Limited data based kinetic modeling and optimization of propionic acid synthesis over supported Rh/C catalyst

Abstract

The importance and need for greener and more sustainable chemical processes and technologies can hardly be overestimated. However, many industries still produce a considerable amount of waste. Reusing these waste streams is an indispensable piece towards realizing fully circular and sustainable economy. Accordingly, it is imperative to operate chemical processes with maximal recycling to minimize waste generation. The study presents in the aforedescribed spirit, the synthesis of propionic acid, where the various goals are optimally realized by unleashing the predictive power of nonlinear process model. A method for synthesizing propionic acid is through the heterogeneous catalytic reaction route between ethylene gas, carbon monoxide, and water. The atom efficiency of the process is 100% with the possibility of fully separating the products and recirculating the unreacted starting materials. Hence, the chemistry of the proposed catalytic route is material efficient. To make the process operate well, we apply the available measured data co construct a kinetic model, and use it to optimize the system subject to various goals. The data were not primarily measured for modeling, but for parametric reactor analysis. Yet, using the apriori knowledge about the process (i.e., the microkinetics of the reactions), the data enabled the kinetic model identification, resulting in a fitting on the measured outputs characterized by a coefficient of determination of 0.76. The operating parameters were optimized by using this model to enhance the energy and material efficiency of the process. The optimized turnover frequency happened to be is 3.974 $\text{mol}/{\text{m}}^3\text{catalyst/s}$, reached at 264 °C, 12 bar and ethylene:CO:H${}_2$O:EtI = 0.379:0.098:0.506:0.016 starting material ratio.