Category Archives: Dissertation

Techno-economic Assessment of Transport Biofuel Production from Bio-oil Using Refinery Upgrading Technologies


Second Year Progress Review Report
Techno-economic Assessment of Transport Biofuel Production from Bio-oil Using Refinery Upgrading Technologies
MOBOLAJI SHEMFE
Supervisor: Prof. Sai Gu
Degree Sought: PhD Mode: Full Time
Date of Initial Registration: 1st January, 2013
Review meeting: 25th September, 2014
Venue: Conference Room 3, Building 52
Process Systems Engineering Group
School of Engineering
Cranfield University,
Cranfield Bedfordshire MK43 0AL.

EXECUTIVE SUMMARY
The effects of greenhouse gas emission from the burning of fossil fuels have been the contention of the energy debate in the last three decades. Today, fossil fuels still represent about 80% of the world’s energy mix and governments and stake holders are joining the bandwagon of introducing more stringent regulations on carbon emission and support policy to enable the development of other sustainable alternatives. The dominance of fossil fuel for transportation fuel demand at 94% is projected to fall to 89% by 2030 and biofuels are expected to play a significant role in the gradual energy transition.
The production of biofuels from fast pyrolysis of biomass holds a great potential for producing renewable transportation fuels. Fast pyrolysis involves the thermochemical conversion of biomass in the absence of oxygen at temperatures ranging from 450oC to 650oC to maximize bio-oil production among other associated products such as char and non-condensable gases. While bio-oil has been demonstrated as a suitable fuel for power plants, it is incompatible with modern internal combustion engines as a transport fuel due to the high composition of oxygen in the oil, hence requires further stabilization and upgrading. Few conventional refinery operations have been identified for upgrading bio-oil into transport fuels including hydroprocessing (hydrotreating and hydrocracking) and zeolite catalytic cracking. These bio-oil upgrading routes present unique technical and economic challenges which can be explored via process modelling. The goal of this research is to develop high fidelity process models, validate them, optimize their energy efficiency and conduct economic assessment.
In the work done so far, a high fidelity process model of a 72 MT/day pine wood fast pyrolysis and bio-oil upgrading plant was developed with rate based chemical reactions using Aspen Plus® process simulator. It was observed from simulation results that 1 kgs-1 pine wooddb produced 0.66 kgs-1 bio-oil, 0.19 kgs-1 gas and 0.15 kgs-1 char. Simulation results also show that the energy required for drying and fast pyrolysis operations can be provided from the combustion of pyrolysis by-products mainly char and non-condensable gas, with sufficient residual energy for miniature electric power generation. The intermediate bio-oil product from the fast pyrolysis process is upgraded into gasoline and diesel via a two-stage hydrotreating process, which is implemented by a pseudo first order reaction of lumped bio-oil species

followed by the hydrocracking process. Simulation results indicate that about 0.2 kgs- 1 of gasoline and diesel range products and 96W of electric power can be produced from 1 kgs-1 pine wooddb. The effect of initial biomass moisture content on the amount of electric power generated and the effect of biomass feed composition on product yields were also reported. Aspen Process Economic Analyser® was used for equipment sizing and cost estimation for an nth plant and the product value was estimated from discounted cash flow analysis assuming the plant operates for 20 years at a 10% annual discount rate. Economic analysis indicates that the plant will require £16.6 million of capital investment and product value is observed at £6.69/GGE. Furthermore, the effect of key process parameters on product value and the impact of electric power generation equipment on capital cost and energy efficiency were also reported.

List of publications, seminars and posters presented since last progress review
1. Shemfe, M.B., Gu, S., Ranganathan, P. (2014). Techno-economic performance analysis of biofuel production and miniature electric power generation from biomass fast pyrolysis and bio-oil upgrading process. Submitted to Fuel. 2. Shemfe, M.B. (2014). Production of transport biofuel production from fast pyrolysis bio-oil using refinery upgrading technologies, Research Student presentation at the weekly Energy and Power Division (School of engineering) Research Programme Seminar, 11th February 2014. 3. Shemfe, M.B. (2014). Techno-economic assessment of transport biofuel production from fast pyrolysis bio-oil using refinery upgrading technologies, a poster presented at E&A DTC Poster Conference, School of Engineering, 4th March 2014.

TABLE OF CONTENTS
LIST OF FIGURES ………………………………………………………………………………………. vii LIST OF TABLES ……………………………………………………………………………………….. viii LIST OF EQUATIONS ……………………………………………………………………………………ix LIST OF ABBREVIATIONS ……………………………………………………………………………. x NOMENCLATURE ………………………………………………………………………………………. xii 1.0 INTRODUCTION ………………………………………………………………………………… 1  Background …………………………………………………………………………………….. 1
Motivation……………………………………………………………………………………….. 3
Aim and Objectives ………………………………………………………………………….. 4
Novelty …………………………………………………………………………………………… 4
2.0 SUMMARY OF FIRST YEAR WORK …………………………………………………….. 6  Literature Survey ……………………………………………………………………………… 6
Methodology …………………………………………………………………………………… 9
Fast Pyrolysis Model Development …………………………………………………… 10
Model development …………………………………………………………………….. 11
Model Inputs ………………………………………………………………………………. 15
Results and Discussion ……………………………………………………………….. 16
3.0 SECOND YEAR WORK …………………………………………………………………….. 22  Process Description ……………………………………………………………………….. 22
Model Development ……………………………………………………………………….. 23
Pretreatment section (A100) …………………………………………………………. 24
Pyrolysis section (A200) ………………………………………………………………. 24
Products separation and recovery (A300-A400) ………………………………. 24
Combustion section (A500) ………………………………………………………….. 25
Power generation (A600)……………………………………………………………… 25
Bio-oil upgrading (A700) ………………………………………………………………. 25
Hydrogen production (A800) ………………………………………………………… 26

Process Economics ……………………………………………………………………….. 29
Model Inputs …………………………………………………………………………………. 30
4.0 RESULTS AND DISCUSSION ……………………………………………………………. 32  Model Validation ……………………………………………………………………………. 32
Energy Efficiency …………………………………………………………………………… 35
Energy efficiency of fast pyrolysis process ……………………………………… 35
Energy efficiency of bio-oil upgrading process ………………………………… 36
Effect of Feed Composition ……………………………………………………………… 36
Effect of Initial Biomass Moisture Content………………………………………….. 38
Economic Analysis …………………………………………………………………………. 39
Economic results ………………………………………………………………………… 39
Sensitivity analysis ……………………………………………………………………… 41
Conclusions ………………………………………………………………………………….. 43
5.0 FUTURE WORK PLAN ……………………………………………………………………… 44 Gantt chart for future work ………………………………………………………………………… 45
References ………………………………………………………………………………………………… 46

LIST OF FIGURES Figure 2.1 Total capital cost vs. plant capacity ………………………………………………….. 7 Figure 2.2 Methodology flowchart …………………………………………………………………… 9 Figure 2.3 Aspen Plus simulation flowsheet ……………………………………………………. 14 Figure 2.4 Mass flow of H2O in dryer exhaust and biomass feed. ………………………. 16 Figure 2.5 Effect of combustor temperature on pyrolysis temperature ………………… 19 Figure 2.6 Effect of combustor temperature on NCG concentration. …………………… 20 Figure 2.7 Effect of combustor temperature on bio-oil yield. ……………………………… 20 Figure 2.8 Effect of combustor temperature on char yield …………………………………. 21 Figure 3.1 Generalised process flow diagram …………………………………………………. 22 Figure 3.2 Fast pyrolysis process flowsheet (A100- A600) ……………………………….. 27 Figure 3.3 Bio-oil hydroprocessing and hydrogen production (A700 – A800………… 28 Figure 3.4 Capital investment estimation methodology …………………………………….. 29 Figure 3.5 Multi-step reaction kinetics of biomass pyrolysis [39] ………………………… 31 Figure 3.6 Reaction kinetic model of lumped bio-oil species ……………………………… 32 Figure 4.1 Aspen plus simulation results vs. experimental data from [20] as a function of reactor temperature …………………………………………………………………………………. 33 Figure 4.2 Fast pyrolysis products and biofuel yield from various biomasses ………. 37 Figure 4.3 Electric power generated from various biomass ……………………………….. 38 Figure 4.4 Effect of initial moisture content in biomass on power generated in the process ……………………………………………………………………………………………………… 39 Figure 4.5 Total capital investment of the two main sub-processes ……………………. 40 Figure 4.6 Total capital investment distribution of pyrolysis plant ……………………….. 40 Figure 4.7 Percentage difference in fuel product value over a ± 20% change (increase/decrease) in process and economic parameters ……………………………….. 42 Figure 4.8 Fuel product value sensitivity to process and economic parameters …… 42

