scheduling Model Based on various validated facility models, a refinery plant with an annual crude processing capacity of 1.5 million tons has been studied in this paper, whose flowsheet has been shown in Figure 1. A refinery plant may have over 20 products. The main products include regular gasoline, midgrade (super) gasoline, premium gasoline, diesel, jet kerosene and naphtha, 20 ACS Paragon Plus Environment

Page 21 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


which account for over 75% of the total production.

For simple representation of plant

scheduling results, main refinery products are classified into two categories: i) regular gasoline, super gasoline, premium gasoline, and naphtha can be combined as the total naphtha (TN), as they have similar component compositions; (ii) similarly, aviation kerosene and diesel can be combined as the total diesel (TD). Since product markets are volatile nowadays, the emissionconsidered scheduling model should be investigated under various product demand scenarios. That means we would like to investigate the big pictures of plant profit and emission solutions under different market demands. Under each scenario, the plant optimization model will be run once. Thus, the emission-considered plant scheduling model will be solved multiple times according to different market demands on TN and TD, and give the optimal production schedule with gross profit and emission results. The model has 264 constraints, 558 variables, and 2 binary variables. It is solved by GAMS-BARON on a Core 2 Quad 2.4 GHz computer. 4857 effective scenarios have been solved, which cover TN amount from 219 to 476 kT/yr and TD amount from 540 to 857 kT/yr. The average solving time for one scenario is 6 seconds. The average relative gap between the solution and best possible is 0.03, and the average absolute gap is 61. Figure 8 gives the big picture on plant profit under various TN and TD demands. It shows when both TN and TD demands are low, the plant profit will be low. The profit generally increases when TN or TD demands increase. When TN and TD demand reaches 472 kT/yr and 758 kT/yr respectively, the profit hits the maximum. When TN and TD demands continue increasing, the profit will actually decrease. This is because excessive TN and TD productions require much more hydrogen generated from dry gas, and lots of fuel gas to support hydrotreating and hydrocracking operations. Thus, the overall dry gas consumption will be larger than the total generation, which



makes a certain amount of LPG be used as fuel gas to fill up the dry gas gap. Since LPG is much more valuable than dry gas, total utility cost will dramatically increase. Therefore, the plant profit will decrease. There is a ridge in the curved profit surface, which indicates when the demand of TN (TD) is fixed, what the best choice for the productivity of TD (TN) should be. Figures 9 through 12 provide 3D emission profiles of CO2, VOC, NOX, and PM under various TN and TD demands, respectively. Generally, the CO2 profile doesn’t show clear decreasing or increasing trend. CO2 emission increases when TD demands increases from 540 to 760 kT/yr. Similar to Figure 8, when TN and TD demands increase too many, CO2 emissions will decrease. VOC emission increases when TD demand increases. However, the minimal VOC emission occurs when TN and TD are 318 kT/yr and 540 kT/yr; while the maximal VOC emission occurs when TN and TD are 262 kT/yr and 812 kT/yr. NOX and PM emissions have very similar profiles. This is because both emissions are largely determined by the amount of coke burning at FCC facility. According to the calculation, 57% of NOX and 97% of PM emissions are coming from FCC. The minimal NOX emission point is located at 231 kT/yr of TN and 623 kT/yr of TD; while the maximal NOX emission point is located at 476 kT/yr of TN and 748 kT/yr of TD. The minimal PM emission is located at 231 kT/yr of TN and 642 kT/yr of TD; while the maximal PM emission is located at 476 kT/yr of TN and 748 kT/yr of TD.

6.2. PER Opportunity Identification and Accomplishment After experiencing big pictures about plant production and emission relations, we would like to conduct in-depth process analysis to identify PER opportunities. Since the total amount of gasoline (so-called TG, including regular, midgrade and premium gasoline) is one major concern for a refinery, the plant profitability and emission scenarios under 101 TG demands are


Page 22 of 60

Page 23 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


investigated through the proposed scheduling model. Other products such as naphtha, kerosene, and diesel are considered as part of control variables during the optimization. Note that besides the TG amount specification for each solution scenario, another constraint is that regular gasoline production should overpass 80% of TG to match gasoline market product distribution.43 Under the above situations, Figures 13(a) and 13(b) respectively provide hydrogen generation and consumption curves with TG demands. In Figure 13(a), hydrogen productivity decreases from 2.7 to 1.2 kT/yr with TG increment. Meanwhile, the hydrogen generated from reforming facility will increase until the gasoline productivity reaches 402 kT/yr.

