Simulation of Solid Oxide Fuel Cell Anode in Aspen HYSYS—A Study on the Effect of Reforming Activity on Distributed Performance Profiles, Carbon Formation, and Anode Oxidation Risk

A distributed variable model for solid oxide fuel cell (SOFC), with internal fuel reforming on the anode, has been developed in Aspen HYSYS. The proposed model accounts for the complex and interactive mechanisms involved in the SOFC operation through a mathematically viable and numerically fast modeling framework. The internal fuel reforming reaction calculations have been carried out in a plug flow reactor (PFR) module integrated with a spreadsheet module to interactively calculate the electrochemical process details. By interlinking the two modules within Aspen HYSYS flowsheeting environment, the highly nonlinear SOFC distributed profiles have been readily captured using empirical correlations and without the necessity of using an external coding platform, such as MATLAB or FORTRAN. Distributed variables including temperature, current density, and concentration profiles along the cell length, have been discussed for various reforming activity rates. Moreover, parametric estimation of anode oxidation risk and carbon formation potential against fuel reformation intensity have been demonstrated that contributes to the SOFC lifetime evaluation. Incrementally progressive catalyst activity has been proposed as a technically viable approach for attaining smooth profiles within the SOFC anode. The proposed modeling platform paves the way for SOFC system flowsheeting and optimization, particularly where the study of systems with stack distributed variables is of interest

System Level Exergy Assessment of Strategies Deployed for Solid Oxide Fuel Cell Stack Temperature Regulation and Thermal Gradient Reduction

Several operational strategies for solid oxide fuel cell (SOFC) temperature regulation and temperature gradient minimization at cell scale have previously been assessed by the authors (Amiri et al., Ind. Eng. Chem. Res., 2016). The application of such strategies at system scale, however, requires a numerical linkage between the cell and the system performance metrics allowing simultaneous evaluation of the dominant process interactions. The objective of this study is to analytically examine the effectiveness and applicability of the mentioned thermal management methods at system scale. To achieve this, a system level exergy analysis is presented by using a modeling platform in which a detailed four-cell short stack module and the balance-of-plant (BoP) are integrated. Linkage between the system performance metrics and the stack internal temperature gradient is specifically emphasized. For this, the exergy intensive points (unit operations) are identified throughout the plant. Subsequently, the effective strategies that had been employed for the cell level thermal management proposed in our previous work (Amiri et al., Ind. Eng. Chem. Res., 2016) are examined at the system level capturing the effects on the state of BoP exergy intensive components. Moreover, fuel design is proposed and evaluated as a potential thermal management strategy. Combination of a variety of measures including the exergy destruction rates, the electrical and thermal efficiencies, and the stack internal temperature gradient provides a comprehensive set of data contributing to the SOFC system thermal management

β-MnO2 ACTIVATION OF PEROXYMONOSULFATE FOR CATALYTIC PHENOL DEGRADATION IN AQUEOUS SOLUTIONS

β-MnO2 was prepared and used to activate peroxymonosulphate for degradation of aqueous phenol. The sample was characterized by N2 adsorption, X-ray diffraction (XRD), scanning electron microscopy (SEM). The catalyst is highly effective in heterogeneous activation of PMS to produce sulfate radicals for phenol degradation compared with homogeneous oxidatiom. β-MnO2 could completely remove phenol in 30 min at the conditions of 25 ppm phenol, 0.4 g/L catalyst, 2 g/L PMS, and 25 oC. A pseudo first order model would fit to phenol degradation kinetics and activation energy was obtained as 38.2 kJ/mol

Planar SOFC system modelling and simulation including a 3D stack module

A solid oxide fuel cell (SOFC) system consists of a fuel cell stack with its auxiliary components. Modelling an entire SOFC system can be simplified by employing standard process flowsheeting software. However, no in-built SOFC module exists within any of the commercial flowsheet simulators. In Amiri et al. (Comput. Chem. Eng., 2015, 78:10-23), a rigorous SOFC module was developed to fill this gap. That work outlined a multi-scale approach to SOFC modelling and presented analyses at compartment, channel and cell scales. The current work extends the approach to stack and system scales. Two case studies were conducted on a simulated multilayer, planar SOFC stack with its balance of plant (BoP) components. Firstly, the effect of flow maldistribution in the stack manifold on the SOFC's internal variables was examined. Secondly, the interaction between the stack and the BoP was investigated through the effect of recycling depleted fuel. The results showed that anode gas recycling could be used for managing the gradients within the stack, while also improving fuel utilisation and water management

SOFC Stack and System Modeling, Fault Diagnosis and Control

This report is an account of research and development activities undertaken by the Centre for Process Systems Computations, Department of Chemical Engineering at Curtin University, Western Australia in the area of solid oxide fuel cells. The focus of work of the group included 1) effect of cell macrostructure and microstructure on electrochemical performance with a view to optimise both macro- and micro-structure 2) electrochemistry modeling for simulating electrochemical performance 3) internal reforming aspects impacting performance at cell/stack and system levels 4) system level modeling investigating cell internal profiles (temperature, gas composition), homogeneity improvement, thermal management, anode recycle, fuel diversity, oxygen quality, and 5) monitoring for diagnostics, optimisation and control. The report summarizes work done over a period of 15 years and highlights areas of research gaps and future directions for research in the path to mass-scale commercialisation of the solid oxide fuel cell technolog

Solid oxide fuel cell reactor analysis and optimisation through a novel multi-scale modelling strategy

The simulation of a solid oxide fuel cell (SOFC) that incorporates a detailed user-developed model was performed within the commercial flowsheet simulator Aspen Plus. It allows modification of the SOFC's governing equations, as well as the configuration of the cell's fuel-air flow pattern at the flowsheet level. Initially, the dynamic behaviour of single compartment of a cell was examined with a 0D model, which became the building block for more complex SOFC configurations. Secondly, a sensitivity analysis was performed at the channel (1D) scale for different flow patterns. Thirdly, the effect of fuel and air flow rates on the predominant distributed variables of a cell was tested on a 2D assembly. Finally, an optimisation study was carried out on the 2D cell, leading to a robust, optimal air distribution profile that minimises the internal temperature gradient. This work forms the foundation of future stack and system scale studies

Pattern-based predictive control for ETBE reactive distillation

Synthesis of ethyl tert-buty ether (ETBE), a high-performance fuel additive, through reactive distillation (RD) is an attractive route, while its operation and control are exceptionally difficult due to its functional combination and complex dynamics. Modern control technology greatly relies on good process models, while a reasonable RD model is too complex for control design. Moreover, RD contains considerable uncertainties that cannot be well described in process modelling. Alleviating the model requirement, this work aims to maintain the product purity in RD of ETBE through developing a pattern-based predictive control (PPC) scheme. Process dynamics, control structure, nonlinear transformation, feature pattern extraction, and pattern-based prediction and its incorporation with a conventional linear controller are discussed. Case studies show the effectiveness of the proposed method