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A. Kokossis - One of the best experts on this subject based on the ideXlab platform.
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Environmental impact assessment of Biomass Process chains at early design stages using decision trees
The International Journal of Life Cycle Assessment, 2019Co-Authors: Paraskevi Karka, Stavros Papadokonstantakis, A. KokossisAbstract: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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Environmental impact assessment of Biomass Process chains at early design stages using decision trees
The International Journal of Life Cycle Assessment, 2019Co-Authors: Paraskevi Karka, Stavros Papadokonstantakis, A. KokossisAbstract: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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Cradle-to-gate assessment of environmental impacts for a broad set of Biomass-to-product Process chains
The International Journal of Life Cycle Assessment, 2017Co-Authors: Paraskevi Karka, Stavros Papadokonstantakis, A. KokossisAbstract:PurposeThis 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.MethodsTwenty-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.Results and discussionFactors 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).ConclusionsThe 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.
Liangcai Peng - One of the best experts on this subject based on the ideXlab platform.
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a finalized determinant for complete lignocellulose enzymatic saccharification potential to maximize bioethanol production in bioenergy miscanthus
Biotechnology for Biofuels, 2019Co-Authors: Aftab Alam, Ran Zhang, Yanting Wang, Peng Liu, Jiangfeng Huang, Meysam Madadi, Dan Sun, Arthur J Ragauskas, Liangcai PengAbstract:Miscanthus is a leading bioenergy crop with enormous lignocellulose production potential for biofuels and chemicals. However, lignocellulose recalcitrance leads to Biomass Process difficulty for an efficient bioethanol production. Hence, it becomes essential to identify the integrative impact of lignocellulose recalcitrant factors on cellulose accessibility for Biomass enzymatic hydrolysis. In this study, we analyzed four typical pairs of Miscanthus accessions that showed distinct cell wall compositions and sorted out three major factors that affected Biomass saccharification for maximum bioethanol production. Among the three optimal (i.e., liquid hot water, H2SO4 and NaOH) pretreatments performed, mild alkali pretreatment (4% NaOH at 50 °C) led to almost complete Biomass saccharification when 1% Tween-80 was co-supplied into enzymatic hydrolysis in the desirable Miscanthus accessions. Consequently, the highest bioethanol yields were obtained at 19% (% dry matter) from yeast fermentation, with much higher sugar–ethanol conversion rates by 94–98%, compared to the other Miscanthus species subjected to stronger pretreatments as reported in previous studies. By comparison, three optimized pretreatments distinctively extracted wall polymers and specifically altered polymer features and inter-linkage styles, but the alkali pretreatment caused much increased Biomass porosity than that of the other pretreatments. Based on integrative analyses, excellent equations were generated to precisely estimate hexoses and ethanol yields under various pretreatments and a hypothetical model was proposed to outline an integrative impact on Biomass saccharification and bioethanol production subjective to a predominate factor (CR stain) of Biomass porosity and four additional minor factors (DY stain, cellulose DP, hemicellulose X/A, lignin G-monomer). Using four pairs of Miscanthus samples with distinct cell wall composition and varied Biomass saccharification, this study has determined three main factors of lignocellulose recalcitrance that could be significantly reduced for much-increased Biomass porosity upon optimal pretreatments. It has also established a novel standard that should be applicable to judge any types of Biomass Process technology for high biofuel production in distinct lignocellulose substrates. Hence, this study provides a potential strategy for precise genetic modification of lignocellulose in all bioenergy crops.
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lignin extraction distinctively enhances Biomass enzymatic saccharification in hemicelluloses rich miscanthus species under various alkali and acid pretreatments
Bioresource Technology, 2015Co-Authors: Shengli Si, Qing Li, Liangcai Peng, Jiangfeng Huang, Yan Chen, Huizhen Hu, Ying Li, Haofeng Liao, Yuanyuan TuAbstract:In this study, one- and two-step pretreatments with alkali and acid were performed in the three Miscanthus species that exhibit distinct hemicelluloses levels. As a result, one-step with 4% NaOH or two-step with 2% NaOH and 1% H2SO4 was examined to be optimal for high Biomass saccharification, indicating that alkali was the main effecter of pretreatments. Notably, both one- and two-step pretreatments largely enhanced Biomass digestibility distinctive in hemicelluloses-rich samples by effectively co-extracting hemicelluloses and lignin. However, correlation analysis further indicated that the effective lignin extraction, other than the hemicelluloses removals, predominately determined Biomass saccharification under various alkali and acid pretreatments, leading to a significant alteration of cellulose crystallinity. Hence, this study has suggested the potential approaches in bioenergy crop breeding and Biomass Process technology.
