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One of the most important drawbacks of life cycle assessment (LCA)-related analysis is the generation of reliable data. In the proposed methodology, this drawback is addressed using data from process simulations, based on first-principles models in the LCA calculations. Furthermore, uncertainty that arises from industrial data and a simulation hypothesis are explicitly incorporated, using Monte Carlo sampling, which allows statistical information to be translated into a set of representative scenarios for which the LCA calculations are performed. The combined use of LCA, process simulation, and sampling techniques results in a powerful environmentally conscious quantitative tool whose objective is to guide decision-makers toward the adoption of more-sustainable process alternatives. The main objective of the methodology is to show the main differences between production options. This novel methodology is applied to the specific case of phosphoric acid (PA) production.

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In batch process scheduling, production trade-offs arise from the simultaneous\ud consideration of different objectives. Economic goals are expressed in terms of plant profitability and productivity, whereas the environmental objectives are evaluated by means of metrics originated from the use of life cycle assessment methodology. This work illustrates a novel approach for decision making by using multiobjective optimization. In addition, different metrics are proposed to select a possible compromise based on the distance to a nonexistent utopian solution, whose objective function values are all optimal. Thus, this work provides a deeper insight into the influence of the metrics selection for both environmental and economic issues while considering the trade-offs of adopting a particular schedule. The use of this approach is illustrated through its application to a case study related to a multiproduct acrylic fiber production plant,\ud special attention is put to the influence of product changeovers.Peer ReviewedPostprint (published version