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 CHEMICAL ENGINEERING TRANSACTIONS  
 

VOL. 43, 2015 

A publication of 

The Italian Association 
of Chemical Engineering 
Online at www.aidic.it/cet 

Chief Editors: Sauro Pierucci, Jiří J. Klemeš 
Copyright © 2015, AIDIC Servizi S.r.l., 
ISBN 978-88-95608-34-1; ISSN 2283-9216                                                                               

 

Development of a Hybrid Methodology for Synthesis of 
Biofuels Production Processes Based On Optimization of 

Superstructures 

Keissy Paredes Morelos, Andrea Boneu Mass, Fainer Gaviria Vergara, Ángel 
Darío González-Delgado*  
 University of San Buenaventura, facultad de Ingeniería, Arquitectura, Artes y Diseño, Ternera calle Real, Diagonal 32 # 30-
966, Cartagena, Colombia 
agonzalezd@usbctg.edu.co 

Biofuels can be obtained using different processing routes and raw materials, so there are infinite 
combinations of possible topologies for raw material processing; it is desirable to develop a methodology for 
evaluation of different combinations in order to select the pathway that best accomplish a specific criterion. In 
this paper, the authors developed a hybrid methodology for the synthesis of biofuel production processes, 
based on a branch of available products and intermediates, the establishment of a superstructure with 
technologies and chemical species involved, mathematical optimization of production routes and depth 
assessment of the most promising alternatives. 
As a case of study, the methodology is applied in the synthesis of a process of production of biofuels from 
wastewater. The results showed correspondence between intermediate and final products for biodiesel and 
hydrogen, and the superstructure was constructed based biodiesel as main product. The optimization of the 
superstructure showed that the most promising route is through hydrothermal liquefaction of organic slurry and 
hydroprocessing of biocrude, with a maximum product yield of 0.61 tonnes of biodiesel/tonne of biomass. 

1. Introduction 

There is a growing interest in the identification of cost-effective, clean, and renewable sources of energy. 
Biofuels are among the most promising alternatives as they offer many benefits related to energy security, 
economic stability and reduction of the environmental impact of greenhouse gases (Brunet, et al., 2014). 
Third-generation biofuels are produced from non-conventional feedstocks as microorganisms (yeast, fungi and 
microalgae), being the most common biogas, biodiesel from lipids, and bioethanol from reducing sugars 
(Barajas-Solano et al., 2014). Microorganisms can grow in domestic and industrial wastewaters, where they 
uses the organic matter present in these effluents as feed, so it is common to find biomass in pools of 
wastewater treatment plants. Composition of biomass can determine the feasibility of a biofuel or high value 
substances production process (Pinzon Frias et al., 2014). By using wastewater as a culture medium for the 
growth of third generation energy feedstocks can be produced a double benefit, reduction of organic load of 
the effluent and generation of usable biomass for biofuels and high added value products (Mata et al., 2014).  
On the other hand, the conceptual design of processes belongs to the area of chemical process synthesis, 
which was pioneered by authors as Douglas (1988) and Rudd & Watson (1968), process synthesis has had 
an important impact in the development, of chemical processes, providing systematic methodologies for 
identifying flowrates, design conditions and optimal networks. Several advances in process synthesis has 
been achieved in recent decades, taking into account proposed strategies, tools and frameworks for process 
synthesis, three types of approaches can be differenced: heuristic-based approaches which uses specialized 
knowledge of a process and specific experience, mathematical programming-based approaches which uses a 

                                

 
 

 

 
   

                                                  
DOI: 10.3303/CET1543059 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Please cite this article as: Paredes K., Boneu A., Gaviria F., Gonzalez-Delgado A.D., 2015, Development of a hybrid methodology for synthesis 
of biofuels production processes based on optimization superstructures, Chemical Engineering Transactions, 43, 349-354   
DOI: 10.3303/CET1543059

349



 

 

superstructure optimization formulation with an objective function desired, and hybrid approaches, which 
combines both hierarchical decomposition and mathematical programming (Yuan et al., 2013). 
Taking into account the enormous number of possible combinations of processing routes, it is necessary the 
development of a methodology for selecting the most convenient pathway for biomass transformation under a 
specific parameter, in this paper is shown the development of a process synthesis methodology that integrates 
various concepts as direct and inverse branching and superstructure optimization, and its application taking as 
a feedstock a biomass from a wastewater treatment plant with a known composition. 

