Abstract
Currently, butanol obtained by fermentation is considered as potential biofuel. In this work, it has been simulated and optimized a process to produce acetone, butanol and ethanol by means of lignocellulosic material. To accomplish this task, initially, it was planned the raw material selection, followed by the simulation in MATLAB of simultaneous saccharification, fermentation and separation reactor (SFS) and finally, the stream coming from fermentation was purified. The separation stage was selected from three different options to purify that effluent. The entire process was evaluated under a robust optimization process considering environmental, economic and energetic objective functions by means of a hybrid stochastic method, differential evolution with tabu list. The obtained results showed that the best scheme to produce and purify butanol was the SFS-3C, which considers thermally coupled columns to purify acetone, butanol and ethanol. In general terms, it was obtained as result 0.138 $/kgbutanol, 0.132 points/kgbutanol and 66.8 regarding to the total annual cost, environmental impact and exergy efficiency, respectively.
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Abbreviations
- ABE:
-
Acetone–butanol–ethanol
- C TM :
-
Capital cost of the plant
- C ut :
-
Utility costs
- DDE:
-
Dynamic data exchange
- DE:
-
Differential evolution
- DETL:
-
Differential evolution with tabu list
- D cn :
-
Column diameter
- F ext :
-
Extractant flow
- F rn :
-
Distillate fluxes
- TAC:
-
Total annual cost
- LLE:
-
Liquid–liquid extraction
- LCA:
-
Life cycle assessment
- N tn :
-
Total column stages
- N fn :
-
Feed stages
- ROI:
-
Return of investment
- SFS:
-
Integrated reactor saccharification–fermentation with simultaneous recovery
- R rn :
-
Reflux ratio
- TAC:
-
Total annual cost
- TL:
-
Tabu list
- x m :
-
Vectors of required purities
- y m :
-
Vectors of obtained purities
- F :
-
Mass flow
- V :
-
Volume
- S :
-
Substrate
- x :
-
Molar fraction
- C :
-
Amount of mass in the reactor
- GEI99:
-
Global ecoindicator 99
- NEB:
-
Net energy balance
- η :
-
Exergy efficiency
- NPV:
-
Net present value
- IES:
-
Ideal energy efficiency of separation
- A :
-
Raw material
- X :
-
Amount of biomass used
- D :
-
Dilution rate
- ENZ:
-
Amount of enzyme
- N fni :
-
Feed stage
- F rni :
-
Flow of interconnection
- MODE-TL:
-
Multi-objetive differential evolution with tabu list
- R s :
-
Yield for butanol fermentation
- LHV:
-
Lower heating value of butanol
- H s :
-
Energy consumption for purification
- PUb :
-
Quantity of each raw material
- RMUb :
-
Unitary ecoindicator of raw material
- EI99PUR:
-
Ecoindicator of purification stage
- β b :
-
Amount of chemical released per unit of reference flow
- α b,k :
-
Damage caused in category
- ω d :
-
Weighting factor for damage in category
- δ d :
-
Normalization factor for damage
- EI99RM:
-
Ecoindicator 99 of total raw material used
- C GR :
-
Total grassroots costs
- \(C_{{{\text{BM}},i}}^{\text{}}\) :
-
Module cost of the equipment
- C BM, i :
-
Module cost of the equipment considers real operation
- C R :
-
Reactor cost
- C T :
-
Column cost
- C IN :
-
Condenser cost
- C IE :
-
Initial investment
- C E :
-
Electricity cost
- C V :
-
Steam cost
- C AE :
-
Cooling water cost
- C S :
-
Substrate cost
- C ENZ :
-
Enzyme cost
- C Ex :
-
Cost due to extractant lost
- NEB:
-
Net energy balance
- LHV:
-
Lower heating value
- IES:
-
Ideal energy efficiency of separation
- R s :
-
Yield ABE
- H s :
-
Energy consumption for purification
- NEt :
-
Net earnings value
- FTDCt :
-
Depreciable capital investment
- φ :
-
Net earnings after tax rate
- Revt :
-
Revenues
- FOCt :
-
Facility operating
- TOCt :
-
Transportation
- E x,ABE :
-
Exergy of produced ABE (MW)
- E x,biomass :
-
Exergy of biomass (MW)
- E x,heating :
-
Exergy of heating (MW)
- E x,reactor :
-
Exergy of reactor (MW)
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The financial support provided by the Universidad de Guanajuato and CONACyT (México) is gratefully acknowledged.
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Quiroz-Ramírez, J.J., Sánchez-Ramírez, E. & Segovia-Hernández, J.G. Energy, exergy and techno-economic analysis for biobutanol production: a multi-objective optimization approach based on economic and environmental criteria. Clean Techn Environ Policy 20, 1663–1684 (2018). https://doi.org/10.1007/s10098-018-1486-6
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DOI: https://doi.org/10.1007/s10098-018-1486-6