Thus, the total hydrogen

generation is the summation of the above two curves, which is exactly equal to the total hydrogen consumption as shown in Figure 13(b). There are two incidents corresponding to the two major changes of these curves. When TG demand excesses 200 kT/yr, the amount of diesel will decrease. Since most diesel are produced from hydrotreating and hydrocracking facilities, their operation loads and hydrogen consumptions will correspondingly decrease. When TG demand excesses 402 kT/yr, more gasoline will be generated from FCC facility instead of reforming facility, because the gasoline yield at FCC facility is higher than reforming facility. Thus the load of reforming facility and the amount of hydrogen generated from reforming will slightly decrease. From Figure 13(b), the amount of hydrogen burnt as a heating utility is increased from 0 to 14.9 kT/yr. Meanwhile, about 7 kT/yr of hydrogen is produced from the hydrogen plant, which is used for hydrocracking and VGO hydrotreating. That means the refinery plant, on the one hand, produces large amounts of hydrogen; while on the other hand, it will burn surplus hydrogen as a heating utility. Such contradictory behavior can be illustrated in Figure 14. It is known hydrocracking facilities need to transform VGO to products such as naphtha, diesel, and



tail oil; and VGO hydrotreating refines VGO to make VGO suitable for FCC processing. Both operations need high-purity hydrogen (99%) produced by the hydrogen plant at expensive costs. The feed of hydrogen plant is dry gas, which comes from many other processing facilities, such as FCC, hydrocracking, coking, and hydrotreating.

Note that reforming facility processes

naphtha to produce gasoline with heating utility requirement of only heavy oils. An important byproduct of reforming is low-purity hydrogen (about 80%). Because the hydrogen generated from the reforming facility has only 80% purity, although it has over sufficient amount, it is not qualified for use in hydrocracking and VGO hydrotreating facilities directly. Thus, except the partial used in kerosene and diesel hydrotreating facilitates, a large quantity of low-purity hydrogen has to be burnt as a heating utility by other facilities. One motivation to improve the manufacturing is that a hydrogen purification facility could be added, such that it can upgrade the low-purity hydrogen from reforming to high-purity hydrogen that could be reused by hydrocracking and VGO hydrotreating facilities directly. The thrust can reduce the operation load of hydrogen plant to save costs and reduce emissions. Meanwhile, since reforming facility in the base process produces much more low-purity hydrogen than the needed, it could be expected that the reforming processing load would decrease. Note that although reforming load reduction will reduce its gasoline production, the total plant TG production will not be affected, because other gasoline production facilities could make up. Certainly, the reforming load decrease would reduce the fuel oil consumptions, which would reduce air emissions also. Therefore, a hydrogen purification facility will be added to the original process.

In the case study, a facility of pressure swing adsorption (PSA) will be

employed. It is a standardized industrial facility, which can transfer low-purity hydrogen to qualified high-purity hydrogen. Figure 15 shows the updated flowsheet with a PSA facility.


Page 24 of 60

Page 25 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Based on Figure 15, a new plant scheduling model has been developed. The new model has 270 constraints, 565 continuous variables, and 2 binary variables. The average solving time for one scenario is 18 seconds. The average relative gap between the solution and best possible is 0.03, and the average absolute gap is 68. Scheduling results under various TG demand changing from 83 kT to 459 kT have been studied for the new process. For comparison, Figure 16 (a-d) and 17 show emissions of CO2, VOC, NOX, PM and profit curves for the base case (process without PSA as shown in Figure 1) and new process (process with PSA as shown Figure 15), respectively. They show total CO2, VOC and NOX emissions are reduced for the new process when gasoline demands are greater than 207 kT/yr; while gross profit is increased when TG demands are larger within 86 kT/yr and 436 kT/yr. This is because to produce the same amount of TG, the consumptions of utility fuel oil and fuel gas are reduced in the new process. Correspondingly, CO2, VOC and NOX emissions from utility fuel are also reduced. Meanwhile, the plant productivity of fuel oil and LPG are increased because less oil and gas are used for utilities. Thus, the plant profit of the new process is different from the base process. Total PM emission is nearly unchanged because FCC productions change little in the new process. As aforementioned, over 97% of PM emission is generated from FCC. Thus, optimal schedules for operating FCC facility are almost the same for the new and base processes. Note that when the TG production is larger than 210 kT/yr, the total gross profit of the new process is much higher than the base process. It means the plant can experience higher profit and lower CO2, VOC and NOX emissions simultaneously during this range. This is a remarkable PER opportunity for the plant, which has been demonstrated by the scheduling model. As aforementioned, the opportunity comes from the new hydrogen purification facility. From Figure 18(a), the operation load of hydrogen plant continues decreasing as TG increases. It



