Economic evaluation of ethanol fuel production from agricultural crops and residues in California

Resources and Conservation, 11 (1984) l-25 Elsevier Science Publishers B.V., Amsterdam - Printed

in The Netherlands

ECONOMIC EVALUATION OF ETHANOL FUEL PRODUCTION AGRICULTURAL CROPS AND RESIDUES IN CALIFORNIA

FROM

MARK ME0 Woods Hole Oceanographic (Received

August

Institution,

1, 1983; accepted

Woods Hole, MA 02543

in revised form December

(U.S.A.) 27, 1983)

ABSTRACT The economic feasibility of producing ethanol fuel from a variety of starch and sugar rich crops and crop residues at an off-farm cooperative scale plant (3,408,OOO L/y) is evaluated within an agriculturally diverse and productive region of the Sacramento Valley, California (U.S.A.). A linear programming model of a county (Yolo) representative of the region is used to compare conventional starch and sugar crop fermentation and distillation technology with innovative cellulosic hydrolysis. Parametric programming is used to estimate breakeven prices for ethanol fuel production for several different planning scenarios. Land use data in the model are based upon 1981 crop hectarage inventory maps, a soil quality map, and a map of different trip distances to a centrally located plant. Vegetable and field crop hectarages of barley, corn, grain sorghum, rice, sugar beet, tomato, and wheat are digitized and upper and lower hectarage flexibility restraints are estimated by regression based upon hectarage changes in the past. High and medium productivity soil groups, Storie Index Rating (SIR) I and II, and III and IV, respectively, are also digitized. SIR is used to classify soils on the basis of pedologic characteristics and their usefulness to agriculture. Trip distance costs for trucking are calculated for 13, 26, and 39 km routes. Candidate starch and sugar feedstocks include barley, corn, corn silage, grain sorghum, wheat, fodder beet, Jerusalem artichoke, sugar beet, and sweet sorghum. Lignocellulosic feedstocks include the residues of barley, corn, grain sorghum, rice, and wheat. Planning options comparing the effect of including or excluding currently available subsidies in the form of tax credits on ethanol fuel breakeven prices are modeled for a base year, 1981, and a forecast year, 1992, when these subsidies are scheduled to expire. Technological innovation is assumed to reduce capital costs by 20% by 1992. Prices of gasoline and diesel fuel are projected to rise from $0.34 and $0.30 per liter respectively, to $0.75 and $0.71 per liter (1981 $), respectively. It was found that in 1981 the least-cost feedstock using conventional fermentation technology was fodder beet. Estimated breakeven prices sufficient to equal fied, variable, and opportunity costs of production were $0.29 and $0.46 per liter, with and without subsidy, respectively. In 1992, the least-cost feedstock will also be fodder beet. Its estimated breakeven price is $0.40 per liter. For lignocellulosic feedstocks, breakeven costs using acid hydrolysis technology ranged from $0.27 to $0.35 per liter depending upon conversion efficiency and subsidization. By 1992, the estimated breakeven cost could range from $0.42 to $0.47 per liter without subsidization. Fodder beet appears more favorable than other fuel crops because of its cheaper storage costs, a factor not shared by sweet sorghum or Jerusalem artichoke when the distillery is operated year round rather than seasonally.

2 INTRODUCTION

In recent years, the production of unconventional or alternative sources of energy from biomass has received a considerable amount of attention at the international [l-3], national [4-6], and regional (state) levels [7-g]. Among the various alternative energy options including direct combustion, biogas generation, and gasification, ethyl alcohol fuel production has become the most widely discussed as well as the most controversial. Ethyl alcohol, or ethanol, can be used as a liquid fuel substitute in combinations with gasoline up to a ten percent addition without any engine modification or resultant engine damage [lo]. When petroleum prices increased over the last decade and interruptions in imported oil supplies to the United States occurred in 1979, it was argued that domestic production of ethanol fuel from agricultural crops or their residues could provide a number of social benefits. These include: (1) a contribution to energy self-sufficiency; (2) a new market for damaged or underutilized crops; (3) enhanced security through a decentralized and less vulnerable energy production system; and (4) the chance to begin a transition to the use of renewable resource when nonrenewable or fossil resources are either depleted or become too costly [ll, 121. Social costs, however, are also apparent, and arguments against ethanol fuel have been raised. These include: (1) the economic inefficiency of subsidizing ethanol fuel production; (2) the claim that ethanol fuel consumes more energy in its production than is available for final use; (3) that the diversion of crops into energy production can only exacerbate the problem of world hunger; and (4) that the negative environmental effects associated with ethanol fuel production are considerable and could outweigh [ 13-171. any benefits generated by its production Although no consensus has arisen concerning these points of disagreement, several studies have been conducted to evaluate the expected benefits and costs of ethanol fuel production in a systematic manner. At the national level, the Solar Energy Research Institute has conducted several simulations of the national agricultural economy and its likely responses to shifts of crops into fuel [l&-20] . Of primary concern in many of these models was the amount of grain, primarily corn, that would be fermented into ethanol with a joint product of distiller’s dried grains and solubles (DDGS). Since DDGS has a protein content of almost 29%, it can easily substitute for soybean meal in the feed markets [21,22]. A food-fuel conflict, however, need not arise. Commoner and his coworkers [23] have shown that the cropping patterns in typical Illinois farms could be altered to include crops with higher C:N ratios, such as sugar cane and sugar beet. In this manner the total food value of the crops produced would not diminish. Further, by using cellulosic hydrolysis, a process that converts cellulose, a polymer of starch, into fermentable sugar, agricultural residues can be used as a feedstock exclusively, and hence the use of crops for fuel can be avoided [5]. California’s agricultural sector is a leading food producing region for the