LIST OF TABLES Table 2.1 Past TEA studies of bio-oil production via fast pyrolysis……………………….. 6 Table 2.2 Previous techno-economic studies of upgrading bio-oil ……………………….. 8 Table 2.3 Proximate and ultimate analyses of corn stover [36] ………………………….. 15 Table 2.4 Char proximate and ultimate analyses. ……………………………………………. 15 Table 2.5 Mass flow of pyrolysis products. ……………………………………………………… 17 Table 2.6 Energy balance around dying operation …………………………………………… 18 Table 2.7 Energy balance around fast pyrolysis ………………………………………………. 19 Table 3.1 Process assumptions ……………………………………………………………………. 23 Table 3.2 Cost inputs and assumptions …………………………………………………………. 30 Table 3.3 Proximate and chemical composition of pine wood [46] ……………………… 30 Table 3.4 Pyrolysis chemical reactions [39] ……………………………………………………. 31 Table 3.5 Bio-oil hydrotreating reactions [21] ………………………………………………….. 32 Table 4.1Pyrolysis model validation with experimental measurements at 500 °C …. 33 Table 4.2 Hydrotreated bio-oil results validated with experimental measurements .. 34 Table 4.3 Composition of various biomasses [46] ……………………………………………. 37 Table 4.4 Economic results ………………………………………………………………………….. 41

LIST OF EQUATIONS Equation 2.1 ………………………………………………………………………………………………… 7 Equation 2.2 ………………………………………………………………………………………………… 7 Equation 2.3 ………………………………………………………………………………………………. 11 Equation 2.4 ………………………………………………………………………………………………. 11 Equation 2.5 ………………………………………………………………………………………………. 15 Equation 2.6 ………………………………………………………………………………………………. 16 Equation 2.7 ………………………………………………………………………………………………. 16 Equation 2.8 ………………………………………………………………………………………………. 19 Equation 3.1 ………………………………………………………………………………………………. 29

LIST OF ABBREVIATIONS AFP Atmospheric flash pyrolysis APEA Aspen Process Economic Analyzer
APR Aqueous phase reforming ASPEN Advanced Simulator for Process Engineering
BP British Petroleum
BTG Biomass Technology Group CC Capital Cost
CSTR Continuous stirred-tank reactor DCF Discounted cash flow
EISA Energy Independence and Security Act EPA Environmental Protection Agency
ESP Electrostatic precipitator
FAO Food and Agriculture Organization of the United Nations FCCU Fluid Catalytic Cracking Unit
FT Fischer-Tropsch GGE Gasoline gallon equivalent
HDO Hydrodeoxygenation
HHA Hydroxyacetaldehyde HHV Higher Heating Value
HMFU Hydroxymethylfurfural HT Hydrotreated
IEA International Energy Agency IRR Internal rate of return
LIPS Liquefaction in pressurized solvents
MM Million MT Metric ton
NCG non-condensable gases NGL Natural gas liquids
NPV Net Present Value

NRTL-NTH Non-random two-liquid – Nothnagel
PC Plant Capacity PR-BM Peng-Robinson-Boston-Mathias
PSA Pressure swing adsorption PV Product Value
RRR Required rate of return WEC World Energy Council
ZSM Zeolite Socony Mobil

NOMENCLATURE
ή energy efficiency A pre-exponential factor Ea activation energy f installation factor T temperature   Subscripts  db dry basis PC project contigency PE equipment cost TCI total capital investment TFCI total fixed captial TIDC total indirect cost   Superscript n scaling factor

1.0 INTRODUCTION
Background Crude oil remains the main source of producing transportation fuels and will continue to dominate the demand for liquid transportation fuels for the next three decades [1]. To reduce the world’s dependence on crude oil due to the environmental implications of burning fossil fuels coupled with stringent regulation on carbon emission, the world is rapidly deploying biofuels as a sustainable substitute [2; 3]. Biomass can be converted into biofuels via biochemical and thermochemical conversion routes. While biochemical conversion processes have been demonstrated in commercial quantities, they are economically unsustainable and exert market pressure on food crops and biodiversity [3-5]. On the other hand, thermochemical conversion processes, which include pyrolysis, gasification and hydrothermal liquefaction, have a great potential for producing the intermediate bio-oil required for advanced biofuels production that can compete directly with fossil fuels [3; 5]. However, the products obtained from these processes vary in physical properties and chemical composition and consequently present unique technical and economic challenges [6]. Biomass fast pyrolysis presents the best case for maximising bio-oil product, which can then be subsequently upgraded into transport fuels [7; 8]. Fast pyrolysis entails the thermochemical decomposition of lignocellulosic biomass at temperatures typically ranging from 450 to 650°C, to produce liquids (bio-oil), solids (char and ash) and non-condensable gas (NCG) at a very short vapour residence time typically 1 ‒ 2s[9]. Fast pyrolysis bio-oil can be upgraded into naphtha range transport fuels via two major conventional refinery operations that have been broadly identified and reviewed in literature, which are hydroprocessing and catalytic cracking [6; 10; 11]. Fast pyrolysis bio-oil has a very high concentration of oxygen — typically about 40% — mainly due to the heavy presences of aldehydes, phenols, ketones, esters ethers, carboxylic acids and alcohols. As a consequence, fast pyrolysis bio-oil is highly corrosive and exhibit acidic properties with a pH of 2.5, and ultimately it is incompatible with downstream equipment and internal combustion engines.  Fast pyrolysis bio-oil can be upgraded into naphtha range transport fuels via two major conventional refinery operations that have been broadly identified and reviewed in literature, which are hydroprocessing and catalytic cracking [6; 10; 11].

Hydroprocessing encompasses two main hydrogen intensive processes namely hydrotreating and hydrocracking. Hydrotreating/ Hydrodeoxygenation involves the stabilization and selectively removal of oxygen from untreated pyrolysis bio-oil through its catalytic reaction with hydrogen over sulfided CoMo or NiMo supported catalysts and non-sulfided precious metal catalysts while hydrocracking involves the simultaneous scission and hydrogenation of heavy aromatic and naphthenic molecules into lighter aliphatic and aromatic molecules[6; 9; 10; 12].The origins of fast pyrolysis biomass feed have a significant effect on the resultant   chemical compositions of the bio-oil produced; consequently, the required operating conditions, hydrogen and catalyst inventory for these bio-oil variants differ from one another [13].  Sheu et al., (1988)[14] demonstrated through experiments that the composition of oxygen in pine wood derived bio-oil can be significantly reduced by hydrodeoxygenation reactions over Pt/Al2O3/SiO3, and sulfided CoMo/Al2O3 and Ni- Mo/Al2O3 catalysts. Elliot et al., [15] also described catalytic hydrotreating of bio-oil through experiments using various catalysts formulations, and quantified the process yields from hydrotreating and hydrocracking processes and their respective hydrogen consumption. While there is limited study on hydroprocessing of bio-oil with more than 1 wt% nitrogen, nitrogen content have been found to be considerably high in some bio-oil especially those obtained from pyrolysis of algae [16]. The main challenge with hydroprocessing of bio-oil is the uneconomic amount of hydrogen required for the hydrogenation reactions of its aromatic rings [9].
Catalytic cracking is a very important r operation used in refineries to crack low value large hydrocarbons into smaller molecules —mainly gasoline— at high temperatures and atmospheric pressure in the presence of a suitable catalyst [12]. The C-C covalent bonds in the reactant are heterolytically scissored and dehydrated to produce lighter hydrocarbon molecules and the oxygen is rejected as water and carbon dioxide. The utilization of catalytic cracking for upgrading pyrolysis oil has been receiving a lot of attention, with a lot of emphasis on catalyst development and reformulation. Zeolite cracking reduces the oxygen content of pyrolysis oil by rejecting oxygen as CO2 and H2O. Zeolite cracking does not require hydrogen and high pressure; however, it is plagued by the high rate of coke formation on active catalyst sites. The production of gasoline range fuels from biomass derived sugars via catalytic pyrolysis over selectively shaped small pore zeolite catalyst-HZSM-5 have been demonstrated on a

pilot scales [11; 17; 18] while other catalytic pyrolysis catalysts including metal oxides, phosphoric acid and other salts have been demonstrated elsewhere [19-21]. Zeolite catalysts are best suitable for upgrading biomass-derived oils as they improve the selectivity of the relevant hydrocarbons found in gasoline such as propylene and relatively achieve more liquid yields; however, the rapid deactivation of zeolite catalyst remains a hurdle to be crossed for the upgrading via this route. Although fuel production via this upgrading route is yet to be economic competitive with fossil fuels [22], the prospect of its economic feasibility is underpinned by suitable operating pressures and the elimination of hydrogen feed requirement as in the case of hydroprocessing.
Motivation The production of transport biofuels from the fast pyrolysis of biomass is yet to be commercialised due to the expensive investment required for production and a lack of competitiveness with fossil fuels; thus this makes process modelling and simulation an indispensable tool to investigate process performance and ensuring its economic viability. Furthermore, supporting processes required for the fast pyrolysis process consisting of grinding and drying processes, which are solid processes that are currently inadequately described in available software. Therefore, a high fidelity process model is required for rigorous analysis of the whole process.  In addition, existing models specify the product yield compositions for the reactor without accounting for the effect of temperature and chemical kinetics due to the complexity of the thermochemical reaction kinetics involved, and most available reaction models in literature are descriptive of the intra-particle relationship rather than predictive of the product distribution [23]. Therefore, a model that is able to predict the process yields and process energy requirements at varying operating conditions with minimal assumptions would be necessary. There are several studies on the techno-economic analysis of biomass fast pyrolysis for bio-oil production available in literature, with very few studies considering the upgrading of fast pyrolysis bio-oil into transportation fuels and quantifying the amount of electric power capable of being generated from fast pyrolysis by-products [22; 24-26]. Construction of pilot plants to explore process performance is infeasible as it requires monumental financial investments, therefore developing rigorous process models, validating them and using them to simulate at different operating conditions is a practical low cost alternative. Furthermore,

