Production of syngas via gasification using optimum blends of biomass

Journal of Cleaner Production 242 (2020) 118499

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Production of syngas via gasification using optimum blends of biomass Ahmed AlNouss a, b, Gordon McKay b, Tareq Al-Ansari b, * a b

Department of Chemical Engineering, College of Engineering, Qatar University, Doha, Qatar Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 September 2018 Received in revised form 30 August 2019 Accepted 18 September 2019 Available online 19 September 2019

Considering changes in the global climate, there is an impetus to diversify away from fossil fuels as part of efforts to reduce greenhouse gas emissions. Biomass, as a source of energy, has the potential to generate sustainable power and fuels and contribute towards a cleaner future. In fact, the utilisation of biomass as a carbon dioxide neutral organic source in an integrated system generates valuable products, and reduces waste and the consumption of non-renewable resources. Gasification, the preferred option for converting biomass to combustible gas, provides higher electrical efficiencies than combustion, whereby the syngas generated from the gasification process can be utilised to generate clean energy. In addition, syngas can be utilised for the production of ammonia and methanol thus reducing their respective dependencies on natural gas. This study will detail an optimised biomass gasification process considering multiple parameters, including the thermodynamic operating conditions, the type of gasifier (gasifying agent) and feedstock. Fundamentally, this study considers the process pathways for the recycling of multiple sources of biomass to generate high energy syngas from the available biomass options when used in combination as blends or individually. To achieve this aim, an Aspen Plus simulation model is developed for four different biomass agent-based gasification techniques using the biomass characteristics of certain Qatar biomass materials, which include date pits, manure and sewage sludge. Outcomes of the study included an optimisation of the gasification processes to yield different blending options of the biomass feedstock satisfying the downstream operations of power and fuels production. The results demonstrate the domination of date pits for two of the considered configurations with over 99% w/w date pit feed composition. Moreover, the sensitivity analysis conducted on the different configurations highlighted specific optimum operation points in terms of temperature, pressure, and oxygen and steam feed ratios. The hydrogen content in the generated syngas, considered important for the downstream production, yields a peak at approximately 850
C and 1 bar with a modified equivalence ratio of approximately 2.5, and a ratio of oxygen supplied by an air-steam combination of approximately 0.6. The process can be further optimised by considering trade-offs between product purity or yield, profit, operating efficiency, quality of raw materials blends, and carbon footprint. © 2019 Elsevier Ltd. All rights reserved.

Handling Editor: Prof. S Alwi Keywords: Biomass gasification Simulation Sensitivity analysis Optimisation Biomass blending Gasification performance

1. Introduction The continuous global demand for energy in the light of climate change has fundamentally influenced energy planning. In this regard, there is a drive for a wider utilisation of renewable energy sources in order to meet the growing energy demand and to reduce the reliance on traditional fossil fuels. The combustion of fossil fuels results in greenhouse gas (GHG) emissions that contribute to

* Corresponding author. E-mail address: [email protected] (T. Al-Ansari). https://doi.org/10.1016/j.jclepro.2019.118499 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

climate change and global warming. These emissions are expected to increase from the present 160 megatons of carbon (MtC) to approximately 640 MtC by 2100. As energy security remains a priority amongst nations, the development of alternative solutions to conventional fossil-based power generation is of fundamental importance. Biomass, an energy source has the potential to generate sustainable power and fuels, while reducing the carbon emissions by approximately 20% (Daioglou et al., 2014). The efficient utilisation of biomass as a renewable energy source forms a challenge due to the low combustion efficiency of the traditional biomass-based combustion technologies (Doherty et al., 2013).

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However, there have been recent advancements in biomass gasification, a promising thermochemical conversion technique, which converts biomass feedstock into a high-energy combustible gas (Sikarwar et al., 2016). Testament to the importance of biomass gasification, Fig. 1 illustrates that the number of publications reported in Scopus concerning biomass gasification has increased by approximately 8 fold between the years 2000 and 2017. The present study will uniquely consider the potential to blend various waste streams available in the urban environment, which in many cases would have be landfilled or incinerated, and to produce optimum pathways for a continuous stream of syngas. In order to account for downstream production requirements, the produced syngas would typically require adjustment through a correction of carbon and hydrogen for instance. By optimising the biomass feedstock blends, it is claimed that the subsequent adjustment requirement would be reduced thus reducing the overall cost of the process (Pala et al., 2017). In addition, the blending of multiple biomass feedstock can add a degree of freedom to the syngas composition and address the challenge of seasonal biomass availability (Inayat, Muddasser et al., 2019a,b). For a Qatar case, this study integrates date palm residues (date pits), sewage sludge, and livestock manure into a process model representing the gasification process and end product utilisation options which include the production of power and fuels. Furthermore, considering the process design, an in-depth analysis is applied to evaluate the influence of key gasification parameters such as pressure, temperature, modified equivalence ratio and ratio of oxygen supplied by air and steam, on the dry syngas compositions, syngas heating content and cold gasification efficiency (CGE). 1.1. Biomass gasification Converting carbon-based biomasses ranging from forestry residues to pet-coke wastes to valuable chemicals and energy through gasification, has various advantages over conventional processes, such as the reduction of environmentally harmful emissions and the ability to produce electricity independently of an external power source (Mahinpey and Gomez, 2016). Natural energy resources such as wind, solar, and hydro are abundantly available, nevertheless their conversion processes lack full adoption because of high capital costs and intermittency (Kumar and Shukla, 2016). Hence, Biomass gasification (BG) can feature in the global energy mix and provide an alternative to existing renewable options

because of its high technology readiness and a relatively smaller capital cost (Ellabban et al., 2014). The BG process can lower the ratio of carbon to hydrogen in the efflux syngas leading to a more favourable H2 fraction and a larger calorific value content (Higman and van der Burgt, 2008). It can result in multiple useful products such as bio-char, bio-fuels, and syngas for the production of heat, power, and fertiliser. The produced syngas, which is mainly a combination of carbon monoxide (CO), carbon dioxide (CO2), hydrogen (H2), and methane (CH4), is a key product of the gasification process. It is an essential intermediate for the generation of power and environmentally-friendly chemicals and fuels (Parthasarathy and Narayanan, 2014). Nevertheless, it can be associated with different types of contaminants such as nitrogen compounds, sulphur compounds, and tars (Abdoulmoumine et al., 2014). The various feedstock materials, gasifying agents, gasifier conditions, and sorbent or catalyst present have their influence on the quality and/or quantity of the different gasification products and their associated contaminants (Farzad et al., 2016). Gasification process feedstock are sourced primarily from lignocellulosic materials, with chemical structures containing lignin, cellulose, and hemicelluloses. These chemical structures are inherent in commonly available wastes such as wood, agricultural waste, municipal solid waste (MSW), sewage treatment waste, and food waste (Kumar and Shukla, 2016). Food waste for instance, has a high-energy content that can be extracted through the gasification process. There is a dual advantage in the reduction of landfilled biomass and also the parallel generation of value-added products from waste derived syngas (Ahmed and Gupta, 2010). Yang et al. (2016) explored the capability of food waste gasification and cogasification with woody biomass in deriving high quality syngas. The results of the fixed bed downdraft gasifier illustrate that utilising a 40:60 food waste to wood waste mixture for a gasification process, produced the highest calorific content syngas in terms of the lower heating value (LHV). Additionally, the study concluded that gasification could mitigate food waste disposal. Simulation has been used to confirm experimental gasification studies, which have considered food waste as a feedstock, such as soya bean, sugarcane, wheat, rapeseed, etc. Yan et al. (2018) built a one dimensional model using Aspen Plus to kinetically simulate the co-gasification of biomass and coal in a DFB. The study examined the effect of key operating parameters, such as biomass blending ratio, feedstock flow rate and initial bed temperature on the produced syngas compositions. The results indicated an enhancement in the

Fig. 1. Uptrend number of publications addressing biomass gasification.

