No abstract

Industrial process monitoring tools require robust and efficient estimation techniques that maintain a high service factor by remaining online during abnormal operating conditions, such as during loss of measurements, changes in control status, or maintenance. Constraints incorporate additional process knowledge into estimation by bounding estimated disturbances within feasibility limits thereby providing robustness to faulty measurements or conditions that violate process models. Moving horizon estimation (MHE) and unscented Kalman filtering (UKF) are two estimation techniques that permit incorporation of constraints prior to evaluating the a priori estimate. This paper evaluates both constrained nonlinear estimators versus the extended Kalman filter (EKF) using industrial process data provided by ExxonMobil Chemical Company. Results provide short-term insight into the fouling process, and parameter estimates produced by UKF and MHE are shown to be more accurate than EKF.

by maintaining reliability parameters during load changes. These reliability parameters are critical to maintain power generation efficiency over an extended life of the SOFC. For SOFCs to be commercially viable, the life must exceed 20,000 hours for load following applications. This is not yet achieved because transient stresses damage the fuel cell and degrade the performance over time. This study relates the development of a dynamic model for SOFC systems in order to predict optimal manipulated variable moves along a prediction horizon. The model consists of hundreds of states and parameters that permit tracking of a realistic response. Previously, this detailed model was too computationally intensive to run in parallel with the SOFC process. The contribution of this paper is an application study to enable a large-scale simulation model to be used in Model Predictive Control (MPC) without simplification. Such a technology permits real time calculation of controller moves while loads are followed during operation. The contribution demonstrates the assumptions and approach necessary to provide real-time calculations for optimal predictive control operations using a rigorous model of the SOFC process. Large-scale process models are rarely employed in real-time control because of the prohibitive computational expense necessary to complete the calculations within the specified cycle time. An efficient model based predictive controller reduces operational fluctuations related to the startup and shutdown conditions, without exceeding reliability limits in the cells.

Extending fuel cell lifetime is a necessary objective for reducing fuel cell power generation cost of electricity. Capital costs comprise the most significant fraction of the cost of electricity. Reducing the frequency of fuel cell replacement can be achieved by implementing a control strategy that prevents excursions into operating regions causing failure. In this paper we implement a constrained MIMO model predictive controller (MPC) to avoid the failure modes relevant for a high-temperature tubular solid oxide fuel cell (SOFC) system while performing load-following. The primary causes of failure are catalyst poisoning, fuel or air starvation, carbon deposition, and microcracking. Prior steady-state thermomechanical stress analysis in literature has demonstrated that the minimum cell temperature and maximum negative radial thermal gradient are primary causes of microcracking in the SOFC. State-of-the-art SOFC control literature often seeks to track a mean or outlet cell temperature. The authors have presented the first approach to control the primary two causes of thermally-driven microcracking in tubular SOFCs using constrained control. Constraints are also incorporated into a steady-state optimization to ensure a feasible optimum.