Synergy in Mergers of Petrochemical Companies ... - ACS Publications

5556

Ind. Eng. Chem. Res. 2008, 47, 5556–5567

Synergy in Mergers of Petrochemical Companies within a Complex Considering Purchasing and Selling Advantage with Process Integration Sung-Geun Yoon,† Seung Bin Park,† Sunwon Park,*,† Jeongseok Lee,‡ Peter M. Verderame,§ and Christodoulos A. Floudas*,§ Department of Chemical and Biomolecular Engineering, Korea AdVanced Institute of Science and Technology, 373-1, Guseong-dong, Yuseong-gu, Daejeon, Korea; Corporate R&D, LG Chem Ltd. 104-1, Moonji-dong, Yuseong-gu, Daejeon, 305-380, Korea; and Department of Chemical Engineering, Princeton UniVersity, Princeton, New Jersey 08544

Mergers and Acquisitions, M&A, have been active in the petrochemical industry. However, the synergy created by the merger of petrochemical companies has rarely been studied, although it is the primary goal of a merger. This study deals with the merger of petrochemical companies located within one complex. Synergy considerations resulting from process network and boiler integration with fixed cost reduction and market power increase in upstream market (i.e., resource purchasing advantage) and downstream market (i.e., product selling advantage) are included. A novel mathematical model is formulated that represents the operation of a process network aiming at increasing the profitability of merged companies. The resource purchasing advantage and product selling advantage options are considered by means of various scenarios. The proposed model is applied to Korean Naphtha Cracking Center, NCC, companies in one complex. The results, presented in three case studies, demonstrate that a merger creates synergy primarily from the purchasing advantage and selling advantage options, while the process network and boiler integration which simply collects various processes can create a little synergy. 1. Introduction The petrochemical industry forms a crucial sector in the manufacture of industrial materials. The effects of the petrochemical industry on the national economy are significant because most industries, especially within the manufacturing sector, use products of the petrochemical industry to add value to their goods. Many countries have tried to foster the petrochemical industry as a basic industry in the early stage of industrialization. The petrochemical industry uses oil and gas extracts as raw materials, which are processed by complicated chemical networks, to manufacture a variety of petrochemical products. One important feature of the petrochemical products is mass production. Due to this characteristic, the petrochemical industry needs large equipment and hence significant capital investment. Another important feature is that it is difficult to differentiate between the products. In order to ensure price competitiveness, companies within the petrochemical industry must achieve proper economies of scale. The strategies used to achieve proper economies of scale are either to increase the scale of unit equipment or to increase the scale of a company. The scale of equipment is related to the operating cost of a plant. Newly built plants in China and the Middle East have annual capacity of 1 million tons but many existing plants have annual capacity much less than 1 million tons. In short, a country or a company building a new plant uses the “larger equipment” strategy. A country or a company already having many petrochemical plants, however, uses the larger company strategy because it is not practical to replace all existing plants with new plants with larger capacities, and * To whom correspondence should be addressed. E-mail: sunwon@ kaist.ac.kr, [email protected]. † Korea Advanced Institute of Science and Technology. ‡ LG Chem Ltd. § Princeton University.

increasing the scale of equipment has a technical limitation. The scale of a company affects its market power and the efficiency of its resource usage. The larger company strategy is primarily achieved by M&A of competing companies. Many global petrochemical majors have been increasing the scale of their companies through the M&A. The goal of the M&A is to create synergy, which is achieved by raising market power, reallocating resources, eliminating duplicate activities, etc.1 The strategic advantage of the M&A is that it achieves a degree of synergy which is not attainable for a single company within the same period. The M&A requires large financial resources, however, which may impose a significant financial burden on a company. Hence, it is important to estimate the potential synergy before the M&A is undertaken.2 This paper addresses the merging of petrochemical companies in one complex considering explicitly the resource purchasing and product selling advantage alternatives, as well as process network and boiler integration with fixed cost reduction. The focus is on estimating the synergy effects of the merger. As a case study, we select three Korean companies having a Naphtha Cracking Center (NCC) and develop a novel mathematical model which identifies how much synergy the merger can facilitate. The Korean petrochemical industry has not been active in M&A but future challenges strongly suggest that it should become active in M&A. The results of this paper may assist the decision makers of a company or government in defining and executing a prudent growth strategy. 2. Previous Research Work A number of research contributions have dealt with M&A issues, especially in the financial and management domains.1 Each approach has its own characteristics, but they all utilize past financial statements and stock market values. The approaches either are based on statistical metrics for M&A or estimated enterprise value in the M&A, which is calculated by

10.1021/ie071447k CCC: $40.75  2008 American Chemical Society Published on Web 06/25/2008

Ind. Eng. Chem. Res., Vol. 47, No. 15, 2008 5557

Figure 1. Process network diagrams of target companies: (a) process network diagram of the company A; (b) process network diagram of the company B; (c) process network of the company C.

simply assuming the future cash flow. There is a scarcity, however, of research work quantifying specifically the synergy of the M&A. A qualitative approach toward expressing the role of synergy in M&A can be found in an article.3 One reason for the scarcity of research rigorously quantifying the synergy resulting from M&A is that factors creating synergy vary significantly from industry to industry. It is difficult to generalize a method of synergy quantification because the operation of the company and industry needs to be well understood. In the chemical engineering field, M&A has not been addressed, although the nature of the petrochemical industry inherently leads to M&A.4,5 This paper introduces a novel framework that connects the M&A issue to the process systems engineering field within chemical engineering. The process systems engineering field involves modeling various subsystems that range from one chemical reaction to complex networks of reactions, units, and entire supply chains. The value creation source of the petrochemical industry is to optimally operate the process network. The scope of this study is to model process networks of petrochemical companies and discover how much synergy can be obtained by acting upon merger opportunities. The petrochemical companies generally form a complex of interconnected processes. The processes producing a large volume of products are continuous and can be connected to each other by pipelines. A product of a process often is used as a raw material for another process. The phases of the product are usually liquid or gas. Forming a complex helps to reduce transportation cost. In order to deal with the synergy resulting from a merger of petrochemical companies in one complex, the

aforementioned factors have to be considered. Although there are various elements of synergy, this study addresses the synergy from (a) process network and boiler integration, (b) raw material purchasing advantage, and (c) product selling advantage. The first comes from the fact that the companies are located nearby in one complex. The second and third ones come from the realistic assumption that the larger scale of a company is, the greater its market power becomes. In order to identify the synergy from process network and boiler integration, we have made a virtual model that can optimize the operation of the current process network and boiler system in a company. The model is based upon the material balances of the various processes. There have been several research contributions dealing with the petrochemical process network. Rudd introduced a linear programming (LP) model to optimize the structure of a petrochemical industry for shortrange planning.6 The model included various process data, the range of which is from cracking processes to polymerization processes. The model allowed for competing production paths to manufacture a product. Among the competing paths, the LP model selected one or two processes to satisfy the demand. The optimal structure of a petrochemical industry is the process network consisting of the selected processes. Stadtherr et al. extended the LP model to a long-range planning model including perturbations in feedstock availability7 and feedstock efficiency index.8 Mikkelsen et al. applied the LP model to the European petrochemical industry for the development of the Norwegian petrochemical industry.9 Jimenez et al. introduced a mixedinteger linear programming model (MILP) to the Mexican