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gasification process with the increase in blending ratio and initial bed temperature. Similarly, Peng et al. (2017) studied the cogasification of coal and biomass utilising a dual circulating fluidised bed (CFB) to experimentally evaluate the catalytic behaviour of three common alkali catalysts during the co-gasification process. The results demonstrated a reduction in the CO2 yield and an increase in the H2 yield with each increase of catalyst mass ratio. Moreover, the addition of coal to biomass improved the overall conversion of the process and increased the cracking of the tars. Hern
andez et al. (2017) explored the optimal blend of organic waste to produce chemicals using biomass wastes mixtures derived from sludge, manure and food waste digested to generate biogas. The study included the price of digestate in the optimum formulation. The results of the fixed generated compositions of methane and carbon dioxide for the different H2 to C ratios, highlighted sludge feedstock as the only source of biomass if the component's price of fertiliser was considered. Whereas, a mixture of 65:35 cattle slurry and urban food waste was determined as the optimal blend if the typical fertiliser composition was applied. Sulaiman et al. (2018) studied the co-gasification of three biomass types that include wood chips, coconut fronds and coconut shells. The study focused on the effect of biomass type, blending ratio, catalyst type and catalyst to biomass loading on the syngas composition and higher heating value (HHV). The highest hydrogen content and HHV approximated at 11.7 vol% and 4.96 MJ/Nm3 was observed at a blending ratio of 70:30 between wood chips and coconut shells. Wood chips demonstrate a potential to replace or blend with coconut wastes with some major geometry changes in the gasifier. In a similar study, Inayat, M. et al., 2019a,b investigated the effect of blending ratio, equivalence ratio (ER), gasification temperature and catalyst loading on the performance of wood-coconut shells cogasification. Among the studied ranges of the different parameters, higher gasification temperature and high presence of coconut shells in the biomass blend demonstrated better syngas quality with high HHV and low tar formation. Reduction in hydrogen content was observed at higher ER with lower tar content, CGE and gas yield. The study concluded that the co-gasification process can be improved sufficiently through the different operating parameters with a trade-off between tar reduction, co-gasification performance and syngas quality. Pala et al. (2017) simulated a biomass gasification model using Aspen Plus based on the minimisation of the Gibbs free energy with a restricted equilibrium methodology. The simulation considered various biomass feedstock including food waste and MSW to examine the changing effect of gasification temperature, shift reaction temperature, and steam to biomass ratio on syngas quality and especially the H2/CO ratio. The optimum ratio was achieved by alternating the hydrogen and CO concentrations based on different biomass feedstock. The results demonstrated that the steam gasification of food waste unlike other feedstock yielded a 2.15 H2/CO ratio that can be used directly in the production of liquid fuels without any requirement for syngas adjustment. Farmers have traditionally used livestock manure as a source of valuable and cost-effective fertiliser for their farms. It improves both soil quality and fertility with the high content of nutrients and organic matter when applied appropriately. However, there is a threshold in the quantity of nutrients that can be productively used on fields. It is essential that alternative uses of manure must be sustainable from economic and environmental perspective. Combined with the global growing demand for renewable energy, an attractive opportunity is presented for manure-based energy. Various technologies; such as gasification, pyrolysis, digestion and composting, can utilise the animal manure as a feedstock to produce heat, power and biochar (KCFE, 2012). Esteves et al. (2019) emphasised the importance of utilising livestock manure to

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produce biogas as an intermediary for the production of fertilisers and power. A systematic review of different studies that have analysed the production cycle of biogas from manure was provided. The study identified the critical points and environmental impacts of the life cycle assessment (LCA) for the production of biogas from manure, and consequently the potential to establish regional sustainability strategies. Naqvi et al. (2016) investigated the off-grid electricity generation from the gasification of dairy waste including wheat straw, rice hulls and cow manure. The levelised cost of electricity (LCOE) indicator was utilised to measure the competitiveness of off-grid electricity generation. The price of offgrid electricity was estimated to be greater than the average governmental electricity tariff. Antoniou et al. (2019) investigated the enhancement of a digestion model through the use of downstream gasification. The digestate, originating from a mixed blend of agricultural wastes of pig manure, cow manure, maize and triticale silages and cereal bran, was experimentally gasified with air in a downdraft fixed-bed reactor. The results demonstrated that a gasification temperature of approximately 850
C increased the syngas yield. Sewage sludge waste is an important resource which can also be utilised as feedstock for gasification. Sewage sludge is essentially the residue of the industrial and municipal wastewater treatment process (Werle, 2016). The disposal and storage of sewage sludge is challenging due to the variety of harmful species such as bacteria, viruses, and heavy metals. Gasification offers a significant potential to address such disposal issues. Calvo et al. (2013) experimentally evaluated the gasification of sewage sludge in a simple fluidised bed gasifier with the adjustment of flow and fuel feed rate to obtain an air-fuel mass ratio of 0.2e0.4. The resulting heat content of the produced gas was approximately 8.4 MJ/m3 at 0
C with a 57% cold gas efficiency and a 70% hot gas efficiency. Champion et al. (2014) established a mathematical model to estimate the syngas production rate, composition and temperature and calibrated it using different lab-scale fluidised-bed sewage sludge gasifiers. The results from the model were in good agreement with the experimental literature data. Ong et al. (2015) conducted an experimental and numerical study to co-gasify woody biomass and sewage sludge. A fixed-bed downdraft gasifier was utilised to perform the experiments and the results demonstrated an average LHV of 4.5 MJ/m3 at 0
C for the syngas using 20 wt% sewage sludge feedstock. The experimental and numerical results were in close proximity with a deviation of less than 10%. Werle (2016) experimentally investigated the gasification of sewage sludge using a fixed bed gasifier. Wide operating ranges were considered where the results demonstrated that: air ratio, gasification agent preheating, and gasification agent compositions have a significant impact on determining the LHV of the produced syngas. Similarly, multiple studies have been developed to examine the effect of manipulating different operating parameters on the quality of the produced syngas and other performance indicators and to capture the different experimental methodologies and their influence on the efficiency of the gasification process. Choi et al. (2015) studied the effect of ash and steam to fuel ratio on hydrogen production and tar removal. Gil-Lalaguna et al. (2014) explored the effect of gasification temperature and composition of the gasification medium on solid yield, tar content, gas production, gasification efficiency, heating value and H2:CO ratio. Nipattummakul et al. (2010) experimentally evaluated the performance of gasification over pyrolysis. Documented progress in gasification based literature includes milestones in the development of gasifier types, gasification processing steps and the general advantages and disadvantages of gasification. Heidenreich and Foscolo (2015) reviewed the concepts in biomass gasification for process integration and combination. Mahinpey and Gomez (2016) reviewed the recent findings on the

