Performance of the coffee stem gasification model
Table 6 collates an overview of the most representative performance parameters of the gasifier–ICE system. The results show that the 100-kWth downdraft gasifier coupled to an ICE and fed with 27.2 kg h−1 of coffee stems produces a fuel gas that meets the minimun standards of LHV (4 MJ Nm-3) for ICE applications. The utilisation of the producer gas in the ICE could generate 20.4 kWel of net electricity and 40.4 kWth of thermal power output from the heat recovery stages.
The gasification performance parameters, cold gas efficiency (CGE) and hot gas efficiency (HGE) show a good conversion efficiency of the gasifier when using coffee stems as feedstock, both ranging within the characteristic numbers for downdraft gasifiers, CGE 30–60%  and HGE 85–90% .
Model validation with experimental results
Table 7 presents the validation of the Aspen simulation results with the experimental data on the producer gas composition, LHV and gas yield reported by Garcia et al.  referred to it as “Exp data 1” and the second dataset of experimental results by Oliveros-Tascón et al.  referred to it as “Exp data 2”.
The simulation results show good agreement with the set of experimental data for the mole fractions of H2, CO, CO2, and N2 gas species, the producer gas yield and the gas LHV. Nonetheless, the methane (CH4) mole fraction is under-predicted by the simulation resulting in a high percentage error, for both cases. On this issue, the methane composition is usually under-predicted when a gasification system is modelled following a thermodynamic equilibrium approach. As theory specifies, the methanation reaction, described by the equation (C + 2H2 ↔ CH4), tends to deviate from chemical equilibrium at high temperatures (above 800 °C) , as is the case of for gasification.
On balance, since the H2 and CO composition are the main combustible components in the producer and are slightly over-predicted in the simulations, the low heating value is not deleteriously affected by the lower CH4 concentration. As a result, the producer gas LHV derives into an average percentage error of 13%, acceptable for the purposes of this modelling work.
The effect of key gasification parameters on the gas temperature and composition, and consequently, on the gas low heating value and cold gas efficiency (CGE) of the gasifier is evaluated in this section. These results inform on the feasibility of practical implementation where feedstock properties may vary and operating parameters could be controlled to improve the gasifier performance.
Effect of biomass moisture content
Figure 4 illustrates the effect of the coffee stem moisture content (MC) on the gas composition and gasification temperature profile. The H2O concentration in producer gas increases steadily over the biomass moisture content range. The excess of H2O demands more energy to evaporate the moisture in the biomass, plunging the gasification temperature. A decline in the temperature favours the inverse direction of the endothermic water–gas reaction and the forward direction of the exothermic CO shift reaction. This results in a sharp drop in the CO concentration and a gradual decrease in H2 concentration for MC values above 25%. On the contrary, the CO2 mole fraction increases slowly up to an MC of 35%, after it starts to stabilize. The methane concentration is very low and slowly decreases with higher moisture contents.
Figure 5 presents the overall effect of the moisture content on the gas LHV and CGE. The decreasing concentration of H2 and CO and rising mole fraction of H2O in the producer gas lower the gas LHV, which consequently, affect the gasifier performance, measured by the CGE of the gasifier. This confirms the importance of controlling the moisture content of the biomass, which for downdraft gasifiers should not exceed 25% wt. [46, 55], keeping MC ranges between 10 and 20% wt. for better performance .
Effect of the equivalence ratio
In authothermal gasifiers, as the one modelled in this work, the gasification temperature can be controlled with the amount of air supplied to the gasifier. Figure 6 illustrates this relation, as the ER increases, the gasification temperature rises favouring the products of the endothermic water–gas reaction (C + H2O ↔ CO + H2). The mole fractions of CO and H2 rise as ER increases, both reaching peak values at ER = 0.25. At this equivalence ratio, downdraft gasifiers are expected to give the best gas yield , as shows the trend of the CO and H2 mole fractions.