3

nation, and supplies a wide variety of cash crops and specialty items. In contrast to the Midwest, where starch crops are primarily grown, California’s mediterranean climate is equally well-suited for the production of starch and sugar crops. Sugar beets are one of several important cash crops in the state. Agricultural residues are also abundant - over 9 million tonnes (Gg) of residue are produced each year [ 241. Concern with rising fuel prices and possible disruptions in supply from foreign sources have become important factors to be addressed by the agricultural sector, which is heavily dependent on petroleum both to maintain productivity and to deliver farm produce to market. Although agriculture could not be able to fulfill national liquid fuel demand in the near of existing food, term, (about 530 X 10’ L/y) without major rearrangements fiber, and feed markets, it could, however, readily meet the needs of the farming sector alone (about 2.1 X 10’ L/y) [5]. Hence, the production of ethanol fuel at a level sufficient to provide for the needs of the agricultural sector, yet capable of capturing economies of scale greater than those available to onfarm plants has become an option that merits careful study [23]. This paper develops a mathematical optimization model for a highly mechanized and agriculturally productive region in California and evaluates the economic feasibility of ethanol fuel production based upon conventional starch and sugar crops, newly introduced “fuel” crops, and agricultural residues as potential feedstocks. The model is used to estimate the breakeven price - the price that captures the combined fixed, variable, and opportunity costs of production - of ethanol fuel produced at a 3,408,OOO L/y offfarm cooperative facility that operates with either conventional fermentation of innovative cellulosic hydrolysis technologies. The model’s resource base is derived from and is constrained by the measured distribution and abundance of field and vegetable crop hectarages for the base year 1981. The effects of subsidies and technological improvement are estimated by comparing the base year to a projected year, 1992, at which time ethanol fuel subsidies are scheduled to be removed. This approach provides a detailed disaggregation of several kinds of important land use data and systematically evaluates a variety of possible energy production options that individuals or groups within the agricultural sector might choose to pursue. The scale of operation and region under study are believed to be representative both of the most promising ethanol fuel production technologies and of the agriculturally productive regions in California with respect to climate, soils, farm mechanization, and advisory services. METHOD

OF ANALYSIS

The method of analysis chosen for this study is linear and parametric programming. The general linear programming model maximizes or minimizes a linear objective function subject to a set of linear constraints. Formally stated :

4

max p’x

subject to Ax
5

negative environmental impacts ethanol fuel production can exert, each plant is equipped with appropriate pollution control and internal recycling systems. Land use

Three different sets of information required for the model were obtained by digitizing separate land use data maps and combining the resultant information into a master file stored within a computer. Land cover data for Yolo County were acquired in the form of 30, 7.5 minute quadrangles for the most recent survey year, 1981 [25]. Winter and spring crop hectarages for wheat, barley, corn, tomato, sugar beet, rice, and grain sorghum were digitized by hand and entered into the computer with the aid of the Land Use Mapping Program (LUMP) developed at the University of California, Davis (see Fig. 1).

Fig. 1. Map of Yolo County,

California

showing

1981 vegetable

and field crop hectarages.

Since crops generally give better yields on soils of higher quality, Storie Index Rating (SIR) data for Yolo County [ 261 were digitized in a similar manner as crops (see Fig. 2). Superimposing the crop data with the soil data

Fig. 2. Map of Yolo County, California showing distributions of high (black) and medium productivity soils.

(gray)

Fig. 3. Map of Yolo from Woodland.

