| 1. |
- Baxevanidis, Pantelis, et al.
(författare)
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Group contribution-based LCA models to enable screening for environmentally benign novel chemicals in CAMD applications
- 2022
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Ingår i: AICHE Journal. - : Wiley. - 1547-5905 .- 0001-1541. ; 68:3
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Tidskriftsartikel (refereegranskat)abstract
- This study considers the development of suitable models for the estimation of life cycle assessment (LCA) indices of organic chemicals. Unlike state-of-the-art models, the tools developed here correlate LCA indices with the molecular composition according to the well-established group contribution (GC) approach. The LCA indices considered here are global warming potential, cumulative energy demand, and Eco-Indicator 99. The model development uses data from existing LCA databases, where each material is associated with its cradle-to-gate LCA metrics. A variety of regression and nonregression methodologies are recruited to achieve the optimum correlation. GC models can be used to screen for molecules with optimal and/or desirable properties, using appropriate molecular design synthesis algorithms. In this framework, the models developed here are linked to the design algorithm to enable the consideration of LCA features together with other properties, for the design of environmentally benign liquid–liquid extraction solvents.
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| 2. |
- Karka, P., et al.
(författare)
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Cradle-to-gate assessment of environmental impacts for a broad set of biomass-to-product process chains
- 2017
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Ingår i: International Journal of Life Cycle Assessment. - : Springer Science and Business Media LLC. - 1614-7502 .- 0948-3349. ; 22:9, s. 1418-1440
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Tidskriftsartikel (refereegranskat)abstract
- This study advocates a modular approach combining unit processes as building blocks to formulate biomass process chains. This approach facilitates a transparent environmental life cycle impact assessment for bio-based products. It also enhances the ability to develop and assess more complex biorefinery systems, identifies critical parameters and offers useful material to support environmental impact assessment in early design stages. Twenty-three different products were assessed with regard to the environmental burden associated with their production paths. Life cycle inventories (LCIs) for 32 unit processes were compiled (using information from pilot plants, simulation and literature data) and organized in biomass process chains. Then, 58 study systems were formed based on various combinations of the unit processes, each study system referring to the production of a selected product. Three indicators were used for quantification of the impacts: non-renewable fossil cumulative energy demand (CED), global warming potential (GWP) and water depletion as defined in the ReCiPe method. Factors influencing the variation of results even for similar products are discussed (e.g. production path and allocation method lead to a range of GWP values for ethylene production from 0.43 to 3.37 kg CO2 eq/kg ethylene). For the majority of bio-products, CED has lower values than fossil-based equivalents (average difference 39-70 MJ eq/kg product depending on the allocation method), while mixed trends are obtained for the GWP and water depletion indicators. Assessments also highlight attributes that have a significant effect in the environmental profile of a production path such as the synthesis path, the process chemistry (water intensity) and process-related factors (energy intensity, degree of energy integration/heat recovery). The analysis of impacts per unit process is able to demonstrate the particular production stages featuring high environmental intensities along a path further hinting to suggestions for amendments and improvements from an overall performance perspective. The study makes a useful source for biorefinery design studies especially in adopting a modular approach to represent and to analyse biomass process chains; it also provides a reference point for comparison (benchmarking) between different process technologies for biomass utilization. Finally, the analysis is compatible with the standards of the LCA methodology, and it is based on the use of the most common LCA databases, which facilitates the comparison of the results with other relevant studies.
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| 3. |
- Karka, Paraskevi, 1982, et al.
(författare)
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Digitizing sustainable process development: From ex-post to ex-ante LCA using machine-learning to evaluate bio-based process technologies ahead of detailed design
- 2022
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Ingår i: Chemical Engineering Science. - : Elsevier BV. - 0009-2509. ; 250
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Tidskriftsartikel (refereegranskat)abstract
- Life Cycle Assessment is a data-intensive process holding great promise to benefit from advanced analytics and machine learning technologies. The present research aims at the development of a data-science based framework with capabilities to estimate LCA metrics of bio-based and biorefinery processes in early design phases. Life cycle inventories may combine experimental (pilot and lab scale) data, property and thermodynamic databases, and model-derived data from simulations and design studies. The framework applies advanced analytics such as classification trees and artificial neural networks (ANN) with a scope to produce input–output relationships through predictor variables that refer to the molecular structure of bio-chemical or bio-fuel products of interest, the feedstocks used, and the process technologies characteristics. The combined use of ANNs and trees demonstrates a coordinated level of complementarity between the approaches, while it improves robustness and streamlines LCA estimations in the early-stage design.
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| 4. |
- Karka, P., et al.
