Safe and Optimal Operation of Commercial μ-CHP SOFC System Under Real-World Condition

Operating under high temperature (> 700 o C), solid oxide fuel cell (SOFC) devices can transform chemical energy from H 2 , ammonia, natural gas and other fuels into electricity and heat with high efficiency. Despite these advantages, the commercial deployment of SOFC systems faces significant challenges in maintaining safe, stable and optimal operation under dynamic power demands. These operational challenges stem from two primary factors: (1) external fuel composition variations, particularly in natural gas grid supplies with temporal hydrocarbon concentration fluctuations, and (2) internal system performance degradation over extended operation periods. To address these challenges, real-time optimization (RTO) emerges as an option for SOFC system control and optimization. RTO encompasses various optimization strategies that integrate process measurements with models to achieve the system performance optimum (efficiency, power output or heat output). Two-step approach, the conventional RTO approach in industry, first uses plant measurements to update the model parameters via nonlinear regression and then solves the optimization problem with the updated model to generate new plant inputs. This approach presents critical limitations: (1) potential constraint violation risks due to model uncertainty, and (2) inherent structural plant-model mismatch that prevents from obtaining true system optimum. Another member in the RTO family is called modifier adaptation, which can overcome the disadvantages of the two-step approach. The most important feature of this method is that it can enforce the plant to reach the optimum even in the presence of structural plant-model mismatch. Thanks to the correction terms to the cost and constraint, the modified optimization problem can match the necessary conditions of optimality with no change in the model. The difference between two methods is illustrated in Figure 1a. Notably, since the optimality of the SOFC systems is expected to be fully determined by active plant constraints (on, e.g., maximum fuel utilization, maximum stack temperature, etc.), constraint adaptation (CA) can be used instead, that is, a simpler version of modifier adaptation that does not require the estimation of plant gradients. In this study, we applied CA-based RTO algorithm to maximize the electrical efficiency of commercial SOFC system manufactured by new enerday GmbH under different power targets. SOFC system mainly consists of (1) a catalytic partial oxidation reactor (CPOX), (2) an afterburner to burn the unreacted fuel, (3) an air heat exchanger to preheat the sweep air of the stack, (4) an air mixing valve to control the ratio of non-superheated air, (5) a water heat exchanger for waste heat recovery, and (6) a stack with power capacity from 550 to 850 W power at nominal condition. As is depicted in Figure 1b, natural gas (mainly CH 4 ) was partially oxidized in the CPOX with air to maintain the stack negatrode inlet temperature arou