County,

California

showing

concentric

areas

13,

26,

and

39

km

distant

8

tallied how much of each crop was grown on high productivity soils (SIR I and II) and on medium-productivity soils (SIR III and IV). SIR, developed in California in the 193Os, is used to classify soils on the basis of pedologic characteristics and their usefulness to agriculture. A soil is judged on four factors: A, B, C, and X. Factor A is a soil profile development factor. Factor B rates surface texture. Factor C rates slope. Factor X rates several properties, including drainage, salinity and alkalinity, fertility, acidity, erosion, and microrelief. A soil is rated from 0 to 100% for each of the four factors. The ratings are then multiplied by each other and 100 to give a single rating between 0 and 100. The distance that crops and agricultural residues must be transported to reach the ethanol fuel plant was also an important factor in the plan evaluation. Three sectors with radii of 13, 26, and 39 km from an origin at Woodland were digitized (see Fig. 3). These regions encompass almost all of the productive land within the County. A frequency distribution was calculated for each crop on each identified soil class within each distance interval from the plant. This was accomplished by calculating the number of occurrences of each of the desired variables from the superimposition of the three data maps (Figs. 1, 2, and 3) in the computer master file. The crop residue available for collection is calculated in two steps. First, the total amount available from each crop is obtained by multiplying each crop’s yield by its respective residue factor [24, 271. As a consequence of the increased potential created for soil erosion and nutrient depletion by removal of residues, an amount deemed sufficient to negate or diminish these impacts to a reasonable level was left in place on the soil [28] . This amount is fairly low in the Sacramento Valley region of California (lo-15%) due to the very low rate of measured erosion and the hot, dry growing season [29]. Calculated crop hectarages and residues for the innermost sector surrounding Woodland are presented in Table 1. Calculated residue tonnages for the inner, middle, and outermost sectors for two soil classes are shown in Table 2. Flexibility

restraints

The year to year variation in hectarages planted to selected crops was not expected to be large due to institutional restraints. It was presumed that the amount of land used to produce feedstock for ethanol fuel would be small in comparison to total hectarage planted in field and vegetable crops. Further, marketing, machinery, price factors, and related institutional factors may serve to moderate changes in land use. Flexibility restraints were estimated to represent institutional barriers to changes in hectarage; these restraints act as upper and lower bounds of the mapped hectarage for selected vegetable and field crops, and represent the maximum and minimum hectarages of crops that will be planted. Flexibility restraints can be expressed mathematically as follows:

9

Upper flexibility

xt <

(1 +

restraints:

Tj,)X,,

Lower flexibility

restraints:

Xf 2 (I- bt)Xt-1 where Xt = level of the activity to be determined in t year Xt-, = level of the activity in t - 1 year 6t, lot = maximum allowable proportionate increase and decrease, respectively, in the level of the activity from the t - 1 year to the t year: these are known as upper and lower flexibility coefficients. Following Condra and Lacewell [ 301, flexibility restraints were estimated for corn, wheat, barley, tomato, grain sorghum, and sugar beet [31]. The estimated flexbility coefficients for each crop are presented in Table 3. If restraints are not put on area planted to a particular crop, the linear programming model will shift all the available cropland to the one or two most profitable crops. This type of cropping pattern shift would not be expected as a result of large equipment investments, anticipation of better prices or yields, established crop rotations, and individual farmer biases.

TABLE I Calculation of crop area and crop residue tonnage for Storie Index Rating (SIR) I-IV (0 to 13 km) radius from Woodland crops

SIR I and II Hectares

corn Barley (dryland) Wheat (dryland) Rice Grain sorghum Tomato Sugarbeet Barley (irrigated) Wheat (irrigated)

2,421

SIR III and IV

Yield/Ha (Mg)

Residue factor (Mg resl Mg crop)

Residue Hectares total (Mg)

12.25

1.215

36,123

0

5.44

0.85

18 372

5.98 8.97

0.85 1.215

94 7,444 825

8.16 68.0 76.0

103 10,003

in inner sector

1.125 0.0468 0.09

6.8

0.85

7.48

0.85

Note: Tonnage figures are field dry weight. Source: refs. 24. 25.

Yield/Ha (Mg)

Residue factor (Mg resl Mg crop)

Residue tow (Mg)

Residue total SIR I-IV

20.790

56,913

1.788

9.57

1.215

0

484

4.32

0.85

1,777

1,777

91 4,054

85 4.790

4.78 8.96

0.85 1.215

346 52.146

436 56,200

863 23.690 5,643

31 2.093 1,313

6.99 58.97 68.24

244 6,776 8,064

1,107 29,466 13.707

595

9

5.41

0.85

41

636

5.98

0.85

63,699

4.065

1.125 0.0468 0.09

20,662

84,261

10 TABLE 2 Crop residue in Mg for Storie Index Rating (SIR) I-IV crops

for inner. middle. and outer sectors*

SIR I and II

Corn Barley (dryland) Wheat (dryland) Rice Grain sorghum Tomato Sugar beet Barley (irrigated) Wheat (irrigated) Total, each sector Total, by SIR Groups Total inner sector (O-13 km) Total, inner + middle sector (O-26 km) Total. aB sectors

SIR III and IV

Inner (O-13 km)

Middle (13-26 km)

outer (26-39

36.123 0 91 4,064 863 23.690 6,643 595 63,599 134,658

31.965 1,473 2,921 3,963 206 18,306 10,366 234 53.207 122,640

3,603 663 3,651 734 206 1,487 2,829 0 12,381 24,214

km)

Inner (O-13 km)

Middle (13-26

20.790 1.777 345 52,146 244 5,776 8.064 41 20,662 109,845

70,572 10,243 7,686 112,112 4,654 11,665 21,000 206 43,228 281.366

281,512

km)

Outer (26-39

km)

35.390 4,651 4,966 10.521 8.361 5,721 13,669 639 37,567 121,485

512.696 244.503

648,509

794.208

Note: Tonnage figures are field dry weight. *Distances on a radius from Woodland.