(författare)
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Environmental impact assessment of biomass process chains at early design stages using decision trees
- 2019
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Ingår i: International Journal of Life Cycle Assessment. - : Springer Science and Business Media LLC. - 1614-7502 .- 0948-3349. ; 24:9, s. 1675-1700
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Life cycle assessment (LCA) is generally considered as a suitable methodology for the evaluation of environmental impacts of processes. However, it requires large amount and often inaccessible process data at early design stages. The present study provides an approach to streamline LCA for a broad set of biomass process chains. The proposed method breaks away from conventional LCA work in that the purpose is to support decision at early stages assuming minimal use of data available and points to most dominant LCA impacts, providing useful feedback to process design. Methods: The prediction mechanism employs decision trees, which form “if-then rules” using a set of critical parameters of the process chain with respect to various environmental impacts. The models classify products into three classes, namely having low, medium, and high environmental impact. Data for model development were obtained from early design stages and include descriptors of the molecular structure of the product and process chain-related variables corresponding to chemistry, complexity, and generic process conditions. Twenty-three LCA metrics were selected as target attributes, according to the ReCiPe and the cumulative energy demand (CED) methods. A broad set of process chains is derived from the work of Karka et al. (Int J Life Cycle Assess 22(9):1418–1440, 2017). Results and discussion: Results demonstrate that the average classification error for the decision trees ranges between 13.4 and 43.8% for the various LCA metrics and multifunctionality approaches. Allocation approaches present a better classification performance (up to 25% error) compared with the substitution approach for LCA metrics, such as climate change, CED, and human health. For the majority of models, low- and high-output classes are characterized by better predictive performance compared with the medium class. The interpretability of selected decision trees is analyzed in terms of pruning levels and “irrational” branches. The results of the application of the decision tress for recently published case studies show for instance that 8 out of 13 cases were correctly classified for CED. Conclusions: The proposed approach provides a first generation of models in the form of computationally inexpensive and easily interpretable decision trees that can be used as pre-screening tools for the environmental assessment of bio-based production ahead of detailed design and conventional LCA approaches. The transparent structure of the decision trees facilitates the identification of critical decision variables providing insights for improvement in terms of process parameters, biomass feedstock, or even targeted product.
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| 5. |
- Karka, P., et al.
(författare)
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Life Cycle Assessment of Biorefinery Products Based on Different Allocation Approaches
- 2015
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Ingår i: Computer Aided Chemical Engineering. - 1570-7946. ; , s. 2573-2578
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Biorefineries constitute representative examples of multifunctional systems which are able to produce, similarly to conventional petroleum refineries, a wide range of chemicals (pharmaceutical constituents, plastics, food additives etc.), energy carriers and power through the optimal use of diverse biomass forms (wheat straw, oils, wood chips, municipal solid waste). For this purpose, biorefineries typically comprise a complicated, integrated network of physical and chemical transformation processes, such as mechanical and physical biomass pretreatment, pyrolysis, gasification, catalytic and enzymatic reactions, and downstream purification processes. For the environmental sustainability assessment of these complicated production systems, Life Cycle Analysis (LCA, ISO-Norm 14040) is considered as a widely acceptable methodology from scientists and engineers including, however, the debated aspect of partitioning the impacts among the co-products' in the biorefinery product portfolio. The aim of this study is to present the influence of the various allocation approaches on the LCA results of biorefinery products. The framework of this analysis systematically incorporates the steps of the LCA methodology as described in the ISO norms and estimates the impacts related with the products of interest, taking into account the contribution of the co- and by-products in the overall production path. For this reason, two wider approaches were adopted, the attributional which describes the impact of the production process itself from a retrospective point of view, and the consequential which focuses on the changes in the level of the output (as well as consumption and disposal) of a product, including market effects from increasing or decreasing demand for the study product, having therefore a more prospective point of view. Several scenarios which describe the possible options for handling those products, were developed and assessed based on different allocation methodologies, namely system expansion (substitution method) and partitioning methods according to the mass, thermal and economic values of the co-products. The estimation of the life cycle impacts of the processes was performed using the Global Warming Potential (GWP), Cumulative Energy Demand (CED) and RECIPE methodologies which provide an assessment of the burdens through the associated LCA indicators. The outcome of this approach provides a range of LCA metrics emphasizing at the variation of the results according to the followed allocation methods and to identify those properties of products (physical, economic, thermal) and system factors (processes to be substituted from the renewable ones, degree of utilization of co- and by-products from the markets etc.) which dominate the LCA results.
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| 6. |
- Karka, P., et al.