In contrast, the CO2 and H2O mole fractions drop between ER values of 0.25 and 0.35, respectively, after which they start increasing gradually. As more O2 is available in the gasifier and the carbon in the biomass has been consumed, the CO and H2 start reacting with the oxygen, producing the combustion products CO2 and H2O. The CH4 mole fraction decreases until almost zero, due to methanation reaction tending towards more reactants than products when the temperature rises.
The concentration of the producer gas combustible components CO and H2 determine the gas low heating value, and consequently, the cold gas efficiency of the gasification system. Figure 7 shows that as ER increases from 0.1 to 0.25, the gas LHV goes up gradually, reaches a peak when the CO and H2 mole fractions are in their maximum values and then falls rapidly as the concentrations of CO and H2 drop. Consequently, the cold gas efficiency (CGE) follows a similar trend, where the highest CGE yields at an ER value of 0.25.
Effect of air preheating
Air entering the gasifier as the gasifying agent at temperatures higher than ambient temperatures (> 25 °C) can improve the gasification conversion efficiency. Figures 8 and 9 show how as the air temperature increases the H2 and CO mole fractions augment, resulting in a higher gas LHV. Opposed to this, the CO2 and H2O mole fractions decrease. The CH4 mole fraction remains almost constant across the whole range. This behaviour is caused by an increase in the gasification temperature due to the higher sensible heat of the air stream.
The trends followed by the producer gas composition and heating value, when varying the above key parameters, show the expected behaviour, also in accordance with previous studies, like Ramzan et al. ; Zainal et al. ; Doherty et al. , Yao et al.  and Altafini et al. . The results of the sensitivity analysis also support the predictive capability and robustness of the model to variations in the producer gas composition.
Heat recovery pathways
The main characteristics of the two heat recycling systems, from the producer gas and flue gas cooling stages, of the coffee stem gasification–ICE systems are presented in Table 8. A higher thermal energy stream is attainable when cooling down the flue gases in the second heat recovery stage, due to a higher mass flow rate of the flue gases. For the baseline setting (20 kWe of net power output), the total maximum heat duty that could be recovered from the gas cooling stages is 40.4 kWth. This results in a thermal power output efficiency of 30.3% for the whole system, with reference to the biomass energy input. The heat duty recovered from PGAS-HX1 (2.69 kW) could be utilized to heat the gasifying air up to 250 °C, resulting in an increase in the LHV of the producer gas from to 4.7 to 5.3 MJ kg−1; another potential application is when the biomass requires external mechanical pre-drying before entering the gasifier. The heat recovered, in the form of a hot air stream from the PGAS-HX2, and AIR-HX could be used to supply totally or partially the process heat demand of the coffee mechanical drying.
Coffee drying is a key step in the grain processing, hence the importance of maintaining a uniform drying process to achieve a standard grain moisture content (10–12% wt.) . The optimum air temperature (48–52 °C) and airflow rate (66 m3 min−1 per ton of coffee for static layers dryers) conditions, as established by Cenicafe in , are in theory achieved by combining both recovered heat duties.
The energy flows of the overall system are presented in Fig. 10, as a Sankey diagram to schematize the transformation of the biomass energy input into useful energy outputs (power and low-grade heat), as well as to take into account the energy losses of the system. In the first stage, the intrinsic chemical energy of the biomass is transformed through gasification into the energy carried by the clean producer gas, with a 71% overall efficiency. A fraction of the raw producer gas thermal energy (13.7%), in the form of sensible heat, is recovered as low-grade heat in the gas cooling stage. The energy losses in the gasifier and gas cleanup stage account for almost 15% of the biomass energy input.
In the second stage, the producer gas energy is converted into electrical power through the ICE generator set with an electrical efficiency of 24%, where a fraction (10%) of the gross electrical output is used to supply the internal plant’s power demand resulting in a net power output of 20 kWe. In addition, part of the sensible heat from the hot flue gases is recovered through a loop of heat exchangers. The heat energy losses coming from the power train section, ICE and the flue gas stream correspond to 52.6% of the producer gas energy input.
