Elsevier

Energy Conversion and Management

Volume 182, 15 February 2019, Pages 412-429
Energy Conversion and Management

Multi-objective optimization of sorption enhanced steam biomass gasification with solid oxide fuel cell

https://doi.org/10.1016/j.enconman.2018.12.047Get rights and content

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Abstract

Biomass is one of the encouraging renewable energy sources to mitigate uncertainties in the future energy supply and to address the climate change caused by the increased CO2 emissions. Conventionally, thermal energy is produced from biomass via combustion process with low thermodynamic efficiency. Conversely, gasification of biomass integrated with innovative power generation technologies, such as Solid Oxide Fuel Cell (SOFC), offers much higher conversion efficiency. Typically, energy conversion process has multiple conflicting performance criteria, such as capital and operating costs, annual profit, thermodynamic performance and environment impact. Multi-objective Optimization (MOO) methods are used to found the optimal compromise in the objective function space, and also to acquire the corresponding optimal values of decision variables. This work investigates integration and optimization of a Sorption Enhanced Steam Biomass Gasification (SEG) with a SOFC and Gas Turbine (GT) system for the production of power and heat from Eucalyptus wood chips. The energy system model is firstly developed in Aspen Plus simulator, which has five main units: (1) SEG coupled with calcium looping for hydrogen-rich gas production, (2) hot gas cleaning and steam reforming, (3) SOFC-GT for converting hydrogen into electricity, (4) catalytic burning and CO2 compression, and (5) cement production from the purge CaO stream of SEG unit. Then, the design and operating variables of the conversion system are optimized for annual profit, annualized total capital cost, operating cost and exergy efficiency, using MOO. Finally, for the implementation purpose, two selection methods and parametric uncertainty analysis are performed to identify good solutions from the Pareto-optimal front.

Introduction

The increase in energy demand and global warming concern worldwide have driven the research and development of innovative power production technology with high conversion efficiency and low carbon footprint. Additionally, fossil fuels including coal, natural gas and petroleum are finite and non-renewable, and their use in the combustion engines to produce power and electricity increases the pollution levels in the environment. Therefore, many researchers are focusing on the development of advanced power generation technologies with high efficiencies and low CO2 emissions. Biomass is assumed to be an attractive feedstock for power and heat generations, as it is renewable and widely-available resource, and might be produced sustainably in the future [1], [2].

Biomass feedstock can be used for both heat and power generations through biochemical and thermochemical processes [3]. The biochemical processes, such as anaerobic digestion and fermentation, involve use of enzymes, microorganisms and bacteria to breakdown biomass into liquid (e.g., bioethanol) and gaseous fuels (e.g., biogas). Conversely, thermochemical processes such as combustion, pyrolysis and gasification involve the use of heat as primary mechanism for converting biomass into hydrocarbons [4]. Among these processes, gasification has a lower emission of pollutants and higher efficiency of power and heat generations [5]. In biomass gasification, biomass is converted into a combustible or synthesis gas (syngas) which contains mainly methane, hydrogen, carbon monoxide and carbon dioxide. Gasification occurs when a gasifying agent (steam, oxygen, air, or mixture of gasifying agents) is reacted with the available carbon in biomass inside a gasifier at high temperature. Gasification of biomass with air produces low quality syngas (higher heating value = 4–7 MJ m−3), whereas use of steam or oxygen as a gasifying agent gives high quality syngas (higher heating value = 10–18 MJ m−3). However, gasification with oxygen has not been applied for biomass because of the higher cost of oxygen production using current commercial technology (e.g., cryogenic air separation) [4]. Steam biomass gasification coupled with calcium looping, called as Sorption Enhanced Steam Biomass Gasification (SEG), is an innovative technology for hydrogen-rich gas production [6], [7], [8]. In gasification process, CO2 and tar yields in product gas can be reduced by the use of CaO [9]. Production of hydrogen-rich gas from biomass using gasification with in-situ CO2 capture and tar reduction (use of CaO chemical looping) has been studied by Udomsirichakorn et al. [10]. They reported that the maximum yield and concentration of H2 were increased to 451.11 ml gbiomass-1 and 78 vol%, respectively, with steam to biomass ratio of 3.41 and gasifier temperature of 650 °C. Further, for the same operating point, lowest CO2 concentration and tar content (4.98 vol% and 2.48 g Nm−3) were reported.