computational process models have the litheness to accommodate varying operating conditions and configuration, thereby paving way for better comprehension of the processes and consequently more efficient economic assessment.
Aim and Objectives  The aim of this research to conduct techno-economic performance analysis of transport biofuel production from fast pyrolysis and bio-oil upgrading processes via process modelling and simulation:
The research objectives include:
 To develop predictive process models for biomass fast pyrolysis process and bio-oil upgrading processes.  To compare and analyse the technical performance of each bio-oil upgrading route.  To identify heat integration opportunities and optimize energy usage in the base process model via pinch methodology.  To perform economic analyses of base and integrated upgrading processes.  To evaluate the impact of key process and economic parameters on process economics and financial outlook.
Novelty This research will make a number of contributions to the scientific body of knowledge. Most process models in existing literature are either non-predictive of product distribution or   based on simple empirical correlations with no consideration for the chemical and thermodynamic interactions in the system. However, the development of a predictive process model of the fast pyrolysis process, and bio-oil hydroprocessing and catalytic cracking processes based on rate based chemical reactions, as proposed in this research, is of upmost importance for better process comprehension, and ensuring that the chemical interactions between reactants as well as the associated enthalpies are adequately captured. The proposed model will be validated with experimental data to ensure the model represents the physical system adequately prior to the model analysis. While there is no report of detailed heat exchanger networks for the biomass fast pyrolysis process integrated with bio-oil upgrading processes is existent in current

literature, an energy optimization proposition for the system will be considered in this research. Energy saving opportunities will be identified by matching hot and cold process streams to calculate the required external utilities, and eventually to optimize energy utilization in the system. Different heat exchanger network designs will be considered via pinch methodology to identify the best heat exchanger network design amongst alternatives to ensure optimum energy efficiency.  To conclude, a thorough economic analysis will be performed by mapping unit operations from process flowsheets to equipment cost mode for equipment sizing based on relevant design codes and estimate their equipment costs for subsequent financial analysis. Reported models in existing literature adopt a lot of technical and economic assumptions which influence economic results significantly. The models proposed in this study will increase the integrity of analysis by adopting minimal process and economic assumptions. Furthermore, the effect of key process parameters and variables on process and economic performance will be explored and ultimately project the process economic outlook.

2.0 SUMMARY OF FIRST YEAR WORK First year work reported in the 9 months review include the extensive literature survey of existing techno-economic studies and the development of a preliminary fast pyrolysis process model based on empirical correlations with appropriate thermodynamic properties without the inclusion of chemical kinetic reactions. These are discussed in the subsequent subsections.
Literature Survey In the first year of this research study, extensive survey of literature was conducted on past process models and techno-economic analysis studies (TEA) for fast pyrolysis and bio-oil upgrading processes existing in literature. While there are several techno-economic analysis studies of biomass fast pyrolysis for bio-oil production in literature, very few studies assess the upgrading of the intermediate bio-oil into transportation fuels. Table 2.1 depicts the comparison between past techno-economic studies for bio-oil production.
Table 2.1 Past TEA studies of bio-oil production via fast pyrolysis.
Study Biomass Cost ($/dryMT)
Plant Capacity (MT/day)
Bio-Oil Cost ($/gal)
Feed
Capital  Cost ($MM)
[27] 44 1,000 0.41 Wood 37
[28] 20–42.50 1,000 0.59–2.46
Wood Pellets and Straw
44–143
[29] 44 250 0.5 Wood 14
[29] 44 1,000 0.5 Wood 46
[22] 46.5 1,000 0.41 – –
[25] 22 24 0.82 Rice Husk. 0.389
[26] 36 400 0.89 Wood Chips 14.3
[26] 36 200 0.99 Wood Chips 8.8
[30] 65 240 1.40** Wood 7.8
[31] 83 2,000 0.83 Corn Stover 110.4
** Calculated using HHV 17.9 GJ/tonne and density of 4.55 kg/gal of bio-oil.

These studies report bio-oil cost ranging from $0.41/gal to $1.40/gal for plant capacity range of 24 to 2,000 MT/day, and capital cost ranging from $7.8 to $143 million. The significant disparity in bio-oil cost between these studies can be pinned down to various assumptions utilized in each study, in this regard no convincing relationship was observed between plant capacity and bio-oil cost.  However, a linear relationship was observed between the capital cost and plant capacity as depicted in Figure 2.1, and expressed in Equation 2.1.
Figure 2.1 Total capital cost vs. plant capacity
CC = 0.0532(PC) − 4.7574 Equation 2.1 A similar sizing equation was derived from a study by Rogers and Brammer [32] on the estimation of the production cost of fast pyrolysis bio-oil. In this study, a logarithmic relationship was observed between total capital cost ($MM) and plant capacity (odt.day) of pyrolysis plant, applicable to plants up to 100odt/day as expressed in the sizing equation in below:
TPC = 2853.8Ln(PC) − 6958.8 Equation 2.2 The conformity in the above sizing equations could be due to similar modelling assumptions made in each study with little or no consideration for the thermochemical interactions in the system. To this end, these sizing equations

need to be verified by a more rigorous process model that considers the chemical interactions and thermodynamic properties of the system with minimal assumptions. The past TEA that cover the downstream process of upgrading the intermediate bio-oil obtained from fast pyrolysis are presented in Table 2.2.
Table 2.2 Previous techno-economic studies of upgrading bio-oil
Study Capital  Cost ($MM)
Upgrading Technique
Product Value ($/Gal)
Products
[33] 86.9
AFP- 2 stage hydrotreating
0.96**
High octane gasoline
[34] 303
Hydrotreating and hydrocracking + hydrogen generation
2.04
Gasoline Diesel
[31] 200–287
Hydrotreating and hydrocracking
2.11–3.09
Naphtha and diesel-range
[35]
203–242
2 stage hydrotreating + FCC

Commodity Chemicals. Synthetic
** Calculated using LHV 41.5 GJ/tonne and density of 2.80 kg/gal for gasoline.
One of the earliest TEA studies conducted for converting wood into gasoline and diesel fuels was carried out by Elliott et al., [33]. In this study, two processes were considered including atmospheric flash pyrolysis (AFP) and liquefaction in pressurized solvents (LIPS) based on a 1,000 dry ton/day plant capacity. The total capital investment for producing transport fuels from the AFP and LIPS processes were estimated at $100 million and $126 million respectively. Another TEA studies on the production of naphtha and diesel distillation range fuel from 2000 MT/day corn stover fast pyrolysis and hydrotreating/hydrocracking plant was conducted by Wright et al., [31]. In this study, two scenarios were considered for providing the required for hydroprocessing including hydrogen purchase from the market and in-situ hydrogen production, and results indicated product value at $2.11/gal and $3.09/gal for the two scenarios respectively. A recent TEA study similar to [31] was conducted by Zhang et al.,[35] to evaluate the economic performance of upgrading bio-oil via two upgrading pathway based on a 2,000 MT/day fast pyrolysis biomass plant. In this study, the scenarios considered included a single stage hydrotreating process integrated with a

succeeding hydrocracking process and a two-stage hydrotreating unit followed by a fluid catalytic cracking unit. The study also compared the two options for providing the hydrogen required for hydrotreating; purchased hydrogen from the market and hydrogen produced from methane reforming. The capital investment for these scenarios ranged between $203 to 296 million. The one of the main limitation of the aforementioned TEA studies is that none of the process models used is predictive as the implementation of reaction kinetics to describe the chemical interaction between the reactant is non-existent. Consequently, the influence of process parameters on the techno-economic performance of the process cannot be assessed with full confidence.
Methodology The methodology adopted in this research is illustrated by the flowchart in Figure 2.2 below:
The summary of the procedures that will be taken for this research project are listed below:
Collection of process and experimental data from literature
Aspen plus®  process simulator
Energy optimization via picnh analysis
Aspen Process Economic Analyser®
Process flowsheets, mass and energy balance
Economic Results
Process parameters
Integrated process model
Figure 2.2 Methodology flowchart

1. The relevant process and experimental data are collected from literature. 2.  Appropriate process model sections were selected with suitable scenarios chosen based on developed criteria. 3. Process model was developed using Aspen plus® simulation software. 4.  Process streams are extracted from the base process flowsheet for subsequent heat integration using pinch methodology.  5. Economic analysis, equipment size and cost are evaluated using Aspen Process Economic Analyzer ® and investment spreadsheet calculations. 6. Product value and internal rate of return is determined by discounted flow analysis and sensitivity analysis on process and economic parameters to identify various cost drivers and feasibility for scale-up.
Fast Pyrolysis Model Development The following assumptions were adopted in initial model development for the fast pyrolysis process model:
1. Biomass feed size is supplied between 10–25mm. 2. Feedstock is corn stover with 25% moisture content provided in the proximate and ultimate analyses of corn stover [36].  3. Plant capacity is 70 dry metric tons per day. 4. Pyrolysis heat is supplied by non-condensable gas combustion with make- up natural gas. 5. The chemical composition of yields from the pyrolysis reactor adapted from experimental data in literature [36]. Since there are several chemical compounds found in bio-oil due to the complexity of the process and feed, only few compounds with same properties can be found on the Aspen plus database; hence, the chemical composition of the bio-oil was assumed to contain 10 major components including HCOOH, CH3COOH, C2H5COOH, C7H8O2, C8H10O, C10H12O2, C6H5OH, C7H8, C6H6 and C5H4O2. The gas included in the model include H2,CH4, C2H5, C3H7, CO, CO2 and NH3,  6. Cyclone particle removal at 95% efficiency. 7. Rapid quenching achieved using an immiscible aromatic hydrocarbon compound–pyrene.

