# Biomass Steam Gasification: A Comparison of Syngas Composition between a 1-D MATLAB Kinetic Model and a 0-D Aspen Plus Quasi-Equilibrium Model

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## Abstract

**:**

_{2}and CO

_{2}and lower decrease of CH

_{4}and CO respect to the kinetic one and the experimental data. Thus, the thermodynamic model, despite being calibrated by experimental data, can be used mainly to analyze global plant performance due to the reduced importance of the discrepancy from a global energy and plant perspective. Meanwhile, the more complex kinetic model should be used when a more precise gas composition is needed and, of course, for reactor design.

## 1. Introduction

_{2}was previously captured by the plants. At present, biomass use in modern big plants (e.g., Integrated Gasification Combined Cycle, IGCC, for power or Biomass To Liquid, BTL, plants) is the most cost-effective biomass use for power (efficiencies up to 45%) or biofuel (efficiency up to 80%) generation [4,5,6,7,8,9]. Analysis shows that every additional 1% in energy savings leads to a reduction of about 2.6% in gas imports [10]. The Renewable Energy Directive, 2009/28/EC, has driven a rapid deployment of renewable energy. In 2012, energy from renewable sources was estimated to have contributed 14.1% of EU final energy consumption meanwhile the EU target to 2040 is 50% of EU primary energy [11,12,13].

_{2}sorbent, as CaO, can shift the thermodynamic equilibrium leading to a H

_{2}content up to 90%), gasifier design (e.g., residence time, etc.), feedstock composition [2,18,19,20]. In any case, high quality syngas is characterized by low level of N

_{2}and CO

_{2}, high level of H

_{2}and CO and low level of contaminants and high Low Heating Value (LHV, that is defined by literature as the amount of heat released by combusting an amount of fuel from 25 °C and returning the temperature of combustion products to 150 °C, not recovering the latent heat of vaporization of water in the reaction products). The investigation of syngas composition, varying the operative parameters, is necessary for the optimization of the design and operation of biomass gasification. In addition, conducting experiments on a wide range of operating conditions at large scale could be problematic for safety and cost reasons [21,22,23]. For this reason, mathematical simulation models have acquired great interest in the prediction of process performance, providing a faithful representation of both chemical and physical phenomena occurring into the gasifier and allowing to evaluate the syngas composition with the aim of optimize the gasifier/plant design and its operation [24]. Gasification, involving heterogeneous reactions, does not reach thermodynamic equilibrium (gasification reaction rates are not fast enough and residence times are not long enough for the equilibrium state to be reached) and so thermodynamic models with experimental corrections and kinetic models are mainly applied. Thermodynamic models, depending only on thermodynamic properties, i.e., temperature and pressure, are independent from reactor/particle typologies. On the other hand, kinetic models can be more realistic but are more complex, requiring the implementation of reaction kinetics, hydrodynamic equations. Aspen Plus and MATLAB represent two of the most used simulation tools for biomass gasification [25,26,27,28,29].

## 2. Materials and Methods

#### 2.1. Biomass Choice and Characteristics

_{2}, 5–6% of H

_{2}, less than 1% of N

_{2}, Cl and S and less than 10% ashes [2]. The % is referred to mol basis). Furthermore, shells present compatible dimensions with fluidized bed (i.e., small size) gasification reactors and moisture content lower than 10%. The proximate and ultimate analysis of hazelnut shells are:

- Proximate analysis (%
_{wt}, dry basis): 1.16 of Ash, 72.45 of Volatile Matter and 26.39 of Fixed Carbon; - Ultimate analysis (%
_{wt}, dry basis): 50.38 of C, 6.03 of H, 0.22 of N, 42.32 of O, 0.38 of Cl and 0.67 of S.

#### 2.2. Aspen Plus Modelling

_{2}, H

_{2}, N

_{2}, Cl, S, according to the ultimate analysis). Since the repartition of the products (gas, char unreacted, tar and contaminants) is unknown a DECOMP is considered more suitable than a RYIELD fixing the products based on specific experimental conditions. Products exiting the DECOMP block are moved to the RSTOIC block to simulate the production of H

_{2}S, HCl and NH

_{3}(N

_{2}, Cl and S as elemental components are known to produce mainly H

_{2}S, HCl and NH

_{3}, and a fractional conversion of 1 is quite in line with experimental data which represents the worst case of maximum contaminants [36]). The products are moved to a SEP block to separate volatile, char and inorganic (H

_{2}S, HCl and NH

_{3}) fractions in order to separate (with other SEP) char (i.e., C) and H

_{2}to form tar in the RYIELD block TARPROD where tar is considered to be formed, using experimental data of 18 g/Nm