5558 Ind. Eng. Chem. Res., Vol. 47, No. 15, 2008

Figure 2. Process network diagrams of the merged company D. Table 1. Premium Rate (spj) of Chemical j for Selling Qsj

scenario 1 scenario 2 scenario 3 scenario 4 scenario 5 scenario 6 scenario 7 scenario 8 scenario 9 scenario 10 scenario 11

0 - 0.25adj 0.25adj - 0.5adj 0.5adj - 0.75adj 0.75adj - 1adj

0 0 0 0

0 0.003 0.006 0.009

0 0.006 0.012 0.018

0 0.009 0.018 0.027

0 0.012 0.024 0.036

0 0.015 0.03 0.045

0 0.018 0.036 0.054

0 0.021 0.042 0.063

0 0.024 0.048 0.072

0 0.027 0.054 0.081

0 0.03 0.06 0.09

Table 2. Discount Rate (pdj) for Naphtha Purchasing Qpj (million tons/year) scenario 1 scenario 2 scenario 3 scenario 4 scenario 5 scenario 6 scenario 7 scenario 8 scenario 9 scenario 10 scenario 11 0-3 3-6 6-9 9-12

0 0 0 0

0 0.005 0.01 0.015

0 0.01 0.02 0.03

0 0.015 0.03 0.045

0 0.02 0.04 0.06

BDa BDb HDPEb HDPEc NCCa NCCb NCCc BTXa BTXb BTXc

0 0.03 0.06 0.09

0 0.035 0.07 0.105

0 0.04 0.08 0.12

0 0.045 0.09 0.135

0 0.05 0.1 0.15

Table 4. Statistics of Model and Solution for MILP and MINLP Model

Table 3. Paremeters for Fixed Cost of Processes processes

0 0.025 0.05 0.075

fixed cost ($/ton)

reduced fixed cost ($/ton)

reduction effect (%)

18.09 18.09 51.98 51.98 25.78 28.45 27.56 11.85 13.55 12.14

16.62 16.62 48.04 48.04 23.93 25.57 24.77 10.08 11.52 10.32

8.13 8.13 7.58 7.58 7.18 10.13 10.13 14.93 14.97 14.98

petrochemical industry10 and extended the MILP model so as to detect an optimal development sequence.11 Many other researchers have addressed this class of problems with different objective functions. The postulated objective

no. of equations no. of variables continuous discrete solution time (s) profit ($)

MILP

MINLP

1331 1447 1153 294 0.109 1,061,377,502

1331 1447 1153 294 0.717 941,604,231

functions have been to minimize the total cost,6,10,11 to minimize raw material consumption,7,8,12,13 to maximize thermodynamic availability,14 to maximize the annual profit,15 and to minimize negative environmental effects.16 Sometimes two or three objectives are considered simultaneously in a multiobjective optimization framework.12,16 Bok et al. proposed a robust petrochemical investment approach for capacity expansion.15 Recently, many Middle Eastern countries have applied this

Ind. Eng. Chem. Res., Vol. 47, No. 15, 2008 5559 Table 5. Optimization Results of 2003 Price Set in Case Study 1 profit ($) scenarios 1 2 3 4 5 6 7 8 9 10 11

D

A+B+C

synergy ($)

synergy effect (%)

1,061,377,502 1,099,987,502 1,138,597,502 1,177,207,502 1,215,817,502 1,254,427,502 1,293,037,502 1,331,647,502 1,370,257,502 1,408,867,502 1,447,477,502

996,346,276 1,003,014,916 1,009,683,556 1,016,352,196 1,023,020,836 1,029,689,476 1,036,358,116 1,043,026,756 1,049,695,396 1,056,364,036 1,063,032,676

65,031,226 96,972,586 128,913,946 160,855,306 192,796,666 224,738,026 256,679,386 288,620,746 320,562,106 352,503,466 384,444,826

6.53 9.67 12.77 15.83 18.85 21.83 24.77 27.67 30.54 33.37 36.16

Table 6. Optimization Results of 2005 Price Set in Case Study 1 profit ($) scenarios 1 2 3 4 5 6 7 8 9 10 11

D

A+B+C

synergy ($)

synergy effect (%)

1,001,518,035 1,072,435,215 1,143,352,396 1,214,269,577 1,285,186,758 1,356,103,938 1,427,021,119 1,497,938,300 1,568,855,481 1,639,772,661 1,710,689,842

969,669,987 981,918,657 994,167,327 1,006,415,997 1,018,664,667 1,030,913,337 1,043,162,007 1,055,410,677 1,067,659,347 1,079,908,017 1,092,156,687

31,848,047 90,516,558 149,185,069 207,853,580 266,522,090 325,190,601 383,859,112 442,527,623 501,196,133 559,864,644 618,533,155

3.28 9.22 15.01 20.65 26.16 31.54 36.80 41.93 46.94 51.84 56.63


D

A+B+C

synergy ($)

synergy effect (%)

966,921,203 1,054,205,513 1,141,489,824 1,228,774,135 1,316,058,446 1,403,342,756 1,490,627,067 1,577,911,378 1,665,195,689 1,752,479,999 1,839,764,310

920,149,792 935,225,359 950,300,926 965,376,493 980,452,060 995,527,627 1,010,603,194 1,025,678,762 1,040,754,329 1,055,829,896 1,070,905,463

46,771,411 118,980,155 191,188,898 263,397,642 335,606,385 407,815,129 480,023,873 552,232,616 624,441,360 696,650,104 768,858,847

