Chemical Industry & Chemical Engineering Quarterly

Available on line at Association of the Chemical Engineers of Serbia AChE www.ache.org.rs/CICEQ

Chem. Ind. Chem. Eng. Q. 25 (4) 329−339 (2019) CI&CEQ

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JOÃO CARDOSO

VALTER SILVA

DANIELA EUSÉBIO TIAGO CARVALHO

PAULO BRITO

VALORIZA, Polytechnic Institute of Portalegre, Portugal

SCIENTIFIC PAPER

UDC 662.63(469):662.6:66

MODELLING AND EXPERIMENTAL ANALYSIS OF A SMALL-SCALE OLIVE POMACE GASIFIER FOR COGENERATION APPLICATIONS: A TECHNO-ECONOMIC ASSESSMENT

Article Highlights • A 2-D CFD multiphase model was employed to study olive pomace gasification • The most suitable end-use applications for the produced gas were assessed • The feasibility of performing olive pomace gasification in small-scale was addressed • A Monte Carlo sensitivity analysis was performed Abstract

A 2-D numerical simulation approach was implemented to describe the gasific-ation process of olive pomace in a bubbling fluidized bed reactor. The numer-ical model was validated under experimental gasification runs performed in a 250 kWth quasi-industrial biomass gasifier. The producer gas composition, H2/ /CO ratio, CH4/H2 ratio, cold gas efficiency and tar content were evaluated. The most suitable applications for the potential use of olive pomace as an energy source in Portugal were assessed based on the results. A techno-economic study and a Monte Carlo sensitivity analysis were performed to assess the feasibility and foresee the main investment risks in conducting olive pomace gasification in small facilities. Results indicated that olive pomace gasification is more suitable for domestic purposes. The low cold gas efficiency of the pro-cess (around 20%) turns the process more appropriate for producer gas pro-duction in small cogeneration facilities. Olive pomace gasification solutions showed viable economic performance in small cogeneration solutions for agri-culture waste-to-energy recovery in olive oil agriculture cooperatives. However, the slender profitability may turn the project unattractive for most investors from a financial standpoint.

Keywords: olive pomace gasification, quasi-industrial biomass gasifier, CFD, small-scale power production facilities, techno-economic analysis.

In the Mediterranean basin, olive cultivation and olive oil extraction constitute a major cultural and economic activity [1]. This region alone accounts for 95% of the world’s olive oil production, corresponding to the total amount of about 3013,000 tons [2]. Glo-bally, most of the olive oil production is assigned to European Union (EU) member states holding 75% of the share, and the remaining 25% belonging to

Correspondence: V. Silva, Campus Politécnico 10 7300-555, Portalegre, Portugal. E-mail: valter.silva@ipportalegre.pt; valter@fe.up.pt Paper received: 9 January, 2019 Paper revised: 5 March, 2019 Paper accepted: 24 March, 2019

https://doi.org/10.2298/CICEQ190109010C

countries such as Tunisia, Morocco, Algeria, Syria and Turkey [2]. Portugal sits as the 8th world’s largest olive oil producer while Spain takes the lead having a huge 40% share of the world’s production [3]. In Por-tugal, olive oil production earned in 2016 an income of 412 million euros, with olive groves representing 52% of the permanent crops surface area (336,000 ha), being the Alentejo region (south-central Portu-gal), the main area of olive production in the country [4]. Figure 1 depicts the Portuguese olive oil product-ion by region. From the olive oil extraction process, it is possible to recover a solid residue know as olive pomace, containing pulp, pieces of olive pits, olive husk and some remaining oil and water. This olive oil industry by-product appears as a suitable biomass

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fuel solution given to its high heating value, with applications in heat and/or electrical power product-ion, namely in fueling boilers and gasification pro-cesses [5,6].

Figure 1. Portuguese olive oil production case scenario.