^{3}, repartitioned into 60% benzene, that does not condense, so it is not a “real” tar but it is the most present hydrocarbon in biomass gasification after methane, 20% toluene and 20% naphthalene [36]) and unreacted C to be sent to the combustor. The gasifier, considered as an indirectly heated fluidized-bed reactor, is modeled by a RGibbs reactor (GASIF in Figure 1) and the bed material is sand. Within the reactor, the restricted chemical equilibrium of the specified reactions is simulated in order to set the product gas composition by specifying a temperature approach for each individual reaction. The reactions considered in the simulation are reported in Table 1. The gasifying agent considered in this paper is steam, however in Figure 1 also the streams of oxygen OXYG and air AIR are reported since the model is able to work with all the combination of oxidizing agents, their mass flow has been set to zero. The stream called UNREACT represents the unreacted char, set as 11% of biomass inlet (dry) according to [40] and feeds the combustor COMB. More details of the model can be found in the work of Marcantonio et al. [36].

#### 2.3. MATLAB Modelling

_{2}, CO and CH

_{4}respectively) considering their mass-based concentrations in the total gas mixture. The thermal balance of the gasifier determines the temperature drop of the circulating bed material (olivine), considering a fixed ratio of 50 kg

_{olivine}/kg

_{biomass}as heat transfer media between the gasifier and the combustor. The specific heat of the olivine is fixed at 750 kJ/(kg·K). Using olivine as bed material also provides a moderate catalytic effect for the heterogeneous reactions in the MATLAB model [5], modifying the syngas composition. The model has been tuned and validated using the experimental results obtained in a bench scale fluidized bed gasifier [43]. The reactions considered in the model are the same of the Aspen Plus (which are reported in Table 1) plus the Boudouard reaction (R6) and the tar reforming (R7), shown below, which were not considered in the Aspen Plus model due to the fact that they are very far from thermodynamic equilibrium. Adding such reactions would lead to inaccurate results respect to the experimental ones (i.e., more CO and H

_{2}and no toluene).

CO_{2} + C → 2 CO | (+172 MJ/kmol) | (R6) |

C_{7}H_{8} + 7 H_{2}O → 7 CO + 11 H_{2} | (+881.74 MJ/kmol) | (R7) |

## 3. Results and Discussion

#### 3.1. Models Comparison

_{steam}/kg

_{biomass,dry}) the Aspen Plus and MATLAB values of H

_{2}and CH

_{4}are similar and are lower and higher than the reference value, respectively. The AE is within −8 and −3 for H

^{2}and +3 and +5 CH

_{4}with a RE within −20% and −8% for H

_{2}and within +33% and +61% for CH

_{4}due to its lower values in the syngas. This is due to the presence of olivine used as natural catalytic bed material in the experiments of Hofbauer et al. [45] and Fercher et al. [44] that emphasizes the water-gas shift (WGS) reaction (R4) and steam-methane reforming (SMR) reaction (R5), determining in this way the reduction of CO that, vice versa, is higher in the two models (AE +17 and +13 which leads to a RE up to +100%). The MATLAB model, that considers the olivine effect, has fewer differences although the olivine contribution is still lower than what seen in the literature reference. The overestimation of CO consequently shifts the equilibrium of (R4) towards the reagents reducing the CO

_{2}prediction (AE −10 and −14, RE in the range of −34% and −45%). Despite the differences in gas composition, regarding LHV, yield and cold gas efficiency, the models shows good agreement with the experimental values (Aspen Plus maximum AE 1.7 and RE 12%, MATLAB maximum AE 0.7 and RE 5%), confirming that, for plant analysis also the thermodynamic models can be used meanwhile for specific gas prediction the kinetic models are more suitable.

_{steam}/kg

_{biomass,dry}) from the comparison of the experimental values obtained by Hofbauer [45] using 0% of nickel catalyst and quartz as bed material it is possible to observe the good correspondence of the developed models for CH

_{4}, CO and CO

_{2}(Aspen Plus maximum AE 4 and RE 31%, MATLAB maximum AE 3 and RE 31%). Instead the H

_{2}is overestimated (Aspen Plus maximum AE 8 and RE 23%, MATLAB maximum AE 7 and RE 21%) mainly because the Aspen Plus model, under equal uncatalyzed conditions, considers equilibrium conditions meanwhile MATLAB overestimates the steam reforming of tars, both factors that increase the H

^{2}content of the syngas at higher S/B. At S/B 0.5 the LHV, yield and cold gas efficiency are in good agreement (maximum AE −3 and RE +32%), the LHV of the MATLAB model, in this case, is in a better agreement (AE −0.45 and RE −3%) respect to Aspen Plus (AE −2.95 and RE −20.13%) since it considers the reforming of tars.