TABLE 3 Flexibility restraints for selected vegetable and field crops in Yolo County crop

Corn Wheat Barley Tomato Grain sorghum Sugar beet

Hectares 1979-1980 average

(1 + T;,

(1 - a,

Flexibility restraints Short run

Long run

Upper

Lower

Upper

Lower

15,385 39,190 13.866 23,787

1.123 1.166 1.148 1.161

0.868 0.883 0.819 0.869

17,277 46,264 15,918 27.616

13.364 34.604 11,366 20,433

21,779 60,321 20,990 37,254

10,060 26,944 7,626 16.056

3,441 6,235

1.142 1.146

0.818 0.874

3.930 7.145

2.815 5,449

5,125 4.933

1,883 4.166

Note:

(1 ?: b) (1979-1980 (1 5 b)s(1979-1980 Source: ref. 31.

average) = short run flexibility restraint. average) = long run flexibility restraint.

Crop budgets The primary source for crop production budgets is the U.C. Budget Generator, a computerized enterprise cost data system developed and maintained by the Cooperative Extension Service at the University of California at Davis. Production budget data were obtained for the following crops: corn, wheat, barley, grain sorghum, sugar beets, and tomatoes. Budget data for fodder beet, sweet sorghum, and Jerusalem artichoke were devel-

11

oped through interviews, literature production budgets [32, 331.

search, and by revisions

to existing crop

Harvesting and transportation Harvesting costs for crops were obtained from the U.C. Budget Generator when available. Costs for harvesting introduced crops that lack operational data were derived from estimates provided from field trials [33] . Harvesting costs for agricultural residues include mechanized collection and usually require some type of automated processing such as baling to facilitate transportation and storage. Residue collection and processing costs were obtained from several sources for rice straw [34, 361, corn stover [35], wheat straw cost ranged from [361, and barley straw [ 371. The average collection approximately $16/Mg for corn stover to $19/Mg for rice straw. This range includes the least-cost estimate for processing. The primary mode of transporting crops or residues within 40 km of Woodland is by truck. Estimates of truck costs were obtained from a commercial hauler based in Woodland [38]. The quoted costs, shown in Table 4 for alfalfa cubes, are the same quotes used by the hauler to justify operating costs in compliance with the State Public Utilities Regulatory Commission (PVC). Interviews with staff at the PUC indicated that minimum rate tariffs for hauling many agricultural residues as an energy source had not been TABLE 4 Summary of proposed revenue and expenses for alfalfa cubes Trip distance, km Round trip, km Proposed per truckload (rate not subject to any surcharge) Expenses: Wages-$0.55/km Hourly $9.92/h 1 Hour Loading* 1 Hour Unloading* Fuel, oil, tires Repair and maintenance - $0.53/km Insurance - fee regulation 5.0% Depreciation and fixed costs 5.7% Indirect cost 6.6%

40 80 $110.000

24 48

8 16

$ 17.00

$10.20

$ 3.40

9.92 9.92

9.92 9.92

9.92 9.92

16.50

9.90

3.30

5.50

5.50

5.50

6.27 7.26

6.27 7.26

6.27 7.26

Total expenses

$ 72.37

$58.97

$46.07

Cost per one way

$

$ 2.44/km

$ 5.72/km

1.79/km

*Stated at maximum time to perform either functions.

12

determined present.

by the Fall of 1982, thus some error in the cost per km may be

Distillery cons true tion and operation There is presently an insufficient number of ethanol fuel plants in operation within California to provide sufficiently detailed information on cost data for all of the candidate feedstocks included in the optimization model. Model distillery data were thus abstracted and modified from several engineering feasibility studies done under contract to the Department of Energy [39, 401, the National Alcohol Fuels Commission [41], and elsewhere [42] . The model distillery’s costs are based upon operating a 568 L/h plant 6,000 h/y. The 3,408,OOO L/y plant capacity is considered to be a farm cooperative venture. Although larger capacity plants can be expected to have lower unit production costs [43], in Yolo County it is likely that nonagricultural demand for ethanol fuel may be limited by the market at the present time. One report concluded that projected “gasohol” (a 10% ethanol mix with gasoline) sales in California would not absorb the output from a 38 ML/y plant located in Woodland using corn as the sole feedstock [44]. Conventional fermentation and distillation The assumed plant is designed to operate year-round necessitating storage facilities for feedstocks. A number of grain elevators able to provide the required storage are situated within Woodland. Typically, harvested grains, including grain sorghum, wheat, barley, and corn, are charged a storage fee for an entire season which ends on the first of June each year. The season storage charge is $7.72/Mg [45]. Storage requirements for sugar crops are different from grain crops. Both sugar beets and fodder beets can be either pulled from the ground or allowed to over-winter in the soil. There will, however, be a drop in the amount of available sugar after the beet is harvested. Beets in storage consume their own sugar by respiration at the rate of 0.16 kg/Mg day of beets at 10°C [41]. Sweet sorghum at Jerusalem artichoke could be utilized without storage for four to six months each year. For the remainder of the year, feedstock sugar could be supplied from molasses, but t.he costs for converting sugar crops into syrup for storage are very high both energetically as well as monetarily [46] . Thus, both the sweet sorghum and Jerusalem artichoke feedstock options required modification of the “front-end”, or pretreatment stage, of the fermentation process to accommodate grain crops for a portion of the year. Distillery costs can be separated into fixed and variable costs. Fixed costs are the investment costs of acquiring and erecting the plant with all necessary equipment including engineering and legal permit fees. Variable costs include feedstock costs, utility and chemical requirements, and labor costs for operating the plant. The fixed and variable costs for a 568 L/h ethanol fuel plant (3,408,OOO) that uses corn as a feedstock are shown in Tables 5 and 6, respectively.