Model development The Aspen Plus® simulation flowsheet for biomass fast pyrolysis is illustrated in Figure 2.3. The wet biomass stream (CHR-1) supplied at 20mm diameter is fed into a multiple roll mill (CHR) in which the particle is reduced to 2mm. The exiting wet biomass stream (CHR-2) is with moisture content of 25% is then fed into a dryer to reduce the moisture content. The dryer is modelled as a steam tube rotary drum. The wet biomass feed (CHR-2) is fed into the dryer with an air stream (AIR) at ambient conditions. The required air is calculated from the assumed exhaust gas humidity ratio. The drying operation is represented by three block models: RSTOIC block (DR-RXN), HEATX block (DR-HEX) and FLASH-2 block (DR-FLSH). Biomass is considered a nonconventional component in Aspen Plus®. Every nonconventional component in Aspen Plus® is assumed to have a molecular weight of 1.0.
BIOMASS Wet  → 0.0555084 H2O Equation 2.3
Although drying operation is not considered a chemical reaction, a stoichiometric reactor block was introduced to convert a portion of the biomass to form H2O represented by Equation 2.3. Furthermore, a calculator block was introduced for the drying operation to control the rate of drying and specify the moisture content of the dried biomass material. The material balance for drying efficiency given in Equation 2.4 was specified in the calculator block using a FOTRAN subroutine.
CONV = H2OIN – H2ODRY 100 – H2ODRY
Equation 2.4
Where:H2ODRY = moisture content of dried biomass, specified at 10%.
H2OIN   = % moisture in the biomass feed in stream (CHR-2), given as from the proximate analysis of corn stover; moisture content is 25%.
CONV = Fractional conversion of biomass to H2O in block (DR-RXN).
The exiting stream from the RSTOIC block (DR-RXN) is fed into a HEATX block (DR-HEX) with a countercurrent heated steam (DR-STM1) supplied at 150 °C. The outlet temperature of the dry biomass mixed with exhaust stream was specified at 100 °C as the cold stream outlet temperature. In the FLASH-2 block

(DR-FLSH), the exhaust stream containing the evaporated water (DR-4) from the feed is separated from the dried biomass stream (DR-3) with its moisture content reduced to 10%.
The yield reactor block (PR-RXN) and heater block (PR-HEX) are used to model the fluidized bed pyrolysis reactor. The process heat required for the pyrolysis reactor is mainly supplied by the combustor modelled by a Gibbs reactor block (CB-BUR), where exiting stream from the combustor (CB-OUT) is used as the fluidizing gas and heat stream (PR-Q) from (PR-HEX) provides the process heat. In the combustor (CR-BUR), a mixture of CH4 (FUEL) and N2 (N2) are combusted with the non-condensable gas stream (ESP-NCG). The combustion temperature is controlled at 945 °C by controlling the extent of combustion of non-condensable gases and the make-up gas. The exiting hot gas from (CR-BUR) is sent to HEATER block (PR-HEX), from which the heat required in the fluidized bed pyrolysis reactor (PR-RXN) is directly supplied by a heat stream (PR-Q).  The reactor feed (DR-3) is sent to the (PR-HEX) before it is fed into the reactor (PR- RXN) as stream (PR-FD). The operating temperature of the pyrolysis reactor (PR- RXN) is controlled at 488 °C by varying the combustion temperature of (CB-BUR). The yields from reactor (PR-RXN) are temperature-dependent.
A FOTRAN user subroutine is used to determine yield composition based on DynaMotive reactor temperature and yield empirical correlations. The pyrolysis product stream (PR-P), consisting of a mixture of gases, hot vapours and char particulates is fed into a cyclone (PR-CYC), where it is separated into two stream made of char (PR-SD) and gases and pyrolysis vapours (PR-VP). Stream (PR- VP) is quenched with pyrene in a 3 phase flash drum (QC) at 50 °C. The exiting streams from (QC) are (QC-VAP) made up of NCG and aerosols, (QC-LLIQ) containing a fraction of bio-vapours quenched into oils and (QC-HLIQ) made of the pyrene and the remaining bio-oil. Pyrene is separated from bio-oil in the decanter (SEP) for re-use. The separated oils (S-1) are then mixed with the second stream (QC-LLIQ from the flash drum to obtain the final bio-oil product (BIO-OIL). The non-condensable gases (QC-VAP) are sent to an electrostatic precipitator (QC-ESP) to remove the remaining char particulates (ESP-SD)

entrained in the gas. A clean non-condensable gas stream (ESP-NCG) is recycled to the combustor (CB-BUR) for burning with mixture of N2 and CH4.

Figure 2.3 Aspen Plus simulation flowsheet

Model Inputs The model inputs utilised for simulation are obtained from literature. Biomass is modelled as non-conventional based on proximate and ultimate analyses of corn stover given in Table 2.3 [36]
Table 2.3 Proximate and ultimate analyses of corn stover [36]
Proximate analysis (wet basis wt. %) Ultimate analysis (dry basis wt. %)
Moisture 25.0 Carbon 47.28
Fix Content 17.7 Hydrogen 5.06
Volatile Matter 52.8 Oxygen 40.63
Ash 4.5 Nitrogen 0.80
Sulphur 0.22
Ash 6.00
Char proximate and ultimate analyses taken from laboratory results of Rover (2008)[37] shown in Table 2.4..
Table 2.4 Char proximate and ultimate analyses.
Proximate analysis (wet basis wt. %) Ultimate analysis (dry basis wt. %)
Moisture  0 Carbon 51.2
Fix Content 51.21 Hydrogen 2.12
Volatile Matter 49.79 Oxygen 11.5
Ash 0 Nitrogen  0.45
Sulphur 0.935
Ash 33.3
Three polynomial equations were obtained by correlating the data collected from DynaMotive (1999)[38] to calculate the temperature-dependent yields of non-condensable gases, liquids and char in the pyrolysis reactor.
The yield of non-condensable gases from biomass pyrolysis is expressed as:
Y Gas [ kg/kg biomass daf ] =13053 – 58.87 T + (5.77 x 10-2) T 2
Equation 2.5

The yield of bio-oil from biomass pyrolysis is expressed as:
Y Oil [ kg/kg biomass daf ] =14458 + 60.799 T  –  (6.36 x 10-2) T 2
Equation 2.6
The yield of char from biomass pyrolysis is expressed as:
Y Gas [ kg/kg biomass daf ] = 1505 – 5.9249 T + (5.9 x 10-3) T 2
Equation 2.7 These equations reflected the reactor data of the BioThermTM reactor more precisely. Product composition the exiting stream was adapted from the experimental product composition of Mullen et al., (2010).
Results and Discussion
2.3.3.1 Mass balance The stream table for biomass drying predicted from the base model is illustrated in Figure 2.4.
Figure 2.4 Mass flow of H2O in dryer exhaust and biomass feed.

The fraction of moisture removed from the wet biomass feed defined in Equation 2.4 is 0.2 and the ratio of wet biomass to steam required for drying is 1:10.
Table 2.5 Mass flow of pyrolysis products.
PR-P ESP-NCG BIO-OIL PR-SD ESP-SD Temperature (°C) 488 50 46 486 50
Mass Flow (kg/hr)      H2 14.15 14.15 0.00 0.00 0.00 CH4 0.84 0.83 0.02 0.00 0.00 C2H6 3.42 3.32 0.10 0.00 0.00 C3H8 3.66 3.48 0.17 0.00 0.00 CO 157.87 156.77 1.10 0.00 0.00 CO2 130.43 121.61 8.82 0.00 0.00 NH3 0.29 0.23 0.06 0.00 0.00 CH3COOH  (Acetic acid) 142.71 7.52 135.19 0.00 0.00 C2H5COOH  (Propionic acid) 175.92 4.00 171.92 0.00 0.00 C7H8O2  (Methyl phenol) 14.68 0.00 14.68 0.00 0.00 C8H10O (Ethylphenol) 91.45 0.06 91.39 0.00 0.00 C10H12O2 (Propyl benzoate) 393.71 0.25 393.45 0.00 0.00 C6H5OH (Phenol) 11.07 0.01 11.06 0.00 0.00 C7H8 (Toluene) 54.63  5.62  49.00 0.00 0.00 C6H6 (Benzene) 18.53  4.47  14.06 0.00 0.00 n 480.82  5.30  475.52 0.00 0.00 HCOOH (Formic acid) 82.06  7.93  74.13 0.00 0.00 H2O 259.90  33.53  226.37 0.00 0.00 CHAR 394.43  0.00 0.00 374.71 19.72 TOTAL (kg/hr) 2430.5 369.08 1667.04 374.71 19.72
Mass Yield (%)  15.00 69.00 15.00 1.00

