Integrating life cycle inventory and process design techniques for the early estimate of energy and material consumption data

Life cycle assessment (LCA) is a powerful tool to identify direct and indirect environmental burdens associated with products, processes and services. A critical phase of the LCA methodology is the collection of representative inventory data for the energy and material streams related to the production process. In the evaluation of new and emerging chemical processes, measured data are known only at laboratory scale and may have limited connection to the environmental footprint of the same process implemented at industrial scale. On the other hand, in the evaluation of processes already established at commercial scale, the availability of process data might be hampered by industrial confidentiality. In both cases, the integration of simple process design techniques in the LCA can contribute to overcome the lack of primary data, allowing a more correct quantification of the life cycle inventory. The present paper shows, through the review of case study examples, how simplified process design, modeling and simulation can support the LCA framework to provide a preliminary estimate of energy and material consumption data suitable for environmental assessment purposes. The discussed case studies illustrate the implementation of process design considerations to tackle availability issues of inventory data in different contexts. By evidencing the case-specific nature of the problem of preliminary conceptual process design, the study calls for a closer collaboration of process design experts and life cycle analysts in the green development of new products and processes

Predicting the cradle-to-gate environmental impact of chemicals from molecular descriptors and thermodynamic properties via mixed-integer programming

Life Cycle Assessment (LCA) has recently gained wide acceptance in the environmental impact evaluation of chemicals. Unfortunately, LCA studies require large amounts of data that are hard to gather in practice, a critical limitation when assessing the processes and value chains present in the chemical industry. We here develop an approach that predicts the cradle-to-gate life cycle production impact of organic chemicals from attributes related to their molecular structure and thermodynamic properties. This method is based on a mixed-integer programming (MIP) optimisation framework that systematically constructs short-cut predictive models of life cycle impact. On applying our approach to a data set containing 88 chemicals, 17 molecular descriptors and 15 thermodynamic properties, we estimate with enough accuracy (for the purposes of a standard LCA) several impact categories widely applied in LCA studies, including the cumulative energy demand, global warming potential and Eco-indicator 99. Our framework ultimately leads to linear models that can be easily integrated into existing modelling and optimisation software, thereby facilitating the design of more sustainable processes

Environmental, health, and safety assessment of chemical alternatives during early process design: The role of predictive modeling and streamlined techniques

Industrial chemicals are important for many aspects of modern life, though they can be harmful to the environment and human health. Environmental or safety concerns identified during the early design and selection of chemicals could motivate choices as to safer alternatives and process setups. There is a growing interest in developing more rapid, and streamlined assessment methods to obtain a first indication of the potential impacts linked to the nature and use of industrial chemicals. This work applies predictive modeling and streamlined techniques to estimate the potential environmental, health, and safety hazards associated with specific chemical structures. The assessment is performed during the design and selection of promising candidates for a particular process as part of the computer-aided molecular design (CAMD) and process setup. The case of phase-change solvents used for post-combustion carbon capture is examined. Furthermore, the refinement of predictive models through the incorporation of knowledge already existing in the field (prior knowledge) is investigated. A procedure for knowledge extraction from scientific articles that applies text mining is proposed. The results show that incorporating impact assessment criteria into the CAMD facilitates the molecular design by enriching the Pareto front of candidates. The use of predictive models that estimate molecular properties, such as acute aquatic toxicity, bioconcentration, and persistency are found to support the identification of the optimal solvents for CO2 capture. Given the role of sustainability-related properties in tasks such as CAMD, the improved performance and the interpretability of the aquatic toxicity predictive models developed here and using prior knowledge are important. The process level assessment of the phase-change solvent systems indicated that phase-change solvent alternatives could provide benefits, not only in terms of reduced energy consumption but also lower impacts on human health and the environment. \ua0However, the degradation behaviors of these compounds should be properly assessed and controlled to ensure beneficial performances compared to conventional carbon capture solvents. Overall, predictive modeling and streamlined life-cycle assessments (LCAs), as well as environmental, health, and safety evaluation methods were revealed to be valuable for defining the critical aspects that influence the potential impacts of chemicals and in supporting decisions concerning the molecular and process designs