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steam explosion distinctively enhances Biomass enzymatic saccharification of cotton stalks by largely reducing cellulose polymerization degree in g barbadense and g hirsutum
Bioresource Technology, 2015Co-Authors: Yu Huang, Yanting Wang, Yuanyuan Tu, Shiguang Zhou, Ao Li, Peng Chen, Xuewen Zhang, Liangcai PengAbstract:In this study, steam explosion pretreatment was performed in cotton stalks, leading to 5–6 folds enhancements on Biomass enzymatic saccharification distinctive in Gossypium barbadense and Gossypium hirsutum species. Sequential 1% H2SO4 pretreatment could further increase Biomass digestibility of the steam-exploded stalks, and also cause the highest sugar–ethanol conversion rates probably by releasing less inhibitor to yeast fermentation. By comparison, extremely high concentration alkali (16% NaOH) pretreatment with raw stalks resulted in the highest hexoses yields, but it had the lowest sugar–ethanol conversion rates. Characterization of wall polymer features indicated that Biomass saccharification was enhanced with steam explosion by largely reducing cellulose DP and extracting hemicelluloses. It also showed that cellulose crystallinity and arabinose substitution degree of xylans were the major factors on Biomass digestibility in cotton stalks. Hence, this study has provided the insights into cell wall modification and Biomass Process technology in cotton stalks and beyond.
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Biomass digestibility is predominantly affected by three factors of wall polymer features distinctive in wheat accessions and rice mutants
Biotechnology for Biofuels, 2013Co-Authors: Zhiliang Wu, Fengcheng Li, Mingliang Zhang, Lingqiang Wang, Yuanyuan Tu, Jing Zhang, Qing Li, Liangcai PengAbstract:Background Wheat and rice are important food crops with enormous Biomass residues for biofuels. However, lignocellulosic recalcitrance becomes a crucial factor on Biomass Process. Plant cell walls greatly determine Biomass recalcitrance, thus it is essential to identify their key factors on lignocellulose saccharification. Despite it has been reported about cell wall factors on Biomass digestions, little is known in wheat and rice. In this study, we analyzed nine typical pairs of wheat and rice samples that exhibited distinct cell wall compositions, and identified three major factors of wall polymer features that affected Biomass digestibility.
Paraskevi Karka - One of the best experts on this subject based on the ideXlab platform.
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Environmental impact assessment of Biomass Process chains at early design stages using decision trees
The International Journal of Life Cycle Assessment, 2019Co-Authors: Paraskevi Karka, Stavros Papadokonstantakis, A. KokossisAbstract: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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Environmental impact assessment of Biomass Process chains at early design stages using decision trees
The International Journal of Life Cycle Assessment, 2019Co-Authors: Paraskevi Karka, Stavros Papadokonstantakis, A. KokossisAbstract: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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Cradle-to-gate assessment of environmental impacts for a broad set of Biomass-to-product Process chains
The International Journal of Life Cycle Assessment, 2017Co-Authors: Paraskevi Karka, Stavros Papadokonstantakis, A. KokossisAbstract:PurposeThis 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.MethodsTwenty-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.Results and discussionFactors 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).ConclusionsThe 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.
Stavros Papadokonstantakis - One of the best experts on this subject based on the ideXlab platform.