reaches zero when TG demand is more than 180 kT/yr, because the hydrogen generated from reforming facility has enough amounts to satisfy demand from other facilities. From Figure 18(b), the amount of burned hydrogen increase from 0 to 10 kT/yr when the TG demand changes from 248 kT/yr to 415 kT/yr. After that, it maintains the value at 10 kT/yr. Also note that the gross profit shown in Figure 17 does not consider the capital costs for the PSA facility. A typical PSA facility for a medium-scale refinery needs about 8 million dollars of capital investment.44 Thus, even the PSA capital costs are both taken into account, there still exist PER opportunities for the plant. For example, if the refinery produces 218 kT/yr of TG with the new process, the annual production profit will increase by 17.9 million dollars, which suggests the new facility investment will be easily paid back within one year. Meanwhile, the emission reductions are significant (CO2, VOC, and NOX will be reduced by 4.9%, 4.7%, and 2.1%, respectively). More instructively, suppose the depreciation rate of PSA facility is about 8%, and the annual interest rate is 5%. Then, the annual depreciation cost is about 0.9 million dollars. By deduction of this cost from the gross profit, meanwhile considering the emission profiles, a PER zone (between 210 kT/yr and 432 kT/yr) could be identified, which has been indicated in Figures 16 (a) through (d). The PER zone does demonstrate that there exist win-win situations for refineries to simultaneously increase profit and reduce emissions through the plantwide production scheduling. Finally, the scheduling model can also provide emission distributions among different sources.

For every species, emission distribution under the base and new processes are

compared as shown in Figures 19(a) through (d), where five TG production scenarios (215, 270, 325, 380, and 430 kT/yr) are disclosed. For the sake of clarity, emission data of each emission source is also given. Figure 19(a) shows CO2 emissions are mainly from gas and oil furnaces,


Page 26 of 60

Page 27 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


and FCC regenerator for burning coke. The CO2 emissions from hydrogen plant in the new process are 0, as compared with 2.7 ~ 4.2 kT in the base process. The CO2 emissions from FCC regenerator are almost unchanged. In Figure 19(b), the major VOC emissions (over 95%) come from gas furnaces in the five scenarios. This is mainly because the total utility gas consumption of the new process is lower than base process. Meanwhile, since the reforming facility load of the new process is lower than base process, the utility oil consumption and the resultant VOC emissions are also lower. Figure 19(c) shows major NOx emissions come from FCC regenerator and gas furnaces, where NOx emissions in the new process are slightly lower than base process. Figure 19(d) demonstrates again that PM emissions mainly come from FCC regenerator.

7. Concluding Remarks

In this paper, the concept of profitable emission reduction for petroleum refinery is proposed. A methodology framework and a new general plant-wide scheduling model have been developed with emission reduction considerations, which can help identify PER opportunities from the entire system point of view. Major air emissions from refineries: such as CO2, VOCs, nitrogen oxides (NOX), and particulate matters (PM) are characterized and quantified. The efficacy of the proposed PER and emission-considered scheduling model has been demonstrated by a real case study. PER thrusts target on emission source reductions in a profitable and systematic way, which are economically attractive, environmentally benign, and technologically viable for petroleum refineries. It may help emission generators (not just limited to petroleum refineries) to proactively and systematically reduce their emissions to meet increasingly strict economic and environmental challenges.



Acknowledgement

This work was partially supported by the Center for Advances in Port Management, President Visionary Project, and Anita Riddle Faculty Fellowship from Lamar University.

Nomenclature

Indices i , i'

general process streams

j

raw material feed of a plant

k

product of a plant

m

component

s , s'

process streams

t

time period

u,v,w

process units

ui , uo

virtual plant input and output units

Sets

I

process stream set

J

raw material feed set of a plant

K

plant product set

M

general component set


Page 28 of 60

Page 29 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


ME

emission concerned component set

S v ,u

special stream set defined as S v ,u = {s | the s-th stream that flow from v to u}

S u ,w

special stream set defined as S u ,w = {s | the s-th stream that flow from u to w}