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effect of experimental gasification procedure on overall reaction, gasification kinetics and methods of kinetic models evaluation. Sikarwar et al. (2016) assessed the fundamentals of gasification such as the effect of different operating parameters, feedstock types, modelling approaches and tar formation along with the recent advances in biomass gasification. According to the World Bank, the State of Qatar on a per capita basis produces waste at a rate of 1.8 kg per capita per day or 7000 tons per day (Ahmad, 2016). As outlined in the Qatar National Development Strategy 2011e2016, environmental waste management strategies are essential in promoting widespread sustainable waste utilisation systems. Additionally, the current energy portfolio of Qatar is almost entirely dependent on hydrocarbons, namely natural gas, to drive the power generation and petrochemicals industries. Hence, there is a need to diversify the energy mix through the incorporation of renewable energy options in order to reduce the release of GHG emissions and to increase system diversity and resilience. Fulfilling this objective through biomass has the additional advantage of eliminating challenges associated with waste disposal. As such, the aim of this study is two-fold; first, to design a process system that reduces national waste streams, and secondly, to do so whilst generating value added products. The proposed system will utilise waste biomass from the Qatar date palm residues (date pits) in addition to sewage sludge, and livestock manure to generate high-energy producer gas/syngas, which contains mainly H2, CO, CO2 and CH4. The considered biomass sources in this study predominately originate from urban and agricultural sources. The availability of manure is expected to increase significantly as part of the food security programs in Qatar. In addition, with the rapid increase in industries and dates manufactories, sludge and date pits quantities are expected to increase. Considering biomass production forecasts for Qatar, a computer simulation model utilising the advanced techniques for biomass gasification is developed using Aspen Plus. The results of the simulated biomass gasification model demonstrate agreement with the actual plant data. Moreover, outputs of the model, i.e. gasifier syngas compositions, heating value and performance are validated with reported literature. For this study, the developed model determines the optimum blending conditions for existing gasification processes using available biomass resources in Qatar in order to generate high-energy syngas. 1.2. Potential of syngas Synthesis gas (syngas) is an important intermediary for the generation of power, chemicals and fuels. Traditionally, it has been produced from coal, natural gas, or by-products from refineries. It consists primarily of CO and H2 with smaller amounts of CH4 and CO, in some cases. Within the Middle East region and in almost all global refineries, particularly in the growing economies, the H2 rich syngas production capacity continues to increase (Iaquaniello et al., 2012). The choice of a feedstock depends on the availability, the cost of the raw material, and the use of produced syngas downstream. Currently, 50% of the syngas produced mainly from natural gas, coal or refinery by-products, is utilised in the production of ammonia, 25% to hydrogen, and the remainder is converted to methanol, Fisher-Tropsch (FT) products and others (Rauch et al., 2014). Approximately 6 Exajoules (EJ) of syngas is produced annually worldwide, which corresponds to almost 2% of the present worldwide energy consumption (Van der Drift and Boerrigter, 2006). Since syngas serves as an intermediate product for several key processes, its quality and specifically the H2/CO ratios differ depending on the application. For instance, the synthesis of methanol requires the use of a unique formula to determine the

two reactants; CO2 and CO, composition. Whereas, syngas compositions with a molar H2 to CO ratio of about 2.0 are required in the Gas-to-Liquid (GTL) applications utilising the Fischer-Tropsch (FT) technology, where CO2 is not a reaction element. Pure carbon monoxide is needed for the carboxylation process and the specific H2/CO molar ratio is lowered to 1.0 for aldehydes generation via olefins hydro-formylation. However, the two key syngas utilisation applications that include the production of fertilisers and oil refining operations require the maximisation of hydrogen content. Syngas is commonly produced via Auto-thermal Reforming (ATR), Partial Oxidation (POx), and Steam Reforming (SR). Auto-thermal Reforming integrates the combustion reactions with the catalytic reforming reactions to produce a syngas composition that is typically applied in the production of large-scale methanol and FischerTropsch liquids. POx technology has a unique possibility of utilising heavy hydrocarbons in a non-catalytic system to produce a CO-rich syngas. The SR is the most energy efficient technology amongst them and it is catalytically utilised with light hydrocarbons to produce an H2-rich syngas (Iaquaniello et al., 2012). Table 1 illustrates a comparison of the three gas reforming technologies (De Klerk, 2012). Solid feedstock are typically converted to synthesis gas through gasification processes that are distinguished by reactor properties or the effluent gas temperature. Table 2 illustrates the classification and main attributes of the different gasification technologies (De Klerk, 2012). Any source of carbon can potentially be used as a feedstock for the generation of synthesis gas. Following its production, the syngas is purified and prepared as a raw material to produce valuable products. In terms of economic feasibility, the cost of the syngas purification units in the FischereTropsch GTL process is by far the highest of any feed-to-liquids (XTL) facility. In the case of solid raw materials such as coal, the cost of the delivery of purified syngas can reach up to 70% of the total capital cost. Similarly, biomass to syngas requires a large investment in the syngas process adjustment to the gasification process and prior to its utilisation in the generation of value-added products. The technology of converting natural gas to syngas is less challenging and the relative capital cost for it is smaller (Pala et al., 2017). The initial syngas adjustment can typically be achieved through manipulating process conditions or the gasifying agent quantity, after which the requirement for further purification is reduced. In this study, the produced syngas from biomass gasification is initially adjusted through optimising the biomass feedstock blends. This can lower the required gasifying agent quantities and operating energy to achieve the same adjustment, whilst achieving the required syngas quality for further utilisation in the production of value added products. This study is part of an integrated project investigation the optimal utilisation options for syngas. This includes defining potential improvements that enable a reduction in capital and energy requirements within the syngas production process. 2. Process description In order to create value added products from waste streams and ultimately reduce the environmental burden associated with waste disposal, this study determines the optimum blending pathways for gasification processes in order to generate high-energy syngas using available biomass in Qatar. A novel simulation model, applicable to any mixed biomass feedstock gasification scenario is developed with Aspen Plus and is demonstrated using both single and blended component analysis for Qatar specific biomass characteristics. The optimisation nature of the work differs from other literature biomass utilisation studies as it integrates various biomass feedstock blends, including date pits, a locally abundant

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Table 1 Comparison of gas reforming technologies for syngas production (De Klerk, 2012). Description

Steam Reforming

Partial Oxidation

Auto-thermal Reforming

Feed H2O:C ratio (mol/mol) Feed O2:C ratio (mol/mol) Outlet pressure (MPa) Outlet temperature (◦C) Product CO2:CO ratio (mol/mol) Product H2:CO ratio (mol/mol) Outlet CH4 content (mol%)

2.5e5.0 0 2.0e2.5 820e880 0.5e1 4e7 3e5

0e0.15 0.55e0.65 2.5e4.0 1300e1400 0.05e0.1 1.6e1.9 0.1

1.5e2.5 0.55e0.6 ~2.5 950e1050 0.2e0.3 2.5e3.5 0.5e1

0.6 0.6 2.5e2.9 1020e1065 0.2 2.2e2.3 0.5e1.2

Table 2 Classification and main attributes of feed-to-syngas gasification conversion technologies (De Klerk, 2012). Attribute

Gasification technology

Reactor technology

Low temperature

Medium temperature

High temperature

Moving bed

Fluidised bed

Entrained flow

425e650 > 2:1 to < 1: 1 High Low Yes

900e1050 < 1: 1 Moderate Moderate Possibly

1250e1600 ~1:2 Low High No

Temperature of syngas (◦C) H2:CO ratio in syngas Steam demand Oxidant demand Pyrolysis products in gas

waste. Furthermore, multiple end-use options for hydrogen rich syngas are considered within the optimisation framework. The description of the base case process has been emphasised in an earlier study that utilises oxygen and steam as gasifying agents (AlNouss et al., 2018). The base case has also been utilised to assess the techno-economic and environmental performance of the gasification process integrated with a number of downstream applications (AlNouss et al., 2019b), to study the sustainability of the system through energy-water-food (EWF) nexus applications (AlNouss et al., 2019c) and to optimise the superstructure of biomass gasification and downstream applications (AlNouss et al., 2019a). The simulated Aspen Plus flowsheet is further enhanced to capture the different biomass gasification technologies that include: 1. 2. 3. 4.

