Abstract
In this contribution our developed framework for data-driven chance-constrained optimization is extended with an uncertainty analysis module. The module quantifies uncertainty in output variables of rigorous simulations. It chooses the most accurate parametric continuous probability distribution model, minimizing deviation between model and data. A constraint is added to favour less complex models with a minimal required quality regarding the fit. The bases of the module are over 100 probability distribution models provided in the Scipy package in Python, a rigorous case-study is conducted selecting the four most relevant models for the application at hand. The applicability and precision of the uncertainty analyser module is investigated for an impact factor calculation in life cycle impact assessment to quantify the uncertainty in the results. Furthermore, the extended framework is verified with data from a first principle process model of a chloralkali plant, demonstrating the increased precision of the uncertainty description of the output variables, resulting in 25% increase in accuracy in the chance-constraint calculation.
Highlights
Environmental sustainability has grown to become a more pressing subject for the chemical industry
In this study we focus on chance-constrained optimization, in line with previous works at our department [16,17]
The case study is based on a life cycle impact assessment step, where the uncertainty of the calculated impact scores are analysed
Summary
Environmental sustainability has grown to become a more pressing subject for the chemical industry. Using chance-constrained optimization for real-time applications would enable the incorporation of environmental models with highly uncertain parameters and still achieve accurate online computation of optimal and stable process operating conditions. A new framework for chance-constrained optimization has been developed at the department, decreasing the computational effort significantly This is achieved by exchanging rigorous models for the optimization with data-driven ones. Implementing more complex distribution functions to model the uncertainty while keeping the computational effort at a minimum It is the aim of this paper to develop and implement a method to improve uncertainty modelling for data-driven chance-constrained optimization. Sustainability 2020, 12, 2450 distribution functions while keeping the computational effort at a minimum This would allow the implementation of environmental models coupled with process models for real-time optimization
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