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Environmental impact assessment of Biomass Process chains at early design stages using decision trees
The International Journal of Life Cycle Assessment, 2019Co-Authors: Paraskevi Karka, Stavros Papadokonstantakis, A. KokossisAbstract: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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Environmental impact assessment of Biomass Process chains at early design stages using decision trees
The International Journal of Life Cycle Assessment, 2019Co-Authors: Paraskevi Karka, Stavros Papadokonstantakis, A. KokossisAbstract: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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Cradle-to-gate assessment of environmental impacts for a broad set of Biomass-to-product Process chains
The International Journal of Life Cycle Assessment, 2017Co-Authors: Paraskevi Karka, Stavros Papadokonstantakis, A. KokossisAbstract:PurposeThis 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.MethodsTwenty-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.Results and discussionFactors 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).ConclusionsThe 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.
Yuanyuan Tu - One of the best experts on this subject based on the ideXlab platform.
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lignin extraction distinctively enhances Biomass enzymatic saccharification in hemicelluloses rich miscanthus species under various alkali and acid pretreatments
Bioresource Technology, 2015Co-Authors: Shengli Si, Qing Li, Liangcai Peng, Jiangfeng Huang, Yan Chen, Huizhen Hu, Ying Li, Haofeng Liao, Yuanyuan TuAbstract:In this study, one- and two-step pretreatments with alkali and acid were performed in the three Miscanthus species that exhibit distinct hemicelluloses levels. As a result, one-step with 4% NaOH or two-step with 2% NaOH and 1% H2SO4 was examined to be optimal for high Biomass saccharification, indicating that alkali was the main effecter of pretreatments. Notably, both one- and two-step pretreatments largely enhanced Biomass digestibility distinctive in hemicelluloses-rich samples by effectively co-extracting hemicelluloses and lignin. However, correlation analysis further indicated that the effective lignin extraction, other than the hemicelluloses removals, predominately determined Biomass saccharification under various alkali and acid pretreatments, leading to a significant alteration of cellulose crystallinity. Hence, this study has suggested the potential approaches in bioenergy crop breeding and Biomass Process technology.
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steam explosion distinctively enhances Biomass enzymatic saccharification of cotton stalks by largely reducing cellulose polymerization degree in g barbadense and g hirsutum
Bioresource Technology, 2015Co-Authors: Yu Huang, Yanting Wang, Yuanyuan Tu, Shiguang Zhou, Ao Li, Peng Chen, Xuewen Zhang, Liangcai PengAbstract:In this study, steam explosion pretreatment was performed in cotton stalks, leading to 5–6 folds enhancements on Biomass enzymatic saccharification distinctive in Gossypium barbadense and Gossypium hirsutum species. Sequential 1% H2SO4 pretreatment could further increase Biomass digestibility of the steam-exploded stalks, and also cause the highest sugar–ethanol conversion rates probably by releasing less inhibitor to yeast fermentation. By comparison, extremely high concentration alkali (16% NaOH) pretreatment with raw stalks resulted in the highest hexoses yields, but it had the lowest sugar–ethanol conversion rates. Characterization of wall polymer features indicated that Biomass saccharification was enhanced with steam explosion by largely reducing cellulose DP and extracting hemicelluloses. It also showed that cellulose crystallinity and arabinose substitution degree of xylans were the major factors on Biomass digestibility in cotton stalks. Hence, this study has provided the insights into cell wall modification and Biomass Process technology in cotton stalks and beyond.
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Biomass digestibility is predominantly affected by three factors of wall polymer features distinctive in wheat accessions and rice mutants
Biotechnology for Biofuels, 2013Co-Authors: Zhiliang Wu, Fengcheng Li, Mingliang Zhang, Lingqiang Wang, Yuanyuan Tu, Jing Zhang, Qing Li, Liangcai PengAbstract:Background Wheat and rice are important food crops with enormous Biomass residues for biofuels. However, lignocellulosic recalcitrance becomes a crucial factor on Biomass Process. Plant cell walls greatly determine Biomass recalcitrance, thus it is essential to identify their key factors on lignocellulose saccharification. Despite it has been reported about cell wall factors on Biomass digestions, little is known in wheat and rice. In this study, we analyzed nine typical pairs of wheat and rice samples that exhibited distinct cell wall compositions, and identified three major factors of wall polymer features that affected Biomass digestibility.