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Keywords = Wet-Biomass (WB)

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27 pages, 5436 KB  
Article
Gaussian Process Regression Model for Crop Biophysical Parameter Retrieval from Multi-Polarized C-Band SAR Data
by Swarnendu Sekhar Ghosh, Subhadip Dey, Narayanarao Bhogapurapu, Saeid Homayouni, Avik Bhattacharya and Heather McNairn
Remote Sens. 2022, 14(4), 934; https://doi.org/10.3390/rs14040934 - 15 Feb 2022
Cited by 39 | Viewed by 7199
Abstract
Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), [...] Read more.
Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop growth and helping farmers make market decisions. Many parametric and non-parametric machine learning techniques have been utilized to estimate these parameters. A general non-parametric approach that follows a Bayesian framework is the Gaussian Process (GP). The parameters of this process-based technique are assumed to be random variables with a joint Gaussian distribution. The purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data. RADARSAT-2 full-polarimetric images and in situ measurements of wheat, canola, and soybeans obtained from the SMAPVEX16 campaign over Manitoba, Canada, are used to evaluate the performance of these GPR models. The results from this research demonstrate that both the full-pol (HH+HV+VV) combination and the dual-pol (HV+VV) configuration can be used to estimate PAI, VWC, and WB for these three crops. Full article
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17 pages, 4579 KB  
Article
Modeling and Experimental Study of a Small Scale Olive Pomace Gasifier for Cogeneration: Energy and Profitability Analysis
by Domenico Borello, Antonio M. Pantaleo, Michele Caucci, Benedetta De Caprariis, Paolo De Filippis and Nilay Shah
Energies 2017, 10(12), 1930; https://doi.org/10.3390/en10121930 - 23 Nov 2017
Cited by 38 | Viewed by 5448
Abstract
A thermodynamic model of a combined heat and power (CHP) plant, fed by syngas produced by dry olive pomace gasification is here presented. An experimental study is carried out to inform the proposed model. The plant is designed to produce electric power (200 [...] Read more.
A thermodynamic model of a combined heat and power (CHP) plant, fed by syngas produced by dry olive pomace gasification is here presented. An experimental study is carried out to inform the proposed model. The plant is designed to produce electric power (200 kWel) and hot-water by using a cogenerative micro gas turbine (micro GT). Before being released, exhausts are used to dry the biomass from 50% to 17% wb. The ChemCad software is used to model the gasification process, and input data to inform the model are taken from experimental tests. The micro GT and cogeneration sections are modeled assuming data from existing commercial plants. The paper analyzes the whole conversion process from wet biomass to heat and power production, reporting energy balances and costs analysis. The investment profitability is assessed in light of the Italian regulations, which include feed-in-tariffs for biomass based electricity generation. Full article
(This article belongs to the Section I: Energy Fundamentals and Conversion)
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11 pages, 788 KB  
Article
Anhydrous Ammonia Pretreatment of Corn Stover and Enzymatic Hydrolysis of Glucan from Pretreated Corn Stover
by Minliang Yang, Weitao Zhang and Kurt A. Rosentrater
Fermentation 2017, 3(1), 9; https://doi.org/10.3390/fermentation3010009 - 17 Feb 2017
Cited by 11 | Viewed by 6044
Abstract
As a promising alternative of fossil fuel, ethanol has been widely used. In recent years, much attention has been devoted to bioethanol production from lignocellulosic biomass. In previous research, it is found that the pretreatment method named low-moisture anhydrous ammonia (LMAA) has the [...] Read more.
As a promising alternative of fossil fuel, ethanol has been widely used. In recent years, much attention has been devoted to bioethanol production from lignocellulosic biomass. In previous research, it is found that the pretreatment method named low-moisture anhydrous ammonia (LMAA) has the advantage of high conversion efficiency and less washing requirements. The purpose of this study was to explore the optimal conditions by employing the LMAA pretreatment method. Corn stover was treated under three levels of moisture content: 20, 50, 80 w.b.% (wet basis), and three levels of particle size: <0.09, 0.09–2, >2 mm; it was also ammoniated with a loading rate of 0.1g NH3/g biomass (dry matter). Ammoniated corn stover was then subjected to different pretreatment times (24, 96, 168 h) and temperatures (20, 75, 130 °C). After pretreatment, compositional analysis and enzymatic digestibility were conducted to determine the highest glucose yield. As a result, the highest glucose yield was obtained under the condition of 96 h and 75 °C with 50 w.b.% and 0.09–2 mm of corn stover. The main findings of this study could improve the efficiency of bioethanol production processing in the near future. Full article
(This article belongs to the Special Issue Biofuels and Biochemicals Production)
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