Multi-objective Optimization of the Economic and Life Cycle Environmental Performance of Industrial Processes - Stochastic Model

The combined use of multi-objective optimization and LCA has recently emerged as a useful tool for minimizing the environmental impact of industrial processes. The main limitation of this approach is that it requires large amounts of data that are typically affected by several uncertainty sources. We propose herein a systematic framework to handle these uncertainties that builds upon the mixed-integer nonlinear programming (MINLP) model presented in our previous (first) article1 (Sabio, N.; Pozo C.; Guillén-Gosálbez, G.; Jiménez, L.; Karuppiah, R..; Vasudevan, V.; Sawaya, N., Farrell, J.T. AIChE Journal, 2012, submitted) of this series of two. The approach proposed in this work takes advantage of recent advances made in modeling of uncertain LCA data and in optimization under uncertainty. We propose a stochastic multi-objective and multi-scenario mixed-integer nonlinear programming (MINLP) approach in which the uncertain parameters are described via scenarios. We investigate the use of two stochastic metrics: (i) the environmental impact in the worst case; and (ii) the environmental downside risk. We demonstrate the capabilities of our approach using the same industrial network described in our first publication, but this time considering the uncertainty of some key life cycle inventory (LCI) parameters that were regarded as deterministic in our previous article