Modeling and optimization of a naphtha pyrolysis process considering carbon emissions
In response to the recent climate crisis, research on greenhouse gas reduction is actively underway. One of the effective ways for carbon neutrality is to reduce carbon dioxide emissions from existing high-carbon-emitting processes. This study performs modeling study for naphtha pyrolysis processes at Naphtha Cracking Center that is a representative high-carbon-emitting process due to LNG combustion gas for heat supply. Specifically, since the interaction between a firebox and tubular reactors appears complex depending on the location inside the cracking furnace, this study proposes a coupled firebox and tubular reactor model to simultaneously predict product yield and carbon emissions according to operating conditions. In addition, model fitting is performed based on the operation data of an actual naphtha pyrolysis furnace to ensure that the developed model properly considers the characteristics of the actual system.
Utilizing the developed coupled firebox and tubular reactor model, optimization problems are designed and solved to compare the operating strategies under various scenarios. Existing optimization studies of the naphtha pyrolysis process have mainly focused on the maximization of profit from the products or minimization of operating cost without considering greenhouse gas emissions that has recently become a major environmental issue. Therefore, this study derives optimal operating conditions to maximize profit from ethylene and propylene while minimizing the carbon tax due to CO₂ emissions. The derived optimal operating conditions can effectively reduce carbon emissions while maintaining a product yield similar to that in the conventional case. Additionally, the influence of carbon tax on the optimal operation is analyzed by applying a wider range of carbon tax values to the optimization framework. The need for the proposed economic optimization framework is expected to increase as the global carbon tax continues to rise.
Computational fluid dynamics based modeling and model predictive control of fluidized-bed dry reforming of methane process
In a fluidized bed DRM (dry reforming of methane) process, solid catalyst particles move at a low velocity in the bubbling fluidization zone at the bottom and descend with a high fluidization velocity at the top, with a non-uniform velocity profile depending on their location inside the reactor. Therefore, for precise fluidized bed DRM process control, a fundamental model that considers DRM reaction, mass and heat transfer as well as bubble and solid catalyst flow is required. STEP Lab is performing fundamental modeling of fluidized bed DRM process based on computational fluid dynamics (CFD)-based compartment modeling methodology. Specifically, the inside of the fluidized bed reactor is divided into hundreds of small zones that can be assumed as a single homogeneous reactor. Subsequently, mass and energy conservation equations for each zone and the entire reactor are formulated based on the DRM mechanism while CFD simulations are performed to calculate the mass flow between adjacent zones and account for the resulting heat and mass transfer.
Despite the obvious advantages from an environmental point of view, most of the existing studies for DRM processes are still on technologies such as catalyst and plasma reactor development, and there are few studies on process efficiency improvement through optimization and optimal control. STEP Lab is conducting research to improve process efficiency by establishing the model predictive control (MPC) system, which is one of the most advanced control methods applied to various chemical processes, in connection with fundamental modeling. Specifically, a MPC system is designed and verified by adopting the developed high-fidelity fundamental model for the fluidized-bed DRM reactor as a virtual plant. Then, the developed MPC system will be applied to an actual fluidized-bed DRM reactor through collaboration with companies or research institutes.
Bio-electrochemical system modeling and offset-free model predictive control
Recent research on bio-electrochemical system (BES) is in the stage of focusing on the possibility of the production of target molecules such as hydrogen, methane, and acetate from carbon dioxide at the cathode through the application of external electricity. In order to gradually move from the stage of development for basic technologies to the commercialization stage, research to improve the performance of the BES process by maximizing the carbon dioxide removal efficiency and productivity of the target molecules while reducing the overall energy consumption is essential. Therefore, STEP Lab is conducting research to optimize the operating conditions of BES and implement the optimal control system on the BES process. Specifically, we are trying to utilize the offset-free model predictive control technique that can effectively compensate for the model-plant mismatch in order to consider the error between the measured value from the practical process and the predicted value from the digital twin built via fundamental modeling.