Chemical Engineering July 2012 - 43

Methanol production remained near
maximum at 8,760 gal/d throughout
the tests. But at 1,600°F, there was a
0.9% increase in methanol output.
The upper dashed line represents
the best model-predicted pre-tax profits,
adjusted for inflation. There is a
noticeable slope change in the curve
at 7,480 Btu/lb, suggesting that lower
heat content will be insufficient for
profitable steam production.
Capital costs
A feasibility estimate of the modeled
process capitalized equipment,
exclusive of environmental permits
indicates a cost of $108,479,360 U.S.
dollars as of March 31, 2011 [3]. This
includes the cost of site excavation,
buildings, parking, concrete, steel, construction
labor, process equipment, installation,
piping, distributed control
(DCS), instrumentation, pneumatic
valves, and administrative costs, including
a 10% contingency. Taxes and
interest charges were not considered.
The process equipment that is depreciated
(but not limited to) includes
electronics, instrumentation, vessels,
motors and vehicles. The total depreciable
sum is $35,782,083.
Assuming a 10% salvage value for
the process equipment and a 40-year
useful plant life, the annual straightline
depreciation is:
(3)
For 30 ton/h of fuel consumed and a
50% fuel blend (10,221 Btu/lb), methanol
production is:
(4)
The predicted steam generation gross
profit is:
(5)
Annual revenue is: ($14,129 + $8,760)
x 313 working d/y = $7,164,257.
The predicted pay out period, assuming
no interest charges:
(6)
Pay out rises to 16.9 years at 20% less
throughput and a $3.56/ton fuel increase.
Scrap
steel sales from the tire
blend were not used to calculate the
pay out in our example. If the scrap
metal composition for a green project
is unknown, EPA recycling data [6]
can provide a workable estimate of
glass, metals and other salvageable
materials in municipal waste. Resale
of these commodities can reduce fuel
production costs, improving overall
economics.
Salable scrap from solid waste often
can amount to tons/d. So salvaging
item 'A' for example, produces 'tons/d'
x 'operating d/yr' x '$/ton' = '$A/yr'
of additional revenue. Assuming, fuel
production costs are '$C/yr':
Adjusted fuel cost:
(7)
The example process has potential
given the fuel tests were restricted to
30 tons/h maximum of blended waste.
It is reasonable to expect that much
higher steam outputs are attainable
since the blended heating values are
comparable to coal.
Payout is acceptable for the green
project and it shows promise, demonstrating
profitability under minimum
throughputs.
The engineer should consider further
investigations of this process
based on model predictions, with an
eye towards improving conversion and
plant throughput for enhanced profitability
and better pay out.
Higher methanol and steam pricing
is not recommended as a means towards
this end as market factors play
an increased roll in product prices.
Conclusions
Dynamic models are useful tools to
evaluate any process that can be represented
mathematically. Engineers can
use them to not only emulate a given
process but to test competing control
strategies, trend cost variables, and
identify and implement process improvements.
Modeling a process provides
the best means of culling out the
most promising projects from the pack.
Dynamic models also give engineers
a development tool for determining
the suitability of solid waste compositions
as a fuel source for a particular
process. As demonstrated here, engineered
fuel pellets produced from
municipal waste sources can provide
a sustainable and reliable fuel, whose
use can help to reduce dependence on
petroleum-derived fuels well into the
future.
n
Edited by Suzanne Shelley
References
1. Brian Bahor, Michael Van Brunt, P.E., Keith
Weitz, and Andrew Szurgot, Life-cycle assessment
of waste management greenhouse
gas emissions using municipal waste combustor
data, J. Envir. Eng., 136, 749 (2010);
doi:10.1061/(ASCE)EE.1943-7870.0000189.
2. Methodology for Allocating Municipal Solid
Waste to Biogenic and Non-Biogenic Energy,,
Energy Information Administration,
Office of Coal, Nuclear, Electric and Alternate
Fuels U.S., May 2007 Report.
3. Peters, Max S., and Timmerhaus, Klaus D.,
" Plant Design & Economics for Chemical
Engineers, " 2nd Ed., MCGraw-Hill, 1968.
4. Williams, A., Wilson, R.G., and Chang, S.,
Reformer model improves control, A. Williams,
R.G. Wilson. S. Chang, Hydrocarbon
Processing, Nov. 1994.
5. Nack, H., Litt, R.D., and Kim, B.C., Multisolid
fluidized bed combustion, CEP, January
1984.
6. Municipal Solid Waste Generation, Recycling,
and Disposal in the United States, Tables and
Figures for 2010, U.S. Environmental Protection
Agency Office of Resource Conservation
and Recovery, http://www.epa.gov/osw
/nonhaz/municipal/pubs/msw_2010_data_
tables.pdf. Tables 1-29, Nov. 2011.
Authors
Allen Williams is a private
consultant with more than 30
years of experience in power
generation and petrochemicals
design (Email: allen_k_
williams_1@netzero.com). He
has developed statistical control
algorithms for the power
industry and has two previous
technical publications. He
holds a B.S.Ch.E. with graduate
study in kinetics from the
University of MO-Rolla and New Mexico State
University. He is a registered professional engineer
in the state of Michigan.
Ken Dunwoody (Email:
Kdunwoody2@aol.com) prior
to his retirement, chairman
and chief operating officer
of RCR Systems, Inc., where
he and the firm's general
partners developed the RCR
solid waste handling system
prototype. His firm holds five
patents in waste-separation
machinery. Prior to that,
Dunwoody was general manager of the glass
manufacturing division in charge of production
at Coors Container. He holds a B.S. in business
administration and Associate degrees in digital
electronics and industrial management the Univ.
of Colorado and the Denver Technical Institute.
CHEMICAL ENGINEERING WWW.CHE.COM JULY 2012 43
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