In the EU, 6.8 million tons of waste derived from the olive oil industry is estimated to be produced per year, containing an encouraging energy content of around 18 MJ/kg [7]. Portugal as a major contributor to the global olive oil industry holds a favorable posit-ion, given the readiness and availability of olive pom-ace residues, particularly in the Alentejo region, turn-ing this residue into a feasible energy supply [5]. In Portugal, the estimated olive pomace amount and its energy potential round to 23.7 t/yr and 0.51 PJ/yr, respectively [8].

Biomass gasification has become an attractive energy conversion process due to its high-efficiency power production and enhanced environmental per-formance, aiding to fulfill the increasingly stringent energy demands while mitigating climate change and its impacts [9-11]. Olive pomace gasification is no dif-ferent, and its energy potential has already been acknowledged by other authors, as pointed out in the following lines.

The effect of temperature on olive pomace gas-ification was studied by Almeida [12], whose expe-riments showed that higher bed temperatures favored gas production as well as other gasification perform-ance parameters. Puig-Gamero [13] compared olive pomace, coal and petcoke feedstocks in co-gasific-ation processes, and the authors indicated that olive pomace blends presented the lowest devolatilization temperature, highest reactivity, highest H2 emission and highest H2/CO ratio. A characterization analysis of pellet blends composed by olive pomace and olive tree prunings was carried out by Barbanera [14]. Physical, chemical and mechanical characteristics showed that these residues carry great potential to

fuel combustion and gasification applications. Borello [15] developed a techno-economic analysis studying the viability of olive pomace gasification in cogener-ation processes, and the results demonstrated the economic viability of the process implemented in Italy.

Thus, the goal of this paper is to assess the gasification of olive pomace and its techno-economic viability in small cogeneration facilities in the Alentejo region. The contributions of this work rely on a thorough application analysis of this abundant sub-strate in this particular region on quasi-industrial con-ditions in a 250 kWth bubbling fluidized bed reactor. For this purpose, olive pomace gasification runs gathered from a quasi-industrial biomass gasifier were employed to validate a previously developed numerical model. The producer gas composition and the cold gas efficiency (CGE) of the process were evaluated. The most suitable olive pomace end-use applications were determined. Lastly, a techno-eco-nomic evaluation alongside with a Monte Carlo sensit-ivity analysis was performed on a small-scale con-cerning the application that carries greater potential from the technical analysis.

EXPERIMENTAL

The olive pomace gasification runs were con-ducted in a quasi-industrial gasification plant located in the Alentejo region at the Polytechnic Institute of Portalegre, Portugal. The proximate and elemental analyses of the used olive pomace feedstock are shown in Table 1. The main specifications of the experimental apparatus are depicted in Figure 2.

Table 1. Olive pomace proximal and elementary analysis

Biomass properties Value

Density [kg m-3] 410.68

Proximal analysis (wt.%)

Humidity 8.90

Ash 0.65

Volatile matter 70.96

Fixed carbon 19.48

Elemental analysis (%, db)

C 53.87

H 8.83

N 2.03

O 26.51

The unit carries a 250 kWth bubbling fluidized bed gasifier, 0.5 m wide and 4.15 m height, thermally isolated by a ceramic refractory material coating its interior. Biomass feedstock is delivered into the reactor

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at a height of 0.4 m from a distributor plate, and pre-heated air flow is delivered within the reactor through a set of diffusers in the distributor plate. The bed has a static height of 0.15 m and is composed of 70 kg of dolomite (calcium magnesium carbonate CaMg (CO3)2; its use as bed material is considered advantageous since it is cheap and exhibits catalytic tar cracking and anti-sintering properties. Bed temperature is con-trolled by means of three sensor probes arranged throughout the reactor. Gas sampling bags are used to collect the producer gas samples at the condenser outlet, once the gasification process reaches equilib-rium. Producer gas samples are then inserted into the chromatograph for further analysis within one hour after collection. Additional details on all remaining components are fully explained in [16,17].

