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A previous Iowa State University (ISU) analysis published in 2010 investigated the technical and economic feasibility of the fast pyrolysis and hydroprocessing of biomass, and concluded that the pathway could produce cellulosic biofuels for a minimum fuel selling price (MFSP) of $2.11/gal. The 2010 ISU study was largely theoretical in that no commercial-scale fast pyrolysis facilities were being constructed at the time of publication.The present analysis expands upon the 2010 ISU study by performing an updated techno-economic analysis of the fast pyrolysis and hydroprocessing pathway. Recent advances in pathway technology and commercialization and new parameters suggested by the recent literature are accounted for. The MFSP for a 2000 MTPD facility employing fast pyrolysis and hydroprocessing to convert corn stover to gasoline and diesel fuel is calculated to quantify the economic feasibility of the pathway.The present analysis determines the MFSP of gasoline and diesel fuel produced via fast pyrolysis and hydroprocessing to be $2.57/gal. This result indicates that the pathway could be competitive with petroleum, although not as competitive as suggested by the 2010 ISU study. The present analysis also demonstrates the sensitivity of the result to process assumptions. Keywords fast pyrolysis, hydroprocessing, catalytic pyrolysis, techno-economic analysis, Bioeconomy Institute, Mechanical Engineering Disciplines Industrial Engineering | Mechanical Engineering | Systems EngineeringComments NOTICE: This is the author's version of a work that was accepted for publication in Fuel. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently publishedin in Fuel, 106, April (2013) NOTICE: This is the author's version of a work that was accepted for publication in Fuel. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently publishedin in Fuel, 106, April (2013)

Manufacturers provide products that have distinct green levels (i.e. higher degree of environmental friendliness) to satisfy consumer demands with different green preferences. A product with a higher green level generate fewer emissions but have higher costs. To encourage those manufacturers to produce environmentally friendly products, a government can implement subsidy policies. This paper focuses on the decision-making problem faced by manufacturers to determine which levels of green products to produce and production quantities at each green level. We develop an optimization model under oligopolistic competition considering green preferences and subsidies, with the objective of profit maximization for the manufacturers. We prove the existence and uniqueness of equilibrium and propose a converged algorithm with theory of finite dimensional variational inequality. Numerical results show that an increase of consumer environmental awareness will incentivize manufacturers to produce more green products with higher green levels, but this does not necessarily lead to higher profits for the manufacturers. Moreover, a well-designed subsidy policy can not only generate more profits for manufacturers, but also save subsidy investment for the government. In addition, with the changes of consumer environmental awareness or/and subsidy policy, manufacturers may obtain more profits even if the competition is more fierce.

This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.

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