#### 3.2. Sensitivity Analysis

_{2}increases while steam increases for both models, this is due to the WGS reaction that is favored by the increase of steam. For S/B ratio equal to 0.25, the concentration of H

_{2}given by the Aspen Plus model is lower than the one given by the MATLAB model, but the yield achieved by Aspen Plus is higher. H

_{2}grows faster in the Aspen Plus model since it is a thermodynamic model. As shown in Figure 3c,d, an increase of the S/B ratio results in an increase of CO

_{2}and in a decrease of CO, this can be explained by the influence of the WGS reaction that consumes CO and produces CO

_{2}. In fact, even though the WGS is disadvantaged by high temperature respect to the steam-methane reforming (SMR) reaction which is instead favored at high temperature, we should be considered that the quantity of reagents in the WGS reaction are significantly higher compared to the ones of the SMR reaction. So, it is true that the constant of equilibrium of the WGS reaction is lower than the one of the SMR reaction at 850 °C but, since the quantity of reagents of the WGS reaction are higher, the WGS is the reaction that dominates the trends of CO and CO

_{2}. Figure 3b reports a clear reducing trend of CH

_{4}, as the steam increases, following the SMR reaction. The reduction of the methane in the Aspen Plus model is greater than the one in the MATLAB model with increasing S/B because, being the Aspen Plus a thermodynamic model, it neglects the residence time (more CH

_{4}is converted). Similar trends were reported in literature references [26,47]. High values of RE for CH

_{4}can be justified due to its low molar fraction. The maximum Relative Error can reach 108.92% although in that operating point (S/B = 0.9) the CH

_{4}molar fraction is quite low (0.04 for Aspen Plus and 0.12 for MATLAB) with an AE equal to 0.08, which is acceptable. The overall error analysis shows that the MAE is between 2.11–5.80 for all syngas components. For H

_{2}, CO and CO

_{2}—which present higher molar fractions (>0.15 mol/mol

_{syngas})—the MRE is contained between 8.95% and 12.64%. The CH

_{4}prediction instead is affected by a larger MRE of 61.45% which is however acceptable, for plant analysis purpose, since the absolute molar fraction is low (0.03–0.15 mol

_{CH4}/mol

_{syngas}).

## 4. Conclusions

_{2}, CO and CO

_{2}gases while for CH

_{4}it is equal to 61.45%. Such high value can be justified by the fact that the contribution of CH

_{4}in the syngas composition is low (<0.15) which affects the overall syngas composition prediction less significantly with an AE of 0.08. The influence of the steam to biomass ratio on the syngas composition of both models was investigated. The concentration of H

_{2}increases while steam increases for both models, due to the WGS reaction, and it grows faster in the Aspen Plus model since it is a thermodynamic model. The values obtained from the simulation by Aspen Plus have a higher error, compared to the literature values, than those of MATLAB. However, this error is acceptable for what regards system simulation (LHV, yield, cold gas efficiency and main gas component) because it is within an error range of 10–20%. For this reason, if the objective of the process modelling is to investigate system coupling and/or integration, thermodynamic models seem to be more suitable than kinetic ones, due to their enhanced simplicity and general applicability; while, at the same time assessing with sufficient accuracy the overall mass and energy balance. On the other hand, if the objective is to predict specific gas composition and design and/or optimize an actual gasifier system, a kinetic model is needed, providing a better accuracy in the syngas composition and, of course, the trends and distributions of the analyzed quantities along the axis of reactor and data regarding the hydrodynamics of the system. For future works, the aim will be to reduce the error between the values obtained from the model and the ones came from experimental data. This could be done, first of all, make a deeper differentiation among the values come from the using of catalyst inside the reactor, and then improving the data-fit of experimental data used for the QET, for Aspen Plus model, and improving the data on kinetic constant and residence time, for MATLAB model.