5.08 12.72 20.12 27.28 34.23 40.96 47.50 53.84 60.00 65.98 71.80

Table 8. Average Synergy Effect According to Each Scenario in Case Study 1 scenarios 1 2 3 4 5 6 7 8 9 10 11

av synergy effect (%) 4.96 10.54 15.96 21.25 26.41 31.44 36.35 41.15 45.83 50.40 54.86

methodology for their petrochemical industry development because they are developing a petrochemical industry using their ample resources.16–18 It is important to note that the study of synergy can also be applied to process synthesis,19–24 process design,25–28 and process operating problems.29,30 The synergy resulting from increasing the scale of a company generally is a consequence of increasing the market power in upstream and downstream markets and reducing the fixed cost per unit product.1,5 This study considers increasing the market power in an upstream market and a downstream market. In other

Table 9. Optimization Results of 2003 Price Set in Case Study 2 profit ($) D

A+B+C

synergy ($)

1,061,377,502 1,070,744,367 1,080,111,231 1,089,478,095 1,098,844,960 1,108,211,824 1,117,578,689 1,126,945,553 1,136,312,418 1,145,679,282 1,155,046,146

996,346,276 997,771,678 999,197,080 1,000,622,482 1,002,047,884 1,003,473,286 1,004,898,688 1,006,324,090 1,007,749,492 1,009,174,894 1,010,600,296

65,031,226 72,972,688 80,914,151 88,855,613 96,797,076

scenarios 1 2 3 4 5 6 7 8 9 10 11

synergy effect (%) 6.53 7.31 8.10 8.88 9.66 10.44 11.21 11.99 12.76 13.53 14.29

112,680,001 120,621,463 128,562,926 136,504,388 144,445,851

Table 10. Optimization Results of 2005 Price Set in Case Study 2 profit ($) D

A+B+C

synergy ($)

synergy effect (%)

1,001,518,035 1,018,597,532 1,035,677,029 1,052,756,526 1,069,836,023 1,086,915,521 1,103,995,018 1,121,074,515 1,138,154,012 1,155,233,509 1,172,313,006

969,669,987 975,727,249 981,784,510 987,841,772 993,899,033 999,956,295 1,006,013,556 1,012,070,818 1,018,128,079 1,024,185,341 1,030,242,602

31,848,047 42,870,283 53,892,519 64,914,754 75,936,990 86,959,226 97,981,461 109,003,697 120,025,933 131,048,168 142,070,404

3.28 4.39 5.49 6.57 7.64 8.70 9.74 10.77 11.79 12.80 13.79

scenarios 1 2 3 4 5 6 7 8 9 10 11


D 966,921,203 982,348,688 999,687,084 1,018,678,734 1,037,670,385 1,056,662,035 1,075,653,686 1,094,645,336 1,113,636,986 1,132,628,637 1,151,620,287

A + B + C synergy ($) synergy effect (%) 920,149,792 922,095,898 925,952,913 931,463,184 936,973,454 942,483,725 947,993,996 953,504,266 959,014,537 964,524,807 970,035,078

46,771,411 60,252,791 73,734,171 87,215,550 100,696,930 114,178,310 127,659,690 141,141,070 154,622,450 168,103,829 181,585,209

5.08 6.53 7.96 9.36 10.75 12.11 13.47 14.80 16.12 17.43 18.72



words, an advantage is assumed to exist when purchasing raw materials and selling products. A petrochemical company requires few raw materials and manufactures many products. The companies in our case study use only naphtha as raw material. We assume that the purchasing advantage varies with the annual purchasing quantity, which is reflected by applying a simple discount rate to the unit price of the raw material. The selling advantage is reflected in same manner as the purchasing advantage but uses premium rates.

5560 Ind. Eng. Chem. Res., Vol. 47, No. 15, 2008 Table 13. Optimization Results of 2003 Price Set in Case Study 3 profit ($) scenarios 1 2 3 4 5 6 7 8 9 10 11

D

A+B+C

synergy ($)

synergy effect (%)

1,061,377,502 1,109,354,367 1,157,331,231 1,205,308,095 1,253,284,960 1,301,261,824 1,349,238,689 1,397,215,553 1,445,192,418 1,493,169,282 1,541,146,146

996,346,276 1,004,440,318 1,012,534,360 1,020,628,402 1,028,722,444 1,036,816,486 1,044,910,528 1,053,004,570 1,061,098,612 1,069,192,654 1,077,286,696

65,031,226 104,914,048 144,796,871 184,679,693 224,562,516 264,445,338 304,328,161 344,210,983 384,093,806 423,976,628 463,859,451

6.53 10.45 14.30 18.09 21.83 25.51 29.12 32.69 36.20 39.65 43.06


D

A+B+C

synergy ($)

synergy effect (%)

1,001,518,035 1,089,514,713 1,177,511,390 1,265,508,068 1,353,504,746 1,441,501,424 1,529,498,102 1,617,494,780 1,705,491,458 1,793,488,136 1,881,484,814

969,669,987 987,975,919 1,006,281,850 1,024,587,782 1,042,893,713 1,061,199,645 1,079,505,576 1,097,811,508 1,116,117,439 1,134,423,371 1,152,729,302

31,848,047 101,538,794 171,229,540 240,920,287 310,611,033 380,301,780 449,992,526 519,683,272 589,374,019 659,064,765 728,755,512

3.28 10.28 17.02 23.51 29.78 35.84 41.69 47.34 52.81 58.10 63.22


D

A+B+C

synergy ($)

synergy effect (%)

966,921,203 1,069,632,999 1,174,255,705 1,280,531,666 1,386,807,628 1,493,083,589 1,599,359,550 1,705,635,511 1,811,911,472 1,918,187,434 2,024,463,395

920,149,792 937,171,465 956,104,047 976,689,885 997,275,723 1,017,861,561 1,038,447,398 1,059,033,236 1,079,619,074 1,100,204,911 1,120,790,749

46,771,411 132,461,534 218,151,658 303,841,781 389,531,905 475,222,028 560,912,152 646,602,275 732,292,399 817,982,522 903,672,646

5.08 14.13 22.82 31.11 39.06 46.69 54.01 61.06 67.83 74.35 80.63



When companies are merged, synergy can take place but costs also occur. These costs are the acquisition premium and the cost for asset rationalization.3 The acquisition premium is paid by an acquiring company for control of a target company. For the case of petrochemical companies, the cost of asset rationalization is the investment for reallocating equipment to create synergy. In this study we will not focus on the acquisition premium but on the synergy effect of the merger. The cost for rationalization is very small compared to the profit which is the objective of the model; therefore, it is assumed to be negligible in the model.