Oxygen-steam biomass gasification with indirect heating; Oxygen-steam biomass gasification with direct heating; Oxygen biomass gasification with direct heating; Steam biomass gasification with indirect heating. The Aspen Plus flowsheet illustrated in Fig. 2 of the biomass

gasification process is simulated on the basis of steady state and isothermal operation, atmospheric pressure, negligible pressure drop, zero-dimensional simulation, char is considered completely as a carbon (C), tar formation is not considered, instantaneous drying and pyrolysis are assumed in the model. All fuel-bound nitrogen, fuel-bound sulphur, and fuel-bound chlorine are converted to ammonia, hydrogen sulphide and hydrogen chloride, respectively. The Peng-Robinson equation of state is used with BostonMathias modifications to model the real and nonpolar components presented in the simulation. The main components of the simulation include a mixer to blend the various biomass feedstock, a drying and decomposition reactor to convert the nonconventional content of the blend into conventional components. The components stream is then fed into a separator to separate the ash content of the blend, which will be then sent to the gasifier along with the gasifying agent. The gasifying agents considered are steam and oxygen, where oxygen is sourced from an air separation unit (ASU). Table 3 summarises the proximate and ultimate analysis and respective empirical formula for the three considered biomass feedstock; manure, sewage sludge and date pits. The empirical formulae characterise the atoms

Fig. 2. Aspen Plus flowsheet illustrating gasification process.

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Table 3 Properties of the different biomass feedstock (AlNouss et al., 2018). Biomass Proximate analyses (dry basis, wt %) Fixed carbon Volatile matter Ash Moisture (wt %) Ultimate analyses (dry basis, wt %) C O H N Cl S Ash Empirical Formula LHV (dry basis) (MJ/kg)

Dried Sewage Sludge

Manure

Date Pits Waste

19.4 8.8 71.8 8.3

13.5 65.0 21.6 27.4

17.2 81.8 1.0 5.0

19.1 5.7 2.3 1.1 0 0.1 71.8

37.1 31.4 5.1 3.7 1.0 0.5 21.4

49.8 37.9 6.8 4.5 0 0 1.0

CH1.45N0.049O0.22S0.0018 20.50

CH1.63N0.085O0.64S0.005Cl0.0089 19.40

CH1.63N0.08O0.57 34.07

present in the chemical compound as a simplest positive integer ratio. The ultimate and proximate analysis of the three biomass feedstock illustrated in Table 3 have been specified in the simulation model as non-conventional streams with thermodynamic conditions at 1 bar and 25
C. A calculator block is implemented to determine the mass yields of the conventional components according to their relation (Table 4) with non-conventional biomass properties. The main reactions occurring in the gasification zone are summarised in Table 5, where reactions number 11, 12 and 13 are used to describe the 100% conversion of the fuel-bound components, which are removed completely from the process after the reactor. Gasification reactions (14e20) occur inside the gasifier depending on the gasification configuration, and considering the minimisation of the Gibbs free energy with a restricted equilibrium method. In the case of oxygen-based gasification, the air is supplied from an air separation unit (ASU) with a quality of 95% O2, 1.6% N2, and balanced Ar. Whereas, in the case of steam-based gasification, the steam flowrate is estimated based on the biomass and its moisture content. Where, WATER represents the moisture content in the biomass defined in the proximate analysis of the biomass. PROXANAL represents the values of proximate analysis of the biomass. FACT represents the dried factor of the biomass. CARBON represents the calculated yield of carbon. ULTANAL represents the values of ultimate analysis of the biomass. In terms of species: O2 represents the calculated yield of oxygen, H2 represents the calculated yield of hydrogen, N2 represents the calculated yield of nitrogen, CL2 represents the calculated yield of chlorine, SULF represents the calculated yield of sulphur, H2O represents the calculated yield of water and ASH represents the calculated yield of ash. The base model of steam gasification is validated against literature published data considering similar systems. The model input data presented in Table 6 is simulated using the steam gasification model and the earlier specified process description. The results, illustrated in Table 6, demonstrate very good agreement with literature values, where the percentage deviation in all cases is less

Table 5 Gasification reactions (AlNouss et al., 2019b). 0.5N2 þ 1.5H2 4 NH3 H2 þ S 4 H2S Cl2 þ H2 4 2HCl C þ 2H2 4 CH4 C þ CO2 4 2CO C þ H2O 4 CO þ H2 CH4 þ H2O 4 CO þ 3H2 CO þ H2O 4 CO2 þ H2 C þ O2 4 CO2 2C þ O2 4 2 CO

[11] [12] [13] [14] [15] [16] [17] [18] [19] [20]

than 6.5%. The model is further developed to accommodate other configurations. Table 7 summarises the main basic input to each of the studied configurations along with the material and energy flow results. Notably, both the indirect and direct air-steam configurations have the same basic inputs and later in section 4.1, the results will demonstrate that both configurations yielded the same optimisation results. 3. Modelling The objective of the present study is to determine the optimum blended feedstock for the four technologies that provide the potential H2:CO ratios to further utilise the produced bio-syngas in aldehydes, liquid fuels and fertilisers applications. Following the development of the different gasification models, the blends of the three biomass feedstock are optimised in order to determine their different contribution towards syngas production. The optimisation is conducted using the built-in capabilities of Aspen Plus. Initially, the formulation of the problem using FORTRAN coding in Aspen Plus is performed to maximise the production of syngas resulting from the blending of the different biomass feedstock, and is constrained by the application unique H2 to CO ratio. A restriction on the modelling terms of the maximum amount of any of the feeds

Table 4 Formulations of the calculator block to determine conventional components yield (AlNouss et al., 2018). WATER ¼ PROXANAL(1) FACT ¼ (100 - WATER)/100 CARBON ¼ ULTANAL (2)/100 · FACT O2 ¼ ULTANAL (7)/100 · FACT H2 ¼ ULTANAL (3)/100 · FACT

[1] [2] [3] [4] [5]

N2 ¼ ULTANAL (4)/100 $ FACT CL2 ¼ ULTANAL (5)/100 · FACT SULF ¼ ULTANAL (6)/100 · FACT H2O ¼ WATER/100 ASH ¼ ULTANAL (1)/100 · FACT

[6] [7] [8] [9] [10]

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Table 6 Base model validation against literature. Parameter

Literature (Doherty et al., 2013)

Present Model

Biomass input (kg/h) Proximate analyses (dry basis, wt %) Volatile matter Fixed carbon Ash Moisture (wt %) Ultimate analyses (dry basis, wt %) C H O N S Cl Ash Gasification temperature (oC) Gasification pressure (bar) Gasifier steam to biomass mass ratio Gasifier steam temperature (oC) Combustor air to biomass mass ratio Combustor air temperature (oC) Combustor air composition (mol%) O2 N2 Combustion temperature (oC)

1508.64 80.00 18.84 1.16 20.00 51.19 6.08 41.30 0.20 0.02 0.05 1.16 850 1 0.75 450 1.12 450 21 79 905

Syngas composition (vol%, dry and NH3, H2S, HCl free)

Literature (Doherty et al., 2013)

Model

Relative error %

H2 CO CH4 CO2 N2 Syngas LHV (MJ/m3, dry at 0
C and 1 atm) CGE (LHV and mass basis) Impurities (ppmv, dry basis) NH3 H2S HCl Char combusted (mass basis)

45.80 21.59 11.02 20.19 1.40 11.60 76.7%

45.80 20.79 11.22 20.79 1.40 11.60 75.3%

0.00% 3.70% 1.79% 2.98% 0.00% 0.03% 1.81%

1514.00 66.12 149.5 12.93%

1609.68 70.31 158.99 12.93%

6.32% 6.34% 6.35% 0.00%

Table 7 Basic material and energy flow inputs and results for each studied configuration. Steam Gasification Material Flow Biomass input (kg/h) Biomass blending Steam (kg/h) Air (kg/h) Syngas Flow (kg/h) Composition (vol%) N2 CO CO2 H2 CH4 H2:CO ratio Syngas LHV (MJ/m3, dry at 0
C) CGE (LHV and mass basis) Char Combusted

Air Gasification

Air-Steam Gasification

The ratios studied in this paper are H2:CO ¼ 1 for the production of aldehydes, H2:CO ¼ 2 for the production of Fischer-Tropsch liquids, and H2:CO ¼ 3 for the production of ammonia/urea which requires the maximisation of H2. Equations (21)e(25) detail the formulation of the optimisation problem.