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 3.**(

**a**–

**d**) Effect of S/B ratio on syngas (

**a**) hydrogen, (

**b**) methane, (

**c**) carbon monoxide; (

**d**) carbon dioxide dry mole fraction.

**Table 1.**Gasification reactions [36].

Reaction | Reaction Name | Heat of Reaction | Reaction Number |
---|---|---|---|

Heterogeneous reaction | |||

C + 0.5 O_{2} → CO | Char partial combustion | (−111 MJ/kmol) | (R1) |

C + H_{2}O CO + H_{2} | Water-gas | (+172 MJ/kmol) | (R2) |

Homogeneous reactions | |||

H_{2} + 0.5 O_{2} → H_{2}O | H_{2} partial combustion | (−283 MJ/kmol) | (R3) |

CO + H_{2}O CO_{2} + H_{2} | Water gas-shift | (−41 MJ/kmol) | (R4) |

CH_{4} + H_{2}O → CO + 3H_{2} | Steam-methane reforming | (+206 MJ/kmol) | (R5) |

Thermal Power Input of the Gasifier (m_{bio}·LHV_{bio}) | 350 kW_{th} |
---|---|

Temperature | 850 °C |

Pressure | 1 bar |

S/B = 0.25 (kg_{steam}/kg_{biomass,dry}) | Literature Data [44,46] | Aspen Plus Model | MATLAB Model |
---|---|---|---|

H_{2} (%_{dry mole fraction}) | 42.1 | 34.0 | 38.7 |

CO (%_{dry mole fraction}) | 16.8 | 33.5 | 29.9 |

CO_{2} (%_{dry mole fraction}) | 31.5 | 20.8 | 17.2 |

CH_{4} (%_{dry mole fraction}) | 8.8 | 11.7 | 14.2 |

LHV (MJ/kg) _{dry} | 13.9–15.1 | 12.8 | 15.2 |

$\mathrm{Gas}\text{}\mathrm{yield}\text{}{\left(\frac{N{m}^{3}\mathrm{syngas}}{\mathrm{kg}\text{}\mathrm{biomass}}\right)}_{dry}$ | 0.85 | 1.40 | 1.25 |

Cold gas efficiency | 0.87–0.96 | 0.88 | 0.92 |

S/B = 0.5 (kg_{steam}/kg_{biomass,dry}) | Literature Data [45,46] | Aspen Plus Model | MATLAB Model |
---|---|---|---|

H_{2} (%_{dry mole fraction}) | 30–40 | 43.1 | 42.2 |

CO (%_{dry mole fraction}) | 20–30 | 26.0 | 22.9 |

CO_{2} (%_{dry mole fraction}) | 15–25 | 24.0 | 21.8 |

CH_{4} (%_{dry mole fraction}) | 8–12 | 6.9 | 13.1 |

LHV (MJ/kg) _{dry} | 14.1–15.2 | 11.7 | 14.2 |

$\mathrm{Gas}\text{}\mathrm{yield}\text{}{\left(\frac{N{m}^{3}\mathrm{syngas}}{\mathrm{kg}\text{}\mathrm{biomass}}\right)}_{dry}$ | 1 | 1.60 | 1.32 |

Cold gas efficiency | 0.89–0.96 | 0.91 | 0.95 |

Error Analysis | Aspen Model | MATLAB Model | ||
---|---|---|---|---|

S/B = 0.25 (kg_{steam}/kg_{biomass,dry}) | Absolute Error | Relative Error (%_{dry mole fraction}) | Absolute Error | Relative Error (%_{dry mole fraction}) |

H_{2} (%_{dry mole fraction}) | −8.1 | −19.24 | −3.4 | −8.08 |

CO (%_{dry mole fraction}) | +16.7 | +99.40 | +13.1 | +77.98 |

CO_{2} (%_{dry mole fraction}) | −10.7 | −33.97 | −14.3 | −45.40 |

CH_{4} (%_{dry mole fraction}) | +2.9 | +32.95 | +5.4 | +61.36 |

LHV (MJ/kg) _{dry} | −1.7 | −11.72 | +0.7 | +4.8 |

$\mathrm{Gas}\text{}\mathrm{yield}\text{}{\left(\frac{N{m}^{3}\mathrm{syngas}}{\mathrm{kg}\text{}\mathrm{biomass}}\right)}_{dry}$ | −0.14 | −11.38 | +0.02 | +1.6 |

Cold gas efficiency | −0.03 | −3.2 | +0.01 | +1.0 |

Error Analysis | Aspen Model | MATLAB Model | ||
---|---|---|---|---|

S/B = 0.5 (kg_{steam}/kg_{biomass,dry}) | Absolute Error | Relative Error (%_{dry mole fraction}) | Absolute Error | Relative Error (% _{dry mole fraction}) |

H_{2} (%_{dry mole fraction}) | +8.1 | +23.14 | +7.2 | +20.57 |

CO (%_{dry mole fraction}) | +1.0 | +4.0 | −2.1 | −8.4 |

CO_{2} (%_{dry mole fraction}) | +4.0 | +20 | +1.8 | +9.0 |

CH_{4} (%_{dry mole fraction}) | −3.1 | −31 | +3.1 | +31 |

LHV (MJ/kg) _{dry} | −2.95 | −20.13 | −0.45 | −3.07 |

+0.32 | +32.0 | +0.32 | +32.0 | |

Cold gas efficiency | −0.02 | −2.1 | −0.02 | +2.1 |

**Table 7.**Absolute error (AE) and Relative Error (RE) between the Aspen Plus and MATLAB model for each S/B operating point. Maximum values (red), minimum values (green). Mean Absolute Error (MAE) and Mean Relative Error (MRE) over the whole range of S/B.

