Linear Programming Approach for Structure Optimization of Renewable-to-Chemicals (R2Chem) Production Networks

For CO₂ reduction in the chemical industries, the massive use of renewable energies and the substitution of fossil based feedstock by implementation of Renewables-to-Chemicals (R2Chem) production systems are of key importance. Due to the multitude of alternative feedstock sources and process technologies a large number of different process pathways are possible for converting renewables into valuable target products. In this work we propose a method for the identification of the optimal R2Chem process structure under consideration of an economic objective function. By introducing process extent variables it is possible to fully avoid binary decision variables, resulting in a purely linear program. The derived cost function includes operational as well as capital cost. Furthermore, a penalty term for the carbon dioxide emission is considered. It is shown that an acceptable trade-off between cost and emissions is realizable by using natural gas as feedstock source, especially if the required energy is supplied from renewable sources. A net consumption of CO₂ of the overall production system is only possible if renewable energies sources are exploited while using CO₂ as feedstock source at the same time. In case of using fossil energy sources, a negative carbon footprint is unavoidable due to high indirect CO₂ emissions due to the energy supply (electricity, heat). Thus, in addition to economic challenges of using CO₂ as feedstock also the ecologic impact strongly depends on the energy source used. The main advantage of the proposed method is the fast screening for the optimal process system within a superstructure which contains many alternative process configurations. The method is exemplified by optimizing process systems for the production of methanol for different feedstock and energy supply sources.