Numerical procedure

The simulations were carried out in a 2-D fluid-ized bed reactor, rectangular domain 0.5 m wide and 4.15 m height, as the real 250 kWth reactor. Initial bed height was set with dolomite up to a height of 0.15 m, air is introduced at the bottom and resulting gas aban-dons the reactor’s vessel throughout an outlet placed at the top right corner of the geometry. A transient calculation method was applied to simulate the sol-ution, using a time step size of 0.001 s, for a total number of 50,000 time steps (50 s). The mesh was built by using quadrilateral cells of uniform grid spac-ing, the selected size was about 12 times the particle diameter so to effectively capture the hydrodynamics [18]. The mesh was selected to be sufficiently refined to capture the steep gradients in the nearby the inlet and outlet of the gasifier. The optimal size of quadri-lateral cells was found by consecutively doubling the grid density up until when no significant differences were obtained considering producer gas composition, temperature and velocity profiles. The selected mesh to picture the behavior within the 250 kWth reactor was built with 80808 elements. This mesh quality has already served as the best solution to fulfill the

purposes of other published studies by the research group [19]. Additional details on the numerical pro-cedure can be found elsewhere [19].

Mathematical model

The implemented 2-D Eulerian-Eulerian mathe-matical model was firstly developed by Silva [17]. Complex phenomena concerning the gasification pro-cess for the fluidized bed reactor is simulated by means of a multiphase (gas and solid) model within the ANSYS Fluent framework. The gas phase was considered as a continuum, and the solid phase was modeled following a Eulerian granular model. Inter-actions between phases were modeled as well, with both phases exchanging heat by convection, mom-entum (due to drag between phases), and mass (given the heterogeneous chemical reactions). Table 2 summarizes the main governing equations for both gas and solid phases, the hydrodynamic model, the devolatilization, main chemical reactions and reaction rates coefficients (based on the Arrhenius law) in the chemical model. The energy conservation equation describes the heat exchange between gas and solid phases, the viscous dissipation and the expansion work of the void fraction. The equation for the gas and solid phases is shown in Figure 3, in which

pqQ is the

heat transfer intensity, hq the specific enthalpy of phase,

qq the heat flux, Sq the source term and hpq

the enthalpy of the interface. For the mass balance model, the gas and solid phases continuity equations are provided, the α is the volume fraction, ρ density, vg velocity and Sgs is defined as the source term (Fig-ure 3). The momentum equations for both phases consider the gas stress tensor τg, gravity g, the gas- -solid interface drag coefficient β, and the solid mean velocity Us (Figure 3). The granular Eulerian model treats the continuous fluid (primary phase) as well as dispersed solids (secondary phase) as interpenetrat-ing continua. Stresses in the granular solid phase are obtained by analogy between the random particle motion and the thermal motion of molecules within a

Figure 2. Schematic respecting the main components of the biomass gasification plant located at

he Polytechnic Institute of Portalegre, Portugal.

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Figure 3. Conservation equations, hydrodynamic and chemical reactions model [17].

gas, accounting for the inelasticity of solid particles. The kinetic energy associated with velocity fluctuat-ions is described by granular temperature which is proportional to the norm of particle velocity fluctu-ations. In the conservation equation for the granular temperature, the γθa is the collisional dissipation rate of granular energy,

sv the diffusive flux of granular

energy, ϕ1s the granular energy exchange between the gas and solid phase, and kθa is the diffusion coefficient (Figure 3). The devolatilization was inc-luded within the ANSYS Fluent framework so to dev-elop a reliable gasification model. In this process, the biomass is thermally decomposed into volatiles, char, and tar. The homogeneous reactions are the CO

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combustion, water-gas shift, reforming of hydrocar-bons, H2 combustion, CH4 combustion (presented in Figure 3 by this order) and include only reactants and products in the same phase. In gasification, these reactions occur in the gas-phase describing the react-ions between volatile gases and gasifying agents. In the heterogeneous reactions, the gas species react with solid char (the solid devolatilization residue) res-ulting in gaseous products and include the partial combustion, CO2 gasification, and steam gasification (Figure 3). The present model has already been ext-ensively documented in recent literature published by the research group. Further details on the model can be found elsewhere [17,19].