13 TABLE

5

Summaries

of investment

(1980

cost) 95% ethanol

fuel from corn

568 L/h--Option (3,408,OOO L/y)

Section

Investment*

Equipment Raw material

preparation

E $ 68,900 NE 14,000 $

82,900 Cooking

and saccharification

E NE

169,100 3,600 197,000

172,700 Fermentation

E NE

124,300 0 165,300

124,300 Distillation

E NE

315,400 16,700 421,000

332,100 E NE

Boiler facility

0 43.200 43,200

Total

E NE

Fees - engineering,

legal permits

Total fixed investment

125,300

677,700 77,500 755,200

15% Contingency

92,000

1,006,600 151,000 1,157,600 120,400 $1,278,000

E = skid mounted/erected. NE = non-erected. *Difference between investment and equipment costs represents installation erected equipment, skid placement and connecting up other equipment. Source: ref. 41.

on non-

0 0

18.9

-

-

-

7.57 7.57 3.79

WW/RW

34.0

226.5

--

-

192.5 -

-

-

PW

11.1

4.8 230.7

-

161.1 20.1 33.6

-

kW*

Electrical

196.0

2.9

141.7 17.9 29.8

3.7

kW**

Source: ref. 41.

CW: cooling water; WW: well water; RW: refrigerated water; PW: process water. *Nameplate requirement. **Actually used. (-_) = Produced.

Total

-

-

-

-

-

CW

Water Consumed (L/min)

(544.31) --

-

Alcohol dehydration

Product and byproduct handling Boiler facility

90.72 453.59

-

Stl?alll usage (g/s)

Raw materials receiving and storage Raw materials preparation Cooking and saccharification Fermentation Distillation

Section

Chemical and Utility requirements 95% fuel ethanol from corn. 568 L/y-option

TABLE 6

114

128 21.8 17.7

40.2

Denaturant 681 Coal 8.6

Chemical sterilant

Enzyme Yeast Hydrated lime

Corn

case. 6000 h/y

L/d Mgid

L/d

kglday kg/day kg/day

Mglday

15

These costs are based upon assumptions of combinations of process technologies available commercially. These include: ?? conventional farm operations for grain handling; ?? grain cracking with roller mills to enhance soaking and shorten soak time; ?? soaking of cracked corn for a period of 4 h to facilitate cooking; ?? cooking with a single-barrel screw extruder to take advantage of energy savings and to eliminate the need for high-pressure steam; ?? commercially proven continuous saccharification and batch fermentation; ?? ethanol spirits distillation at atmospheric pressure, eliminating the need for high-pressure steam; ?? dehydration with molecular sieves, yielding energy savings over atmospheric distillation; and ?? recovery of stillage as distillers dried grains and solubles (DDGS) with appropriate air and water pollution control technology. The fixed costs of the plant are amortized over ten years by the formula: U.S.p_V.

=A

’ - (’+i)-” = A(U.S.P.V. i

Table Factor)i,,

where U.S.P.V. = the uniform series present value of an item A = the annuity or periodic receipt or payment i = the interest rate or opportunity cost n = the number of periods the receipt or payment occurs Production of ethanol fuel from starch and sugar crops is dependent upon the fraction of fermentable carbohydrate in each, and the efficiency with which it can be coverted into alcohol. Crop carbohydrate data and production efficiency calculations done in an earlier study [47] were used without modification. Cellulosic hydrolysis Agricultural residues that contain lignocellulose are a potential source of liquid fuel. Ethanol fuel can be produced from lignocellulose by either acid or enzymatic hydrolysis followed by conventional carbohydrate fermentation [48] . With cellulosic hydrolysis technology, presently underutilized or surplus biomass could be converted into liquid fuel without any diversion of edible crops from the food market. Of the two techniques, acid hydrolysis was selected for the county model as a result of its perceived technical simplicity compared with enzymatic hydrolysis and the likelihood that higher overall production reliability could be obtained [49]. Plant cost data for a 17 ML/y acid hydrolysis plant [42] were used to estimate model plant costs by using the six-tenths rule* and *

