(739c) Optimal Process Design for a Sustainable Methanol Production Using Renewable Energies By Applying the Fluxmax Approach

1. Introduction

In the context of energy transition, one of the major goals of the
chemical industry is to replace fossil raw materials with renewable resources
by using sustainable process technologies. However, even if in the main focus
of interest, not only the substitution of feedstock, but also an increase in
energy efficiency will be decisive for a successful transition towards a more
sustainable production of chemicals. In order to enhance the overall process
efficiency, challenges must be faced at different levels of detail. While at
the plant level, more general questions and early stage decisions of chemical
production networks are addressed, at the process level and process unit level
the detailed optimization of chemical processes and process units is in the focus.
We developed the FluxMax approach that enables the simultaneous flux
optimization and heat integration by discretization of the thermodynamic state
space. As a consequence, process-based nonlinearities are decoupled effectively
from the flow optimization problem, which allows the optimization of chemical
processes across different length. Heat integration is considered as integrated
part of the optimization problem by introducing additional inequality
constraints, which results in an outperformance compared to classical,
sequential approaches.

2. Methods

The general idea of the FluxMax approach is an effective
decoupling of process-based nonlinearities from the subsequent network flux
optimization by discretization of the thermodynamic state space. The
discretization allows the representation of chemical process across different
length scales, which enables the transformation of a nonlinear process
optimization problem into a convex flux optimization on a defined network graph.
The chemical process is represented as directed graph, where the nodes
correspond to thermodynamic substances, elementary processes and heat and work
utilities. While each mixture is uniquely determined by thermodynamic
coordinates, the elementary processes are uniformly described by stoichiometric
equations. The edges, that connect the nodes, correspond to mass- and energy
fluxes, and are decision variables of the optimization problem. As a result, the
FluxMax approach can be divided into the three steps: i) discretization of the
thermodynamic state space; ii) modeling of elementary processes; and iii) formulation
and solution of the flux optimization problem.

3. Results and discussion

The methanol synthesis process was selected as example to apply
the FluxMax approach to different levels of details. At the plant level [1,2]
we systematically analyzed the influence of feedstock and energy sources on the
specific methanol production cost and its specific CO₂ emissions. It
could be shown that an economically competitive production process can be
designed also under the usage of renewable energies (Figure 2 A). As
a consequence of the simultaneous consideration of heat integration, the
FluxMax approach identified energy-optimal process configurations [3], which
outperform configurations identified in a sequential procedure (Figure 2 B).
The proof-of-concept for the application at process unit level was provided for
the reactor (Figure 2 C) and compressor cascade design of the
methanol synthesis [4].

Figure 1 Pareto optimum of competing objectives, specific cost and
CO₂ emissions (A), optimal trajectory of methanol synthesis process within
discretized thermodynamic state space (B), and kinetic rate optimization of
reactor part (C).

4. Conclusions

In this contribution, the FluxMax approach for the optimization of
chemical processes across different length scales is presented, which enables
the simultaneous flux optimization and heat integration. The introduction of
nodes corresponding to mixtures, elementary processes and utilities allows the
representation of any chemical process as a directed graph, with the edges
corresponding to the mass and energy fluxes to be optimized. As a consequence, the
FluxMax approach effectively decouples process based nonlinearities from the
optimization problem. The heat integration is considered by additional
constraints in the optimization. Using the methanol synthesis process as
example, the FluxMax approach was applied to different levels of details. In
particular the outperformance compared to classical approaches makes the
FluxMax approach a powerful tool for designing chemical processes across
different length scales.

References

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