3000 Equally 2209.29 e 2520.09

e 1800 3183.94

2209.29 1800 3425.37

2.42 14.56 21.63 56.73 4.66 3.90 9.87 53.14% 20.17%

0.85 23.51 47.77 26.28 1.58 1.11 6.78 26.46% 0 (direct heat)

0.89 13.91 62.13 43.62 0.32 3.14 5.86 26.12% 0 (direct heat)

0.93 0.93

850 6.26 direct heat

5.83 direct heat

n X

Maximize

xi , Syngas

c i 2Biomass Sources

i¼1

(21)

Subject to

n X

xi ¼ 1

(22)

i¼1

yH2 , Syngas ¼ Application unique yCO , Syngas

(23)

Energy Flow Gasification T (oC) Gasifier (MW) Combustor (MW)

used is identified to be 2000 kg/h which is twice the initial feedstock. This restriction is implemented to study the contribution of each biomass type on the overall blended feed to the gasifier on an equal basis.

mi  2; 000

kg h

(24)

Equation (21) represents the objective function of the optimisation formulation with the aim of maximising the syngas production. Equations (22)e(24) represent the constraints of the optimisation problem and it aims to ensure the fraction summation of biomass contribution is one and below the 2000 kg/h restriction, and that the ratio of hydrogen to carbon monoxide in the produced syngas is achieved at the application requirement value. Where,

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A. AlNouss et al. / Journal of Cleaner Production 242 (2020) 118499

Syngas is the molar flowrate of the generated H2-rich synthesis gas, yH2 & yCO are molar fractions of hydrogen and carbon monoxide, x is the blending fraction of each biomass feedstock and m is the mass flowrate of each biomass feedstock. The constraint in equation (24) is modified to equation (25) for the case of methanol production to account for the role of H2, CO2, and CO presence in syngas.

yH2  yCO2 ¼2 yCO þ yCO2

Subject to

n X

xi *Ai

BAi MWi



max

(26)



(27)

BAi MWi

Where BA is the new blending attribute, x is the blending ratio of each biomass, A is the original attribute, xEF is the ratio of each atom in the empirical formula and MW is the molecular weight. The second objective of this paper is to examine the effect of varying gasification performance parameters on the produced results and operating conditions. The main gasification parameters studied here are: 1. Gasification Temperature (Tg) 2. Gasification Pressure (P) 3. Modified Equivalence Ratio (ERM) which is defined according to equation (28).

ERM ¼

Stoichiometric Oxygen Actual Oxygen

(28)

Where, stoichiometric oxygen represents the required oxygen supply as per the reaction mechanism and actual oxygen represent the oxygen supplied to the process by both air and steam. This definition is the modified ratio for systems that can use both air and steam as the gasification agent (Gordillo et al., 2009). 4. Ratio of oxygen supplied by air and steam (ASTR) which is defined according to equation (29):

ASTR ¼

Oxygen by Air Oxygen by Air þ Oxygen by Steam

m_ syngas , LHVsyngas m_ biomass , LHVbiomass

(30)

Where m_ syngas is the mass flowrate of syngas, m_ biomass is the mass flowrate of biomass, LHVsyngas is lower heating values of syngas and LHVbiomass is lower heating values of biomass. 4. Results and discussion

i¼1

xEF ¼

CGEð%Þ ¼

(25)

Where, yCO2 is the molar fraction of carbon dioxide. The results of the optimisation problem are then categorised and analysed based on the downstream application. The blending option is then linked with the proximate and ultimate analysis for the different biomass through determination of the blendedbiomass attributes and its corresponding empirical formula. The empirical formula is determined through calculating the simplest positive ratio of each atom from the new blending attribute using equations [26e27].

BAi ¼

biomass LHV calculated as:

(29)

This definition yields a finite range of ASTR from 0 to 1 where 0 represents steam gasification and 1 represents oxygen gasification and in between the oxygen/steam gasification. 5. Biomass feedstock moisture content The main observation results of the sensitivity analyses include the dry syngas composition, the dry mass LHV, and the Cold Gas Efficiency (CGE) which is a measure of the produced syngas LHV to

The results of this study are classified into two parts; (i) the optimum biomass blends of each configuration for different application requirements, and (ii) the sensitivity analysis of the key gasification performance parameters. 4.1. Optimum blends As the objective of the study is to determine the optimum blends of the three biomass feedstock according to the four technologies, the results will be discussed based on the application sinks. First, the H2:CO ¼ 1 for the production of aldehydes, demonstrates only one case, where only the oxygen-based gasification yielded the correct constraints. This demonstrates similar behaviour to the POx technology which dominates this field and the results of H2:CO originally presented in Table 7. The result of this study is summarised in Table 8 and the resulting blending option is represented in Fig. 3. The resulting blending option is distributed amongst the three biomass feedstock without any domination. The lower percentage of manure demonstrates its potential to increase the H2:CO ratio, where the original 1.11 ratio is lowered by decreasing the manure feedstock flow. Second, the H2:CO ¼ 2 for the production of FischerTropsch liquids, demonstrates that only the oxygen based gasification case yielded an incorrect constraint with a ratio of only 1.5. This illustrates similar behaviour to the SR technology, which dominates this field and the results of H2:CO originally presented in Table 7. The results of this study are summarised in Table 9 and the resulting blending options are illustrated in Fig. 4. The resulting blending options in this case demonstrates a domination for the date pits for three configurations out of the four. The oxygen gasification configuration illustrates a different blending option distributed between manure (54%) and sludge (46%). This highlights the potential of date pits specially and food waste generally in the production of value added products. In addition, the indirect and direct oxygen-steam configurations

Table 8 Results of biomass feedstock blends optimisation for the case of H2:CO ¼ 1 (ERM ¼ 1.67, ASTR ¼ 1, Tg ¼ 850
C, Pg ¼ 1 bar). Technology Syngas composition (mol%) N2 CO CO2 H2 CH4 Feed Blend (wt%) Manure Date Pits Sludge Empirical Formula ERM ASTR Syngas Yield (kg product/kg Feed) LHV (MJ/m3, dry at 0
C)

Oxygen Gasification (direct heat) 0.8 26.1 44.8 26.1 2.3 16.6 41.7 41.7 CH1.60N0.072O0.50S0.0012Cl0.0015 1.67 1 1.30 7.22

A. AlNouss et al. / Journal of Cleaner Production 242 (2020) 118499

Fig. 3. Biomass feedstock blending option for the case of H2:CO ¼ 1 (ERM ¼ 1.67, ASTR ¼ 1, Tg ¼ 850
C, Pg ¼ 1 bar).

