Abstract
In this work we present a new approach that we use to simulate and optimize multiple dividing wall columns at the same time. Instead of considering all model equations as constraints and all process variables as optimization variables in a large and highly nonlinear optimization problem we only incorporate a subset of the model equations as constraints and a subset of the process variables as optimization variables. The remaining process variables are calculated from this subset by a robust and fast calculation procedure. This calculation procedure also ensures that the remaining model equations are satisfied. A comparison with the commercial process simulator Aspen Plus® shows that with the new approach multiple dividing wall columns can be optimized more stable and better solutions are found. Moreover the time needed to find an optimal design decreases significantly.
Highlights
IntroductionThe problem of finding a steady state can be formulated as a system of nonlinear equations: gi(xvar) = 0, i ∈ IMESH, (1)
In process design, the problem of finding a steady state can be formulated as a system of nonlinear equations: gi(xvar) = 0, i ∈ IMESH, (1)where xvar =j∈Ivar are the process variables and gi(xvar), i ∈ IMESH are the model equations
In this work we present a new approach that we use to simulate and optimize multiple dividing wall columns at the same time
Summary
The problem of finding a steady state can be formulated as a system of nonlinear equations: gi(xvar) = 0, i ∈ IMESH, (1). Where xvar = (xj)j∈Ivar are the process variables and gi(xvar), i ∈ IMESH are the model equations. The user has to specify a value for each remaining degree of freedom. The simulation software tries to determine values for all process variables by solving the underlying non-linear system of equations given in Equation (1). If not just any design is to be found, the specifications are modified until a good design is found. This can for example be achieved using an optimization routine
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