3. Problem Definition: The Korean Petrochemical Industry and Target Companies This study deals with three petrochemical companies having a Naphtha Cracking Center, NCC, in Korea. The Korean petrochemical industry has developed rapidly during the last 30 years. During its early stages, the petrochemical industry attempted to import a few petrochemicals, and its production capacity was very small. The production capacity was expanded gradually through the 1970s and 1980s due to a strong development policy initiated by the Korean government, and by then, most of the domestic demand was satisfied by the domestic production and the investment in the petrochemical industry was controlled by the government. In 1986, the Korean government liberated investment in the petrochemical industry, and the existing companies decided to expand their capacities and new companies participated in the industry. Since the 1990s, Korea has been exporting petrochemicals, and now the Korean petrochemical industry exports half of its total production. The projected situations in the near future may not be favorable to the Korean petrochemical industry. China, which is a major export market, is quickly increasing its production capacity to satisfy its domestic demand, and the Middle Eastern countries are significantly expanding their own petrochemical industry. The Korean petrochemical industry will have to confront these major challenges and has to secure price competitiveness in order to sustain its petrochemicals exports. As a result, M&A have become a very important strategy that Korea can adopt. At this stage, many companies participate in the relatively small petrochemical industry, and the scale of the companies is much smaller than the major global petrochemical companies and the newcomers in China and the Middle East. It is important to explain the rationale for selecting NCC companies as our case study. A petrochemical industry starts from a NCC company and the NCC company can affect most of the other petrochemical companies. Korea has eight NCC companies, which are located in three petrochemical complexes. Most of the other petrochemical companies in Korea are also located in these three complexes, which suggests that the Korean petrochemical companies already have a great advantage in exploring the M&A alternative. The synergy from process network integration with fixed cost reduction can occur when companies are in close proximity. The three companies, A, B, and C, in our case study operate in one of the three complexes. They have common NCC and BTX processes, but each company has different downstream processes. In addition, A, B, and C have two steam boilers each. Figure 1 depicts the process network diagrams of the three companies. In Figure 1, a square represents a process and an ellipse denotes a chemical. If the three companies are merged, the merged company has all the processes of each company. We assume that company D is the aggregate of companies A, B, and C. The process network diagram of the company D is shown in Figure 2. In addition, the company D has six steam boilers. 4. Mathematical Modeling In this section, a mathematical model is introduced for the optimization of a process network. The model determines the optimum process levels when considering profit maximization under given price data. The merged companies are fixed. The sets, variables, and parameters in the model, are presented below. (1) Sets


i ) 1, 2, ..., I processes

equivalent mixed-integer linear form is explained, and the complete mixed-integer linear programming model is presented. (A) Mass Balance Equations.

j ) 1, 2, ..., J chemicals k ) 1, 2, ..., K boilers l ) 1, 2, ..., L division of chemical purchasing and selling amount

aijXi ) qij,

{

{

naphtha, ethylene, propylene, C4 mixture, pyrolysis fuel oil, raw pyrolysis gasoline, hydrogen, high-density polyethylene, butadiene, C4-raffinate, C5 mixture, C9+, benzene, toluene, xylene, chemicals ) phenol, acetone, methyl methacrylate, polypropylene, styrene monomer, bisphenol-A, ethylene oxide, monoethylene glycol, diethylene glycol, butene-1, methanol, methyl tert-butyl ether

}

}

boiler ) {Ba1, Ba2, Bb1, Bb2, Bc1, Bc2} (2) Variables Xi ) annual amount of input to process i (tons/year) ri ) operating ratio of process i bi ) binary for process operation used to denote the activation of process i qij ) annual amount of produced or consumed chemical j in process i (tons/year) Qpj ) annual amount of chemical j purchased (tons/year) ypj ) binary for chemical j purchased Qsj ) annual amount of chemical j sold (tons/year) ysj ) binary for chemical j sold bbk ) boiler activation binary brk ) boiler operating ratio BXk ) boiler operating level pdj ) purchasing discount rate for chemical j spj ) selling premium rate for chemical j ypdlj ) binary for discount rate determination of chemical j ysdlj ) binary for premium rate determination of chemical j Qpdlj ) amount of purchased chemical j in division l (tons/ year) Qsdlj ) amount of sold chemical j in division l (tons/year) (3) Parameters aij ) material balance coefficient of chemical j in process i Pj ) price of chemical j ($/ton) Ui ) process iutility cost exzept steam ($/ton) Fi ) process i fixed cost ($/ton) Xi ) process capacity (tons/year) Cj ) capacity check of chemical j (0 or 1) BXk ) boiler capacity Si ) steam demand for process I (tons/year) SP ) steam generation cost adj ) annual supply of chemical j in Korean petrochemical industry (tons/year) The equations for the mass balance of chemicals are presented first, followed by constraints for process operations and the mixed-integer nonlinear objective function. Then, the transformation of the mixed-integer nonlinear objective function to an

(1)

∀i

(2)

0 e Xi e Xi,

Eighteen processes, 27 chemicals, 6 boilers, and 4 divisions are involved in the case study. The processes, chemicals, and boilers are: NCCa, NCCb, NCCc, BTXa, BTXb, BTXc, process ) BDa, BDb, SMa, HDPEb, HDPEc, PPc, MMAc, PHENOLb, BPAb, EOc, EGc, MTBEa

∀ i, j

ri )

Xi

∀i

,

Xi

(3) ∀i

0.9 × bi e ri e bi,

(4)

I

Qpj +

∑q

∀j

ij - Qsj ) 0,

(5)

i

0 e Qpj e M·ypj,

∀ j, M ) large positive value

(6)

0 e Qsj e M·ysj,


(7)

ypj + ysj e 1,

∀j

(8)

Equation 1 represents the amount of produced or consumed chemicals, qij. Negative aij means chemical j is consumed in process i, and positive aij means chemical j is produced in process i. The amount produced or consumed is constrained by eq 2. Equation 3 represents the process operating ratio. Equation 4 is a constraint for minimum operating ratio at 90% and for binary variable of process operation. Petrochemical processes are usually operated at high level. Equation 5 represents the mass balance of chemical j in a company. Equations 6, 7, and 8 are constraints on Qpj and Qsj. For all chemicals, Qpj and Qsj cannot take on a positive value simultaneously. (B) Constraints for Process Operation. ∀j

Cj·Qpj ) 0,

(9)