RESULTS AND DISCUSSION

Model validation

In order to evaluate the model fitting regarding the different producer gas composition species, a total of five experimental gasification runs using olive pomace as fuel and atmospheric air as gasification agent were performed. Table 2 shows the operating conditions and producer gas quality indices compo-sition for the five experimental runs used to validate the numerical model. Throughout this study, special care will be delivered to producer gas quality indices, since these allow assessing the possible different applications based on the obtained producer gas composition. Figure 4 presents the experimental and numerical molar producer gas composition for the five experimental runs. Results show that the mathe-matical model was capable to correctly predict pro-ducer gas compositions for the five experimental runs by providing a good agreement with the experimental results. The largest deviations were measured for the

smaller fractions, methane gas (CH4), around 20%. Such behavior is justifiable once smaller fractions tend to favor higher relative errors. This deviation is a reasonable margin for such a complex process as biomass gasification in a quasi-industrial fluidized bed reactor [17]. For simplification purposes, only five experimental runs were here considered, once the mathematical model applied in this study has already been thoroughly validated concerning the producer gas compositions from multiple gasification agents and feedstocks at various operating conditions, strength-ening the accurate predictability of the mathematical model in a broad range of applications [20,21].

Figures 5a and b show the effect of temperature and equivalence ratio (ER) on the producer gas calo-rific value for both experimental and numerical data. The increase in the gasification temperature has a positive effect on LHV (lower heating value) as inc-reasing temperature enhances H2 and CO content, which in turn increases the producer gas LHV. In this work, ER is set by varying the air flow while main-taining all other operating conditions constant. Res-ults show that ER presents a contradictory trend once with the ER increase the N2 dilution effect comes enhanced, further leading to LHV decrease. Both trends go in hand with the current literature [17,22].

Table 2. Experimental operating conditions and producer gas molar quality indices composition

Gasification run 1 2 3 4 5

Temperature [K] 1023 1074 1073 1123 1126

Biomass admission [kg h-1] 46 46 56 26 56

Feed system screw speed [%] 50 70 70 30 70

Producer gas quality indices

CH4/H2 ratio 1.17 1.09 1.05 1.02 1.00

H2/CO ratio 0.21 0.20 0.20 0.22 0.21

Figure 4. Experimental and numerical molar producer gas composition for the five experimental runs.

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Producer gas quality indices, CGE and tar content

Numerical models provide valuable information in predicting the producer gas composition in gasific-ation processes, particularly when determining the producer gas quality indices to appraise possible applications concerning the obtained producer gas characteristics. Figure 6a depicts the CH4/H2 and H2/ /CO producer gas mole ratios as a function of the three tested gasification temperatures. Higher CH4/H2 indices were measured, with ratios tending to dec-rease with the gasification temperature increase, as higher temperatures favor H2 production. Lower H2/ /CO ratios are due to constantly increased CO quan-tities, therefore revealing a constant profile along the temperature range. Generally, producer gas produced from biomass tends to have a rather low H2/CO ratio (<1) [17]. Producer gas quality indices allow gauging the correct end-use application for each obtained producer gas. From an application point of view, higher H2/CO ratios are particularly suitable for fuel

cell integration purposes, and chemical industry (>1.7), while producer gas with higher CH4/H2 ratios is preferable for domestic purposes [23]. Figure 6b illus-trates the CGE and tar content for the various reactor operating temperatures. Results show that gasific-ation temperature has a strong influence on CGE and tar content. CGE increases with the gasification tem-perature, as the gaseous products yield become enhanced. The pyrolysis tar content becomes gradu-ally reduced with the temperature increase, promoting tar decomposition, while lower temperatures promote tar condensation. The olive pomace gasification pro-cess showed a low CGE, around 20%, making this process unfit for broad electricity production due to very low overall yields. Instead, it may be used to pro-duce producer gas with certain characteristics for small facilities. This assumption goes in hand with the preliminary results provided by the producer gas qual-ity indices, indicating that the producer gas obtained from this process is more suitable for domestic pur-poses.