Vu

unit set defined as Vu = {v | units that have streams flowing to u}

UINV

inventory unit set

Parameters PARt

process air unit price

PETt

electricity unit price

PFD j ,t

unit price for the j -th plant feed during t

PPOk ,t

unit price for the k -th plant product during t

PVu , t

unit price of inventory in unit u during t

PWA t

unit price vector of various process water

PSWt

unit price vector of various steams

PFG t

unit price vector of various fuel oils

PRC t

unit price vector of various catalysts

xVOCs , yVOCs

average molecular formula of C and H of VOCs

xPM , y PM

average molecular formula of C and H of PM

φ SI

emission factor in SI unit as T/kT

φ EN

emission factor in pound per thousand barrels



φSI ,CO

2

emission factor of CO2 in SI unit

φSI ,VOCs

emission factor of VOCs in SI unit

φ SI , PM

emission factor of PM in SI unit

ρ

density of an emission source

Continuous variables or variable vectors C u ,t

processing capability of unit u during t

max Cumin ,t , Cu ,t

lower and upper bounds for processing capability of u during t

Cost _ FDt

total cost for plant feedstock purchase during t

Cost _ UDt

total cost for plant utility purchase during t

CRin , CR0

carbon residue index

fi

flowrate of the i -th input stream to a mixer

fo

out flowrate of a mixer

Fv ,s ,u ,t

flowrate of the s -th stream from v to u during t

FD j , t

flowrate of the j -th plant feed during t

FEtm

total emissions of the m -th pollutant during t

FPk , t

flowrate of the k -th plant product during t

K, K in , K 0

K factor of a petroleum stream

IVu , t

inventory amount in unit u during t

∆IVu , t

inventory value increment


Page 30 of 60

Page 31 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Pam u,t

vector of operating parameters for unit u during t

Sale _ POt

total sale value of plant products produced during t

Sale _ UOt

total sale value of plant utilities produced during t

SG

specific gravity at 15.56 °C

TR , T0

reaction temperature

T10% , T30% , T50%

ASTM D1160 temperature (°C)

T70% , T90%

ASTM D1160 temperature (°C)

UARuin,t , UARuout,t

consumption and generation amount of process air for operating u during t

UETuin,t , UETuout ,t

consumed and generated amount of electricity for operating u during t

UFG inu,t , UFG u,outt consumed and generated fuel oil vectors for operating u during t URCinu,t , URCu,outt consumed and generated catalyst vector for operating u during t USWu,int , USWu,outt consumed and generated steam vectors for operating u during t UWA inu,t , UWA u,outt consumed and generated process water vectors for operating u during t

V ABP

volumetric average boiling point in unit of °C

X v ,s ,u ,t

property vector of the s -th stream from v to u during t , defined as

[

]

T

X v ,s ,u ,t = xvρ,s ,u ,t , xvv, s ,u ,t , xvw,s ,u ,t , xvsv, s ,u ,t , xviv,s ,u ,t , z v , s ,u ,t , ∀ s ∈ S v ,u XD j,t

property vector of the j -th plant feed during t

XPk ,t

property vector of the k -th plant product during t



UI u,t

Page 32 of 60

utility input vector of unit u during t ; defined as

[

UI u ,t = UETuin,t , UARuin,t , UWA inu ,t , USWuin,t , UFG inu ,t , URC inu ,t

UI ui, t

vector of purchased utilities by a plant during t

UO u,t

utility output vector of unit u during t ; defined as

]

T

[

out out out out out UO u ,t = UETuout ,t , UARu ,t , UWA u ,t , USWu ,t , UFG u ,t , URC u ,t

]

T

UOuo, t

vector of generated utilities by a plant during t

ΩFv ,u ,t

flowrate vector for all streams from v to u during t , defined as ΩFv ,u ,t = [K , Fv ,s ,u ,t , K] , ∀ s ∈ S v ,u T

ΩX v ,u ,t

property vector for all streams from v to u during t , defined as ΩX v ,u ,t = [ K , X v ,s ,u ,t , K] , ∀ s ∈ S v ,u T

