Clean Technologies and Environmental Policy

, Volume 15, Issue 5, pp 783–799

Systematic approach for conceptual design of an integrated biorefinery with uncertainties

  • Mei Chee Tang
  • Markus W. S. Chin
  • King Ming Lim
  • Yee San Mun
  • Rex T. L. Ng
  • Douglas H. S. Tay
  • Denny K. S. Ng
Original Paper

DOI: 10.1007/s10098-013-0582-x

Cite this article as:
Tang, M.C., Chin, M.W.S., Lim, K.M. et al. Clean Techn Environ Policy (2013) 15: 783. doi:10.1007/s10098-013-0582-x

Abstract

The objective of this work is to present a systematic approach for conceptual design of an integrated biorefinery with maximum economic potential accounting for the predefined uncertainties in energy economics. Various parameters commencing from raw biomass feedstock, desired end products, to market price trend, technological constraints and system uncertainties at multi-periods are to be considered. A structural framework, integrated biorefinery pathway map which embeds and interconnects the latest processing technologies is first developed. Then, a robust optimisation model is adopted to determine the optimum network which handles the predefined sets of uncertainties in energy economics. To illustrate the proposed approach, a case study with two different scenarios of uncertainties is solved. Furthermore, a sensitivity analysis is also performed to identify the critical parameters of an integrated biorefinery.

Keywords

BiofuelDesignIntegrated biorefineryRobust optimisationUncertainties

List of Symbols

Abbreviations

Btu

British thermal units

CnHmOp

C—carbon atom, H—hydrogen atom, O—oxygen atom

CRPM

Chemical reaction pathway map

DEE

Diethyl ether

DME

Dimethyl ether

DTBG

Di-tert-butyl ether of glycerol

ETBE

Ethyl-tert-butyl ether

FAEE

Fatty acid ethyl esters

FAME

Fatty acid methyl esters

FT diesel

Fischer–Tropsch diesel

HMF

Hydroxymethylfurfural

IBPM

Integrated biorefinery pathway map

MINLP

Mixed-integer non-linear programming

MSW

Municipal solid waste

RMG

Renewable methane gas

RNFA

Reaction network flux analysis

TTBG

Tri-tert-butyl ether of glycerol

2015EP

EP achieved under the portfolio of raw biomass feedstock supply in year 2015

2016EP

EP achieved under the portfolio of raw biomass feedstock supply in year 2016

2017EP

EP achieved under the portfolio of raw biomass feedstock supply in year 2017

2018EP

EP achieved under the portfolio of raw biomass feedstock supply in year 2018

2019EP

EP achieved under the portfolio of raw biomass feedstock supply in year 2019

2020EP

EP achieved under the portfolio of raw biomass feedstock supply in year 2020

2015/2016EP

EP achieved under the portfolio of raw biomass feedstock supply in year 2015 or 2016

2018/2019EP

EP achieved under the portfolio of raw biomass feedstock supply in year 2018 or 2019

Sets

b

Index for processing technology on biogas platform

c

Index for processing technology on carbon-rich chains platform

i

Index for raw biomass feedstock

j

Index for bioprecursor

h

Index for biorefinery platform

k

Index for intermediate

k

Index for intermediate other than k

l1

Index for first reactant in the same processing technology w

l2

Index for second reactant in the same processing technology w

n

Index for special case

p

Index for final product

q

Index for uncertainty set

s

Index for processing technology on sugar platform

t

Index for processing technology on thermochemical platform

v

Index for conversion operators

w

Index for same processing technology

Parameters

\( C_{i}^{\text{Bio}} \)

Cost of raw biomass feedstock i

\( C_{p}^{\text{Fresh}} \)

Purchase price of product p

\( C_{p}^{\text{Prod}} \)

Market price of the final product p

\( F_{i}^{\text{Available}} \)

Available supply raw biomass feedstock i

\( F_{i}^{\text{Demand}} \)

Market demand of product p

n1
Total number of components in raw biomass feedstock layer in Fig. 1
https://static-content.springer.com/image/art%3A10.1007%2Fs10098-013-0582-x/MediaObjects/10098_2013_582_Fig1_HTML.gif
Fig. 1