The sixth-tenths Cost of Plant A Cost of Plant B I(

rule is: =

Capacity

of Plant A

Capacity

of Plant B

CG

16

results from the literature [39-411 . Calculated fixed and variable costs are shown in Table 7. These estimates also include the costs of adequate pollution control technologies. Cellulosic hydrolysis feedstocks in the model are limited to the residues of wheat, barley, corn, grain sorghum, and rice. Residue composition is listed in Table 8. TABLE

7

Capital investment hydrolysis)

and operating

costs for 17

X

lo6 L/y ethanol

fuel process

(acid

Investment (1979) Hydrolysis tanks l-115 m3 l-17.4 m3 Rotary vacuum filters 2-1300 m’ Rotary dryer 8.2 Mg/d Impregnator 305 Mg/d Fermentors 2-3.7 m I.D. by 15.2 m Centrifuges 2 -helical conveyor Distillation column reboiler and condenser Acid recovery (electrodialysis Miscellaneous Heat exchangers Pumps and piping Heaters Compressor and tanks

$

394,000 277,700 334,500 512,100 389,900 670,200 2,472,OOO

unit)

113,200 253,300 203,500 27,700 Subtotal 30% Contingency Total

Acid recovery Make up acid Utilities Neutralizer Yeast extract and nutrients Labor Maintenance Depreciation, taxes, inst.

89,200 55,800

5,793,100 1,737,900 $7,531,000 300,700 421,700 381,200 266,000 265,000 409,400 409,400 818,800 $3,262,200

Source:

ref. 42.

17

TABLE

8

Compositions

of crop residues,

Residue

Ash

Corncob Corn stover

9.72

Grain sorghum stover Oat straw Rice hull Rice straw Soybean stover Wheat straw Source:

9.35

dry matter

basis (%) Cellulose

Hemicellulose

0.4 7.0-8.3

34.2-43.3 29.9-41.0

37.3-49.8 23.4-33.4

5.9-8.1 6.1-9.7

3.7 2.5-4.0 21.4 4.9-16.5

33.1-38.0 35.2-42.8 34.2-43.0 33.2-42.6 37.0-44.9 40.9-49.9

20.0-21.7 22.4-26.1 7.0-21.0 15.0 13.8-22.4 24.5-27.4

7.8-9.3 8.0-11.5 14.7-17.2 6.0-7.0 9.1-16.8 9.3-12.5

Silica

7.65 3.5

Lignin

ref. 17.

Technological innovation and subsidies The production of ethanol fuel has been criticized as economically inefficient and energetically wasteful when its evaluation has been based upon the operation of beverage distilleries neither designed nor built to conserve energy [43] . Laboratory studies of innovative technologies have shown that substantial savings in cost and expended energy can be obtained [47]. Promising improvements in vacuum distillation, genetic engineering, and heat recycling could become commercially available within the industry, but this is not likely without some form of economic subsidy. Energy research, development, and demonstration programs, including ethanol fuel production, have usually received some form of governmental support in the past. It is assumed that public support of ethanol fuel production research will continue to serve as an inducement to technological innovation in the future. The federal tax code provides two investment tax credits: a regular investment tax credit and an energy investment tax credit that applies to equipment that converts biomass into ethanol fuel. The energy credit is scheduled to terminate at the end of 1985; coal may not be used as a feedstock after 1982 [51]. Farm distillers can receive up to 10% of the cost of qualifying ethanol distillery investment components as an investment tax credit for each of the federal investment tax credits making the total one-time credit equal to 20% of the cost of the investment. The investment tax credit is not available for property financed by tax exempt industrial development bonds or related government subsidized energy financing. Qualifying farm distillers can receive up to 13.2 4/L as an ethanol fuel credit. There are two types of credit; one is for ethanol mixtures, and the other is for just ethanol. Only one of the credits can be taken. The ethanol fuel credits are available until the end of 1992. California has allocated funds for demonstration projects,

18

design competitions, distilleries [ 521 .

and low interest

loans to subsidize

construction

of

Scenarios The Yolo County linear programming model is formulated in such a way that purchasing, production, and selling activities are kept separate and distinct. This disaggregation is convenient for changing selected variables through parametric programming while maintaining an optimal solution. With parametric programming, the purchase prices of the liquid fuels, gasoline and diesel, can be raised independently of other purchased inputs. Although ethanol fuel cannot substitute for diesel fuel, the latter is an essential input to crop production and harvesting. Therefore, an estimation of the cost of producing ethanol fuel from crops must include the contribution that the cost of purchased diesel fuel makes to the overall process. In an iterative manner, the prices of gasoline and diesel that allow for an alcohol fuel sales price that just equals production costs can be estimated for any feedstock and technology option. Breakeven estimations were carried out for several alternative plans. For conventional fermentation, a base case year, 1981, was modeled both with and without available subsidies. Cellulosic hydrolysis was modeled in the base year with subsidization, but for two different conversion efficiencies. In the near future, improvements in design and operation of alcohol fuel plants are expected to lower costs and improve overall efficiency. The planning year 1992 was selected for analysis because: (1) the 13.2 $/L tax exemption is expected to terminate in that year; and (2) technological innovation is expected to have lowered plant costs. At the same time, expected improvements in crop yields and projected liquid fuel increases ought to make biomass-based fuel more competitive. Horner et al. [53] projected crop production and concomitant residue availability in California to 1985 and 1990. Percentage increases in crop yields for 1992 were estimated to be: wheat, 5%; rice, 6.5%; corn, grain sorghum, and sweet sorghum, 8%; barley, 11%; sugar beet, fodder beet, and Jerusalem artichoke, 12.2%; and tomatoes, 12.3% . Liquid fuel price forecasts made for California [ 541 indicate that real gasoline prices will increase over time. A medium range forecast indicates that gasoline could cost $0.52/L in 1985, $0.68/L in 1990, and $0.70/L in 1995 (in constant 1980 $). These estimates are based in part on a consensus that oil prices will continue to rise more rapidly than the rate of inflation; that OPEC will play a dominant role in oil price determination; and that competition of competing fuels including oil shale, tar sands, and gasified coal will reduce the rate of oil price increases in the 1990s. RESULTS