I

(1 - Cj)·Qpj e -(1 - Cj)·

∑a X, ij i

∀j

(10)

i

Equations 9 and 10 represent the operational strategy for petrochemical companies. If a downstream process is operated, the upstream process supplies as much as possible of the relevant intermediate chemicals regardless of the profitability of the upstream process. Cj is a binary parameter and is activated when ∑iI aij Xi g 0. ∑iI aij Xi means chemical j production or consumption when processes operate at their maximum operating ratio. When chemical j is an intermediate chemical, ∑iI aij Xi is the amount produced in upstream processes subtracted by the amount consumed in downstream processes. A positive value of ∑iI aij Xi means that upstream processes can supply the intermediate chemical needed in downstream processes if it is needed. If Cj is activated, Qpj is forced to be zero. A negative value of ∑iI aij Xi means that upstream processes cannot supply whole the amount of the intermediate chemical needed in downstream processes if the downstream processes operate at their maximum operating ratio. In this case, the level of Qpj is restricted by eq 10. When a downstream process is operated, it means that the downstream process is profitable, and the operating level becomes maximal. By eq 10, the upstream process supplies the chemicals as much as possible of the given chemicals and then deficient amount can be purchased. (C) Equations for Boiler Operation. K

∑ k

I

BXk g

∑SX

i i

(11)

i

0 e BXk e BXk,

∀k

(12)


brk )

BXk

∀k

,

BXk

(13)

(18)

∀j

Qsj ) Qsd1j + Qsd2j + Qsd3j + Qsd4j,

(19)

∀j

0·ysd1j e Qsd1j e 0.25·adj·ysd1j,

∀ k, M ) large positive value

brk e bbk e M·brk,

∀j

ysd1j + ysd2j + ysd3j + ysd4j ) 1,

(20)

(14)

0.25·adj·ysd2j e Qsd2j e 0.5·adj·ysd2j,

∀j

(21)

Equation 11 represents that boilers have to satisfy process steam demand. Equation 12 is for capacity limitation of boiler. Equation 13 calculates boiler operating ratio. Equation 14 is constraint for binary variable for boiler operation. (D) Objective Function.


∀j

(22)

maximize{annual profit ) z} annual profit ) revenue by chemicals selling cost of chemicals purchasing - cost of process operation J

z)

J

j

j

j

j

j

j

i

j

0.6(1 - ri)) + Fi)Xi -

J

purchase )

k

k

i

k

(15)

k

In the objective function (15), the first term is the revenue generated by selling chemicals, the second term is the cost associated with purchasing chemicals, and the third term is the cost of process operation. Revenue by chemicals selling: J

revenue )

∑ P Qs (1 + sp ) j

j

(16)

j

j

Equation 16 represents the annual revenue generated by selling chemicals. This term is the product of the price, Pj, of chemical j, the amount sold, Qsj, and the selling advantage, 1 + spj. This term is nonconvex due to the bilinear term, Qsj · spj. A petrochemical company sells various types of products to other petrochemical companies or manufacturing companies. Because every petrochemical company intends to sustain a high operating level, a company faces excess competition bringing about a negative margin.31 Increasing market power helps a company to overcome this excess competition. Although it is not true for every case, a larger market share may lead to better price. We have discovered that there is proportional relation between selling amount and selling price for some petrochemical products in Korea.32–37 The larger quantities also affect transportation and inventory cost. Selling premium results from the market power and the cost reduction. spj represents the selling advantage as a premium rate. It is difficult to discover exact relationship between selling and purchasing amounts and price. We assume that spj has a step value according to Qsj, and adj representing the annual supply of chemical j. In addition, we consider various scenarios for spj due to the variability of spj. Table 1 shows eleven scenarios of spj. Values in the Table 1 are created on the basis of average selling prices of various Korean petrochemical companies. Through 11 scenarios, the proportional relation between selling amount and selling price for some petrochemical products in Korea is reflected. In order to apply the spj to the optimization model, we first define a set of continuous variables, Qsdlj, and a set of binary variables, ysdlj. Qsdlj denotes the division of the selling amount, Qsj, and ysdlj is binary variable which activates Qsdlj. Then we introduce the following mathematical model, which is written here for illustrative purposes for scenario 2 but is applicable for every scenario: spj ) 0·ysd1j + 0.003·ysd2j + 0.006·ysd3j + 0.009·ysd4j,

∀j

∑ P Qp (1 - pd ) j

j

(24)

j

j

i

∑ SP·bb (1 - 0.6(1 - br ))BX

(23)

where spj is expressed by eqs 17 and 18 using ysdlj. Equation 19 represents Qsj and Qsdlj. By eqs 20–23, Qsj is assigned to Qsdlj, and Qsdlj and ysdlj are connected. Cost of chemicals purchasing:

I

∑ P Qs (1 + sp ) - ∑ P Qp (1 - pd ) - ∑ (b U (1j

0.75·adj·ysd4j e Qsd4j e 1·adj·ysd4j, ∀ j

(17)

The above term represents the annual cost of purchasing chemicals. This term is the product of the price, Pj, of chemical j, its amount purchased, Qpj, and the discounted rate, 1 - pdj. This term is nonconvex due to the bilinear term, Qpj · pdj. A petrochemical company purchases chemicals from the refinery industry or other petrochemical companies. The purchasing amounts of the chemicals generally exceed tens or hundreds of tons per year. In particular, the required amount of naphtha or ethane is very large due to the large number of petrochemical derivatives resulting from it. The target company uses naphtha as raw material, which is consumed on the order of 10 million tons per year by the target company. Securing raw materials at a lower cost is an important objective for a petrochemical company. The purchasing advantage option results from purchasing larger quantities of raw material. Larger quantities influence not only market power but also can reduce the transportation and inventory cost. We have discovered that there is proportional relation between naphtha purchasing amount and cost of unit naphtha price. pdj represents the purchasing advantage as a discount rate, which is a dependent upon Qpj and annual demand of naphtha in Korea, which is approximately 30 million tons. Table 2 shows 11 scenarios for pdj. We assume that the degree of purchasing advantage in the upstream market is larger than the degree of selling advantage in the downstream market. The 11 scenarios reflect the proportional relation between purchasing amount and cost of unit naphtha purchasing in Korea. In order to apply the pdj to the optimization model, we define a set of continuous variables, Qpdlj, and a set of binary variables, ypdlj. Qpdlj denote the division of the purchasing amount, Qpj, and ypdlj are binary variables which activate Qpdlj. Then we introduce the following mathematical model, which is written for scenario 2 but is applicable for every scenario: pdj ) 0·ypd1j + 0.005·ypd2j + 0.01·ypd3j + 0.015·ypd4j,

∀j

ypd1j + ypd2j + ypd3j + ypd4j ) 1,

∀j

Qpj ) Qpd1j + Qpd2j + Qpd3j + Qpd4j, 0·ypd1j e Qpd1j e 3 × 10 ·ypd1j, 6

∀j

3 × 10 ·ypd2j e Qpd2j e 6 × 10 ·ypd2j, 6

∀j

6

(26) (27) (28)

∀j

6·106·ypd3j e Qpd3j e 9·106·ypd3j, ∀ j 9 × 106·ypd4j e Qsd4j e 12 × 106·ypd4j,

(25)

(29) (30)

∀j

(31)

where pdj is expressed by eqs 25 and 26 using ypdlj. Equation 27 represents Qpj and Qpdlj. By eqs 28–31, Qpj is assigned to Qpdlj, and Qpdlj and ypdlj are connected.


