Optimizing methane and methanol production from integrated steelworks process off-gases through economic hybrid model predictive control

Abstract

Within integrated steelworks, process off-gases are important energy carriers. After suitable treatment, they are typically exploited for heating purposes and for production of steam and electricity. However, their main compounds (CO x and H 2 ) can also be valorized through synthesis reactors, which provide valuable products, such as methane and methanol, while reducing CO 2 emissions. To this aim, large quantities of cheaply and greenly produced hydrogen must be available to enrich process off-gases and make them suitable to the synthesis processes. The enrichment and valorization of process off-gases requires an advanced control system that ensure optimal economic valorization and safe operation of the plants. This paper proposes a solution relying on a dispatch controller based on Economic Hybrid Model Predictive Control, which integrates a set of process models based on physical/chemical laws and machine learning-based models for disturbances forecasting. The controller implements a mixed integer linear programming approach after the linearization of the dynamics of controlled systems every control step. The optimization problem also includes a set of constraints related to the operating condition limits of each equipment. Economic and environmental impacts of the proposed approach are compared with respect to the standard use of process off-gases. The feasibility of the approach strictly depends on the cost of hydrogen, and, in the case of low-cost green electricity sources, the results are highly encouraging. The approach was successfully tested on-line to supervise the operation of pilot methanol and methane reactors.