Optimal process design across process hierarchies for the efficient utilization of renewable energy sources

Abstract

Without strong efforts and effective climate protection measures, the world’s population will have only a few more years to achieve the goals announced in the Paris Agreement. As one of the largest consumers of energy and thus a large contributor of harmful greenhouse gas emissions, the chemical industry bears a special responsibility for a successful energy transition. Consequently, one of the major goals of the chemical industry is to replace fossil raw materials with renewable resources by using sustainable process technologies. For a significant CO2 reduction, the massive use of renewable energies by implementation of Renewables-to-Chemicals (R2Chem) production systems is of key importance. The principal step of R2Chem-concepts is the electrochemical splitting of water into hydrogen and oxygen via electrolysis and the subsequent utilization and/or storage of hydrogen. Since in many areas, (synthetic) hydrocarbons will still be required in future, also sustainable carbon sources have to be identified. Carbon capture and utilization (CCU) technologies seem to be very attractive since they offer the possibility to close the carbon cycle by recycling the bounded carbon atom in the CO2 molecule. Initially, this dissertation evaluates different target molecules by introducing the storage efficiency as the ratio of the stored energy content to the total energy input required via the R2Chem pathway. Methanol proves to be an optimal target molecule due to its advantageous carbon-hydrogen feed ratio and beneficial storage properties. It is shown that the main energy consumption is caused by the electrolysis of water, although the carbon source also has a significant influence: The use of CCU and the direct capture of CO2 from the air increases the energy demand significantly compared to concentrated CO2 sources such as flue gases from power plants. While theoretically the annual methanol production in Germany could be covered by the available electricity from renewable energies, the consumption (and thus the reduction potential) of CO2 is only 0.2 % of the annual emissions. This shows that not only the substitution of fossil raw material and energy sources but also an increase in the energy efficiency of the processes involved is crucial for a successful transition to a more sustainable chemical production. In order to increase the overall efficiency of the processes, challenges must be faced at different process hierarchies. While at the production system level, more general questions and early-stage decisions in chemical production networks are addressed, at the plant and process unit level the focus is on the detailed optimization of chemical processes and process units. In the context of this dissertation the FluxMax approach was developed as a scale-independent network approach for process design and synthesis and simultaneous heat integration. The basic idea of the FluxMax approach is an effective decoupling of process-related non-linearities from the subsequent network flow optimization by discretizing the thermodynamic state space. The discretization allows the representation of chemical processes across different length scales, which enables the transformation of a non-linear process optimization problem into a convex flow optimization task on a defined network graph. The chemical process is represented as a directed graph, with nodes corresponding to thermodynamic substances, elementary processes and heat and work utilities. While each mixture is uniquely defined by thermodynamic coordinates, the elementary processes are uniformly described by stoichiometric equations. The edges connecting the nodes correspond to the mass and energy flows and are decision variables of the optimization problem. Consequently, the FluxMax approach can be divided into three steps: i) discretization of the thermodynamic state space; ii) modeling of the elementary processes; and iii) formulation and solution of the flow optimization problem. The FluxMax approach was developed in collaboration with Georg Liesche at the Max Planck Insitute Magdeburg. Using the methanol synthesis process as a case study, the FluxMax approach was applied to different levels of the process hierarchy. First, the FluxMax approach was applied at the production system level for the systematic analysis of different feedstocks and energy sources. It is shown that an acceptable trade-off between costs and emissions can be achieved by using natural gas as raw material source if the required energy is supplied from renewable sources. However, a net consumption of CO2 of the entire production system is only possible if renewable energy sources are used and at the same time CO2 is used as a raw material source. With the use of fossil fuels, a significant carbon footprint is unavoidable due to high indirect CO2 emissions from the energy supply (electricity, heat). Thus, in addition to economic challenges of using CO2 as feedstock also the ecological impact strongly depends on the energy source used. Secondly, the FluxMax approach was applied at the plant level by representing the overall process of methanol production through the elementary processes of reaction, separation, heating/cooling, mixing and compression. As a result of the simultaneous consideration of heat integration, an energy-optimized process configuration is identified that outperforms the configuration identified in a sequential design procedure. The simultaneous approach results in a heat saving potential of almost 99 % compared to 88 % in the sequential approach. This result underlines the need for a simultaneous approach to identify energy efficient processes that lead to significant reductions in CO2 emissions. Thirdly, the FluxMax approach was used for the design of distillation columns, as these account for the largest contribution to the total energy consumption of chemical processes due to their high heat demand. The representation of a distillation process by elementary processes results in additional degrees of freedom for optimization compared to classical column modeling approaches. The energy-optimized configuration is a column with improved heat transfer between vapor and liquid streams. MESH equations are used for the validation of the FluxMax design, as they represent the state-of-the-art in process modeling tools. While the new design reduces the energy requirements by up to 64 % compared to the classic design, additional heat exchange area is required to exploit the energy saving potential. A multi-objective optimization – energy demand vs. heat exchange area – was performed to determine the optimal trade-off between energy demand and capital costs related to heat exchange area to be installed. The highly energy-efficient designs identified by the FluxMax approach can be realized in practice by horizontal columns or modularized container solutions. The results presented in this dissertation demonstrate the versatility of the FluxMax approach, which uses a unique description of each type of chemical conversion process. Consequently, the approach is applicable to any level of the process hierarchy. In particular, the representation of the chemical process by elementary processes – resulting in increased degrees of freedom – and the simultaneous consideration of heat integration and process design make the FluxMax approach a powerful tool for the design of chemical processes on different length scales. At different levels of the process hierarchy, it is demonstrated that highly energy-efficient processes can be designed that can contribute to a reduction in the energy consumption of the chemical industry. Nevertheless, the results also show the limits of the chemical industry’s potential in terms of reducing global energy consumption and the corresponding GHG emissions. However, the chemical industry needs to be considered in a sector-coupled perspective, as the chemical industry has a major impact on other sectors: from the sustainable production of alternative fuels to the sustainable production of consumer goods.