278979 “Green” Solvents: Multi-Criteria Knowledge-Based and Statistical Approaches for Their Evaluation

Monday, October 29, 2012: 3:40 PM
333 (Convention Center )
Stavros Papadokonstantakis, Tianhe Liu, Alireza Banimostafa and Konrad Hungerbühler, Institute for Chemical and Bioengineering, Swiss Federal Institute of Technology, Zurich (ETHZ), Zurich, Switzerland

“Green” solvents: Multi-criteria knowledge-based and statistical approaches for their evaluation

Stavros Papadokonstantakis, Tianhe Liu, Alireza Banimostafa, Konrad Hungerbühler

Institute of Chemical- and Bioengineering, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland

Sustainable process design has been recognized as one of the key research challenges for process systems engineering. In fact, the experience gained so far has pointed out that the applicability of sustainability principles can be more advantageous in earlier phases of process design characterized by more degrees of freedom for decision making. Typical decisions made in these early process design phases involve the selection of chemical synthesis path, chemical auxiliaries, unit operation conditions up to a basic flowsheet design.

Solvents belong to chemical auxiliaries of particular interest for the chemical industry due to their multi-functional use (e.g., to achieve desired reaction rates while protecting from the reaction exothermicity, to ensure solid product quality requirements through effective crystallization, to perform liquid-liquid separations or azeotropic distillations etc.) and their large amounts (e.g., in fine-chemical and pharmaceutical production large amounts are used per mass of final products). Historically, solvents have been designed to maximize technical performance and minimize cost. But nowadays, an effort to find ‘green' solvents gains interest, for replacing conventional solvents with environmentally benign substitutes while, of course, maintaining their effectiveness for the intended use. In this context, the concept of ‘‘green'' solvents expresses the goal to minimize the environmental impact resulting from their use in chemical production, both from a life cycle impact assessment (LCIA) and a safety, health and environmental (SHE) hazard assessment point of view.

Several shortcut, index-based methodologies have been proposed for evaluating the relevant LCIA and SHE effects, both for independent substances and for chemical production processes. These index-based frameworks encapsulate prior knowledge regarding main SHE hazards, cradle-to-gate resources consumption, and emission related effects. The indices are typically defined on the basis of substance properties, derived from available databases or estimated using available techniques (thermodynamic property estimation methods, QSAR models, etc.), and process features derived either from a preliminary process flowsheet or even on the basis of the chemical synthesis path. On the one hand, the simplicity of the index-based approaches makes them attractive, especially for early stages of process design lacking detailed process information, but on the other hand they face criticism regarding limited coverage of considered effects, subjectivities for the calculation of aggregated end-point indices, and unknown resolution of the scaled final scores according to which the ranking of process alternatives is inferred.

Especially for the problem of subjective aggregation to end-point scores principal component analysis (PCA) has been recently proposed as a potential remedy. In this approach the rows of the PCA input matrix refer to the evaluation objects (e.g., solvents, chemical process steps, chemical process sections or overall chemical processes) and the columns refer to metrics for their assessment (e.g., LCIA and/or SHE hazard assessment indicators). The evaluation objects are ranked according to the obtained principal component (PC) scores, which in the simplest PCA version can be viewed as linear combinations of the assessment metrics. The weights of these linear combinations express the importance of the original assessment metric for a specific PC and are derived in such a way that the first PC covers most of the variability of the PCA input matrix, the second PC most of the remaining variability under the condition that it is orthogonal to the first one, and so on. The concept behind this PCA-based method, e.g., when it is used for the assessment of substances, is that dependencies must exist between the assessment metrics, since the molecular structure of a substance should, in theory, provide (although most of the times via unknown functional relationships) all necessary information for the assessment metrics. The PCA-based method will, therefore, detect these dependencies in a theoretically sound way and this information will be used instead of a priori weighting schemes for the diverse metrics. In this way, two objectives are achieved simultaneously, i.e., the inter-relationships between the diverse metrics reveal the “true” dimensionality of the multi-criteria assessment and the level of multi-criteria similarity among individual solvents can be derived. Moreover, the same PCA-based concept can be used in an expectation-maximization iterative procedure for filling data gaps, typically occurring in the assessment metrics.

The proposed framework is demonstrated (see Figure 1) on the basis of 138 organic solvents used in industrial applications. The performance of the PCA-based method for filling data gaps is compared with other approaches based on priority settings between substance properties and reveals in which extent this should be preferred (i.e., relative amount and patterns of data gaps). Moreover, two case studies are presented in which the results of the PCA-based framework are compared to the results of other studies (from the Safety and Environmental Technology group in ETHZ and GlaxoSmithKline, respectively) using different index-based methods with a priori postulated weighting schemes. The results indicate that the presented framework is useful for covering most of the information contained in the other index-based methods for the evaluation of “green” solvents, while being inherently flexible with respect to the number and type of assessment metrics, as well as to the existence of data gaps. Finally, the present study discusses the advantages and limitations of the PCA-based approach in terms of simplicity, interpretability and statistical robustness.

Figure 1: Integrating a PCA-based approach for the evaluation of “green solvents” and comparison with existing index-based approaches.

 


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