2. Formulation of models for superstructure optimization 

The superstructure contains a number (NP) of layers of chemical species designated under the index i , and 
(NP - 1) layers of processing technologies, designated by the index k. Performance model for the extraction of 
metabolites and processing in the kth layer flows in the related chemical species into and out of the conversion 
operator, namely: 
 

, ,1 , , , .( ,..., ,..., )i i i
out out out

g k g k c g k NCF F F = ig i ,ψ , ,1 , , , .( ,..., ,..., , , )i i i i i
in in in

g k g k c g k NC g gF F F d O      ig∀ , k∀                (1) 
 

Where , ,i
out

g k cF and , ,i
in

g k cF  are the flowrates in tonnes per year of chemical species c leaving and entering 
transformation technology gi in layer k. The design and operating variables of each technology gi are denoted 

by ig
d

and ig
O

, respectively. The mass balance of chemical species c of the chemical species i in the 
technology layer extraction / transformation layer k is given as follows: 
 

, 1 , , ,i
i

c k c k g c k
g

F F r+ = +  ig∀ , k∀    (2)          
For including the economic criteria into the optimization, the term total annualized cost (TAC) is introduced, 
and is defined as the summation of annualized fixed costs (AFC) and annual operating costs (AOC) (e.g., El-
Halwagi, 2012).  
 

TAC = AFC + AOC   (3) 

The annualized fixed costs ( ),ig kAF C  of technology evaluated gi in layer k is given by a cost factor ( ,ig kα ) 
times the flowrate of the limiting component entering the transformation technology, or the flowrate of the 
feedstock containing the chemical specie to extract, capacity differences between data reported in literature 
and this work were adjusted using the seven tenths factor rule, because it applies for large equipment and 
because it is a new and untested process so it’s necessary to be more conserved in the use of technologies to 
scale, additionally it is a number that has proven successful in the optimization of superstructures (Bao et.al 
2011). 
 

lim
0.7

, , , ,
( )

i i i gi

in
g k g k g c k

AFC Fα=
   (4) 

The annual operating costs ( ),ig kAOC  of technology evaluated gi in layer k is given by a cost factor ( ,ig kβ ) 
times the flowrate of the limiting component entering the transformation technology, or the flowrate of the 
feedstock containing the chemical specie to extract, i.e. 
 

lim, , , ,i i i gi

in
g k g k g c k

AOC Fβ=    (5) 
 
The objective function involves the maximization of revenue derived by the selling of final product defined as 
the value of the product less the cost of feedstock and the TAC of the chemical species processing. 

 

Maximize 
Product Biomass Biomass

, i
i

p NP g
k g

C F TAC C F− −                 (6) 

350



 

 

After the superstructure optimization, additional issues must be considered for selecting the processing 
topology. One of these issues is the cost of co-products obtained using the promising pathway without any 
further processing. Therefore, the objective function is modified as follows: 

Product Co-Product Co-Product Biomass Biomass
, , , i

i

p NP m k m k g
k m k g

C F C F TAC C F+ − −                                     (7) 
Where CProduct is the selling price of the product (e.g., $/t), CBiomass is the cost of the feedstock (e.g., $/t) and 

FBiomass is flowrate of the feedstock.  

3. Results 

A literature review and selection of existing and emerging technologies for the production of different chemical 
species involved in the process was performed taking into account the information available in the literature 
and experimental results (Bao et al., 2011). Information of the composition of solids in wastewater, which was 
obtained in previous experimental unpublished work developed by authors, cost parameters for solids 
separation and drying if it is necessary for any technology and economic data was summarized (Table 1). In 
cases where no information was available on the costs of the technology applied to the biomass from 
wastewater, were used the economic data for the same technologies that affect similar raw materials 
(González-Delgado, 2014). 

Table 1. Wastewater composition modelled and economic parameters for case study. 

