Integrated management of mixed biomass for hydrogen production from gasification

https://doi.org/10.1016/j.cherd.2022.01.012Get rights and content

Highlights

  • Integrated optimization model for biomass-based renewable hydrogen production systems is proposed.

  • We calculate and evaluate the biomass utilization potential of a certain area.

  • We made predictions through chemical reaction kinetics and thermodynamic models.

  • Inventory model is embedded in it to decide the optimal production and operation management.

  • The effectiveness of the model was tested through a mixed biomass project in Sichuan Province, China.

Abstract

In this study, we propose a novel optimization method for designing and operating various types of biomass-based renewable hydrogen production systems. To this end, we considered the coupling of multiple biomass hybrid utilization processes for hydrogen production. First, we calculated and evaluated the biomass utilization potential of a given area. Second, we predicted the biomass gasification products using chemical reaction kinetics and thermodynamic models. Finally, we determined the optimal annual total cost of the hydrogen production supply chain by optimizing the microscopic operation of the biomass gasification plant. Through the proposed method, it is possible to effectively conduct a comprehensive assessment of regional biomass material hydrogen production; moreover, the optimal design of the biomass hydrogen production supply chain can also be determined based on the availability of biomass and hydrogen demand. More precisely, the logistics operations under fluctuating demand conditions were realized, enabling strategic decision-making for planning a biomass hydrogen production system. To validate the model, a case study analysis of biomass hydrogen production, to be launched in Sichuan Province, China, is presented.

Introduction

The production and application of hydrogen energy is a hot topic in current research. Traditional hydrogen production processes, such as electrolysis of water and coal conversion, consume large amounts of energy and emit large amounts of carbon dioxide. Identifying an environmentally friendly and effective hydrogen production technology has been a primary focus for many domestic and foreign scholars. As one potential solution, biomass has the characteristics of zero carbon dioxide emissions and is an effective renewable energy resource; therefore, utilizing biomass to achieve hydrogen production technology has attracted increasing attention. Currently, hydrogen is mainly obtained by the electrolysis of water, use of petrochemical energy sources, and biomass combustion. Hydrogen production by electrolysis of water entails low environmental pollution but high energy consumption; as such, there are obstacles in the implementation of this process at the economic level. Hydrogen production from petrochemical energy also includes hydrogen production from water gas and natural gas. Although the cost of such techniques is generally low, they are based on petrochemical energy. As such, obtaining hydrogen causes the release of substantial amounts of carbon emissions; hence, there are environmental restrictions associated with these methods. Alternatively, biomass hydrogen production is a method of converting biomass into hydrogen-rich combustible gas through chemical methods, whereby pure hydrogen is obtained through separation.

Hydrogen production by gasification refers to the process of converting hydrocarbons into hydrogen-containing combustible gases in a gasification agent (such as air or water vapor). The biomass enters the gasification furnace and is heated and dried, whereby the water evaporates. As the temperature increases, the material then undergoes decomposition and produces hydrocarbon gas. Subsequently, the coke and pyrolysis products undergo an oxidation reaction with the gasification agent that is passing in. As the temperature further increases, the oxygen in the system is exhausted, and the product begins to undergo reduction (Puig-Arnavat et al., 2010, Watson et al., 2018). Biomass gasification agents mainly include air, water vapor, and oxygen. When oxygen is used as the gasification agent, the amount of hydrogen produced is high. When air is used as the gasification agent, although the cost is low, nitrogen is produced in large quantities and is difficult to separate. Yao et al. (2016) proposed a method of hydrogen production from biomass gasification using biochar as a catalyst/support. In a similar study, Xu et al. (2018) prepared alkali metal titanate catalysts for biomass pyrolysis raw gas reforming and also evaluated the catalysts using continuous pyrolysis raw gas with no extra steam. In another recent study, Couto et al. (2017) focused on a thermodynamic analysis of the steam gasification of Portuguese municipal solid waste.

Some technological problems remain unresolved in gasification that have prevented a robust market penetration. Of them, the main challenge is related to the optimization and understanding of the reactor behavior, which is the lowest efficiency component of a gasification plant (Ahmad et al., 2016, Basu, 2010). In practice, the development and implementation of mathematical models are essential for understanding and predicting the process behavior and analyzing the effects of different variables on the process performance. Examples of such models include the kinetic rate, thermodynamic equilibrium, and Aspen Plus gasification models. Kinetic models provide essential information on kinetic mechanisms to describe the conversion during biomass gasification, which is crucial for the design, evaluation, and improvement of gasifiers. These rate models are relatively accurate and detailed, but also computationally intensive (de Lasa et al., 2011, Loha et al., 2014, Onel et al., 2014). Thermodynamic equilibrium calculations, which are independent of gasifier design, may be more suitable for process studies on the influence of the most important fuel process parameters. At chemical equilibrium, the reaction system is at its most stable composition. However, thermodynamic equilibrium may not be achieved, mainly because of the relatively low operation temperatures (Jarungthammachote and Dutta, 2007, Zainal et al., 2001, Couto et al., 2015, Ruggiero and Manfrida, 1999, Timmer and Brown, 2019). Aspen Plus is a problem-oriented input program that is used to facilitate the calculation of physical, chemical, and biological processes. It can be used to describe processes involving solids in addition to vapor and liquid streams (Abdelouahed et al., 2012, Adeyemi and Janajreh, 2015, Noh et al., 2017).