Figure 6. a) Producer gas CH4/H2 and H2/CO mole ratios as a function of the temperature; b) influence of temperature

on CGE and tar content.

Figure 5. a) Effect of gasification temperature on experimental and numerical producer gas LHV; b) effect of ER on experimental and

numerical producer gas LHV.

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Techno-economic analysis

Given the strong availability of olive pomace residues in the Alentejo region and the suitability of its application for domestic use, the present study pur-poses the cogeneration of this by-product in rural small-scale facilities within an olive oil agriculture cooperative. Thus, a techno-economic analysis is developed to assess the economic feasibility from an investor point of view of this project along a certain lifetime period. As an endeavor to bring this analysis as close as possible to a real practical application, this study was built based on literature review con-cerning investment projects in olive pomace gasific-ation in small cogeneration facilities [15,24-26]. The purposed unit consists of an 800 kW small-scale cogeneration plant lodged within the agriculture coop-erative. The project is considered to extend to a total of 20 years lifetime (start-up phase in 2018 for initial investment and construction, and an operational phase from 2019 to 2038). The olive pomace feed-stock is collected from the associated farmers’ country estates and then transported to the cooperative facil-ities for gasification, therefore, only transportation costs are weighted in and no biomass cost is con-sidered which aids the supply viability of the project. An average consumption of 2344 tons/year of olive pomace is estimated, with an electric power output of 5280 MWh/yr, considering a baseload annual oper-ation time of 6600 h (in accordance with the literature

for olive pomace gasification in small-scale cogener-ation processes) [24]. The unit is operated in shift regime by a total of six workers (one engineer, one assistant, and four employees). Most of the electrical energy generated is used to reduce the cooperative associate’s primary energy demands; 45% of the pro-duction is assumed to be sold to the national grid for profitability purposes. In this study, no heat sales are considered, and all produced thermal power is assumed to be used for biomass drying and pro-ducing additional electricity by means of an internal heat recovery system.

Table 3 details the economic assumptions used to build the spreadsheet-based economic model dev-eloped to calculate the project’s net present value (NPV), internal rate of return (IRR) and payback period (PBP). These three methods are important common indicators in investment decisions [26]. The considered cash flows for costs and revenues calcul-ations were: initial investment (equity and borrowed capital), amortizations, operating and maintenance (O&M) costs, employees costs, revenues from elec-tricity sales to the grid, and electricity savings. The estimated electricity savings for this project were about 30%, which goes accordingly to the literature’s estimations for small-scale cogeneration projects [27]. All cash flows, except the initial investment occurring only in the start-up phase of the project, extend throughout the 20-year lifetime of the project, with all

Table 4. Economic assumptions for the 800 kW small-scale cogeneration plant

Economic parameters Value Observations

Discount rate [%] 10 -

Inflation rate [%] 1.6 Inflation rate applied in 2020.

Equity capital (30%) [k€] 279 Values applied during the investment period. Comprises costs related to creditopening, whole plant equipment acquisition (gasifier, turbine, producer gas cleaning system) and electric power line construction.

Borrowed capital (70%) [k€] 651

Amortizations [k€] 88

Amortizations value in 2019. Comprises the regular debt payment throughout theplant lifetime and insurance.

O&M costs [k€]

93

Value applied in 2019 (10% of the capital cost). Comprises all consumption costs withthe facility, namely olive pomace transportation to the agricultural cooperative, olivepomace pre-treatment, ash transport and deposition into landfill, and maintenance ofthe equipment and facility.

Employees costs [k€] 67

Value applied in 2019. Comprises wages, benefits, employees’ insurance expensesand social security charges, all in agreement with the Portuguese legislation.