ΨFu,t

input flowrate vector of u during t , ΨFu ,t = [K , ΩFv ,u ,t , K ] , ∀ v ∈ Vu

ΨX u,t

input property vector of u during t , ΨX u ,t = [K , ΩX v ,u ,t , K ] , ∀ v ∈ Vu

ΓFu,t

output flowrate vector of u during t , ΓFu ,t = [K , ΩFu ,w,t , K] , ∀ w ∈ Wu

ΓX u,t

output property vector of u during t , ΓX u ,t = [K , ΩX u ,w,t , K] , ∀ w ∈ Wu

T

T

T

T

Binary variables

y u ,t

binary variable, it is 1 if u is in operation; otherwise, it is 0


Page 33 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


References

1. Energy Information Administration (EIA). Refinery Capacity and Utilization. 2016. Accessed January 5, 2018. 2. Energy Information Administration (EIA). Performance profiles of major energy producers 2011. Accessed January 5, 2018. 3. US Census Bureau. Annual Survey of Manufactures. 2016. https://www.census. gov/data/tables/2016/econ/asm/2016-asm.html. Accessed January 5, 2018. 4. Ragothaman, A. and W. Anderson. Air Quality Impacts of Petroleum Refining and Petrochemical Industries. Environments. 2017; 4(3): 66. 5. South Coast Area Air Quality Management District. Control of Emissions from Refinery Flares. 2005. Accessed January 5, 2018. 6. Bay Area Air Quality Management District. Flare Monitoring at Petroleum Refineries. 2003. Accessed January 5, 2018. 7. Bay Area Air Quality Management District. Flares at Petroleum Refineries. 2006. Accessed January 5, 2018. 8. Environmental Protection Agency. Emissions Estimation Protocol for Petroleum Refineries. 2015. Accessed January 5, 2018. 9. California Air Resources Board. Cap-and-Trade Program. Accessed January 5, 2018. https://www.arb.ca.gov/cc/capandtrade/capandtrade.htm. 10. Lou HH, Huang YL. Profitable Pollution Prevention: Concept, Fundamentals and Development. Journal of Plating & Surface Finishing. 2000; 87: 59-66.



11. Xu Q, Arnesh T, Lou HH, Huang YL. Integrated Electroplating System Modeling and Simulation for Near Zero Discharge of Chemicals and Metals. Industrial & Engineering

Chemistry Research. 2005; 44: 2156-2164. 12. Kassinis GI. A model for estimating pollution emissions for individual economic activities.

Environmental Impact Assessment Review. 1998; 18: 417-438. 13. Tehrani A. Allocation of CO2 emissions in petroleum refineries to petroleum joint products: A linear programming model for practical application. Energy Economics. 2007; 29: 974-997. 14. Moreno T, Querol X, Alastuey A, Gibbons W. Identification of FCC refinery atmospheric pollution events using lanthanoid- and vanadium-bearing aerosols. Atmospheric Environment. 2008; 42: 7851-7861. 15. Xu Q, Yang X, Liu C, Li K, Lou HH, Gossage JL. Chemical plant flare minimization via plantwide dynamic simulation. Industrial & Engineering Chemistry Research. 2009; 48: 3505-3512. 16. Xu Q, Liu CW, Yang XT, Li KY, Lou HH, Gossage JL. Study on Near-Zero Flaring for Chemical Plant Turnaround Operation. Proceedings of FOCAPO. 2009; 603-611. 17. Xu Q, Li KY, Yang XT, Liu CW, Romero RO, Mekala UR, Lou HH, Gossage JL. Flare Minimization for Chemical Plant Turnaround Operation via Plant-wide Dynamic Simulation,

Proceedings of FOCAPO. 2008; 247-250. 18. Zhang SJ, Wang SJ, and Xu Q. Emission Constrained Dynamic Scheduling for Ethylene Cracking Furnace System. Industrial & Engineering Chemistry Research. 2017; 56(5): 13271340. 19. Sear TN. Logistics planning in the downstream oil industry. Journal of Operational Research

Society. 1993; 44: 9-17.


Page 34 of 60

Page 35 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


20. Escudero LF, Quintana FJ, Salmeron J. CORO: A modeling and an algorithmic framework for oil supply, transformation and distribution optimization under uncertainty. European

Journal of Operational Research. 1999; 114: 638-656. 21. Dempster MAH, Pedron NH, Medova EA, Scott JE, Sembos A. Planning logistics operations in the oil industry. Journal of Operational Research Society. 2000; 51: 1271-1288. 22. Shah N. Mathematical programming techniques for crude oil scheduling. Computers and

Chemical Engineering. 1996; 20: S1227-S1232. 23. Lee H, Pinto JM, Grossmann IE, Park S. Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management. Industrial

& Engineering Chemistry Research. 1996; 35: 1630-1641. 24. Ierapetritou MG, Floudas CA. Effective Continuous-Time Formulation for Short-Term Scheduling. 1. Multipurpose Batch Processes. Industrial & Engineering Chemistry Research. 1998; 37: 4341-4359. 25. Ierapetritou MG, Floudas CA. Effective Continuous-Time Formulation for Short-Term Scheduling. 2. Continuous and Semicontinuous Processes. Industrial & Engineering