In order to develop advanced power generation technology, biomass gasification can be coupled with high-efficiency power generation units such as Solid Oxide Fuel Cell (SOFC). A SOFC is one of the most promising technologies for electricity generation with higher thermodynamic efficiency compared to the conventional technologies [11], [12]. A SOFC directly converts chemical energy into electricity with high efficiency and operates at a very high temperature (∼600 to 1000 °C) [11]. Recently, biomass gasification integrated with a SOFC system for power generation has been given significant attention by researchers. Colpen et al. [13] simulated an integrated biomass gasification and SOFC system, and studied the use of gasification agents (steam, oxygen, air) on the system performance. Their system includes biomass gasification, hot gas cleaning and fuel cell. The results show that use of steam inside the gasifier gave maximum electrical efficiency (41.8%), exergy efficiency (39.1%) and power/heat ratio (4.65), but minimum efficiency of fuel utilization (50.8%). For oxygen and air gasification agents, exergy destruction had maximum values inside the gasifier. Bang-Moller et al. [14] investigated an integrated plant, biomass gasification, a SOFC and a micro GT, for the combined power and heat generations. They reported that the optimized plant (production capacity ∼ 290 kWe) has electrical efficiency of 58.2% based on the lower heating value of feedstock. Recalde et al. [15] showed that biomass gasifier integrated with a SOFC is more suitable for energy recovery, than any other process such as biochar production by pyrolysis, with the electrical efficiency of 65% and exergy efficiency of 58%.

Usually, energy conversion process has multiple performance criteria, namely thermodynamic conversion efficiency, capital and operating costs, annual profit and environment impact. Multi-objective Optimization (MOO) method is used to find quantitative trade-offs among the conflicting performance criteria and also to get the optimal values of decision variables. Many researchers have studied trade-offs between two or more objective functions using MOO methods [16], [17]. Abdollahi and Meratizaman [18] accomplished the thermodynamic and environmental optimization of a small-scale distributed combined heating, cooling and power generation system (CCHP) using genetic algorithms to find the Pareto-optimal front. Quddus et al. [19] investigated optimization of a SOFC for oxidative coupling of methane, using Non-dominated Sorting Genetic Algorithm with Jumping Genes (NSGA-II-aJG). Modeling and optimization of SOFC has thus become a great tool to understand its performance. The optimization results provided better distributed and wider spread of optimal solutions which helps in running a SOFC at desired optimum. Jokar et al. [20] performed thermodynamic study of a molten carbonate fuel cell and supercritical CO2 Brayton cycle hybrid system. The multi-objective evolutionary method (NSGA) was used to get the Pareto-optimal front, and three efficient decision making methods namely Fuzzy, LINMAP and TOPSIS were used to select the best solution from the Pareto-optimal front. Sadeghi et al. [21] investigated a syngas fed SOFC power plant using a downdraft gasifier and performed a multi-objective optimization of maximizing the total exergy efficiency and minimizing the normalized CO2 emission using genetic algorithms method. The optimization results show that minimization of total product unit cost of system as the only criterion leads to the higher value of normalized CO2 emission and lower value of system exergy efficiency.

This study investigates the integration and optimization of a SEG, SOFC and GT units, based on several performance criteria, to generate electrical power and heat from Eucalyptus wood chips. The energy system model is developed in Aspen Plus simulator (version 8.8). The integrated SEG-SOFC-GT plant is an attractive choice for heat integration because waste heat released by SOFC at high temperature can be consumed in CaO regeneration. In order to optimize the performance of the proposed integrated energy plant, four relevant bi-objective optimization problems have been investigated: (1) maximization of annual profit, minimization of annualized capital cost (ACC), (2) minimization of ACC, maximization of exergy efficiency, (3) minimization of ACC, minimization of operating cost, and (4) minimization of levelized electricity cost, minimization of ACC per kWh. OSMOSE platform is a computer-aided tool which is developed for the design and integration of industrial processes and energy systems, and is also used to study MOO of the proposed integrated energy plant [23], [24], [25].