Error Analysis | $\mathbf{AE}\text{}\left|{\mathit{x}}_{\mathit{i}}{}_{\mathit{M}\mathit{A}\mathit{T}\mathit{L}\mathit{A}\mathit{B}}-{\mathit{x}}_{\mathit{i}}{}_{\mathit{A}\mathit{S}\mathit{P}\mathit{E}\mathit{N}}\right|$ | $\mathbf{RE}\text{}(\%)\text{}\frac{\left|{\mathit{x}}_{\mathit{i}}{}_{\mathit{M}\mathit{A}\mathit{T}\mathit{L}\mathit{A}\mathit{B}}-{\mathit{x}}_{\mathit{i}}{}_{\mathit{A}\mathit{S}\mathit{P}\mathit{E}\mathit{N}}\right|}{{\overline{\mathit{x}}}_{\mathit{M}\mathit{A}\mathit{T}\mathit{L}\mathit{A}\mathit{B},\mathit{A}\mathit{S}\mathit{P}\mathit{E}\mathit{N}}}$ | ||||||
---|---|---|---|---|---|---|---|---|

S/B (kg_{steam}/kg_{biomass}) | H_{2} | CH_{4} | CO | CO_{2} | H_{2} | CH_{4} | CO | CO_{2} |

0.25 | 5.63 | 1.90 | 4.53 | 3.79 | 15.93% | 14.13% | 13.64% | 20.61% |

0.3 | 4.07 | 2.90 | 3.82 | 3.53 | 10.98% | 22.96% | 12.30% | 18.19% |

0.4 | 1.61 | 4.41 | 3.34 | 2.88 | 4.04% | 38.68% | 12.00% | 13.68% |

0.5 | 0.70 | 5.56 | 3.30 | 2.28 | 1.66% | 53.04% | 13.01% | 10.18% |

0.6 | 2.75 | 6.80 | 3.32 | 1.63 | 6.24% | 70.86% | 14.23% | 6.97% |

0.7 | 4.01 | 7.60 | 3.01 | 1.28 | 8.81% | 84.45% | 13.98% | 5.27% |

0.8 | 4.89 | 8.36 | 2.47 | 1.00 | 10.50% | 98.57% | 12.49% | 3.96% |

0.9 | 6.40 | 8.85 | 1.72 | 0.45 | 13.40% | 108.92% | 9.46% | 1.74% |

MAE | MRE (%) | |||||||

S/B (kg_{steam}/kg_{biomass}) | H_{2} | CH_{4} | CO | CO_{2} | H_{2} | CH_{4} | CO | CO_{2} |

0.25–0.9 | 3.76 | 5.80 | 3.19 | 2.11 | 8.95% | 61.45% | 12.64% | 10.07% |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Marcantonio, V.; Monforti Ferrario, A.; Di Carlo, A.; Del Zotto, L.; Monarca, D.; Bocci, E.
Biomass Steam Gasification: A Comparison of Syngas Composition between a 1-D MATLAB Kinetic Model and a 0-D Aspen Plus Quasi-Equilibrium Model. *Computation* **2020**, *8*, 86.
https://doi.org/10.3390/computation8040086

**AMA Style**

Marcantonio V, Monforti Ferrario A, Di Carlo A, Del Zotto L, Monarca D, Bocci E.
Biomass Steam Gasification: A Comparison of Syngas Composition between a 1-D MATLAB Kinetic Model and a 0-D Aspen Plus Quasi-Equilibrium Model. *Computation*. 2020; 8(4):86.
https://doi.org/10.3390/computation8040086

**Chicago/Turabian Style**

Marcantonio, Vera, Andrea Monforti Ferrario, Andrea Di Carlo, Luca Del Zotto, Danilo Monarca, and Enrico Bocci.
2020. "Biomass Steam Gasification: A Comparison of Syngas Composition between a 1-D MATLAB Kinetic Model and a 0-D Aspen Plus Quasi-Equilibrium Model" *Computation* 8, no. 4: 86.
https://doi.org/10.3390/computation8040086