Chemistry Research. 1998; 37: 4360-4374. 26. Jia Z, Ierapetritou M, Kelly JD. Refinery short-term scheduling using continuous time formulation: Crude-oil operations. Industrial & Engineering Chemistry Research. 2003; 42: 3085-3097. 27. Jia Z, Ierapetritou M. Efficient short-term scheduling of refinery operations based on a continuous time formulation. Computers and Chemical Engineering. 2004; 28: 1001-1019. 28. Reddy PCP, Karimi IA, Srinivasan R. A new continuous-time formulation for scheduling crude oil operations. Chemical Engineering Science. 2004; 59: 1325-1341.



29. Mendez CA, Grossmann IE, Harjunkoski I, Kabore P. A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations. Computers and Chemical

Engineering. 2006; 30: 614-634. 30. Pinto JM, Joly M, Moro LFL. Planning and scheduling models for refinery operations.

Computers and Chemical Engineering. 2000; 24: 2259-2276. 31. Neiro SMS, Pinto JM. A general modeling framework for the operational planning of petroleum supply chains. Computers and Chemical Engineering. 2004; 28: 871-896. 32. Kelly JD, Zyngier D. Hierarchical decomposition heuristic for scheduling: Coordinated reasoning for decentralized and distributed decision-making probloems. Computers and

Chemical Engineering. 2007; 32: 2684-2705. 33. Kelly JD. Smooth-and-dive accelerator: a pre-MILP primal heuristic applied to scheduling.

Computers and Chemical Engineering. 2003; 27: 827-832. 34. Turkay M, Grossmann IE. Logic-based MINLP algorithms for the optimal synthesis of process networks. Computers and Chemical Engineering. 1996; 20: 959-978. 35. Vecchietti A, Grossmann IE. Modeling issues and implementation of language for disjunctive programming. Computers and Chemical Engineering. 2000; 24: 2143-2155. 36. Karuppiah R, Furman KC, Grossmann IE. Global optimization for scheduling refinery crude oil operations. Computers and Chemical Engineering. 2008; 32: 2745-2766. 37. Xu JL, Zhang SJ, Zhang J, Wang SJ, Xu Q. Simultaneous scheduling of front-end crude transfer and refinery processing. Computers & Chemical Engineering. 2017; 96: 212-236. 38. Xu JL, Qu HL, Wang SJ, Xu Q. A new proactive scheduling methodology for front-end crude oil and refinery operations under uncertainty of shipping delay. Industrial &

Engineering Chemistry Research. 2017; 56(28): 8041-8053.


Page 36 of 60

Page 37 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


39. Brooke A, Kendrick D, Meeraus A. GAMS: A User’s Guide. Palo Alto, CA: Scientific Press, 1992. 40. Sahinidis NA. BARON: A general purpose global optimization software package. Journal of

Global Optimization. 1996; 8: 201-205. 41. CPLEX Optimization Inc. Using the CPLEX callable library. 1995. 42. Murtagh, B. A.; Saunders, M. A. MINOS 5.5 User’s guide. 1998. 43. Energy Information Administration (EIA). Prime Supplier Sales Volumes. 2016. http://tonto.eia.doe.gov/dnav/pet/pet_cons_prim_dcu_nus_m.htm . Accessed January 5, 2018. 44. HCE

LLC.

PCM:

Substitute

Natural

Gas

Production

Cost

http://www.hceco.com/HCEI1104002.pdf. Accessed January 5, 2018.


Estimate.

2004.


Page 38 of 60

Table 1. Emission Characterization under Various Sources

Emission Source

Dry gas

LPG

Fuel oil

Burning coke

CO2

Emission Factor from Literature12 (lb/1,000 barrels) ---

VOCs

5.8

1.25269

NOx

140

30.2372

PM

3

0.64794

CO2

---

3014.20

VOCs

6.5

1.40387

NOx

150

32.397

PM

3.5

0.75593

CO2

---

3233.13

VOCs

1.28

0.16763

NOx

55

7.20265

PM

7

0.91670

CO2

---

3655.62

VOCs

N/A

N/A

NOx

71

9.50574

PM

45

6.02476

Main Emission Species


Emission Factor (T/kT) 2864.79

Page 39 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


LIST OF FIGURES

Figure 1.

Flowsheet of a general refinery plant.

