Abstract
In this paper we describe an approach that combines system optimization under environmental constraints with a detailed simulation of the processes of a working plant. Specifically, a flow-sheet-based model of a potable water production plant was developed and coupled with a Python script which allowed variation of operational parameters of the plant (dissolved organic carbon (DOC), UV Absorbance at 254 nm (UVA), pH of coagulation unit, etc.) and automatic determination of the values of the objectives (e.g. quantity of sludge produced as a result of the settling phase, CO2 emissions, climate change impact), which are not known in closed form. The Python script runs an optimization algorithm, seeking the global minimum of an objective function in the parameter space. The search procedure is based on the Nelder-Mead algorithm, which does not assume smooth functions.
The script was applied to a simplified model of a potable water production plant, which includes pumping, ozonation, powdered activated carbon (PAC) addition, recirculation, coagulation, settling, filtration and disinfection. Every unit is modelled with a set of equations describing input and output mass flows of the process, as well as the chemical reactions taking place within the unit. Therefore, while simplified, the model has all the complexity of a full-fledged simulation of the plant.
Systematic exploration of the objective surfaces shows that they are irregular and multimodal in all three cases tested, although there are systematic trends in relation to parameter values.
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Wallace, R.J., Marvuglia, A., Benetto, E., Tiruta-Barna, L. (2014). A New Approach to Optimization with Life Cycle Assessment: Combining Optimization with Detailed Process Simulation. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8581. Springer, Cham. https://doi.org/10.1007/978-3-319-09150-1_52
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