Total annual costs [k€] 248 Value for 2019.

Power output parameters

Electricity production [MWh/year] 5280 Considering the baseload operation time of 6600 h.

Output sold to the grid [MWh/year] 2376 45% of the total electricity production is sold to the grid.

Electricity sales tariff [€/MWh] 121.34 Tariff applied in 2018.

Revenues

Electricity savings [k€/year] 18 Value for 2019. Estimated savings of around 30%.

Annual revenue [k€/year] 288 Revenues from electricity sales to the grid in 2019.

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costs and revenues being updated to the year they correspond to. The total annual cash flow is given by the sum of all the costs and revenues for each year. The annual revenue is given by multiplying the annual electricity production by the electricity sales tariff in the same year. The annual cash flow is resolved by balancing the total annual costs, revenues, and the electricity savings. A discounted cash flow is deter-mined by dividing the annual cash flow in each year by the unit plus the calculated discount rate elevated to the relating year. Lastly, the cumulative NPV is det-ermined to provide the present worth of negative and positive investment cash flow. All the analysis is per-formed at current prices, revenues, and current value- -added taxes rates. The implemented inflation rates for 2019 and 2020 are based on the forecasts pro-vided by the Bank of Portugal. After 2021 the applied inflation rate is the calculated average of the last 10 years. All implemented tariffs and prices progression along the project lifetime are amended in agreement with the consumer price index provided by the Nat-ional Statistical Institute of Portugal. Amortization rates are in accordance with the straight-line method, and all interest rates considered result from the quot-ations provided by Portuguese banking for this type of projects.

Figure 7 shows the economic model results for the NPV, IRR and PBP calculations. The NPV method states that an investment should be accepted if the NPV > 0 and rejected if the NPV < 0. The cal-culated NPV for the small-scale cogeneration plant project at the discount rate of 10% is of 89 k€. The IRR is the expected return rate offered by the project and is given by the moment at which the NPV equals to zero. For this project, the calculated IRR rate is 11%. The PBP is the year in which the cumulative

cash flow turns positive. Here, the calculated PBP is of 17.2 years, providing the exact amount of time needed to recover the initial capital investments made. Thus, in this project with a lifetime of 20 years, the initial investment will only be recovered by 2035.

In general, the financial indicators point out that this project is economically feasible, by presenting a positive NPV, an IRR higher than the discount rate, and a PBP inferior to the cogeneration plant lifetime. Nevertheless, one must look beyond these indicators and assess the attractiveness of the project from an investor standpoint. According to typical financial benchmarks for biomass projects present in the lite-rature, the NPV must be positive, IRR greater than 10%, and PBP less than 10 years [26]. Indeed, these criteria may differ by country risk and project-specific conditions, notwithstanding, these will be brought into consideration for reference purposes. Despite the positive NPV, the value obtained is rather low com-pared to the initial investments made, pointing out that the cash inflows only slightly overcome the cash outflows, foreseeing rather low future investment earnings. On the other hand, the IRR positively res-ulted superior to 10%, however, the obtained PBP shows that it would require 86% of the project’s life-time to recover the capital investment costs. Such long PBP may turn this project undesirable to most investors.

Sensitivity analysis

To measure the risks associated with the pro-ject, a Monte Carlo sensitivity analysis is implemented within the economic model spreadsheet so to assess the most critical variables for the performance of the project. The variables that most affect the viability of the project are electricity production, electricity sales

Figure 7. NPV profile variation as a function of the discount rate showing the NPV of the project at a discount rate of 10%, the IRR and

the PBP given out by the cumulative NPV.