Figure 2.

General Methodology Framework.

Figure 3.

Input-output representation scheme for a general unit.

Figure 4.

Representation scheme for a plant feedstock model.

Figure 5.

Representation scheme for a plant output model.

Figure 6.

Flowsheet of a fluid catalytic cracking (FCC) facility.

Figure 7.

Comparison between the FCC model predictions and the real measurements.

Figure 8.

3D profit profile with respect to TN and TD demands.

Figure 9.

3D profile for total CO2 emission with respect to TN and TD demands.

Figure 10.

3D profile for VOC emission with respect to TN and TD demands.

Figure 11.

3D profile for NOx emission with respect to TN and TD demands.

Figure 12.

3D profile for PM emission with respect to TN and TD demands.

Figure 13.

Hydrogen (a) generation curves and (b) consumption curves for the base refinery process.

Figure 14.

PER opportunity with internal hydrogen reuse.

Figure 15.

Flowsheet of a modified refinery plant with a PSA facility.

Figure 16.

Comparisons of (a) CO2, (b) VOC, (c) NOx, and (d) PM emissions between the base and new refinery processes

Figure 17.

Total profit comparisons between base and new processes.

Figure 18.

Hydrogen (a) generation curves and (b) consumption curves for the new refinery process.

Figure 19.

Spatial distribution of (a) CO2, (b) VOC, (c) NOx, and (d) PM emissions between the base and new refinery processes.



Figure 1. Flowsheet of a general refinery plant. Reprinted with permission from ref 37. Copyright 2017 Elsevier.


Page 40 of 60

Page 41 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Scheduling Model Development

Start

Facility PFD and Design Data Plant Real Operation Data Product Property Characterization Utility Flow Characterization Emission Characterization

Product Market Data Emission Concerns

Facility Models Development and Validation

Emission-Considered Plant Scheduling Model Development

Comprehensive Analysis of Scheduling Results

Fundamental Analysis

PER Opportunity Iidentification

Industrial Expertise

Scheduling Model Modification

Product Market Data Emission Concerns

Troubleshooting

New Scheduling Result Analysis

PER Accomplished?

No

Yes PER Zone Generation

End

Figure 2. General Methodology Framework. 41 ACS Paragon Plus Environment

Scheduling Model Improvement for PER Accomplishment


ΨFu ,t

ΓFu,t

ΩX v ,u ,t

ΩFu ,w,t

...

...

...

... ΩFv ,u ,t

Fv ,s ,u ,t

ΩX u ,w,t

...

...

v

ΓX u,t

...

ΨX u ,t

... Xv, s, u, t

u

Fu ,s ,w,t

X u , s , w, t

...

...

...

...

...

...

...

...

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 42 of 60

UI ui,t

UOui,t

Figure 3. Input-output representation scheme for a general unit.


w

Page 43 of 60

...

FD j , t

XD j , t

...

Feed materials

...

...

...

X ui ,s ,w, t

UI ui,t

UO ui,t

Figure 4. Representation scheme for a plant feedstock model.


w

...

...

ui

Feed utilities

Fui ,s ,w, t

...

...

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60



v

Fv ,s , uo , t

FPk , t

X v , s , uo, t

XPk ,t

Page 44 of 60

Product materials

uo

UO uo,t

UI uo,t

Product utilities

Figure 5. Representation scheme for a plant output model.


Page 45 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


F5,1,12,t

F5 ,1, 7 ,t

F5,1, 9,t

F8,1,5,t

F10,1,5,t

F2, 3, 5,t

F5,1,8,t

F5,1,15,t

Figure 6. Flowsheet of a fluid catalytic cracking (FCC) facility.


Gasoline Yield Diesel Yield

LPG Yield

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Fuel Gas Yield


Figure 7. Comparison between the FCC model predictions and the real measurements.


Page 46 of 60

Page 47 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 8. 3D profit profile with respect to TN and TD demands.



Figure 9. 3D profile for total CO2 emission with respect to TN and TD demands.


Page 48 of 60

Page 49 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 10. 3D profile for VOC emission with respect to TN and TD demands.



Figure 11. 3D profile for NOx emission with respect to TN and TD demands.


Page 50 of 60

Page 51 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 12. 3D profile for PM emission with respect to TN and TD demands.



Figure 13. Hydrogen (a) generation curves and (b) consumption curves for the base refinery process.


Page 52 of 60

Page 53 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 14. PER opportunity with internal hydrogen reuse.