Process operating cost

J

∑

I

operating cost )

∑ (b U (1-0.6·(1 - r )) + F )X + i

i

i

i

i

j

k

∑ P Qs (1 + (0·ysd j

1j + 0.003·ysd2j +

j

j

0.006·ysd3j + 0.009·ysd4j))

i

∑ SP·bb (1 - 0.6·(1 - br ))BX k

J

PjQsj(1 + spj) )

k

J

)

(32)

k

The operating cost term consists of the utility cost, the fixed cost. The utility cost consists of Ui and SP. Ui includes the cost for electricity, water, gas, etc. SP means steam production cost. The utility cost and steam production cost per unit product varies with the operating ratio.38 The higher the operating level is, the lower the utility cost per unit product is. It is difficult to find a general formula representing the relation between the operating ratio of utility equipment and the utility cost. We used the formula of SRI International, which has various experiences in industrial cost estimation.38 By the formula we used, the proposed mathematical model can reflect more reality than simple utility cost model which does not include the variable operating cost with the operating ratio of the utility equipment. The binary variable term forces the utility cost to be zero when the operating level is zero. The fixed cost occurs when a plant is installed, so the operating level does not affect the fixed cost. This is why petrochemical companies intend to keep their operating ratio high. The fixed cost of a process consists of maintenance materials, maintenance labor, operating labor, operating supplies, control laboratory, plant overhead, etc.39 Merger of companies can create fixed cost reduction due to elimination of duplicate works. This paper calculates synergy from fixed cost reduction under assumption that operating supplies and plant overhead are reduced by merger. The operating supplies mean service for plant operation. The plant overhead includes wider activities than the operating supplies: cafeteria; employment and personnel; fire protection, inspection, and safety; first aid and medical; industrial relations; janitorial; purchasing, receiving, and warehousing; automotive and other transportation; and recreation. It is very difficult to make a general mathematical model for the fixed cost reduction because there can be many different cases. This paper assumes constant reduction in the operating supplies and the plant overhead of the fixed cost, when there are same processes in the merged company. If the merged company has two processes producing the same product (BD and HDPE processes), 25% reductions of the operating supplies and the plant overhead are assumed. If there are three processes producing the same product in the merged company (NCC and BTX processes), 50% reductions of the operating supplies and the plant overhead are assumed. In order to calculate the synergy, reduced fixed cost parameter is used. Table 3 shows parameters of reduced fixed cost for BD, HDPE, NCC, and BTX processes. Equation 32 introduces nonconvexities due to the bilinear products of the binary variable, bi, and the continuous variable, ri, and of the binary variable, bbk, and the continuous variable, brk. (E)Transformation of Mixed-Integer Nonlinear Model to Mixed-Integer Linear Model. Although the above model expresses the petrochemical network in a company, it results in nonlinearities in the objective function, namely eqs 16, 24, and 32. When the optimization problem is solved, the nonconvexities consume much computational time and can result in a suboptimal solution.40,41 To address this issue, we transform the nonconvex bilinear terms for scenario 2, eqs 16, 24, and 32, into an equivalent mixed-integer linear optimization model,40 as follows. A similar approach is followed for all other scenarios.

∑ P (Qs + (0·Qsd j

1jysd1j +

j

j

0.003·Qsd2jysd2j + 0.006·Qsd3jysd3j + 0.009·Qsd4jysd4j)) J

)

∑ P (Qs + (0·Qsd j

1j + 0.003·Qsd2j +

j

j

0.006·Qsd3j + 0.009·Qsd4j)) J

(33)

J

∑ P Qp (1 - pd ) ) ∑ P Qp (1 - (0·ypd j

j

j

j

j

1j + 0.005·ypd2j +

j

j

0.01·ypd3j + 0.015·ypd4j)) J

)

∑ P (Qp - (0·Qpd j

1jypd1j +

j

j

0.005·Qpd2jypd2j + 0.01·Qpd3jypd3j + 0.015·Qpd4jypd4j)) J

)

∑ P (Qp - (0·Qpd j

1j + 0.005·Qpd2j +

j

j

0.01·Qpd3j + 0.015·Qpd4j))

(34)

I

∑ (b U (1-0.6·(1 - r )) + F )X + ∑ SP·bb (1 - 0.6·(1 i

i

i

i

i

k

i

k

brk))BXk I

)

∑ (U (0.4·b + 0.6·r b )) + F )X + ∑ SP(0.4·bb + i

i

i i

i

i

k

k

i

0.6·bbk·brk)BXk I

)

∑ (U (0.4·b + 0.6·r )) + F )X + ∑ SP(0.4·bb + i

i

i

i

i

k

i

k

0.6·brk)BXk

(35) 0.9 × bi e ri e bi,

∀i

(4)

∀ k, M ) large positive value

brk e bbk e M·brk,

(14) (F) The Complete Mathematical Model. The proposed mathematical model is a mixed-integer linear optimization model which can be solved to optimality, and it is applied to companies A, B, C, and D. The summation of the profits of A, B, and C are compared to the profit of D to identify the role of the merger synergy. This study uses CPLEX42 along with a GAMS43 package to solve this optimization problem. The complete model for scenario 2 is presented below. Maximize J

z)

∑ P (Qs + (0·Qsd j

1j + 0.003·Qsd2j + 0.006·Qsd3j +

j

j

J

0.009·Qsd4j)) -

∑ P (Qp - (0·Qpd j

1j + 0.005·Qpd2j +

j

j

I

0.01·Qpd3j + 0.015·Qpd4j)) -

∑ (U (0.4·b + 0.6·r )) + F )X i

i

i

i

i

i

K

∑ SP(0.4·bb + 0.6·br )BX k

k

k

k


Subject to aijXi ) qij,

∀ i, j

(1)

∀i

(2)

0 e Xi e Xi, ri )

Xi

∀i

,

Xi

(3)

0.9 × bi e ri e bi,

∀i

(4)

I

Qpj +

∑q

ij - Qsj ) 0,

∀j

(5)

i

the capacities of the various processes and the annual demand of chemicals were determined by the annual report of the Korea Petrochemical Industry Association and information from the interview,44 and are provided in the Appendix Tables A1 and A2. The price data was collected from the SRI PEP Yearbook and weekly spot price data38 and are provided in Appendix Table A3. Three sets of annual average prices were used in the optimization studies. Due to a company’s sensitivity toward market trends, one data set can provide a biased result. The 2003 price data are from the SRI PEP yearbook, and data for 2005 and 2006 are from the spot price data. The operating cost data for the processes are from SRI PEP Year book38 and are provided in Appendix Table A4.