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tariff, initial investment, discount rate and O&M costs. The five considered variables sensitivity bounds are defined as unfavorable or favorable by varying the baseline value up to a ±10% range. The simulation is conducted for a total of 10,000 iterations. All other variables within the economic model are maintained unchanged during this analysis. A triangular distri-bution is considered for each variable due to its mathematical simplicity and ability to generate enough random samples, requiring the input of a minimum (favorable value), a mode (baseline value), and maxi-mum (unfavorable value) [28]. Figure 8 depicts the NPV sensitivity analysis to each one of the considered critical variables. For the sake of simp-lification, only the sensitivity analysis to the NPV is presented, since the analysis showed that higher risks of investment loss are more likely to occur due to NPV failure. The curves with higher gradients are the ones that require special concern, thus, from all considered variables electricity production and elec-tricity sales tariff are the ones to which the NPV is more sensitive. These two variables may greatly com-promise the NPV from a negative value of about -1.2 to 2.9 M€, while the remaining variables do not com-promise the NPV to a negative range. Unsurprisingly, electricity production and electricity sales tariff are the variables that carry greater impact over the NPV, as the calculated annual revenues (given by the product of the annual electricity production for the electricity sales tariff) are strongly dependent on these. All curves intersect at the conditional mean given by the horizontal yellow line which points the previously calculated NPV value of 89 k€ (0.89 M€).

A set of final remarks must be addressed in order to better evaluate this investment project. The investment in question was built based on a practical

implementation mindset with the plant being lodged in an agriculture cooperative, meaning more funds are available which would ease the acquisition of such an expensive piece of equipment as a small-scale cogeneration plant. Costs related to biomass are often a significant part of an investment of this nature. Here, no biomass costs are considered, otherwise, this project would present no viability whatsoever. Regardless of these positive economic factors, at least 45% of the generated electric power is con-sidered to be sold to the national grid, contrarily, the viability of the project would be broadly compromised. In addition, the plant must be operated in an almost continuous fashion with an annual baseload of 6600 operating hours being considered, so to guarantee a sufficient power output production capable of main-taining the feasibility of the project. The CGE gener-ally comes as a great deal whenever conversion pro-cesses of this nature are involved, however, one must consider the best achievable CGE for the intended needs. In this study the low CGE (about 20%) does not hamper significantly the process at this rural small-scale [15]. Here, the system output aspect is particularly crucial once the project NPV relies con-siderably on revenues coming from the electricity pro-duction and national electricity sales tariff (which com-prises a certain uncertainty due to its dependence on energy market prices fluctuations and subsidies). Hence, one must balance the pros and cons, and even when considering an estimated 30% on elec-tricity savings, most farmers could be discouraged from investing in a project whose evaluation, despite predicting positive earnings, may not convince far-mers less available to take risks, due to the asso-ciated uncertainties, demanding higher assurances before moving towards such investment. Thus, the

Figure 8. Sensitivity analysis range to input variables for NPV.

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tenuity and the risks associated with an investment of this type are easily recognizable. In this manner, it is important to look beyond the numbers provided by the economic model, and assess each situation indepen-dently considering all potential factors that can easily reverse the initially predicted viability of the project.

CONCLUSION

The gasification process of olive pomace was studied in a quasi-industrial fluidized bed reactor by employing a 2-D CFD model. A set of experimental gasification runs were gathered from the fluidized bed reactor at different temperatures for validation pur-poses. The numerical model effectively predicted the acquired experimental data with generally good agreement. Higher CH4/H2 ratios measured indicate that olive pomace gasification is more suitable for domestic purposes. The low CGE of the process, around 20%, turns it more suitable for producer gas production in small cogeneration facilities, or even synthetic compounds production. Based on these assumptions, an 800 kW small-scale cogeneration plant was purposed, located in an olive oil agriculture cooperative. The techno-economic analysis showed that the project is viable under current market condit-ions, showing an NPV of 89 k€, an IRR of 11%, and PBP of 17.2 years. The sensitivity analysis showed higher risk of failure in the NPV, with the electricity production and electricity sales tariff causing higher impact change over this method. Final remarks point that olive pomace gasification projects carry great potential in small facilities applications in the Alentejo region with economic viability, however, special con-cerns must be considered regarding the project attractiveness to potential investors.

