Local Self-Optimizing Control with Average Loss Minimization

Efficiency Maximization of a Direct Internal Reforming Solid Oxide Fuel Cell in a Two-Layer Self-Optimizing Control Structure

Control system configuration is essential for the efficiency performance of a solid oxide fuel cell (SOFC). In this paper, we aim to report a novel two-layer self-optimizing control (SOC) system for the efficiency maximization of a direct internal reforming SOFC, where the efficiency index is defined as the profit of generated electricity penalized by carbon (CO₂) emission. Based on the lumped-parameter model of the SOFC, comprehensive evaluations are carried out to identify the optimal controlled variables (CVs), the control of which at constant set-points can optimize the efficiency, in spite of operating condition changes. In the lower SOC layer, we configure single variables as the CVs. The results show that the stack temperature is the active constraint which should be controlled to maintain the cell performance. In addition, the outlet hydrogen composition is identified as the optimal CV. This result differs from several previous proposals, such as methane composition. In the presence of operating condition changes, the set-point of hydrogen composition is further automatically adjusted by the upper SOC layer, where a linear combination of the SOFC measurements is configured as the CV, giving negligible efficiency losses. The cascaded two-layer SOC structure is able to maximize the SOFC efficiency and reduce carbon emission without using online optimization techniques; meanwhile, it allows for smooth and safe operations. The validity of the new scheme is verified through both static and dynamic evaluations.

Enhanced control structure design for an industrial off-gas system: Simple reconfigurations benefit the economy

For control structure design of the industrial off-gas benchmark system, application of the Skogestad's state-of-art design procedure has suggested the scrubber inlet pressure (P _si in the roaster) and one of the fan speeds (N _fan1 or N__fan2 in the furnace) as the self-optimizing controlled variables CVs. In this study, we stress and advocate the gSOC-plus-BAB approach as an enhanced design toolkit for the classical and systematical design procedure. The gSOC (global self-optimizing control) is able to efficiently solve measurement combinations as CVs with improved economic performances, while the BAB (branch and bound) algorithm serves to fast screen promising measurement subsets for large-scale problems. Using the enhanced design for the off-gas system, our new findings are to control the combination of roaster's ID fan outlet pressures, 0.494P _IDfan1+0.506P _IDfan2 (setpoint: -257.04 Pa), and the furnace's fan pressure difference, P _IDfan1- P _IDfan2 (setpoint: 0). Such simple reconfigurations can dramatically reduce the average economic loss by 53.3% for the roaster and even achieve perfect optimal control for the furnace. Both steady state and dynamic evaluations are carried out to validate the reconfigured control structures. © 2017 Elsevier Inc. All rights reserved.

A Retrofit Hierarchical Architecture for Real-Time Optimization and Control Integration

To achieve the optimal operation of chemical processes in the presence of disturbances and uncertainty, a retrofit hierarchical architecture (HA) integrating real-time optimization (RTO) and control was proposed. The proposed architecture features two main components. The first is a fast extremum-seeking control (ESC) approach using transient measurements that is employed in the upper RTO layer. The fast ESC approach can effectively suppress the impact of plant-model mismatch and steady-state wait time. The second is a global self-optimizing control (SOC) scheme that is introduced to integrate the RTO and control layers. The proposed SOC scheme minimizes the global average loss based on the approximation of necessary conditions of optimality (NCO) over the entire operating region. A least-squares regression technique was adopted to select the controlled variables (CVs) as linear combinations of measurements. The proposed method does not require the second order derivative information, therefore, it is numerically more reliable and robust. An exothermic reaction process is presented to illustrate the effectiveness of the proposed method.