9

yielded the same results demonstrating that there is no split of char required to achieve the 850
C gasification temperature. Furthermore, the results indicate that in the case of steam and oxygensteam configurations, decreasing the manure and sludge flows lowers the H2:CO ratio, where the original respective 3.90 and 3.14 ratios (Table 7) decrease to 2. In the case of oxygen gasification, the original 1.11 H2:CO ratio requires an increase which is achieved by lowering the date pits mass flow and increasing both the manure and sludge mass flows. Third, the H2:CO ¼ 3 for the production of Ammonia/Urea, demonstrates a difficult convergence step for the oxygen-based gasification case. For the remaining three cases, the results demonstrate some distribution of the three-biomass feedstock over the optimum blends. This illustrates similar behaviour to the ATR technology that dominates this field and the results of H2:CO originally presented in Table 7. The result of this study is summarised in Table 10 and the resulting blending options are illustrated in Fig. 5.

Table 9 Results of biomass feedstock blends optimisation for the case of H2:CO ¼ 2 (Tg ¼ 850
C, Pg ¼ 1 bar). Note: the oxygen based gasification case yielded an incorrect constraint with a ratio of only 1.5. Technology

Oxygen Gasification (direct heat) Steam Gasification (indirect heat) Oxygen þ Steam (direct heat) Oxygen þ Steam (indirect heat)

Syngas composition (mol%) N2 1.3 CO 9.9 CO2 73.9 H2 14.8 CH4 0.0 Feed Blend (wt%) Manure 54.3 Date Pits 0.0 Sludge 45.7 ERM 1.67 ASTR 1 Empirical Formula CH1.58N0.074O0.51S0.0037Cl0.0061 Syngas Yield (kg product/kg Feed) 1.2 LHV (MJ/m3, dry at 0
C) 3.75

2.1 22.2 19.3 44.3 12.1

0.4 20.2 35.8 40.4 3.1

0.4 20.2 35.8 40.4 3.1

0.3 99.4 0.3 2.67 0 CH1.63N0.077O0.57 1.6 12.02

0.2 99.7 0.2 1.5 0.5 CH1.63N0.077O0.57 2.3 8.20

0.2 99.7 0.2 1.5 0.5 CH1.63N0.077O0.57 2.3 8.20

Fig. 4. Biomass feedstock blending option for the case of H2:CO ¼ 2 (Tg ¼ 850
C, Pg ¼ 1 bar) when gasifying with a) Oxygen (ERM ¼ 1.67, ASTR ¼ 1), b) Steam (ERM ¼ 2.67, ASTR ¼ 0), c) oxygen and steam with direct heat source (ERM ¼ 1.5, ASTR ¼ 0.5), and d) oxygen and steam with indirect heat source (ERM ¼ 1.5, ASTR ¼ 0.5).* * Note: Option c) oxygen and steam with direct heat source, and d) oxygen and steam with indirect heat source have exactly the same result as option (b).

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A. AlNouss et al. / Journal of Cleaner Production 242 (2020) 118499

Table 10 Results of biomass feedstock blends optimisation for the case of H2:CO ¼ 3 (Tg ¼ 850
C, Pg ¼ 1 bar). Technology Syngas composition (mol%) N2 CO CO2 H2 CH4 Feed Blend (wt%) Manure Date Pits Sludge ERM ASTR Empirical Formula Syngas Yield (kg product/kg Feed) LHV (MJ/m3, dry at 0
C)

Steam Gasification (indirect heat)

Oxygen þ Steam (direct heat)

Oxygen þ Steam (indirect heat)

2.3 17.4 21.4 52.1 6.9

0.7 12.6 48.6 37.7 0.4

0.7 12.6 48.6 37.7 0.4

41.6 58.4 0.0 2.67 0 CH1.63N0.079O0.59S0.0015Cl0.0031 1.7 10.50

39.2 39.2 21.7 1.5 0.5 CH1.61N0.077O0.56S0.0019Cl0.0033 2.1 6.21

39.2 39.2 21.7 1.5 0.5 CH1.61N0.077O0.56S0.0019Cl0.0033 2.1 6.21

The blending option result in this case illustrates an overall distribution for the three feedstock. This highlights the potential of all the biomass options in the production of value-added products. As observed earlier, the indirect and direct oxygen-steam configurations yielded identical results demonstrating that there is no split of char required to achieve the 850
C gasification temperature. The results also indicate that in the case of steam and oxygen-steam configurations and since the objective of H2:CO is near the original H2:CO; 3.90 and 3.14, respectively, decreasing the sludge flow lowered the ratio to 3. Finally, the unique ratio for the production of methanol, demonstrates the difficultly in converging for the oxygen based gasification cases. For the remaining steam gasification case, the results demonstrate a domination for the sludge biomass over the other two-biomass feedstock. This illustrates similar behaviour to the SR technology that dominates this field and the results of H2:CO originally presented in Table 7. The result of this study is summarised in Table 11 and the resulting blending option is illustrated in Fig. 6. The result indicates that increasing the flow of sludge has yielded an increase in hydrogen production compared to CO and CO2. The resulting syngas compositions from all cases are compared

Table 11 Results of biomass feedstock blends optimisation for the case of special ratio (ERM ¼ 2.67, ASTR ¼ 0, Tg ¼ 850
C, Pg ¼ 1 bar). Technology Syngas composition (mol%) N2 CO CO2 H2 CH4 Feed Blend (wt%) Manure Date Pits Sludge Empirical Formula ERM ASTR Syngas Yield (kg product/kg Feed) LHV (MJ/m3, dry at 0
C)

Steam Gasification (indirect heat) 3.2 8.0 19.7 68.0 1.1 0.1 0.0 99.9 CH1.45N0.05O0.22S0.0018Cl0.00001 2.67 0 1.0 9.18

against the literature values illustrated in Table 12. Evidently, the results from the four configurations demonstrate syngas compositions outcomes that are in agreement with published literature.

Fig. 5. Biomass feedstock blending option for the case of H2:CO ¼ 3 (Tg ¼ 850
C, Pg ¼ 1 bar) when gasifying with a) steam (ERM ¼ 2.67, ASTR ¼ 0), b) oxygen and steam with direct heat source (ERM ¼ 1.5, ASTR ¼ 0.5), and c) oxygen and steam with indirect heat source (ERM ¼ 1.5, ASTR ¼ 0.5).

A. AlNouss et al. / Journal of Cleaner Production 242 (2020) 118499

11

60

16

50

14 LHV (A+S) LHV (S)

40

LHV (A)

30

10 CGE (A+S) CGE (S)

8

CGE (%)

LHV (MJ/kg)

12

20

CGE (A)

10

6

4 Fig. 6. Biomass feedstock blending option for the case of (H2eCO2:CO þ CO2) ¼ 3 when gasifying with steam (ERM ¼ 2.67, ASTR ¼ 0, Tg ¼ 850
C, Pg ¼ 1 bar).

600

800

1000

1200

0 1400

Tg (oC) Fig. 8. Effect of changing gasification temperature (Pg ¼ 1 bar, 1000 kg/h of each biomass feedstock) on LHV and CGE (for A option, ERM ¼ 1.67 and ASTR ¼ 1, for S option, ERM ¼ 2.67 and ASTR ¼ 0 and for A þ S option, ERM ¼ 1.5 and ASTR ¼ 0.5).