There are several approaches for choosing one of the optimal solutions found for a MOO problem [26]. For the implementation purpose, the preferred optimal solutions from the Pareto front are chosen by Net Flow Method (NFM) and Gray Relational Analysis (GRA). At the end of this study, market economics and plant operating conditions, namely electricity price, wood price, oxygen price, CaCO3 price, Portland cement price, yearly operation, interest rate, capital cost, and economic life time, are used to study parametric uncertainty analysis of the optimal solution found by OSMOSE. Optimal solutions or plant designs for different MOO problems are ranked based on the best and five good designs, using a number of randomly generated economic scenarios. For details on methodology of parametric uncertainty analysis, interested readers are referred to the literature [24], [25], [27].

In summary, the main contributions of this study are: integration of SEG, SOFC-GT and cement plant, for the production of electricity and cement (side product), multi-objective optimization of the integrated plant based on four relevant bi-objective problems, selection of final plant design for implementation purpose, using NFM, GRA and parametric uncertainty analysis. The performance of the optimized plant found to be better than reference plant (i.e., gasifier with SOFC-GT).

Section snippets

Process simulation of SEG-SOFC-GT plant

In this study, the integrated plant (SEG, SOFC and GT) for producing electricity and heat from Eucalyptus wood chips has been modeled in Aspen Plus simulator. The integrated energy conversion plant has five main parts: (1) sorption enhanced steam biomass gasification coupled with calcium looping for the production of hydrogen-rich gas, (2) hot gas cleaning and steam methane reforming, (3) solid oxide fuel cell with gas turbines to convert hydrogen into electricity, (4) catalytic burning and CO2

Exergy analysis

The energy analysis method is based on the first law of thermodynamics while the exergy analysis method is based on the first and second laws of thermodynamics. Energy is a measure of only the quantity whereas exergy also takes into account the quality of energy. Moreover, exergy analysis can be used to identify and quantify the locations, causes and magnitudes of inefficiencies in process. Therefore, the exergy analysis method is used to enhance system understanding and to examine the

Optimization problem formulation

The formulated MOO problems for SEG-SOFC-GT plant is shown in Table 3 (Eqs. (57)–(62)). In this study, four important bi-objective optimization problems are studied: (1) maximization of annual profit, minimization of ACC, (2) maximization of exergy efficiency, minimization of ACC, (3) minimization of both ACC and operating cost, and (4) minimization of both levelized electricity cost and ACC per kWh. Here, the method of economic analysis is annual profit, which has been defined based on the

Maximization of annual profit, minimization of ACC

The optimization results for the simultaneous maximization of annual profit and minimization of ACC (MOO problem 1) are presented in Fig. 7. These results were found using OSMOSE: population size, NP = 50; number of function evaluations, NFE = 3000. As anticipated, both these objective functions (annual profit, ACC) are conflicting with each other. This figure also presents changes in the main decision variables with annual profit. Temperature of gasifier, fuel utilization in SOFC, and inlet

Ranking of Pareto solutions of the integrated SEG-SOFC-GT plant

The solution of a MOO problem is a set of Pareto solutions, collectively called as Pareto front. All the Pareto solutions are equally good mathematically, and each solution represents a distinct plant design. In order to implement these results in practice, decision makers have to select one of the Pareto solutions. The selection of Pareto solution may be performed based on the judgement of decision makers, or using a ranking method that usually provides few solutions, based on the provided

Conclusions

In this work, thermo-economic performance of the sorption enhanced steam biomass gasification (SEG) with solid oxide fuel cell, for the production of power and heat from Eucalyptus wood chips, was investigated using multi-objective optimization approach. The plant model was developed in Aspen Plus simulator, which includes SEG, CaO looping, hot gas cleaning, steam reforming, SOFC-GT, catalytic burning, CO2 compression and cement production. In total, four relevant bi-objective optimization

Declaration of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

Support from The Royal Golden Jubilee Ph.D. Program (The Thailand Research Fund) and Chulalongkorn Academic Advancement into Its 2nd Century Project is gratefully acknowledged. A. Arpornwichanop would also thank the financial support provided by the “Research Chair Grant”, National Science and Technology Development Agency (NSTDA).

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