Parameter Unit Value 

Wastewater composition (solids)   
    
 Carbohydrates % 10 
 Lipids % 33 
 Proteins % 55 
 Special substances % 2 
   
Selling price of main product $/t 900 
Processing capacity t biomass/y 100,000 
Cost of feedstock (Collecting and transport included) $/t 50 
   
Biomass harvesting   
    
 Annualized fixed cost parameter for different capacity $*y-0.3*t-0.7 97 
 Annualized operating cost parameter for different capacity $/t 1.92 
    
Biomass drying   
    
 Annualized fixed cost parameter for different capacity $*y-0.3*t-0.7 348 
 Annualized operating cost parameter for different capacity $/t 200 

 
Table 2 shows the results of the economic evaluation for each technology in layer k of the superstructure. 
Technologies such as oil or alkane secretion feature high values of α, caused by the special cultivation 
conditions required for obtaining the hydrocarbons related to the need to avoid the presence of undesirable 
microorganisms into the culture media which can consume the released products for their growth, in practical 
terms, these technologies based on secretion of products are difficult to be applied in wastewater treatment 
plants. The β values for oil and alkane secretion (which are related to the separation of desired compounds) 
are lower in comparison to other technologies present in superstructure. With wastewater as raw material and 
Biodiesel as the main product, a superstructure with chemical species obtainable from solid phase of 
wastewater and extraction / processing technologies (Figure 1) is built. 
 
 

351



 

 

Table 2: Cost parameters for wastewater-to-diesel superstructure optimization from an economic point of view 
(Modified from González-Delgado, 2014) 

Process Product α($*y-0.3*t-0.7) β($/t feed) 

Alkane secretion Diesel-like 22,130.40 173.99 

Direct transesterification Diesel-like 1,001.94 439.63 

Wet extraction Lipid 869.93 194.32 

Enzymatic degradation Lipid 20,087.15 242.64 

Oil secretion Lipid 22,130.40 164.92 

Solvent extraction Lipid 929.59 309.82 

Supercritical extraction Lipid 2,383.97 479.92 

Esterification/transesterification Diesel-like 738.14 353.00 

Hydrotreatment Diesel-like 595.94 199.00 

Transesterification (heterogeneous) Diesel-like 721.77 211.87 

Transesterification (homogeneous) Diesel-like 369.07 154.55 

Supercritical  transesterification Diesel-like 545.17 80.03 

Supercritical Water Gasification Methane 3,464.65 462.32 

Supercritical Water Gasification Syngas 3,464.65 462.32 

Gasification Syngas 2,976.40 423.10 

Gasification Methane 2,976.40 423.10 

Pyrolysis Bio-Oil 1,576.65 265.50 

Pyrolysis Syngas 1,576.65 265.50 

HTL Biocrude 2,869.42 227.10 

Fischer-Tropsch Diesel-like 2,409.82 150.10 

Steam reforming Syngas 2,593.16 623.40 

Autothermal reforming Syngas 1,880.04 592.23 

Partial oxidation Syngas 2,333.84 529.89 

Cracking C2H4 7,584.73 35.87 

Hydroprocessing Diesel-like 595.94 199.00 

Oligomerization Diesel-like 706.23 55.79 

  

 

Figure 1: Superstructure of chemical species and technologies for extraction / transformation for the 
production of biodiesel from wastewater. 

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After optimization of the superstructure, only one alternative showed positive results as shown in Figure 2. The 
path starts with the liquefaction process of hydrothermal slurry (with biomass), which creates an aqueous 
phase and a phase organic, known as bio-oil or bio crude. The bio crude liquid is improved biofuel using 
hydroprocessing technologies, taking diesel range alkanes as the main product. The product flow on this route 
is 60,649 t/y of biodiesel. 

 

 

Figure 2: Optimal topology for the production of biodiesel from PTAR microbiota after optimization of the 
superstructure. 

A schematic representation of the optimal routes superstructure obtained after application of the concept of 
co-product valorisation can be seen in Figure 3. For the production chain of HTL-based wastewater 
transformation into biodiesel, the aqueous phase obtained after liquefaction can be recycled to the first stage 
of the process, the main products obtained in this case of study are gasoline-like biofuel and biodiesel-like 
biofuel. 
 

 

Figure 3: Solution for the superstructure maximization considering additional economic parameters taking into 
account the valorisation of co-products. 

4. Conclusions 

A methodology for the synthesis and optimization of production processes for biofuels has been proposed, the 
methodology is based on several integrated approaches including branching by forward and reverse selection 
of the main product, superstructure optimization, applying the concept of co-product valorisation, and 
optimized multicriteria comparison of alternatives. For the case study, the economic viability of pool- PTAR for 
biofuel production showed, raising the possibility of synergy with companies that handle wastewater, providing 
added value to the waste effluent. 
 
Acknowledgments 

The authors express their gratitude to the University of San Buenaventura-Cartagena for their financial 
support through the internal call for research projects. 
 
 
 

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