0 e Qpj e M·ypj,


(6)

0 e Qsj e M·ysj,


(7)

6. Optimization Results

(8)

The optimization studies of the four target companies, A, B, C, and D were carried out using three price sets under 11 discount and premium rate scenarios. The results show profit differences between D and the sum of A, B, and C. The difference represents the role of synergy in a merger. The proposed MILP model is solved to optimality and typical execution times are less than 0.1 s for a company in a scenario. Table 4 exhibits statistics of model and solution for the MILP model of company D. Core 2 CPU 2.93 GHz is used to solve the model. In addition, statistics of model and solution for the MINLP model are exhibited for comparison. DICOPT along with a GAMS package is used to solve the MINLP model. The results show that the MILP model is better than the MINLP model. DICOPT could not find optimal solution. As soon as the objective function of the NLP subproblem started to deteriorate, the search was stopped. Profit of MINLP model in Table 4 is the best solution found. We have three case studies. Case Study 1 considers purchasing advantage only, Case Study 2 considers selling advantage only, and Case Study 3 consider purchasing and selling advantage together. 1. Case Study 1: Purchasing Advantage Only. In Case Study 1, only the purchasing advantage, pdj, is included in the optimization model, and the selling advantage, spj, is zero for all scenarios. Tables 5, 6, and 7 show the optimization results for 2003, 2005, and 2006 price sets, respectively. Scenario 1 does not consider the naphtha discount rate. Hence, the results of the scenario 1 only reflect synergy from process network and boiler integration with fixed cost reduction. Scenarios 2-11 reflect synergy from process network and boiler integration and the purchasing advantage. The results from scenario 1 show that process network and boiler system achieves synergy effect of 4.96% on average. The results for the other scenarios (2-11) show that the synergy created from larger purchasing scale advantage is dominant. Table 8 shows the average synergy effects of three price data sets according to each scenario. 2. Case Study 2: Selling Advantage Only. In Case Study 2, only the selling advantage, spj, is included in the optimization model, and the purchasing advantage, pdj, is zero for all scenarios. Table 9, 10 and 11 show the optimization results for 2003, 2005 and 2006 price sets, respectively. Scenario 1 does not consider the selling premium rate. Hence, the results of scenario 1 only reflect synergy from process network and boiler integration with fixed cost reduction and are exactly equal to scenario 1 in Case Study 1. Scenarios 2-11 reflect synergy from process network and boiler integration and the selling advantage. The results for scenarios 2-11 show that the synergy created from selling advantage is smaller than the

ypj + ysj e 1,

∀j ∀j

Cj·Qpj ) 0,

(9)

I

(1 - Cj)·Qpj e -(1 - Cj)·

∑a X,

∀j

ij i

(10)

i

K

I

∑ BX g ∑ S X

(11)

0 e BXk e BXk, ∀ k

(12)

k

i i

k

i

brk ) brk e bbk e M·brk,

BXk BXk

,

∀k

(13)

∀ k, M ) large positive value (52)

spj ) 0·ysd1j + 0.003·ysd2j + 0.006·ysd3j + 0.009·ysd4j, ∀j

ysd1j + ysd2j + ysd3j + ysd4j ) 1, 0·ysd1j e Qsd1j e 0.25·adj·ysd1j,

(18)

∀j

Qsj ) Qsd1j + Qsd2j + Qsd3j + Qsd4j,

∀j (17)

∀j

(19) (20)


∀j

(21)


∀j

(22)

∀j

0.75·adj·ysd4j e Qsd4j e 1·adj·ysd4j,

pdj ) 0·ypd1j + 0.005·ypd2j + 0.01·ypd3j + 0.015·ypd4j, ∀j

ypd1j + ypd2j + ypd3j + ypd4j ) 1,

∀j

Qpj ) Qpd1j + Qpd2j + Qpd3j + Qpd4j, 0·ypd1j e Qpd1j e 3 × 106·ypd1j,

∀j

(23) ∀j (25) (26) (27) (28)

3 × 10 ·ypd2j e Qpd2j e 6 × 10 ·ypd2j,

∀j

(29)

6 × 106·ypd3j e Qpd3j e 9 × 106·ypd3j,

∀j

(30)

9 × 106·ypd4j e Qsd4j e 12 × 106·ypd4j,

∀j

(31)

6

6

5. Input Parameters The mathematical model needs various input parameters, which include the material balance coefficients, the capacities of different processes, the price of chemicals, annual supply of chemicals and the operating cost parameters. In this study, a literature survey and interviews with engineers in the target companies were conducted. The material balance coefficients,


purchasing advantage. Table 12 shows the average synergy effects of three price data sets according to each scenario. 3. Case Study 3: Purchasing and Selling Advantage Together. In Case Study 3, the selling advantage, spj, and the purchasing advantage, pdj, are included in the optimization model. Tables 13, 14 and 15 show the optimization results for 2003, 2005, and 2006 price sets, respectively. Scenario 1 considers no market power advantage. Hence, the results of the scenario 1 only reflect synergy from process network and boiler integration and are exactly equal to scenario 1 in Case Studies 1 and 2. Scenarios 2-11 reflect synergy from process network and boiler integration and the purchasing and selling advantage. The results for scenarios 2-11 show that synergy in Case Study 3 is equal to the sum of synergy in Case Studies 1 and 2 subtracted by synergy from process network and boiler integration because the optimization model is linear. Table 16 shows the average synergy effects of three price data sets according to each scenario. The three case studies show that market power increase due to larger scale affects predominantly the synergy in the merger. These results coincide with the M&A record of major petrochemical companies, which have carried out many ‘out of a complex’ M&A.45 The results of three case studies also demonstrate that the synergy effects from larger scale advantage vary with the price data sets. Synergies from process network and boiler system integration are less than synergies from larger scale. However synergies created from larger scale vary by price data sets, implying that synergy from market power is very sensitive to market conditions. Oil prices in 2003, which directly affected naphtha prices, were much lower than in 2005 and 2006, and the synergy effect in 2003 is accordingly lower than when compared to 2005 and 2006. The higher the value of a raw material, the more synergy the M&A can achieve. Oil prices are expected to increase gradually and many new petrochemical plants are now under construction; therefore, the price of naphtha will increase. The Korean petrochemical companies have to consider the advantages of a merger alternative in order to