4.2. Sensitivity analysis The results of the second objective, which is concerned with studying the changing effect of the key gasification's performance parameters on the produced results and operating conditions,

Table 12 Typical dry gas compositions of different oxygen/steam gasification techniques of biomass (Rauch et al., 2014). Compound

Steam Gasification

Oxygen Gasification (Fluidised Bed)

Oxygen Gasification (Entrained Flow)

CO (vol %) H2 (vol %) CO2 (vol %) CH4 (vol %) N2 (vol %) Tar content at 0
C (g/m3) LHV at 0
C (MJ/m3)

20e25 30e45 20e25 6e12 0e1 1e10 10e14

20e30 20e30 25e40 5e10 0e1 1e20 10e12

40e60 15e20 10e15 0e1 0e1 < 0.1 10e12

80%

Syngas Compositions (vol%, dry)

70%

CO2 (A+S) CO (A+S)

60%

CH4 (A+S) N2 (A+S) H2 (A+S)

50%

CO2 (S) CO (S)

40%

CH4 (S) N2 (S)

30%

H2 (S) CO2 (A)

20%

CO (A) CH4 (A) N2 (A)

10%

H2 (A)

0% 600

700

800

900

1000

Tg

1100

1200

1300

1400

(oC)

Fig. 7. Effect of changing gasification temperature (Pg ¼ 1 bar, 1000 kg/h of each biomass feedstock) on produced syngas composition (for A option, ERM ¼ 1.67 and ASTR ¼ 1, for S option, ERM ¼ 2.67 and ASTR ¼ 0 and for A þ S option, ERM ¼ 1.5 and ASTR ¼ 0.5).

12

A. AlNouss et al. / Journal of Cleaner Production 242 (2020) 118499

80%

100% 90%

CO2 (A+S) CO (A+S)

60%

Syngas Compositions (vol%, dry)

Syngas Compositions (vol%, dry)

70%

CH4 (A+S) N2 (A+S) H2 (A+S)

50%

CO2 (S) CO (S)

40%

CH4 (S) N2 (S)

30%

H2 (S) CO2 (A)

20%

CO (A) CH4 (A)

10%

N2 (A)

5

10

15

20

25

CO (A+S) CH4 (A+S) N2 (A+S)

70%

H2 (A+S)

60%

CO2 (S) CO (S)

50%

CH4 (S)

40%

N2 (S)

30%

CO2 (A)

H2 (S) CO (A)

20%

CH4 (A) N2 (A)

10%

H2 (A)

0% 0

CO2 (A+S)

80%

H2 (A)

0%

30

0

Pg (bar)

2

4

6

8

10

ERM

Fig. 9. Effect of changing gasification pressure (Tg ¼ 850
C, 1000 kg/h of each biomass feedstock) on produced Syngas composition (for A option, ERM ¼ 1.67 and ASTR ¼ 1, for S option, ERM ¼ 2.67 and ASTR ¼ 0 and for A þ S option, ERM ¼ 1.5 and ASTR ¼ 0.5).

Fig. 11. Effect of changing ERM on produced Syngas composition (Tg ¼ 850
C, Pg ¼ 1 bar, 1000 kg/h of each biomass feedstock).

demonstrate some optimum values for the gasification temperature, pressure and performance ratios. The results of each sensitivity analysis study are presented in the Figures for each category and the different technologies. The terms ‘A þ S’ stands for Air and Steam gasification, ‘S’ stands for Steam gasification and ‘A’ stands for Air gasification.

produced syngas until an optimum peak at around 850
C. The results of LHV and CGE illustrate similar behaviour where an optimum value of 850
C is obtained. Whereas, the carbon content in the produced syngas demonstrates a different behaviour with a decrease in the composition of CO2 and CH4 as the temperature increased.

4.2.1. Gasification temperature (Tg) The influence of the gasification temperature on the produced syngas composition and key performance parameters are plotted in Figs. 7 and 8 for the four different technologies. The resulting compositions from changing the gasification temperature demonstrate an increase in the content of H2 in the

4.2.2. Gasification pressure (Pg) The influence of the gasification pressure on the produced syngas composition and key performance parameters are plotted in Figs. 9 and 10 for the four different technologies. The resulting compositions from changing the gasification pressure demonstrate a decrease in the content of H2 in the

16

60

LHV (S)

12 LHV (MJ/kg)

50

LHV (A+S)

40

LHV (A)

10

30

CGE (A+S)

8

CGE (S)

CGE (A)

6

CGE (%)

14

20

10

4

0 0

5

10

15

20

25

30

Pg (bar) Fig. 10. Effect of changing gasification pressure (Tg ¼ 850
C, 1000 kg/h of each biomass feedstock) on LHV and CGE (for A option, ERM ¼ 1.67 and ASTR ¼ 1, for S option, ERM ¼ 2.67 and ASTR ¼ 0 and for A þ S option, ERM ¼ 1.5 and ASTR ¼ 0.5).

A. AlNouss et al. / Journal of Cleaner Production 242 (2020) 118499

18

and air to the gasification reactor. Consequently, the carbon reactions occur in an oxygen and steam deficient environment, which generates CO-rich syngas and an increased content of CH4 since less C is converted to CO and CO2. Moreover, more H atoms are incorporated into CH4 formation and less leaves as H2. A modified equivalence ratio of around 2.5 gives an optimum value for lowering the carbon content and increasing the H2 in the produced syngas. This implies moderate results for LHV and CGE as per Fig. 12.

60

16

LHV (A+S)

50

14 LHV (S)

LHV (MJ/kg)

LHV (A)

10

30 8

CGE (A+S)

6

CGE (%)

40

12

20

CGE (S)

4.2.4. Ratio of oxygen supplied by air and steam (ASTR) The influence of the ratio of oxygen, supplied by air only, to the oxygen supplied by air and steam, on produced syngas composition and key performance parameters are plotted in Figs. 13 and 14. The effect of changing the ASTR on the produced syngas compositions is illustrated in Fig. 14. Increasing the ASTR at constant temperature implies suppling a higher oxygen mass flow from the air rather than steam. Consequently, the carbon reactions occur in an oxygen rich environment, which generates a CO2-rich syngas and a lower H2 content, since less C is converted to CO and more is available to be coupled with H2 to produce CH4. Since less H atoms are converted to H2, an optimal point for the oxygen value supplied by air and steam of less than 0.6 is highlighted for the production of H2-rich syngas. These results agree with the values of LHV and CGE in Fig. 14, where the two parameters increase with lowering the ASTR value below 0.6.

4 10

CGE (A)

2

0

0 0

2

4

6

8

13

10

ERM Fig. 12. Effect of changing ERM on LHV and CGE (Tg ¼ 850
C, Pg ¼ 1 bar, 1000 kg/h of each biomass feedstock).

produced syngas indicating a low pressure optimum point of approximately 1 bar. The results of LHV and CGE illustrate a constant behaviour with the increase in gasification pressure. Whereas, the carbon content in the produced syngas demonstrates a different behaviour with a decrease in the composition of CO2 and CH4 as the pressure increased.

4.2.5. Moisture content The influence of the moisture content presented in the three biomass feedstock on the produced syngas composition and key performance parameters are plotted in the following figures for the different technologies that utilise gasification air or/and steam. The effect of increasing moisture content in the biomass feedstock on the produced syngas compositions is illustrated in Fig. 15. The increasing moisture content demonstrates varying results with the different gasification processes. The steam gasification process illustrates an increase in the production of H2 with the increase of

4.2.3. Modified equivalence ratio (ERM) The influence of the modified Equivalence Ratio (ERM) on the produced syngas composition and key performance parameters are plotted in the following figures for the different technologies that utilise gasification air or/and steam. The effect of changing the ERM on the produced syngas compositions is illustrated in Fig. 11. Increasing the ERM at constant temperature implies suppling a lower oxygen feed in both steam

100% CO2 CO CH4 N2 H2

Syngas Compositions (vol%, dry)

90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

ASTR Fig. 13. Effect of changing ASTR on produced syngas composition (Tg ¼ 850
C, Pg ¼ 1 bar, 1000 kg/h of each biomass feedstock).

