Duckweed growth model for large-scale applications: Optimizing harvesting regime and intrinsic growth rate via machine learning to maximize biomass yields
Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to e...
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| Autor*in: |
Calicioglu, Ozgul [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Self-assembled 3D hierarchical MnCO - Rajendiran, Rajmohan ELSEVIER, 2020, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:324 ; year:2021 ; day:15 ; month:11 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jclepro.2021.129120 |
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ELV055660568 |
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| 520 | |a Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. | ||
| 520 | |a Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. | ||
| 650 | 7 | |a Duckweed yield |2 Elsevier | |
| 650 | 7 | |a LASSO regression |2 Elsevier | |
| 650 | 7 | |a Deep learning |2 Elsevier | |
| 650 | 7 | |a Wastewater upcycling |2 Elsevier | |
| 650 | 7 | |a Stella architect |2 Elsevier | |
| 650 | 7 | |a Duckweed harvesting |2 Elsevier | |
| 700 | 1 | |a Sengul, Mert Y. |4 oth | |
| 700 | 1 | |a Femeena, Pandara Valappil |4 oth | |
| 700 | 1 | |a Brennan, Rachel A. |4 oth | |
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10.1016/j.jclepro.2021.129120 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001580.pica (DE-627)ELV055660568 (ELSEVIER)S0959-6526(21)03309-6 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Calicioglu, Ozgul verfasserin aut Duckweed growth model for large-scale applications: Optimizing harvesting regime and intrinsic growth rate via machine learning to maximize biomass yields 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. Duckweed yield Elsevier LASSO regression Elsevier Deep learning Elsevier Wastewater upcycling Elsevier Stella architect Elsevier Duckweed harvesting Elsevier Sengul, Mert Y. oth Femeena, Pandara Valappil oth Brennan, Rachel A. oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:324 year:2021 day:15 month:11 pages:0 https://doi.org/10.1016/j.jclepro.2021.129120 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 324 2021 15 1115 0 |
| spelling |
10.1016/j.jclepro.2021.129120 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001580.pica (DE-627)ELV055660568 (ELSEVIER)S0959-6526(21)03309-6 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Calicioglu, Ozgul verfasserin aut Duckweed growth model for large-scale applications: Optimizing harvesting regime and intrinsic growth rate via machine learning to maximize biomass yields 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. Duckweed yield Elsevier LASSO regression Elsevier Deep learning Elsevier Wastewater upcycling Elsevier Stella architect Elsevier Duckweed harvesting Elsevier Sengul, Mert Y. oth Femeena, Pandara Valappil oth Brennan, Rachel A. oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:324 year:2021 day:15 month:11 pages:0 https://doi.org/10.1016/j.jclepro.2021.129120 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 324 2021 15 1115 0 |
| allfields_unstemmed |
10.1016/j.jclepro.2021.129120 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001580.pica (DE-627)ELV055660568 (ELSEVIER)S0959-6526(21)03309-6 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Calicioglu, Ozgul verfasserin aut Duckweed growth model for large-scale applications: Optimizing harvesting regime and intrinsic growth rate via machine learning to maximize biomass yields 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. Duckweed yield Elsevier LASSO regression Elsevier Deep learning Elsevier Wastewater upcycling Elsevier Stella architect Elsevier Duckweed harvesting Elsevier Sengul, Mert Y. oth Femeena, Pandara Valappil oth Brennan, Rachel A. oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:324 year:2021 day:15 month:11 pages:0 https://doi.org/10.1016/j.jclepro.2021.129120 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 324 2021 15 1115 0 |
| allfieldsGer |
10.1016/j.jclepro.2021.129120 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001580.pica (DE-627)ELV055660568 (ELSEVIER)S0959-6526(21)03309-6 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Calicioglu, Ozgul verfasserin aut Duckweed growth model for large-scale applications: Optimizing harvesting regime and intrinsic growth rate via machine learning to maximize biomass yields 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. Duckweed yield Elsevier LASSO regression Elsevier Deep learning Elsevier Wastewater upcycling Elsevier Stella architect Elsevier Duckweed harvesting Elsevier Sengul, Mert Y. oth Femeena, Pandara Valappil oth Brennan, Rachel A. oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:324 year:2021 day:15 month:11 pages:0 https://doi.org/10.1016/j.jclepro.2021.129120 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 324 2021 15 1115 0 |
| allfieldsSound |
10.1016/j.jclepro.2021.129120 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001580.pica (DE-627)ELV055660568 (ELSEVIER)S0959-6526(21)03309-6 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Calicioglu, Ozgul verfasserin aut Duckweed growth model for large-scale applications: Optimizing harvesting regime and intrinsic growth rate via machine learning to maximize biomass yields 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. Duckweed yield Elsevier LASSO regression Elsevier Deep learning Elsevier Wastewater upcycling Elsevier Stella architect Elsevier Duckweed harvesting Elsevier Sengul, Mert Y. oth Femeena, Pandara Valappil oth Brennan, Rachel A. oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:324 year:2021 day:15 month:11 pages:0 https://doi.org/10.1016/j.jclepro.2021.129120 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 324 2021 15 1115 0 |
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Duckweed growth model for large-scale applications: Optimizing harvesting regime and intrinsic growth rate via machine learning to maximize biomass yields |
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Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. |
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Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. |
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Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day. |
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