Where: PR-P is the total pyrolysis products exiting the reactor; ESP-NCG is the non- condensable gases from the ESP unit; BIO-OIL is the final bio-oil product; PR-SD is the char exiting the cyclone and ESP-SD is the char particulates leaving the ESP unit. The pyrolysis fast process produced 1667.04 kg/hr of bio-oil. The mass yield of bio-oil produced from the process is calculated as 68.5%.
2.3.3.2 Energy Balance  Table 2.6 Energy balance around dying operation
For drying
operation, the thermal efficiency as follows:
2917 kg of wet corn stover contains 25 % moisture
= 2915*0.25 = 729.25 kg of moisture.
Bone dry corn stover = 2915*(1-0.25) = 1641 kg bone dry corn stover.
Moisture removed = 486 kg
Final product from the dryer contains 10% moisture = 243.25 g
Latent heat of evaporation = 2260 kJ/kg.
Using 1 hour basis:
The heat required for drying = 486 *2257 =1.1 x 103 MJ/hr.
Heat used from Aspen Plus® = 2903.25 MJ
Thermal efficiency of dryer =
1.1 x 103 2903.25  = 0.38 ≈ 40%
Process Stream Energy in (MJ) Energy Out (MJ)
CHR-2 (Biomass-wet) 66530 –
AIR 158 –
Heat Added 2903 –
Humid Air – 17180
Exhaust – 6346
Heat removed – 1575
DR-3 (Dry Biomass) – 44490

Table 2.7 Energy balance around fast pyrolysis
2.3.3.3 The effect of combustor temperature The effect of combustion temperature on pyrolysis reactor temperature and product distribution was explored as depicted in Figures 2.5, 2.6, 2.7 and 2.8.
Figure 2.5 Effect of combustor temperature on pyrolysis temperature
Furthermore, a linear relationship was observed between combustion temperature and the reactor temperature as expressed in Equation 2.8.
𝑇𝑝 = 1.4442(𝑇𝑐) − 876.69 Equation 2.8
Process Stream Energy in (MJ) Energy Out (MJ)
DR-3 (Dry Biomass) 44490 –
Fluidizing Gas (Heat) 1135 –
Quench Liquid 1210 –
Bio-oil – 30,903
Quench Effluent – 1210
Char – 8600
Heat removed – 8542

Figure 2.6 Effect of combustor temperature on NCG concentration.
Figure 2.7 Effect of combustor temperature on bio-oil yield.

Figure 2.8 Effect of combustor temperature on char yield
The results from the model developed in this study were benchmarked against the simulation work done by [31] and yield claims of Dynamotive (1999)[38].
Simulation [31] [31; 38] NCG yield (%) 15 47 10–20 Bio-oil yield (%) 69 43 60–75 Char yield (%) 16 10 15–25 Dried wood moisture content (%) 10 7 <10 Bio-oil Relative Yield (%) 0.92 0.57

3.0 SECOND YEAR WORK In second year work, improvements were made on the fast pyrolysis process by the inclusion of chemical reaction kinetics of pyrolysis reactions. Furthermore, more emphasis is made on the detailed process modelling of each process equipment. The fast pyrolysis reactor model is developed based on rate based multi-step chemical reactions [39] using Aspen Plus® process simulator and validated with experimental results reported by Wang et al.[40]. Auxiliary processes consisting of grinding, screening, drying, combustion, bio-oil collection system and power generation are modelled based on design specifications with the appropriate thermodynamic property methods. The hydrotreating process is modelled based on a pseudo first order reaction kinetic lumped model over Pt/Al2O3 catalysts [14]. Based on validated process models, the effect of process input parameters on the process and economic performance are explored.
Process Description The overall process of transportation fuel production from biomass is divided into eight main processing units described by the generalised process flow diagram in Figure 3.1.
Figure 3.1 Generalised process flow diagram
In the feed pre-treatment processing (A100), the feed undergoes grinding and drying operations to meet the minimum pyrolysis reactor feed requirement of 2mm diameter and 10% moisture content. Next, it is passed on to fast pyrolysis fluidized bed reactor (A200)
Biomass (A100)  Pretreatment
(A200)        Fast Pyrolysis
(A300)      Solid Removal

(A500)  Combustion

(A400)         Bio-oil Recovery

Exhaust
NCG
(A600)   Power Generation

Process heat and fluidization gas
Char
(A700)        Bio-oil Upgrading

(A800)  Hydrogen generation

Gasoline /Diesel

where the biomass feed is thermochemically converted in the absence of oxygen into non- condensable gases(NCG), hot pyrolysis vapours and char at a temperature range of 450 – 500°C. The product from the reactor is fed into the solid removal section (A300), where the char is separated from the pyrolysis vapour before bio-oil is to be condensed. The condensation of pyrolysis vapours is done (A400) by quenching it into liquid in the bio-oil recovery section, which contains vapour quenching process units that separate the desired product (bio-oil) from non-condensable gases. NCG and char separated from bio-oil, are then combusted to generate the energy (hot flue gas) required for biomass drying and fast pyrolysis processes in the combustion section (A500). The residual heat from combustion, if any, is used to generate the high pressure steam for power generation (A600). The bio-oil is upgraded into gasoline and diesel fraction products in the upgrading section containing hydrotreating and hydrocracking processes (A700). Hydrogen required for hydroprocessing is generated in the hydrogen generation section (A800).
Model Development The main model assumptions used in this study are presented in Table 3.1/
Table 3.1 Process assumptions
Process Section  Process Assumption
Bio- oil production and power generation
Pretreatment(A100) Biomass size as received is 20mm with 25% initial moisture content.  Fast Pyrolysis(A200) Process heat supplied by NCG and char combustion  Solid Removal(A300) Solid products are separated from the hot vapours stream by high efficiency cyclones at 95% separation efficiency.  Bio-oil Recovery(A400) A direct contact spray tower used for rapid quenching of bio- vapours to 49°C using previously stored bio-oil as quench liquid.  Combustion(A500) Char is combusted  in 60% theoretical air to obtain 1269°C to prevent ash melting at adiabatic flame temperature up to 1700°C Power Generation (A600) Steam Rankine cycle with an isentropic efficiency of 75% and turbine efficiency of 85%.
Bio-oil
upgrading
and
Hydrogen
production
Bio-oil upgrading (A700) 2 stage hydrotreating reactions over Pt/Al2O3/SiO2 catalysts.
Hydrogen Generation(A800)
Hydrogen generated from the reforming of bio-oil aqueous phase and supplementary natural gas.
The comprehensive process flow diagrams for bio-oil production and electric power generation (A100-A600) and bio-oil upgrading (A700 – A800) is shown in Figures 2 and 3 respectively.

Pretreatment section (A100) The wet pine wood stream (CHR-1) supplied at 20mm diameter is fed into a multiple roll crusher (CHR) in which the particle size is reduced to 2mm and followed by a screen (SCRN) for particle separation. The exiting wet biomass stream (CHR-2) with initial moisture content of 25% is then fed into a rotary dryer (DRYER) at operating temperature of 300°C to reduce its moisture content. A rotary dryer was adopted in the model due to its flexibility in operation, low maintenance costs and high operating temperature range [41]. The energy required for drying is supplied by a fraction of flue gas (DYR-FLS) from the combustor (CB-BUR) which exits the dryer as a mixture of hot air and water vapour (DR-4), while the dried pine wood exits the dryer with 10% moisture content (DR-3). The dried biomass feed then goes into the fluidised bed reactor.
Pyrolysis section (A200) Three model blocks (PYR-DEC, PYR-FLD and PYR-RXN) were used to model a bubbling fluidised bed pyrolysis reactor. In the yield reactor (PYR-DEC), biomass is fragmented into its subcomponents (cellulose, hemicellulose and lignin). The fluidised bed (PYR-FLD) is used to model reactor fluid dynamics with specified bed pressure drop of 150 mbar and inert sand bed mass ratio to biomass particle at 1:1.25, and reactor  temperature of  500°C by varying the fluidizing gas flowrate comprising on inert nitrogen gas (FLGAS-1). The transport disengagement height in fluidized bed is calculated using Fournol et al.[42] empirical correlation for FCC powders with particles classified as Geldart B particles. The process heat and fluidizing gas for the fluid bed is supplied at 863°C with a 1:1 mass ratio to biomass feed. The rate based chemical reactions of each biomass subcomponent was modelled inside the CSTR (PYR-RXN) using multi-step reactions kinetics of biomass pyrolysis developed by Ranzi et al. [39]. The bio-oil vapour residence time is specified at 2s. The reactor products composing of a mixture of hot volatile vapours, gas and char is sent into a cyclone (SP-CYC) to separate the solid char (PYR-SD).
Products separation and recovery (A300-A400) Char and unreacted biomass (PYR-SD) are separated from the hot vapour and gas stream (PYR-VAP) in a cyclone (PYR-CYC) at 95% separation efficiency, and the separated solids are subsequently fed into the combustor. The remaining stream of hot vapour and gas (PYR- VAP) at 500°C goes into a spray tower (QUENCH), where the hot vapours are quenched to 49°C using previously stored bio-oil liquid at 25°C (QC-LIQ) as the quench liquid with a mass ratio of 10:1 to the hot vapour  stream. The spray tower is modelled using Non-random two-