Acknowledgments

The authors would like to express their gratitude to the Portuguese Foundation for Science and Tech-nology (FCT) for the grant SFRH/BD/146155/2019 and for the projects IF/01772/2014, CMU/TMP/0032/ /2017 and DMAIC-AGROGAS: Implementation of DMAIC methodology to enhance the recovery of agro-industrial wastes through small-scale gasific-ation solutions, under the framework of ALENTEJO 2020.

Nomenclature

Cp – specific heat capacity [J kg-1 K-1] D0 – diffusion rate coefficient [m2 s-1] Gn – turbulence kinetic energy [m2 s-2] hpq – heat transfer coefficient [W m-2 K-1] Ji – diffusion flux [m2 s-1]

k – thermal conductivity [W m-1 K-1] m – biomass flow [m3 s-1] M – total mole flow of carbon [g cm-2 s-1] Mc – molecular weight [kg mol-1] p – gas pressure [Pa] qq – heat flux [W m-2] qth – specific enthalpy [J kg-1] R – universal gas constant [J mol-1 K-1] Rc – reaction rate [mol L-1 s-1] Sn – source term T – temperature [K] U – mean velocity [m s−1] v – instantaneous velocity [m s−1] Xc – carbon fraction in the biomass Y – mass fraction Qpq – heat transfer intensity

Other symbols α – volume fraction [-] ρ – density [kg m-3] ϕ1s – energy exchange kθa – diffusion coefficient [m2 s-1]

Θ ∇Θa sk – diffusion energy

γθa – collisional dissipation of energy [W m-3] τ – tensor stress [N m-2] μ – viscosity [kg m-1 s-1] γc – stoichiometric coefficient

Subscripts g – gas phase s – solid phase i – component db – dry basis wt – weight

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JOÃO CARDOSO VALTER SILVA

DANIELA EUSÉBIO TIAGO CARVALHO

PAULO BRITO

VALORIZA, Polytechnic Institute of Portalegre, Portugal

NAUČNI RAD

MODELOVANJE I EKSPERIMENTALNA ANALIZA GASIFIKATORA PULPE MASLINE MALOG KAPACITETA PRIMENJENOG ZA KOGENERACIJU: TEHNO-EKONOMSKA PROCENA

Da bi se opisao proces gasifikacije pulpe masline u reaktoru sa fluidizovanim slojem, primenjena je 2-D numerička simulacija. Numerički model je validiran eksperimentalnim istraživanjima gasifikacije u kvazi-industrijskom gasifikatoru biomase od 250 kWth. Pro-cenjen je sastav gasa, odnos H2/CO, odnos CH4/H2, efikasnost hladnog gasa i sadržaj katrana. Na osnovu rezultata procenjene su najprikladnije primene pulpe masline kao izvora energije u Portugalu. Izvršene su tehno-ekonomska studija i analiza osetljivosti Monte Karlo da bi se procenila izvodljivost i predvideli glavni investicioni rizici u spro-vođenju gasifikacije pulpe maslina u malim postrojenjima. Rezultati su pokazali da je gasifikacija pulpe masline pogodnija za domaće potrebe. Mala efikasnost hladnog gasa u procesu (oko 20%) čini proces prikladnijim za dobijanje proizvodnog gasa u malim kogeneracijskim postrojenjima. Rešenja za gasifikaciju pulpe masline pokazala su odr-žive ekonomske performanse u malim kogeneracionim rešenjima za poljoprivredni otpad koji se koristi u poljoprivrednim zadrugama za maslinovo ulje. Međutim, mala pro-fitabilnost može učiniti projekat neprivlačnim sa finansijskog stanovišta za većinu inves-titora.

Ključne reči: gasifikacija pulpe masline, kvazi-industrijski gasifikator biomase, CFD, malo postrojenje za proizvodnju električne energije, tehno-ekonomska analiza.