Trajectory optimization of an oscillating industrial two-stage evaporator utilizing a Python-Aspen Plus Dynamics toolchain

https://doi.org/10.1016/j.cherd.2019.12.015Get rights and content
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Highlights

  • A model-based, dynamic optimization of an industrial evaporator system is presented

  • Optimization performed with Python toolchain; system modeled in Aspen Plus Dynamics

  • The SciPy implementation of deterministic derivative-free algorithm COBYLA utilized

  • Steam consumption trajectory found to minimize oscillations of evaporator system

  • Variance of oscillations reduced by 99.7 % (including bang-bang penalty)

Abstract

Evaporators are integral parts of many separation processes across production industries, and they need to be well understood in order to be operated well, thereby enabling high resource-efficiency and productivity. In a previous investigation, the effects of disturbing oscillations in a two-stage evaporator system were quantified. In the current study, these oscillations were reduced through trajectory optimization using steam consumption as a temporally discretized decision variable, taking advantage of a dynamic process flowsheet model in Aspen Plus Dynamics (APD) employed as if it were a black-box model. The optimization was performed utilizing a Python-APD toolchain with the SciPy implementation of COBYLA. The optimal trajectory was able to successfully reduce the objective function value (including the product stream mass flow variance and a bang-bang penalty on the trajectory itself) to slightly less than 0.3 % of that of the nominal case, in which a time-invariant steam consumption was employed. This in turn grants opportunities to increase throughput of the process, leading to significant financial gains.

Keywords

Aspen Plus Dynamics
Python
Dynamic optimization
Derivative-free optimization
Evaporator system
Oscillations

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