liquid activity coefficient model with Nothnagel equation of state as vapour phase model (NRTL-NTH). The non-condensable gas (QC-GAS) then goes into a high pressure vapour- liquid separator (DEMISTER) operated at 10 bar to collect the bio-oil vapours entrained as aerosol particles. An electrostatic precipitator (ESP) could be used instead but this was avoided due to its very high equipment cost [9]. The resultant dry non-condensable gas goes to a combustor along with char while the quenched bio-oil is sent for further upgrading.
Combustion section (A500) The combustion section is modelled by a yield reactor (CB-DEC) and a Gibb’s reactor (CB- BUR). Unreacted biomass separated from the cyclone goes into the yield reactor (CB-DEC) to decompose into its constituent elements before it is fed into along with char (assumed to be 100% carbon in elemental constitution) and non-condensable gas (NCG) into the Gibb’s reactor (CB-BUR), which calculates multi-phase chemical equilibrium by minimizing Gibb’s free energy and was modelled using Peng-Robinson-Boston-Mathias (PR-BM) equation of state. The fuel mixture of solids and NCG are combusted in 60% theoretical air at combustion temperature of 1269°C in order to mitigate ash melting and prevent material failure at severe temperatures, although a maximum temperature of 1700°C can be achieved at complete combustion. Ash is separated from the resultant combustion gases by hot cyclone (ASH- SEP). The resultant flue gas (FL-GAS) is sent into a splitter (GAS-SPLIT), where it is divided into two streams: (PYR-FLGS) and (DRY-FLGS). These are supplying heat for the feed nitrogen gas, which goes to the fluidized bed pyrolysis reactor and for the feed air, which goes to dryer via two-steam heat exchangers. The residual flue gas heat at 800°C is used for superheated steam generation for subsequent electric power generation.
Power generation (A600) The residual heat generated in the combustion process is exchanged with water in a the two- stream heat exchanger to generate superheated steam at 450°C and 50bar with an outlet flue gas temperature at 90°C. The superheated steam is supplied to a steam turbine (TURB), modelled at 75% isentropic efficiency and turbine efficiency of 85% to generate electricity (P3).
Bio-oil upgrading (A700) Bio-oil product (BIO-OIL) is hydrotreated in a two-stage hydrotreating process over Pt/Al2O3 catalyst due to increased aromatic yield compared with conventional catalysts such as sulfided CoMo/Al2O3 and sulfided Ni-Mo/Al2O3 [14]. Two hydrotreaters were considered and

modelled by two CSTRs (HDO1 and HDO2) using a pseudo first order reaction kinetic model of lumped species based on  previously reported study [14]. A yield reactor was introduced afore the hydrotreaters to lump bio-oil into five pseudo-components namely light non-volatile; heavy non-volatile; phenolics; aromatics + alkanes; Coke + H2O + outlet gases. Since all chemical compounds in the bio-oil are primarily composed of carbon, hydrogen and oxygen, the pseudo components are grouped solely based on their molecular weights. The lumped bio-oil species goes into the first hydrotreater (HDO-1) operating at mild conditions 270°C and 87bar and then fed into the second hydrotreating unit (HDO-2) under more severe operating temperature 383 °C and 87bar in a hydrogen-rich environment of about 5 wt. % [43]. The weight hourly space velocity (WHSV) for the reactors is specified as 2 hr-1. The hydrotreater product (HO-P) is sent into a flash drum (F-DRUM) operated at 40°C and 20 bar to separate hydrotreater gas (HO-VP) from hydrotreated oil (HO-LQ). Hydrotreated oil goes into a phase separator to separate the polar phase from the non-polar phase; with the former going into a reformer to generate hydrogen and the latter fed to a hydrocracker (HYD-CYC) to obtain gasoline and diesel range fuels. Due to lack of adequate knowledge of bio-oil hydrocracking reaction kinetics, a yield reactor was adopted for modelling the unit, and yields are specified based on hydrocracking product oil composition from the work conducted by Elliot et al [15]. The hydrocrackates are finally separated into gasoline and diesel products in a naphtha splitter (SPLITTER).
Hydrogen production (A800) The aqueous phase reforming unit entails two reactors: a pre-reformer modelled with a yield reactor (PRFM) and an aqueous phase reformer (APR) represented by a Gibbs reactor based on UOP bio-oil aqueous reforming process scheme [43]. The pre-reformer is operated at 260°C to generate synthesis gas, which is subsequently fed to the aqueous reformer along with supplementary natural gas to undergo equilibrium reforming reactions with superheated steam supplied at 500°C. The aqueous reformer modelled by a Gibb’s reactor Peng- Robinson-Boston-Mathias (PR-BM) thermodynamic property method calculates the reforming phase and chemical equilibrium reactions by minimizes Gibbs free energy with the products specified as CO, H2, CO2 and H2O. The target hydrogen product flowrate is specified by varying the flowrate of superheated steam required in the reformer using a design specification block. The product from the aqueous reformer goes into a flash drum where the gas mixture is separated from water and then the gas mixture is sent to a pressure swing adsorption (PSA) unit which separates hydrogen from the gas mixture.

Figure 3.2 Fast pyrolysis process flowsheet (A100- A600)

Figure 3.3 Bio-oil hydroprocessing and hydrogen production (A700 – A800

Process Economics Equipment cost estimation and sizing is conducted in Aspen Process Economic Analyser® V8.2 (APEA) based on Q1. 2013 cost data. APEA maps unit operations from Aspen Plus® flowsheet to equipment cost models, which in turn size them based on relevant design codes, and estimate the Purchased Equipment Costs (CPE), and Total Direct Costs (CTDC) based on vendor quotes. The cost of the equipment that cannot be estimated from APEA, are estimated from the cost equation below using costs from Wright et al. [31] as the basis for estimation.
𝐶1  = 𝐶𝒐  ∗ (
𝑺𝟏 𝑺𝒐
)
𝑛
Equation 3.1
Where C1 is the new estimated cost with S1 capacity, Co is the initial equipment cost with S0 capacity and n is the scaling factor typically 0.6. The hypothetical plant is situated in North-Western England, hence material costs and wage rates from the UK are applied, and costs are given in Pound Sterling. The capital investment estimation methodology adopted in this study for the nth plant scenario is illustrated in Figure 3.4. Total Indirect Cost (CTIDC), which includes design and engineering costs, and contractor’s fees, is taken as 20% of CPE.  Project Contingency (PC) is taken as 20% of the sum of Total Direct and Indirect Costs. Total Fixed Capital Investment (CTFCI) is estimated from the sum of CTDC, CTIDC and PC, and Total Capital Investment (CTCI) is estimated from the summation of working capital (5% of CTFCI) and CTFCI.
Figure 3.4 Capital investment estimation methodology

Total operating cost is also estimated from APEA, considering various costs including operating labour cost, raw material cost, hydroprocessing catalyst cost, reformer catalyst cost, PSA packing, ash disposal cost, maintenance cost, utilities cost, operating charges, capital charges, plant overhead, and general and administration (G & A) costs. For discounted cast flow (DCF) analysis, the following investment parameters are assumed:  tax rate of 40%; required rate of return (RRR) 10% and 20 years project economic life. The main economic inputs and assumptions adopted for economic analysis are presented in Table 3.2.
Table 3.2 Cost inputs and assumptions
Parameter Value Parameter Value
Pine wood cost (£/ton)[44]  90 Annual RRR (%) 10 5 wt.% Pt/Al2O3 catalyst cost (£/kg)[45] 4,500 Project Contingency (%) 20 Ash disposal cost (£/ton)[31] 0.11 Project economic life (year) 20 Supplementary natural gas (£/GJ) 3.59 Working Capital (%) 5 Electricity price ((£/kWh)[44] 0.15 Depreciation method Straight Line PSA  operating cost (£/ton) 21 Plant Overhead (%)  50 50
Model Inputs The model inputs including proximate analysis of pine wood and biomass subcomponent composition are shown in Table 3.3. Multi-step reaction kinetics of biomass pyrolysis as shown in Figure 3.5 was implemented in this work. Bio-oil hydrotreating reaction kinetics was implemented by lumping approach of bio-oil components, which is shown in Figure 3.6. The kinetic parameters for biomass pyrolysis and bio-oil hydrotreating reactions are given in Tables 3.3 and 3.4 respectively.
Table 3.3 Proximate and chemical composition of pine wood [46]
Proximate Analysis
wt.%
Subcomponent Composition
wt.%  Moisture content 25 Cellulose  42 Fixed Carbon 20 Hemicellulose  23 Volatile Matter 55 Lignin 24 Ash 0.7 Water 10