Breakeven sales prices for ethanol fuel produced from starch and sugar crops are presented for three scenarios in Table 9. The listed prices are

19

estimated by parametric programming and represent the dollar amount required to equal the sum of fixed, variable, and opportunity costs (market value foregone) that the farm cooperative must receive to just break even on its energy investment. Ethanol fuel prices under Scenario 1 are based upon production costs exclusive of any available subsidy for the year 1981. Gasoline and diesel prices are $0.34 and $0.30/L, respectively. For comparison, subsidies available to the producer are included under Scenario 2. These include a 13.2 t/L tax credit and a 20% investment tax credit. Under this set of credits, ethanol fuel produced from fodder beet is competitive with gasoline on a volumetric basis. TABLE Breakeven scenarios -._

9 prices ($/L) for ethanol

fuel produced

from starch

Feedstock

Scenario 1 1981 no subsidy

Scenario 1981 subsidy

Barley Wheat Corn Corn silage Grain sorghum Sugar beet Fodder beet Sweet sorghum Jerusalem artichoke

$0.58 0.58 0.52 0.53 0.52 0.60 0.46 0.55 0.64

$0.40 0.40 0.33 0.34 0.33 0.43 0.29 0.35 0.47

2

and sugar feedstocks

for 3

Scenario 3 1992 no subsidy $0.54 0.54 0.47 0.48 0.43 0.55 0.40 0.50 0.55

By 1992, several changes are expected to occur. Both the energy investment tax credit and the 13.2 e/L tax credit are scheduled to expire before or at that time. Technological innovations restricted to research laboratories and of limited availability in 1981 ought to have attained commercial distribution in ten years. Agricultural productivity is also expected to rise in the interval between 1981 and 1992. Finally, liquid fuel prices may escalate rapidly. Based upon California Energy Commission mid-range forecasts, gasoline and diesel prices may rise to $0.75 and $0.71/L, respectively. Scenario 3 breakeven prices were calculated under the assumption that the above changes would take place (see Table 9 for assumptions specific to each scenario). All the ethanol fuel breakeven prices under Scenario 3 are higher than those in Scenario 2, but all are lower than the projected prices of gasoline on a volumetric basis. Again, the least-cost feedstock is fodder beet. Evaluation of cellulosic hydrolysis was based upon five sources of agricultural residue as potential feedstocks: barley straw, wheat straw, rice straw,

20

corn &over, and grain sorghum straw. Two different efficiencies for converting residue lignocellulose into ethanol fuel were assumed. This is due in part to the limited amount of data from operating facilities. Additionally, plant construction costs have been calculated with a capacity cost formula; quoted costs for a 3,408,OOO L/y acid hydrolysis plant were not available. Scenarios 4 and 5 in Table 10 both include tax credits and are based upon crop yields, liquid fuel prices and costs for 1981. Average ethanol fuel conversion efficiencies (80%) are used in Scenario 4, and above average efficiencies (90%) are used in Scenario 5. In both cases there was sufficient grain sorghum residue within Yolo County to produce only 2,840,OOO L/y. Wheat straw is the least-cost feedstock. Scenarios 6 and 7 in Table 10 depict breakeven prices for ethanol fuel produced in 1992; tax credits are excluded (see Table 10 for assumptions specific to each scenario). The grain sorghum limitation still occurs, and wheat straw remains the least-cost feedstock. TABLE 10 Breakeven prices ($/L) ethanol fuel produced from agricultural residues by cellulosic hydrolysis (acid treatment) for average and above average conversion efficiencies Feedstock