increase their market power and in turn increase their competitiveness. The model considers the purchasing and selling advantages and process network and boiler system integration resulting from M&A. M&A also can bring about additional synergy through lowering fixed cost, the elimination of duplicate activities, etc. It is important to emphasize though that the purchasing and selling advantages brings about 7-57% profit increase alone. If other elements of merger synergy of petrochemical companies are considered, the effect of a merger may increase further. 7. Conclusions This paper proposes a method to estimate the degree of synergy created by a merger of petrochemical companies in a complex considering purchasing and selling advantages and process network and boiler integration with fixed cost reduction. A novel mathematical model based on the process network for a company is developed and applied to three NCC companies in Korea. The purchasing and selling advantages are reflected by assuming a discount rate in chemical purchasing and a premium rate in selling, respectively. The discount rate and premium rate are varied through 11 scenarios. The results show that a merger creates synergy primarily from purchasing advantage followed by selling advantage and then process network and boiler integration. The resulting synergy varies with the scenarios. The process network and boiler integration, which simply collects processes and boilers, can create a little synergy. This result means that increasing the market power should be the primary objective of M&A. The Korean chemical industry has to consider M&A in order to remain competitive in the future. Acknowledgment S.G.Y., S.B.P., and S.P. gratefully acknowledge support from BK 21 project of Korea Ministry of Education and Human Resource Development, and P.M.V. and C.A.F. acknowledge partial support from National Science Foundation.

Table A1. Material Balance Coefficients (aij) and Process Capacities (Xi) NCCa NCCb NCCc BTXa BTXb BTXc Bda

BDb

SMa HDPEb HDPEc PPc Phenolb BPAb EOc

EGc MTBEa MMAc

naphtha -1.00 -1.00 -1.00 ethylene 0.29 0.32 0.31 -0.26 -1.00 -1.00 -0.50 propylene 0.15 0.16 0.17 -1.00 -0.28 C4-mix. 0.08 0.09 0.10 -1.00 -1.00 PFO 0.03 0.03 0.02 RPG 0.23 0.21 0.20 -0.99 -0.99 -0.99 H2 0.02 0.02 0.01 -0.01 -0.01 -0.01 HDPE 0.99 0.99 BD 0.45 0.45 C4-raff. 0.51 0.51 -0.86 -0.75 C5-mix. 0.12 0.12 0.19 C9+ 0.13 0.13 0.15 benzene 0.31 0.36 0.28 -0.74 -0.51 toluene 0.17 0.16 0.15 xylene 0.10 0.07 0.10 phenol 0.56 -0.75 acetone 0.34 -0.25 MMA 0.39 PP 0.99 SM 0.94 BPA 0.88 EO 0.64 -0.71 MEG 0.91 DEG 0.08 butene-1 0.26 MTBE 0.53 methanol -0.14 -0.25 capacity 5052 2375 2323 1155 563 536 378 300 151 298 364 384 321 136 625 440 321 128 (103 tons/ year)


Appendix

Table A5. Process Steam Demand (Si)

This appendix includes tabular displays of data regarding the material balance coefficient (Table A1), the annual supply of various chemicals in the Korean petrochemical industry (Table A2), price of the chemicals/processes (Table A3), operating cost and steam demand of the processes (Tables A4 and A5, respectively), and boiler capacity (Table A6). Table A2. Annual Supply of Chemical j (adj) in Korean Petrochemical Industrya chemicals

103 tons/year

chemicals

103 tons/year

ethylene propylene C4-mix. HDPE BD C9+ benzene toluene xylene acetone

5900 3900 1600 900 1600 3400 2000 2500 2900 1400

MMA PP SM BPA EO MEG DEG butene-1 MTBE

700 2000 2700 500 300 300 150 500 900

process

Si (ton/input chemical ton)

NCCa NCCb NCCc BTXa BTXb BTXc BDa BDb SMa HDPEb HDPEc PPc Phenolb BPAb EOc EGc MTBEa MMAc

0.00 0.00 0.00 1.34 1.54 1.38 0.95 0.95 1.41 0.35 0.35 0.40 2.46 1.32 -0.10 -0.48 0.47 1.37

Table A6. Boiler Capacity (BXk) boiler

Ba1

Ba2

Bb1

Bb2

Bc1

Bc2

capacity (103 tons/year)

2000

2000

1300

1300

1200

1200

a

Chemicals not in Table A2 have little market or are rarely traded so they are not included in calculating selling and purchasing advantages. Table A3. Price Data (Pj) process

2003 ($/ton)

2005 ($/ton)

2006 ($/ton)

naphtha ethylene propylene C4-mix. PFO RPG H2 HDPE BD C4-raff. C5-mix. C9+ benzene toluene xylene phenol acetone MMA PP SM BPA EO MEG DEG butene-1 MTBE methanol

264 700 562 400 171 272 611 740 705 283 261 171 374 325 315 625 683 1250 1080 926 882 595 639 282 323 328 272

485 867 903 735 314 500 1122 1024 1219 514 479 314 816 672 663 1047 774 2272 1063 1045 1543 1386 866 731 587 564 264

597 1117 1048 611 400 450 700 1199 600 1301 679 600 500 834 831 916 900 1000 1112 692 1600 1197 1116 1303 1275 855 734

Table A4. Process Operating Cost Data (Ui and Fi)a process

Ui ($/ton)

Fi ($/ton)

process

Ui ($/ton)

Fi ($/ton)

NCCa NCCb NCCc BTXa BTXb BTXc BDa BDb SMa

33.50 36.96 35.81 1.80 2.06 1.84 2.57 2.57 43.71

25.78 28.45 27.56 11.85 13.55 12.14 18.09 18.09 24.16

HDPEb HDPEc PPc Phenolb BPAb EOc EGc MTBEa MMAc

15.25 15.25 26.53 20.38 50.07 21.18 3.00 5.51 18.17

51.98 51.98 60.09 52.81 85.1 40.32 23.3 13.62 52.46

a

Steam generation cost (sp): $ 19.38/steam ton.

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ReceiVed for reView October 25, 2007 ReVised manuscript receiVed March 31, 2008 Accepted May 2, 2008 IE071447K

Synergy in Mergers of Petrochemical Companies ... - ACS Publications

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