Figure 3.5 Multi-step reaction kinetics of biomass pyrolysis [39]
Table 3.4 Pyrolysis chemical reactions [39] Reaction A(s-1) E (kj/mol) 1 Cell → CellA 8 x 1013 192.5 2 Cell → 5H2O + 6 Char 8 x 107 125.5 3 CellA → Levoglucosan 4T 41.8 4 CellA → 0.95HAA +0.25Glyoxal +0.2Acetaldehyde+0.25HMFU+ 0.2Acetone+0.16CO + 0.23CO+0.9H O+0.1CH +0.61Char 1 x 109 133.9 5 HCell → 0.4HCell1 +0.6 HCell2 1 x 1010 12.9.7 6 HCell → 0.75H2 +0.8CO2 +1.4CO + 0.5Formaldehyde 3 x 109 113 7 HCell1→ Xylan 3T 46 8 HCell2 →CO2 + 0.5CH4 +0.25 C2H4 + 0.8CO + 0.8H +0.7Formaldehyde+0.25 Methanol +0.125Ethanol + 1 x 1010 138.1 9 LigC →0.35LigCC + 0.1pCourmaryl + 0.08Phenol + 0.14C2H4 + H O + 0.495CH + 0.32CO + CO+ H + 5.735Char 4 x 1015 202.9 10 LigH → LigOH +Acetone 2 x 1013 156.9 11 LigO →LigOH +CO2 1 x 109 106.7 12 LigCC →0.3pCoumaryl + 0.2Phenol + 0.35Acrylic  + 0.7H2O + 0.65CH + 0.6C H + 1.8CO + H + 6.4Char 5 x 106 131.8 13 LigOH → Lig + H2O + Methanol + 0.45CH4 + 0.2C2H4 + 2CO + 0.7H + 4.15Char 3x 108 125.5 14 Lig → Lumped Phenol 8T 50.2 15 Lig → H2O +2CO+0.2Formaldehyde +0.4Methanol +0.2Acetaldehyde +0.2 Acetone+0.6CH + 0.65C H + 0.5H + 1.2x 109 125.5

Figure 3.6 Reaction kinetic model of lumped bio-oil species
Table 3.5 Bio-oil hydrotreating reactions [21]
Reaction A(s-1) E (kj/mol) 1 Heavy non-volatiles → Light non-volatile 6.40 x 10 78 2 Heavy non-volatiles → [Alkanes + Aromatics] 1.26 x 103 91.8 3 Light non-volatiles → Phenolics 1.38 x 102 80.6 4 Phenolics → [Alkanes  + Aromatics] 1.58 x 10 62.3 5 [Alkanes + Aromatics] → [Coke + Water + Gases] 7.75 x 10 75
4.0 RESULTS AND DISCUSSION
Model Validation Experimental work by Wang et al.[40] on a fluidized bed pyrolysis reactor using pine wood is used to validate the fast pyrolysis reactor model developed in Aspen Plus®. The fast pyrolysis reactor model result and experiment data at 500°C reactor temperature are presented in Table 4.1, which indicate that the gas, bio-oil and char yields of the model agree with reported experimental data, and is consistent with pyrolysis product distribution reported in literature[7-9].  Furthermore, the comparison between fast pyrolysis reactor model prediction and experimental measurements of pyrolysis products as function of reaction temperature is depicted in Figure 4.1.

Table 4.1Pyrolysis model validation with experimental measurements at 500 °C
Pyrolysis products Model (wt. %)
Experiment[40] (wt. %)
Relative error (%)
Gas 21 21.5 2.33% Bio-oil 65 64 1.56% Char 14 14.5 3.45%
Figure 4.1 Aspen plus simulation results vs. experimental data from [20] as a function of reactor temperature
It is found that pyrolysis reaction model results agree considerably with experimental data, particularly between 475 and 550°C which is the typical temperature range at which bio-oil yield is highest. The hydrotreating reactor model result was validated with experimental work by Sheu et al. [14] at 400°C reaction temperature, 87.2 bar pressure and WHSV of 2hr-1 over Pt/Al2O3 catalyst as shown in Table 4.2.

Table 4.2 Hydrotreated bio-oil results validated with experimental measurements
Lumped bio-oil components
HT Model (wt. %)
Experiment [14] (wt. %).
Relative error (%) Heavy nonvolatiles 22.94 24.57 6.64% Light nonvolatiles 29.83 29.41 1.41% Phenolics 10.55 10.63 0.76% [Aromatics +Alkanes] 19.82 19.52 4.33% Gases + H2O + Coke 16.86 15.87 5.40%
It can be seen from the above table that hydrotreating model results are in adequate agreement with experimental data. The summary of simulation results from the validated model is presented in the in Table 4.3
Table 4.3 Stream summary of whole process
Component (wt. %) Dried Biomass Dryer Exhaust NCG Bio-oil Char Fuel
Nitrogen – 73.45 80.90 0.03 – – Oxygen – 21.94 – – – – Hydrogen – – 0.32 0.00 – – Methane – – 1.71 0.00 – – Ethylene – – 1.63 0.01 – – Carbon monoxide – – 6.22 0.00 – – Carbon dioxide – – 6.20 0.03 – – Water – 4.61 0.19 20.55 – – Levoglucosan – – – 48.23 – – HAA – – 0.00 3.29 – – Glyoxal – – 0.20 0.45 – – Acetaldehyde – – 0.29 0.03 – – HMFU – – – 1.82 – – Acetone – – 0.84 0.47 – – Acrylic – – 0.00 0.01 – – Xylan – – – 0.36 – – Formaldehyde – – 1.35 2.06 – – Phenol – – 0.00 0.74 – – Methanol – – 0.00 2.71 – – Ethanol – – 0.16 1.12 – – pCoumaryl – – 0.00 1.48 – – L-Phenol – – 0.00 1.37 – – Naphthenes – – – – – 70.00 Aromatic – – – – – 12.00 n/i-Alkanes – – – – – 18.00

Cellulose – – – – 27.43 – Hemicellulose – – – 2.15 5.39 – Lignin Derivatives – – – 12.43 1.28 – Biomass 100 – – – – – Char – – 0.00 0.65 60.14 – Ash – – 0.00 0.00 5.76 – Total Mass flow (kg/hr) 2,489 10,833 3,090 1,597 303 503
The moisture content of the biomass feed after undergoing drying operation is reduced to 10% with the remainder purged as dryer exhaust with 499 kg/hr as water vapour. The product yield for non-condensable gas, bio-oil and char produced from the process is 12 wt. %, 66 wt. % and 22 wt. % respectively. These values are comparable to previously published studies [7-9]. The amount of water in the bio-oil product is 20 wt. %, which is 31% more than the moisture remaining in the biomass after drying. The increase in moisture content in the bio-oil product can be attributed to the water generated during pyrolysis reactions. The combustible non-condensable gases produced mainly consist of H2, CH4, C2H4, CO and trace amounts of light volatile organic alcohols and aldehydes which collectively account for 64 wt. % while CO2 make up 35 wt. % of the gas. Residual solids from the pyrolysis process mainly consist of char (100% carbon) and unreacted biomass. The hydrotreated bio-oil generates 31 wt. % long chained aromatics, phenolics and aliphatic compounds which are subsequently hydrocracked into smaller hydrocarbon molecules.
Energy Efficiency In order to estimate the energy efficiency effectively, the whole process is divided into two main sub-processes: biomass pyrolysis process (drying, fast pyrolysis and electric power generation) and bio-oil upgrading process (hydrotreating, hydrocracking and aqueous reforming).
Energy efficiency of fast pyrolysis process The total energy input (EB) into the biomass pyrolysis process is estimated from the energy content in pine wood of 25 wt.% wet basis in terms of its calorific value [44] and mass flow rate, which is  about 11.32MW.  The electricity input requirement (Winput) required for dryer air blower, pyrolysis air blower, compressors and bio-oil pumps is 0.08MW. The energy content (EBO) of fast pyrolysis bio-oil in terms of its HHVbio-oil [9]

and mass flow rate, is estimated to be 7.56MW. Furthermore, the amount of 0.24MW of electric power is generated from the steam cycle (WHE).
The efficiency of fast pyrolysis without electricity generation, ήp, is determined as:
𝐸𝐵𝑂  𝐸𝐵+ 𝑊𝐼𝑛𝑝𝑢𝑡
= 66.3%
Next, the net electrical efficiency ήel is determined as:
𝑊𝐻𝐸 𝐸𝐵+ 𝑊𝐼𝑛𝑝𝑢𝑡
= 2.1%
The overall energy efficiency of the fast pyrolysis process with electric power generation, ήpel, is determined as:  ήp + ήel = 68.4%
The energy efficiency of the process without electric power generation is 66.3% which increased by 2.1% when a steam cycle is integrated with the fast pyrolysis process to generate electricity. However, the marginal increase in efficiency as a result of power generation may not be sufficient to justify the additional investment in power generation equipment.
Energy efficiency of bio-oil upgrading process Energy content (EBo) in pyrolysis bio-oil is 7.56MW and energy content of supplementary natural gas (EN.G) fed to the aqueous reformer is 0.35MW. The electricity input requirement (Winput) required for upgrading process pumps and compressors is 0.1MW. The energy content (EFuel) of the product biofuel is 6MW. Thus, local energy efficiency of the bio-oil upgrading process is 75%, and the overall energy efficiency of the process of converting biomass into biofuel products and electric power is 52.8%.
Effect of Feed Composition Various biomass feeds were compared with pine wood to examine the effect of feed composition on fast pyrolysis products and biofuel yields. The composition of various biomasses used in the comparative simulation is shown in Table 4.3. The effect of the biomass composition on fast pyrolysis products and biofuel yield is presented in Figure 4.2.

