Fuel composition transients in solid oxide fuel cell gas turbine hybrid systems for polygeneration applications

The potential of Solid Oxide Fuel Cell Gas Turbine (SOFC/GT] hybrid systems for fuel flexibility makes this technology greatly attractive for system hybridization with various fuel processing units in advanced power generation systems and/or polygeneration plants. Such hybrid technologies open up the possibility and opportunities for improvement of system reliabilities and operabilities. However, SOFC/GT hybrid systems have not yet reached their full potential in term of capitalizing on the synergistic benefits of fuel cell and gas turbine cycles. Integrating fuel cells with gas turbine and other components for transient operations increases the risk for exposure to rapid and significant changes in process dynamics and performance, which are primarily associated with fuel cell thermal management and compressor surge. This can lead to severe fuel cell failure, shaft overspeed, and gas turbine damage. Sufficient dynamic control architectures should be made to mitigate undesirable dynamic behaviours and/or system constraint violations before this technology can be commercialized. But, adequate understanding about dynamic coupling interactions between system components in the hybrid configuration is essential. Considering this critical need for system identification of SOFC/GT hybrid in fuel flexible systems, this thesis investigates the dynamic performance of SOFC/GT hybrid technology in response to fuel composition changes. Hardware-based simulations, which combined actual equipment of direct-fired recuperated gas turbine system and simulated fuel cell subsystem, are used to experimentally investigate the impacts of fuel composition changes on the SOFC/GT hybrid system, reducing potentially large inaccuracies in the dynamic study. The impacts of fuel composition in a closed loop operation using turbine speed control were first studied for the purpose of simplicity. Quantification of safe operating conditions for dynamic operations associated with carbon deposition and compressor stall and surge was done prior to the execution of experimentation. With closed loop tests, the dynamic performance of SOFC/GT hybrid technology due to a transition in gas composition could be uniquely characterized, eliminating the interactive effects of other process variables and disturbances. However, for an extensive system analysis, open loop tests (without turbine speed control] were also conducted such that potential coupling impacts exhibited by the SOFC/GT hybrid during fuel transients could be explored. Detailed characterization of SOFC/GT dynamic performance was performed to identify the interrelationship of each fuel cell variable in response to fuel composition dynamics and their contributions to operability of the system. As a result of lowering LHV content in the fuel feed, which involved a transition from coal-derived syngas to humidified methane composition in the SOFC anode, the system demonstrated a dramatic transient increase in fuel cell thermal effluent with a time scale of seconds, resulting from the conversion of fuel cell thermal energy storage into chemical energy. This transient was highly associated with the dynamics of solid and gas temperatures, heat flux, heat generation in the fuel cell due to perturbations in methane reforming, water-gas shifting, and electrochemical hydrogen oxidation. In turn, the dramatic changes in fuel cell thermal effluent resulting from the anode composition changes drove the turbine transients that caused significant cathode airflow fluctuations. This study revealed that the cathode air mass flow change was a major linking event during fuel composition changes in the SOFC/GT hybrid system. Both transients in cathode air mass flow and anode composition significantly affected the hybrid system performance. Due to significant coupling between fuel composition transitions and cathode air mass flow changes, thermal management of SOFC/GT hybrid systems might be challenging. Yet, it was suggested that modulating cathode air flow offered promise for effective dynamic control of SOFC/GT hybrid systems with fuel flexibility

Fuel utilization effects on system efficiency in solid oxide fuel cell gas turbine hybrid systems

A computational analysis was conducted to optimize the design of a solid oxide fuel cell - gas turbine hybrid power generator, focusing on the impact that fuel utilization within the fuel cell has on system efficiency and installed costs. This is the first ever design-study considering the effect of fuel utilization on performance, as well as on the optimum power split. This hybrid system attained high electric generation efficiencies (\u3e70%) over a wide range of operating conditions (60% \u3c fuel utilization \u3c 90%) while the fuel cell stack size decreased in proportion to decreasing the fuel utilization. A one-dimensional fuel cell model was used to simulate the fuel cell while GateCycle® was used to simulate the performance of the associated recuperated turbine and various subsystems necessary for thermal management. For each test case, the size of the solid oxide fuel cell, gas turbine, and recuperator, as well as the fuel and air flow rates, hot-air bypass set point, and heat exchange effectiveness in the solid oxide fuel cell manifold were varied to obtain 550 MWe output. In addition, anode recycle, turbomachinery efficiency, and various thermal management options were tested. The maximum system efficiency (75.6%) was attained for the single-pass solid oxide fuel cell with highly efficient turbomachinery when the solid oxide fuel cell used 80% of the incoming fuel. Efficiency was essentially flat from 75% fuel utilization through 85% fuel utilization. Employing anode recycle starting at 65% resulted in roughly 1 percentage point efficiency decrease for each percent increase in fuel utilization. For minimized solid oxide fuel cell degradation, a near 50:50 power split case was studied resulting in 68.6% efficiency and the solid oxide fuel cell using 55% of the incoming fuel. Because of shifting half of the power generation to the gas turbine, the size of the fuel cell stack was reduced by 25% as compared to that at maximum efficiency (80% fuel utilization)

Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Hybrid gas turbine–fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossil-fueled power generation. A systematic controlled variable (CV) selection method is deployed for a hybrid gas turbine–fuel cell system in the HyPer (hybrid performance) facility at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) for maximizing its economic and control performance. A three-stage approach is used for the CV selection comprising a priori analysis, multiobjective optimization, and a posteriori analysis. The a priori analysis helps to screen off several candidate CVs, thus reducing the size of the combinatorial optimization problem for multiobjective CV selection. For optimal CV selection, a transfer function model of the HyPer facility is identified. By considering several candidate models, the final transfer function model is selected using Akaike’s Final Prediction Error criterion. Experimental data from the HyPer facility are used to estimate the noise in the measurement data. For solving the combinatorial multiobjective optimization problem for CV selection, a multiagent optimization platform comprising simulated annealing, genetic algorithm, and efficient ant colony optimization algorithms is used. Pareto-optimal CV sets exhibit a high trade-off between the economic and control objective. The a posteriori analysis is undertaken for several top Pareto-optimal CV sets. An optimal CV set is selected that shows the best compromise between process economics and controllability under both nominal and off-design conditions

Cooperative Problem-Based Learning (CPBL): a practical PBL model for engineering courses

Problem-Based Learning (PBL) is an inductive learning approach that uses a realistic problem as the starting point of learning. Unlike in medical education, which is more easily adaptable to PBL, implementing PBL in engineering courses in the traditional semester system set-up is challenging. While PBL is normally implemented in small groups of up to ten students with a dedicated tutor during PBL sessions in medical education, this is not plausible in engineering education because of the high enrollment and large class sizes. In a typical engineering course, implementation of PBL consisting of students in small groups in medium to large classes is more practical. However, this type of implementation is more difficult to monitor, and thus requires good support and guidance in ensuring commitment and accountability of each student towards learning in his/her group. To provide the required support, Cooperative Learning (CL) is identified to have the much needed elements to develop the small student groups to functional learning teams. Combining both CL and PBL results in a Cooperative Problem-Based Learning (CPBL) model that provides a step by step guide for students to go through the PBL cycle in their teams, according to CL principles. Suitable for implementation in medium to large classes (approximately 40-60 students for one floating facilitator), with small groups consisting of 3-5 students, the CPBL model is designed to develop the students in the whole class into a learning community. This paper provides a detailed description of the CPBL model. A sample implementation in a third year Chemical Engineering course, Process Control and Dynamics, is also described

Cooperative Problem-Based Learning (CPBL): A Practical PBL Model for a Typical Course

Problem-Based Learning (PBL) is an inductive learning approach that uses a realistic problem as the starting point of learning. Unlike in medical education, which is more easily adaptable to PBL, implementing PBL in engineering courses in the traditional semester system set-up is challenging. While PBL is normally implemented in small groups of up to ten students with a dedicated tutor during PBL sessions in medical education, this is not plausible in engineering education because of the high enrolment and large class sizes. In a typical course, implementation of PBL consisting of students in small groups in medium to large classes is more practical. However, this type of implementation is more difficult to monitor, and thus requires good support and guidance in ensuring commitment and accountability of each student towards learning in his/her group. To provide the required support, Cooperative Learning (CL) is identified to have the much needed elements to develop the small student groups to functional learning teams. Combining both CL and PBL results in a Cooperative Problem-Based Learning (CPBL) model that provides a step by step guide for students to go through the PBL cycle in their teams, according to CL principles. Suitable for implementation in medium to large classes (approximately 40-60 students for one floating facilitator), with small groups consisting of 3-5 students, the CPBL model is designed to develop the students in the whole class into a learning community. This paper provides a detailed description of the CPBL model. A sample implementation in a third year Chemical Engineering course, Process Control and Dynamics, is also described