Superstructure of the Robust MINLP model

n2

Total number of components in bioprecursor layer in Fig. 1

n3

Total number of components in intermediate layer in Fig. 1

n4

Total number of components in secondary intermediate layer in Fig. 1

n5

Total number of components in product layer in Fig. 1

n6

Total number of components in conversion layer in Fig. 1

\( x_{ij} \)

Conversion of raw biomass feedstock i to bioprecursor j

\( x_{sk} ,x_{{sk^{\prime}}} \)

Yield of chemical reaction in conversion operators of sugar platform

\( x_{tk} ,x_{{tk^{\prime}}} \)

Yield of chemical reaction in conversion operators of thermochemical platform

\( x_{bk} ,x_{{bk^{\prime}}} \)

Yield of chemical reaction in conversion operators of biogas platform

\( x_{ck} ,x_{{ck^{\prime}}} \)

Yield of chemical reaction in conversion operators of carbon-rich chains platform

z

Very small real number close to 0

\( \alpha \)

Probability of occurrence

\( \alpha_{q} \)

Probability of occurrence of different uncertainty sets q

Variables

EP

Economic potential

\( F_{i}^{\text{Bio}} \)

Flow rate of raw biomass feedstock i

\( F_{j}^{\text{Comp}} \)

Flow rate of bioprecursor j

\( F_{h}^{\text{Plat}} \)

Flow rate of biorefinery platform h

\( F_{k}^{\text{Int}} \)

Flow rate of intermediate k

\( F_{p}^{\text{Prod}} \)

Flow rate of final product p

\( F_{p}^{\text{Fresh}} \)

Flow rate of fresh product p to be purchased

\( f_{ij} \)

Splitting of raw biomass feedstock i to bioprecursor j

\( f_{jh} \)

Splitting of bioprecursor j to biorefinery platform h

\( f_{hs} \)

Splitting of component from platform h to initial feed of processing technology s

\( f_{ht} \)

Splitting of component from platform h to initial feed of processing technology t

\( f_{hb} \)

Splitting of component from platform h to initial feed of processing technology b

\( f_{hc} \)

Splitting of component from platform h to initial feed of processing technology c

\( f_{ks} \)

Splitting of intermediate k to initial feed of processing technology s

\( f_{kt} \)

Splitting of intermediate k to initial feed of processing technology t

\( f_{kb} \)

Splitting of intermediate k to initial feed of processing technology b

\( f_{kc} \)

Splitting of intermediate k to initial feed of processing technology c

\( f_{{k^{\prime}s}} \)

Splitting of intermediate k′ to initial feed of processing technology s

\( f_{{k^{\prime}t}} \)

Splitting of intermediate k′ to initial feed of processing technology t

\( f_{{k^{\prime}b}} \)

Splitting of intermediate k′ to initial feed of processing technology b

\( f_{{k^{\prime}c}} \)

Splitting of intermediate k′ to initial feed of processing technology c

\( f_{lw}^{1} \)

Flow rate of first reactant l1 in the same processing technology w

\( f_{lw}^{2} \)

Flow rate of second reactant l2 in the same processing technology w

\( f_{lw}^{{2^{\prime}}} \)

Available flow rate of second reactant l2 in the same processing technology w

Rll

Flow ratio of second reactant l2 to first reactant l1 in the same processing technology w

\( I_{n} \)

0–1 binary variable

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mei Chee Tang
    • 1
  • Markus W. S. Chin
    • 1
  • King Ming Lim
    • 1
  • Yee San Mun
    • 1
  • Rex T. L. Ng
    • 1
    • 2
  • Douglas H. S. Tay
    • 1
  • Denny K. S. Ng
    • 1
  1. 1.Department of Chemical and Environmental Engineering, Centre of Excellence for Green TechnologiesUniversity of Nottingham, MalaysiaSemenyihMalaysia
  2. 2.GGS Eco Solutions Sdn BhdKuala LumpurMalaysia