(författare)
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Predictive LCA - a systems approach to integrate LCA decisions ahead of design
- 2019
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Ingår i: Computer Aided Chemical Engineering. - 1570-7946. ; , s. 97-102
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Bio-refineries are promising production options of chemicals production, capable to produce a wide range of fuels and chemicals equivalent to the conventional fossil-based products. To establish bio-refineries as mature choices and achieve the commercialization of their technologies, the application of sustainable solutions during the design and development stages are crucial. The innovative character of bio-based production and therefore data availability and access on process modelling details, is a challenging point for decision makers to move towards this direction. Considering the environmental dimension out of the three aspects of sustainability, Life Cycle Assessment (LCA) is a suitable methodology for the evaluation of environmental impacts of bio-based processes because it highlights the stages with the greatest impact along a production chain. LCA studies require large amount of information, usually extracted from detailed flowsheets or from already completed pilot plants, making this procedure, costly, time consuming and not practical to act as a decision- support tool for the development of a bio-refinery. The aim of this study is to develop predictive models for the assessment of LCA metrics and use them to highlight sustainable design options for bio-refineries. Models require the least possible information, which can be obtained from chemistry - level data or early (conceptual) design stages. The modelling techniques used in this study are decision trees and Artificial Neural Networks (ANN), due to their easily interpretable structure and high computational capabilities, respectively. Models are based on the extraction of knowledge from a wide dataset for bio-refineries (it refers to 32 products that is, platform chemicals (e.g., syngas, sugars and lignin) and biofuels (e.g., biodiesel, biogas, and alcohols), starting from diverse biomass sources (e.g., wood chips, wheat straw, vegetable oil)). Input parameters include descriptors of the molecular structure and process related data which describe the production path of a study product. Models are able to predict LCA metrics which cover the most critical aspects of environmental sustainability such as cumulative energy demand (CED) and Climate Change (CC). The average classification errors for decision- tree models range between 17% (± 10%) to 38% (± 11%) whereas for ANN models the average R2cv values (coefficient of determination) range between 0.55 (± 0.42%) to 0.87 (± 0.07%). Demonstration of models is provided using case studies found in literature. Models are used to rank options in various design problems and support decisions on the selection of the most profitable option. Examples of such cases are the selection of the appropriate technology or feedstock to produce a desired product or the preliminary design of a bio-refinery configuration. The proposed approach provides a first generation of models that correlate available and easily accessed information to desirable output process parameters and assessment metrics and can be used as pre-screening tools in the development of innovative processes, ahead of detailed design, thus saving time and money.
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| 7. |
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| 8. |
- Papadokonstantakis, Stavros, 1974, et al.
(författare)
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Challenges for Model-Based Life Cycle Inventories and Impact Assessment in Early to Basic Process Design Stages
- 2016
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Ingår i: Sustainability in the Design, Synthesis and Analysis of Chemical Engineering Processes. - 9780128020647 ; , s. 295-326
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Sustainability assessment can be quite advantageous in the early stages of process design, where a vast number of alternatives are screened and changes are easier to implement. Among various sustainability assessment frameworks for process design, the life cycle assessment (LCA) is widely used for the environmental impact of normal process operation based on systematic procedures, well defined in ISO norms. However, LCA often requires data that are not available in conceptual-to-basic design stages or can only be estimated with great inaccuracy. In particular a detailed cradle-to-gate analysis of mass and energy flows can be a cumbersome task; typically, only a specific gate-to-gate part is detailed based on the specific interest and know-how of the process designer (e.g., a chemical production company). Thus short-cut approaches for filling life cycle-related data gaps are an interesting alternative. This chapter discusses diverse challenges for estimating life cycle inventories (LCIs), which lie in the core of any LCA study, using model-based approaches. Issues such as the availability of LCIs in existing databases and their compatibility with model-based LCI estimations, and the importance of the process scale and its impact on allocation approaches in multifunctional processes are highlighted and further demonstrated in three case studies. The first case study refers to the design of solvent-based CO2 capture in the very early stage of solvent screening. It focuses on dealing with severe data gaps for the cradle-to-gate life cycle impacts associated with the production of make-up solvent because of solvent degradation and fugitive emissions. The second case study refers to model-based assessment in upcoming processes converting biomass to fuels and chemicals in a biorefinery concept. The impact of various allocation methods is discussed and the importance of information about subdivision of the multifunctional systems into its main building blocks is emphasized. The third case study refers to the design of recycling processes for poly(methyl methacrylate) (PMMA), a multifunctional material found in many end products of everyday use. Besides using process modeling for comparing alternatives and filling in data gaps for LCA, this case study highlights the importance of specific information such as the impurities following the PMMA-containing waste material, the degree of process integration depending on the type of the purification processes, and the availability of market-related information for estimating the generated PMMA-containing waste material. Throughout this chapter the use of models for LCA lies at the center of discussion. Considering the standardized procedures of LCA, this chapter concludes with a discussion about the need for further standardizing the respective model-based approaches used for filling data gaps in the estimation of LCIs.
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