Overall, the cogeneration system efficiency, results in 45.6%, agreeing with numbers reported in similar works about the gasification of agricultural residues in gasifiers-CHP systems [63, 71, 72]. The maximum low-grade heat recovered from both cooling systems could be used to supply the internal heat requirements of the system and/or external heat demands of the coffee drying process.
Furthermore, considering the significant thermal energy that the producer gas carries within, this gas could also be applied for direct utilization in boilers for on-site heat production. Even though this energy pathway is not studied here, it is pertinent to highlight the versatility of the producer gas as a fuel for small-scale bioenergy applications in rural areas.
Biomass availability and energy demand of the Colombian coffee sector
The electricity generated from this bioenergy system can be used to meet the electrical power demand of coffee farms or community coffee processing plants in rural areas. In addition, the low-grade heat recovered from the gas cooling phases can supply partially the process heat required for the coffee mechanical drying. Yet, for this to start materializing, the match between the biomass resource availability and the farm’s energy demand has to be considered.
During the coffee harvesting periods in Colombia, occurring twice a year, the operation of the 100 kWth gasifier ICE for 336 h per month could result in a net electricity generation of 6720 kWh per month and a maximum net thermal power output of 12,400 kWh per month,Footnote 1 requiring approximately 9 tons per month of coffee wood chips as fuel input to run. This net power output could meet the electricity monthly demand of two large-scale coffee farms (cultivated areas ≥ 10 ha) in Colombia, with similar coffee production and average electricity consumption of 2700 kWh per month per farm . This demand includes the power requirements of the farm household appliances and the coffee processing plant.
Medium (areas between 5 and 10 ha) to large (areas > 10 ha) scale farms with coffee processing plants that generally require mechanical drying  could benefit from part of the recovered heat of the system by transforming it in a hot air stream (50 °C) to dry up to 11 tons per month of washed coffee beans, assuming a thermal efficiency of 52% for a common coffee mechanical dryer . As a result, this could provide 5 tons of green coffee per month ready for market trading.
Alternatively, this power and heat generation could also supply the power demand of a large community-based coffee processing plant, requiring on average 25 kW of power capacity for the processing equipment.
The operation of this unit requires approximately 9 tons of coffee stems per month, consistent with 75% of the combined biomass average production of two medium-scale (areas between 5 and 10 ha) coffee farms. At this scale, each farm could produce a minimum of 25 tons per year of dry parchment coffee that yields, in theory, 72 tons per year of coffee stems, following the equivalence reported by Rodriguez  of 0.6 kg of stems per 1 kg of coffee cherries.
This amount of biomass, although is well above the system’s resource demand, is also constrained by each farms management systems and their coffee plantations age (generally requiring pruning after 5 to 6 years of cultivation ). This implies that storing facilities in the farms would be likely necessary to facilitate sustained feedstock availability and protect the coffee wood from rain and prevent decomposition. Direct application to small-scale farms (~ 1–5 ha of cultivated land) would be less viable as it would require the integration of several small farms to guarantee a regular biomass feedstock supply. Instead, small-farm holders could beneficiate of the bioenergy supply to community-based coffee processing plants, as they usually organized themselves in cooperatives for the coffee processing and trade.
An initial country-level estimation of the total coffee stem potential of 3,000,000-ton dry coffee wood  indicates that sustainable utilization of the residue could yield a biomass availability of 1,500,000-ton dry coffee wood. This considers that approximately 50% of total residues can be removed sustainably to avoid soil degradation . This biomass resource could supply the feedstock requirements of 20,000 gasification–ICE plants of similar operation capacity, having the potential to contribute with 270 MW of power installed capacity through the implementation of distributed generation systems in the rural regions of Colombia. This would have a positive impact from small to large-scale coffee farmers in the country, with direct application in coffee farms or through their deployment in community coffee processing plants. This significant bioenergy potential, yet, requires further research to evaluate how the biomass availability and energy demand balance behaves across the coffee regions in Colombia by conducting a detailed geographical biomass supply-energy demand analysis.