Scenario 4 average efficiency 1981

Scenario 5 above average efficiency 1981

Scenario 6 average efficiency 1992

Scenario I above average efficiency 1992

Barley Wheat straw Rice straw Corn stover Grain sorghum straw

$0.32 0.29 0.33 0.30

$0.29 0.27 0.30 0.28

$0.45 0.43 0.47 0.44

$0.43 0.41 0.44 0.42

0.35

0.29

0.47

0.43

DISCUSSION

The 3,408,OOO L/y cooperative distillery is designed to operate year round. For conventional technology, sugar crops such as Jerusalem artichoke and sweet sorghum require some type of treatment such as juice desiccation into molasses for post-harvest season storage. In comparison, sugar beet and fodder beet can be over-wintered in the ground. Fodder beets are the leastcost feedstock for the cooperative distillery as a result of their cheaper storage costs and the absence of a food, feed, or fiber market for the raw product. Acid hydrolysis of agricultural residues appears to be a promising technology. The model results, however, are based upon data with notable limitations. Actual acid hydrolysis plant costs may vary widely from those used in

21

this study. While the cost data for conventional fermentation and distillation equipment is based upon years of professional practice, the cost for acid recovery equipment may be optimistic at this time [48] . With the current set of economic subsidies, the breakeven price of ethanol fuel is $0.29/L in 1981. With gasoline prices at a peak of $0.34/L in 1981, ethanol fuel appears to be competitive. On an energetic basis, however, the situation is different. Ethanol fuel at 95% contains approximately 22,579 MJ/L, gasoline contains approximately 34,565 MJ/L, and diesel fuel approximately 39,025 MJ/L. Thus 1.53 and 1.78 L of 95% ethanol fuel would be required to equal one liter of gasoline and diesel fuel, respectively. This energetic equivalency fails to credit ethanol fuel’s high octane rating or any economies obtained in using ethanol fuel to increase the octane rating of unleaded gasoline in the refining process. Consequently, an energy equivalent breakeven price of ethanol fuel compared to gasoline and diesel would be $0.49 and $0.56/L, respectively. By 1992, projected liquid fuel prices may alter the situation. The energy equivalent breakeven price of ethanol fuel will be $0.61/L ($0.40 X 1.53) for gasoline and $0.69/L ($0.40 X 1.73) for diesel. At that time gasoline and diesel prices (in constant 1980 $) are forecast to be $0.75 and $0.71/L, respectively. Ethanol fuel may be used as a substitute for gasoline in mixes or blends, without modification to spark ignition engines. As a substitute for diesel fuel, however, ethanol fuel is not useful. Compression ignition engines that operate with diesel fuel require engine modification for use with ethanol fuel. This is an important consideration. For Yolo County, field and vegetable crop production in the model required 4,011,347 L of gasoline and 24,235,892 L of diesel fuel in 1981, and 4,007,448 L of gasoline and 24,170,397 L of diesel fuel in 1992. The difference in volume of liquid fuel consumed reflects the response of cropping patterns to higher purchased liquid fuel prices. With the cropping patterns bounded by flexibility restraints, gasoline consumption was roughly 16.5% of diesel consumption, or, alternatively, 14.2% of the total liquid fuel demand. The farm cooperative ethanol fuel distillery’s annual production could provide 85% of the gasoline demand on a volumetric basis used by Yolo County’s field and vegetabel crops in 1981. On an energy equivalent basis, annual production would meet 55.5% of the gasoline demand. The land required to meet the distillery’s annual capacity is not excessive. Using fodder beet as the feedstock, the model’s optimal solution allocates fodder beet production on 416 hectares of medium productivity soil in the middle sector and on 70.4 hectares of medium productivity soil in the inner sector for a total of 486.4 hectares. This land allotment is less than one percent of the 72,402 hectares planted to vegetable and field crops in the spring. Thus, total liquid fuel requirements for the country’s agricultural sector could be satisfied with cooperative scale distilleries. Land use requirements for cellulosic hydrolysis should not alter any

22

cropping hectarages. It is the preferred alternative for individuals concerned about an expected substitution of fuel for food. By using only the fraction of agricultural residues that can be removed without inducing any measurable negative environmental impacts, cellulosic hydrolysis technologies could become a permanent addition to the agricultural sector. CONCLUSION

Farm level ethanol fuel production at a cooperative scale could provide sufficient liquid fuel for agricultural energy self-sufficiency. Although ethanol fuel production is not presently economically feasible, an end of the 1983-84 oil glut and a return to increasing oil prices could change the situation. Removal of existing subsidies combined with the adoption of technological innovations by 1992 could provide for economically competitive ethanol fuel producton if liquid fuel prices continue to rise. Although the land requirement to meet partial energy self-sufficiency is not large for conventional fermentation technologies, the use of cellulosic hydrolysis should prove even less disruptive of land used for agriculture. ACKNOWLEDGEMENTS

Scott Sachs skillfully digitized the land use maps of Yolo County. Their quality testifies to his craftsmanship. I thank George Miller, Roy Sachs, and Lynn Williams, for their assistance throughout the stages of this research. Seymour Schwartz, the editor and two anonymous referees critically reviewed the manuscript and improved its presentation. This research was supported by a grant from the Public Service Research and Dissemination Program at the University of California, Davis. Revision of the manuscript was supported by the Pew Memorial Trust and the Woods Hole Oceanographic Institution’s Marine Policy and Ocean Management Center.

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