In addition to considering the aforementioned evaluation of the gasification process, industries need to convert the operations of a plant/system into an efficient system in terms of utilization of energy, resources, and materials. Hence, one method is to find the optimal operating conditions to minimize the cost of the production/operation. Optimization can be applied to search for the optimal conditions and geometric parameters that provide extremes of the selected objective functions in the frame of previously defined constraints For instance, Woo et al. (2016) presented a new optimization-based approach for the design and operation of a renewable hydrogen system from various types of biomass. More recently, Li et al. (2020) focused on developing a mathematical model that covers the entire hydrogen supply network. The classical hydrogen supply chain network design model was integrated with the hydrogen fueling station planning model to generate a new formulation. Kim and Kim (2017) developed a new optimization-based approach for the strategic planning of a renewable hydrogen supply system using onshore and offshore wind energy. In addition to the above macro-scale planning model, there exists a micro-level operation management model. Let us consider the inventory control model as an example. Memişoğlu and Üster (2016) considered the planning and design of an extended supply chain for bioenergy networks in an integrated fashion while simultaneously addressing strategic and tactical decisions pertaining to location, production, inventory, and distribution in a multi-period planning horizon setting. As another example, Üster and Memişoğlu (2018) considered an integrated supply pricing and biomass logistics network design problem in which the yield rates were uncertain.

Although there have been numerous studies on evaluation and optimization of the biomass gasification process, as well as related decision-making work in the hydrogen production stage, only a few studies comprehensively evaluate the hydrogen production process from a systematic perspective to the best of our knowledge. In particular, a comprehensive assessment of the potential of available biomass fuels in a given area is rare, as is the assessment of wild biomass materials and urban solid waste materials. This study developed an integrated optimization model that contributes to the relevant strands from two aspects:

First, a comprehensive assessment of the potential of regional biomass hydrogen production was conducted. For this, the supply potential of usable biomass was obtained through a multi-dimensional assessment of environmental carrying capacity and efficiency of straw usage. On this basis, the kinetics and thermodynamic equations of the gasification process were used to predict the gasification composition of the corresponding biomass materials. Specifically, the focus of this study was to apply a chemical kinetics method to predict the hydrogen production from biomass gasification and to provide relevant theoretical parameters for further optimization in the next step. For instance, previous similar work such as Yao et al. (2016), Zainal et al. (2001) and Noh et al. (2017) has unilaterally discussed the efficiency of hydrogen production or the chemical environment for hydrogen production, yet has not formed a correlation to the entire hydrogen production management process. The work here can be seen as promoting the application of the previous work, with the pre-processing carried out in multiple sub-modules.

On the other hand, for the above two preparation steps, the predicted economic parameters were sent to the final operation management optimization module. Through the description of several states in dynamic inventory management, the corresponding inventory control decision was finally obtained to maximize the profit of hydrogen production. Similar to the aforementioned contribution level, the economic and technical parameters calculated in advance were also used when the inventory decision-making optimization was conducted. At present, most of the existing studies have only conducted unilateral supply chain or inventory optimization, and have not considered the entire hydrogen production process system (Memişoğlu and Üster, 2016, Üster and Memişoğlu, 2018, Woo et al., 2016). Here, the benefit of the integration and optimization of the integration of related modules is to facilitate the analysis of the direct impact of upstream biomass supply on downstream decision-making, as well as evaluate the impact of the environmental parameters of the biomass gasification process on the results. The proposed model provides the best judgment for the overall biomass-to-hydrogen (B2H2) supply chain decision-making, and at the same time analyzes the level of related by-products in the hydrogen production process. The findings of this study lay a solid foundation for further implementation of green hydrogen production and waste collection.

Section snippets

Problem description

The three main objectives of this study were to establish a comprehensive system optimization model, integrate the operation management from biomass gasification to the onlooker level into the B2H2 production process, manage the logistics operation according to the fluctuation of biomass production and demand, plan the B2H2 system, and make strategic decisions such as capital investment. Fig. 1 shows the main modules involved in B2H2 in this study. The module was primarily used to predict the

Composition of the fuel gas produced

In this part of the study, an equilibrium modeling approach was employed to predict the composition of gas from gasification by biomass. It was assumed that biomass contains the elements carbon (C), hydrogen (H), and oxygen (O). Hence, the chemical formula of the biomass was defined as CHxOy. Other elements such as nitrogen (N) and sulfur (S) accounted for a relatively small proportion, so they were not considered here. The global gasification reaction can be written as follows:CHxOy+wH2O+mO2=x1

Gasification hydrogen production parameters

To predict the production of hydrogen from biomass gasification, it was necessary to use the method described in Section 3.1. Here, we aimed to determine the gasification temperature, T2 = 750 °C (1023 K) and the ambient temperature, T1 = 25 °C (298 K). The general equations for lnK1 and lnK2 refer to Zainal et al. (2001) for the reaction ofC+2H2CH4and reaction ofCO+H2OH2+CO2are K1 = 0.073 and K2 = 0.99 for 750 °C.

In Zhang et al. (2019), the water content and elemental composition (mass percentage) of

Biomass inventory management

Fig. 3 illustrates the purchase quantity, use quantity, and processing quantity of different biomasses. In the figure, the bright and wide rectangles indicate the quantity of biomasses purchased, and the dark and narrow rectangles indicate the use for gasification of the corresponding biomasses. The corresponding inventory level is shown in the lower part of the figure. We obtained the procurement level, usage level, and inventory level for each of the twelve decision-making cycles. The total

Conclusion

Although it is reasonable for biomass gasification to result in hydrogen production, the regional biomass optimization management strategy based on the industrial chain has not received much attention. Based on this point, this study proposes a three-stage integrated optimization model. The composition of different biomasses combined with the gasification process at different temperatures can greatly impact the production of hydrogen. Therefore, it is necessary to predict their production under

Conflict of interest

None declared.

Acknowledgements

This study was supported by the National Natural Science Foundation of China under Grant No. 72171222.

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