Figure 15. Flowsheet of a modified refinery plant with a PSA facility. Modified based on ref 37. Copyright 2017 Elsevier.


Page 54 of 60

Page 55 of 60

PER Zone

400

95

PER Zone

90 350 Emitted VOC (T/yr)

Emitted CO2 (kT/yr)

85

300

250

80 75 70 65

Refinery without PSA


200

Refinery with PSA

Refinery with PSA 60 150

100

150

200

250 300 350 Total Gasoline Demand (kT/yr)

400

450

55

500

100

150

200

(a) 7000


400

450

500

400

450

500

(b) 3500

PER Zone

PER Zone

6500 3000

6000

Emitted PM (T/yr)

5500 Emitted NOx (T/yr)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


5000 4500 4000

2500

2000

1500

3500


Refinery without PSA 3000

1000

Refinery with PSA

Refinery with PSA

2500 2000

100

150

200


400

450

500

500

100

150

(c)

200

250 300 350 Total Gasoline Demand (kT/yr) (d)

Figure 16. Comparisons of (a) CO2, (b) VOC, (c) NOx, and (d) PM emissions between the base and new refinery processes


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Plant Profit (Million dollars/yr)


Figure 17. Total profit comparisons between base and new processes.


Page 56 of 60

Page 57 of 60

PER Zone

20 18

H2 Amount (kT/yr)

16 14 12 10 8 6

Total H2 Generation

4

H2 Generated by Reforming H2 Generated by Hydrogen Plant

2 0

100

150

200


400

450

500

400

450

500

(a) PER Zone

20 18

H2 Amount (kT/yr)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Total H2 Consumption

16

H2 Consumed as Heating Utility

14

H2 Consumed by Hydrocracking and Hydrotreating Facilities

12 10 8 6 4 2 0

100

150

200

250 300 350 Total Gasoline Demand (kT/yr) (b)

Figure 18. Hydrogen (a) generation curves and (b) consumption curves for the new refinery process.



400

From FCC Regenerator

From Hydrogen Plant

From Gas Furnaces

From Oil Furnaces

100 58.5

350

54.1

35.9 27.2

1.9

70 199.9 195.6 181.0

150

158.6

85.41 79.01

40 2.8

0.0

101.0

84.5

73.56 67.35

30

117.0

101.0

133.5

118.5

20

133.8

10

0 Base New

Base New

Base New

215

270

325 TG Demand (kT/yr)

Base

New

0

Base New

Base New

Base New

430

380

215

Base New

270


From Gas Furnaces

6000

3000

120

16.5 44.7

16.6 45.1 14.9 43.1

15.3 44.2

2500 2106 2062

2087 12.6 40.9

2013

2000 1869

12.3 40.0

9.8 37.2

10.2 38.0

62

61

1776 1671

1734

1500

7.9 34.8

7.7 35.8

2758

1626 2418

4352

2000

3814 3292 2757

1000

From Gas Furnaces

From Oil Furnaces

77 1907

3000


117

97

5000

4000

430

130

130

99

Base New

380

(b)

From Oil Furnaces

80

Base New


(a)

7000

83.40

77.42

71.84

0.0

0.0 66.9

69.24

0.0

0.0

84.5

86.45

1.4

50

2.7

4.2

87.26

2.2

60

198.0

177.3

2.9

3.0

1.8

164.6

3.1

66.9

1.4

3.0 2.7

191.0

154.3

100

50

2.8 2.3

43.4

27.8

168.5

From Oil Furnaces

52.6

34.7

200

From Gas Furnaces

90 80

44.5

300

250

58.2

Page 58 of 60

2183

2086

1000

4363

1747

3866

3292

1384

2765

2450

2086

1747

1384

2757

500

2183

0

0 Base New

Base New

Base New

Base New

Base New

215

270


380

430

Base New

Base New

215

(c)

270

Base New


Base New

Base New

380

430

(d)

Figure 19. Spatial distribution of (a) CO2, (b) VOC, (c) NOx, and (d) PM emissions between the base and new refinery processes.


Page 59 of 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


Plant Profitability

Profitable Emission Reduction (PER)

Emission Reduction

Modeling

Simulation

Optimization

For Table of Contents Only



Supporting Information

The detailed refinery crude processing model constraints similar to the previous study with additional nomenclature are summarized in the Supporting Information. This information is available free of charge via the Internet at http://pubs.acs.org/.


Page 60 of 60

Plant-wide Scheduling for Profitable Emission ... - ACS Publications

Recommend Documents