12

60

10

50

8

40

6

30

4

20 LHV

2

10

CGE

0 0.4

CGE (%)

A. AlNouss et al. / Journal of Cleaner Production 242 (2020) 118499

LHV (MJ/kg)

14

0.45

0.5

0.55

0.6

0.65

0.7

0 0.75

ASTR Fig. 14. Effect of changing ASTR on LHV and CGE (Tg ¼ 850
C, Pg ¼ 1 bar, 1000 kg/h of each biomass feedstock).

moisture content while the CO presence is lowered. This implies that more steam is available to react with carbon to produce H2 and a smaller amount of carbon is converted to CO2, CO and CH4. For the cases of oxygen gasification and oxygen/steam gasification, the results demonstrate different behaviour for the CO2 production. With the increase of moisture content, more carbon is converted to CO2 and lower amounts of H2, CO and CH4 are produced. Since the aim is to increase the generation of H2, it is preferred to maintain a high moisture content in the case of steam gasification, whereas drying to lower levels of moisture content is preferred for the cases of oxygen and oxygen/steam gasification. The effect on the LHV is presented in Fig. 16 and shows a continual decrease in the LHV with increasing moisture content in all cases. The decrease rate is less severe in the case of CGE (S).

5. Conclusion and practical implications Biomass resources have demonstrated a potential for the generation of clean energy and environmentally-friendly chemicals and fuels. This paper emphasises the importance of optimising the process of gasifying the different biomass feedstock in terms of raw material blending and operating conditions. The importance of optimising the biomass feedstock blending originates from the large variation in the types and quantities of biomass available, and the need to adapt a gasification system that is capable of intelligently blending the feedstock to achieve the desired syngas quality. The simulation model considered in this study utilises various biomass feedstock including date pits, manure and sewage sludge

70%

14

CO2 (A+S) CO (A+S) CH4 (A+S) N2 (A+S) H2 (A+S) CO2 (S) CO (S) CH4 (S) N2 (S) H2 (S) CO2 (A) CO (A) CH4 (A) N2 (A) H2 (A)

40% 30% 20% 10%

12

5

10

15

20

25

30

35

60

LHV (S)

50

LHV (A)

40

CGE (A+S)

30

CGE (S)

20

CGE (A)

10

10 8 6 4 2

0% 0

LHV (A+S)

40

Moisture Content Fig. 15. Effect of changing biomass feedstock moisture content (Tg ¼ 850
C, Pg ¼ 1 bar, 1000 kg/h of each biomass feedstock) on produced syngas composition (for A option, ERM ¼ 1.67 and ASTR ¼ 1, for S option, ERM ¼ 2.67 and ASTR ¼ 0 and for A þ S option, ERM ¼ 1.5 and ASTR ¼ 0.5).

CGE (%)

50%

LHV (MJ/kg)

60%

Syngas Compositions (vol%, dry)

70

16

0

0 0

10

20 Moisture Content

30

40

Fig. 16. Effect of changing biomass feedstock moisture content (Tg ¼ 850
C, Pg ¼ 1 bar, 1000 kg/h of each biomass feedstock) on LHV and CGE (for A option, ERM ¼ 1.67 and ASTR ¼ 1, for S option, ERM ¼ 2.67 and ASTR ¼ 0 and for A þ S option, ERM ¼ 1.5 and ASTR ¼ 0.5).

A. AlNouss et al. / Journal of Cleaner Production 242 (2020) 118499

to determine the optimal biomass blending options linked to the H2/CO ratio of the downstream application. The results can be used as a base line for the utilisation of any mixed biomass feedstock gasification scenario to be optimised. Typically, the production of syngas requires a huge investment in the subsequent adjustment prior to its utilisation in the production of value-added products. The initial syngas adjustment can normally be achieved through manipulating process conditions or the gasifying agent quantity, upon which the requirement for further purification is reduced. However, in this paper, the syngas that is produced using biomass gasification is initially adjusted through the optimisation of biomass feedstock blending. This can lower the required gasifying agent quantity and operating energy to achieve the same adjustment whilst maintaining the required quality of syngas to be utilised to produce valuable products. The study further demonstrates the sensitivity analysis for the changing effect of operating conditions on the key performance parameters in biomass gasification including, CGE, LHV and produced syngas compositions. The simulation integrates multiple gasification technologies with the objective of generating optimal characteristics for the various utilisation techniques considered. Acknowledgment The authors acknowledge the support of Qatar National Research Fund (QNRF) (a member of Qatar Foundation, Qatar) by GSRA grant No GSRA4-1-0518-17082. The statements made herein are solely the responsibility of the authors. References Abdoulmoumine, N., Kulkarni, A., Adhikari, S., 2014. Effects of temperature and equivalence ratio on pine syngas primary gases and contaminants in a benchscale fluidized bed gasifier. Ind. Eng. Chem. Res. 53 (14), 5767e5777. Ahmad, F., 2016. Sustainable Solutions for Domestic Solid Waste Management in Qatar, College of Engineering. Qatar University, p. 128. Ahmed, I.I., Gupta, A.K., 2010. Pyrolysis and gasification of food waste: syngas characteristics and char gasification kinetics. Appl. Energy 87 (1), 101e108. AlNouss, A., McKay, G., Al-Ansari, T., 2018. Optimum utilization of biomass for the production of power and fuels using gasification. In: Friedl, A., Klemes, J.J., Radl, S., Varbanov, P.S., Wallek, T. (Eds.), Computer Aided Chemical Engineering. Elsevier, pp. 1481e1486. AlNouss, A., McKay, G., Al-Ansari, T., 2019a. Superstructure optimization for the production of fuels, fertilizers and power using biomass gasification. In: € Kiss, A.A., Zondervan, E., Lakerveld, R., Ozkan, L. (Eds.), Computer Aided Chemical Engineering. Elsevier, pp. 301e306. AlNouss, A., McKay, G., Al-Ansari, T., 2019b. A techno-economic-environmental study evaluating the potential of oxygen-steam biomass gasification for the generation of value-added products. Energy Convers. Manag. 196, 664e676. AlNouss, A., Namany, S., McKay, G., Al-Ansari, T., 2019c. Applying a sustainability metric in energy, water and food nexus applications; a biomass utilization case study to improve investment decisions. In: Kiss, A.A., Zondervan, E., € Lakerveld, R., Ozkan, L. (Eds.), Computer Aided Chemical Engineering. Elsevier, pp. 205e210. Antoniou, N., Monlau, F., Sambusiti, C., Ficara, E., Barakat, A., Zabaniotou, A., 2019. Contribution to Circular Economy options of mixed agricultural wastes management: coupling anaerobic digestion with gasification for enhanced energy and material recovery. J. Clean. Prod. 209, 505e514. Calvo, L.F., García, A.I., Otero, M., 2013. An experimental investigation of sewage sludge gasification in a fluidized bed reactor. Sci. World J. 2013 (8). Champion, W.M., Cooper, C.D., Mackie, K.R., Cairney, P., 2014. Development of a chemical kinetic model for a biosolids fluidized-bed gasifier and the effects of operating parameters on syngas quality. J. Air Waste Manag. Assoc. 64 (2), 160e174. Choi, Y.K., Cho, M.H., Kim, J.S., 2015. Steam/oxygen gasification of dried sewage sludge in a two-stage gasifier: effects of the steam to fuel ratio and ash of the activated carbon on the production of hydrogen and tar removal. Energy 91, 160e167. Daioglou, V., Faaij, A.P.C., Saygin, D., Patel, M.K., Wicke, B., van Vuuren, D.P., 2014. Energy demand and emissions of the non-energy sector. Energy Environ. Sci. 7 (2), 482e498. De Klerk, A., 2012. Fischer-Tropsch Refining. John Wiley & Sons, Weinheim,

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