Table 4.3 Composition of various biomasses [46]
Component Pine wood Switch grass Poplar Pine bark
Cellulose  0.42 0.36 0.47 0.22
Hemicellulose 0.23 0.31 0.22 0.23
Lignin 0.24 0.18 0.20 0.47
Water 0.10 0.10 0.10 0.06
Ash 0.01 0.05 0.01 0.02
Figure 4.2 Fast pyrolysis products and biofuel yield from various biomasses
It was observed that poplar wood has the highest bio-oil yield at 68 wt. % while pine bark has the lowest bio-oil yield at 57 wt. %. This could be attributed due to fact that the composition of cellulose in polar wood is higher than that in pine bark, which in turn results in significant variation in the amount of fuel produced from each biomass as the highest fuel yield is observed for poplar at 31 wt. % of bio-oil produced, and the lowest fuel yield observed for pine bark at 27 wt. % of bio-oil produced. The non- condensable gas yield follow an opposite pattern with the highest yield at 25 wt. % for pine bark and lowest yield of 18 wt. % for poplar. Also, the highest char yield is

obtained from pine bark at 18 wt. % and the lowest char yield was observed for poplar at 13.wt%.
The amount of electricity generated from each biomass was also investigated, and is depicted in Figure 9. The highest electricity of 0.38MW is generated from pine bark while the lowest electricity of 0.21MW is generated from poplar. Thus verifying the hypothesis that lignin is the main precursor of char formation for combustion, which is higher in pine bark.
Figure 4.3 Electric power generated from various biomass
Effect of Initial Biomass Moisture Content The initial moisture content in biomass has no significant effect on product yields as it is reduced to 10% prior to its entry into the pyrolysis reactor but it has an effect on the amount of combustor flue gas available for electric power generation. The impact of the initial moisture content in the biomass feed on the amount of power generated from the process is explored, by varying moisture content between 20 and 30 wt. %. As expected the higher the initial moisture content in the biomass more energy is required to reduce its moisture content to 10% moisture content requirement in the pyrolysis reactor. The effect of the initial moisture content in biomass on the amount of heat available for power generation is depicted in Figure 4.4, implying that the initial moisture content of the biomass has an effect on the overall efficiency of the process.

Figure 4.4 Effect of initial moisture content in biomass on power generated in the process
Economic Analysis
Economic results Total Capital Investment (CTCI) for the 72MT/day pine wood fast pyrolysis, bio-oil upgrading and hydrogen production plant is estimated at £16.6 million, which accrues from the summation total direct cost (CTDC), indirect cost (CTIDC), project contingency at 20% of total direct and indirect costs, and working capital. The percentage of contribution to CTCI from the two main sub-processes including the fast pyrolysis and bio-oil upgrading is presented in Figure 4.5. The result indicates that the upgrading process accounts for 61% of Total Capital Investment at £10 million, while the pyrolysis accounts for the remaining 39% at £6.6 million.

Figure 4.5 Total capital investment of the two main sub-processes
The proportion of CTCI for various process units in fast pyrolysis process is illustrated in Figure 4.6.  Result indicates that the pyrolysis and pre-treatment sections account for most of the capital investment required for the fast pyrolysis process, which are about 2.48 and 2.08 £MM respectively while char separation and combustion contribute the lowest to CTCi in the fast pyrolysis sub-process i.e. 0.07 and 0.26 £MM respectively.
Figure 4.6 Total capital investment distribution of pyrolysis plant

The result of the economic analysis is presented in Table 4.4. Annual operating cost for the plant is estimated at £6.4 million which accounts for operating labour cost, maintenance cost, and supervision cost, utilities cost and raw material cost. In addition, catalysts replacement cost of £7.6 million is applied in the first and tenth production years assuming a 10 year catalyst lifespan. Hydrocarbon (gasoline and diesel) fuel yield for the plant is 1.8 million gallon per year and electric power generated per annum is 2.01 GWh. Income is generated from the sales of hydrocarbon fuels and excess electricity produced. Electricity price is assumed at £0.15/kWh based on average market rates [44].  The fuel product value (PV) is obtained at zero Net Present Value (NPV) based on a 10% discount rate. Product value for this plant is observed at £6.69 per GGE when the NPV is zero.
Table 4.4 Economic results
Parameter Value
Plant Size MT/day) 72
Total Capital Investment (£ MM) 16.6
Annual Operating Cost (£ MM) 6.4
Fuel Yield (MMGGE/Year) 1.8
Product Value (£ /GGE) 6.69
Sensitivity analysis To evaluate the effect of changes in process parameters on the project viability, a sensitivity analysis was conducted over a ±20% range by varying fuel yield, operating cost, electricity generated and capital investment as shown in Figures 4.7 and 4.8. Sensitivity analysis indicates that the product value (PV) shows the highest sensitivity to variation in fuel yield; 10% and 20% increase in fuel yield result in 9% and 17% decrease in product value respectively, showing a positive impact on the project economic viability. Conversely, 10% and 20% decrease in fuel yield result in 11% and 25% increase in the product value of the project, which in other words signifies a negative impact on the economic viability of the project. Operating cost shows the second highest sensitivity to PV with 10% and 20% increase in operating cost resulting in 7% and 15% increase in PV respectively and vice versa. 10% and 20% increase in tax result in 7.34% and 7.66% increase in PV respectively.

Figure 4.7 Percentage difference in fuel product value over a ± 20% change (increase/decrease) in process and economic parameters
Figure 4.8 Fuel product value sensitivity to process and economic parameters
Conversely, a decrease in tax shows a more rapid impact on PV; 10% and 20% decrease in tax result in 6.40% and 12.06% decrease in PV respectively.
Variation in capital investment indicates a relatively marginal impact on PV, with 10% and 20% increase in capital investment resulting in 1.4% and 3% increase in PV respectively and vice versa. Electricity generation indicated the lowest sensitivity to

the PV, with 10% and 20% increase in electricity generated yielding minimal 0.48% and 0.90% decrease in PV respectively and vice versa.
Conclusions A high fidelity process model of a 72 MT/day pine wood fast pyrolysis and bio-oil upgrading plant was built in Aspen Plus® and validated with experimental data from literature. Major conclusions drawn from this study are as follows:
Simulation results indicate an overall energy efficiency of 52.8% for an integrated plant while the local energy efficiency of biomass fast pyrolysis process with and without electric power generation indicates 66.3% and 68.4% respectively. The combustion of biomass fast pyrolysis by-products (char and NCG) provides the energy required for both drying and the fast pyrolysis processes with sufficient residual energy to generate electric power.
The inclusion of power generation equipment increase the total capital investment  of the pyrolysis process by 16% whilst generating only 0.24MW which contributes  2.1% increase to energy efficiency, hence it doesn’t justify additional capital investment in power generation equipment; nevertheless the amount of energy available for power generation is highly dependent of the amount of moisture in the biomass.
The amount of moisture in the biomass has an effect of the overall energy efficiency of the process; thus a prior dried biomass is more suitable to increase the overall energy efficiency of the process. Also, process heat integration can be further explored to increase process energy efficiency by identifying possible areas of energy savings for optimised energy use.
Economic analysis indicates that gasoline and diesel products can be produced from biomass fast pyrolysis and bio-oil upgrading at a product value of £6.69/GGE and require total capital investment and annual operating costs of £16.6 million and £6.4 million respectively based on Q1. 2013 cost year over a 20 year project cycle and a 10% annual discount rate.
The bio-oil upgrading process contributes about 61% to total capital investment while fast pyrolysis accounts for the remaining 39%; thus further equipment optimization may be required to minimize capital cost in the upgrading process.

Sensitivity analysis of process parameters indicates that the fuel product value is highly susceptible to changes in fuel yield, operating cost and tax while capital investment and electric power generated shows a minimal impact on product value. Since, catalyst development for upgrading bio-oil is being researched extensively, new advances in low cost catalysts to improve fuel yield will reduce the cost of production significantly. Furthermore, tax breaks from government will have a significant impact on the process commercial viability and outlook.
5.0  FUTURE WORK PLAN Future work in this research is discussed in the subsequent subsections:
 The development, validation and analysis of bio-oil catalytic cracking process model.  Identification of heat integration opportunities and optimization of energy usage by developing heat exchange network designs to reduce utility load and cost.  Economic analyses of base and integrated upgrading processes and the evaluation of the impact of key process and economic parameters on process economics and financial outlook.

Gantt chart for future work
2014 2015
Task
Jan- Mar
Apr- Jun
Jul- Sep
Oct- Dec
Jan- Mar
Apr- Jun
Jul- Sep
Oct- Dec
Literature Review
Task 1 Conceptual model development and simulation- (Pre-treatment and fast pyrolysis process).
Review of past techno-economic studies.
Write-up, revision and submission for 9 months review.
Task 2 Model development and simulation of proposed bio-oil upgrading routes- (Hydrodeoxygenation process and Zeolite cracking)
Task 3 Model development and simulation of the process integration of different upgrading pathways.

Write-up, Revision and submission for 21 months review.                 Task 4: Model analyses of Tasks 2 and 3 and respective economic analyses.
Analyse economic sensitivities to identify various cost drivers and feasibility for scale–up application.
Thesis write-up, revision and submission.

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