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Biofuel has received worldwide attention as one of the most promising renewable energy sources. Particularly, in many countries such as the U.S. and Brazil, first-generation ethanol from corn and sugar cane has been used as automobile fuel after blending with gasoline. Nevertheless, in order to continuously increase the use of biofuels, efforts are needed to reduce the cost of biofuel production and increase its profitability. This can be achieved by increasing the efficiency of a sequential biofuel production process consisting of multiple operations such as feedstock supply, pretreatment, fermentation, distillation, and biofuel transportation. This study aims at investigating methodologies for predicting feedstock yields, which is the earliest step for stable and sustainable biofuel production. Particularly, this study reviews feedstock yield estimation approaches using machine learning technologies that focus on gradually improving estimation accuracy by using big data and computer algorithms from traditional statistical approaches. Given that it is becoming increasingly difficult to stably produce biofuel feedstocks as climate change worsens, research on developing predictive modeling for raw material supply using the latest ML techniques is very important. As a result, this study will help researchers and engineers predict feedstock yields using various machine learning techniques, and contribute to efficient and stable biofuel production and supply chain design based on accurate predictions of feedstocks. [ABSTRACT FROM AUTHOR]

Biodiesel has received worldwide attention as a renewable energy resource that reduces greenhouse gas (GHG) emissions. Unlike traditional fossil fuels, such as coal, oil, and natural gas, biodiesel made of vegetable oils, animal fats, or recycled restaurant grease incurs higher production costs, so its supply chain should be managed efficiently for operational cost reduction. To this end, multiple machine learning technologies have recently been applied to estimate feedstock yield, biodiesel productivity, and biodiesel quality. This study aims to identify the machine learning technologies useful in particular areas of supply chain management by review of the scientific literature. As a result, nine machine learning algorithms, the Gaussian process model (GPM), random forest (RF), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor (KNN), AdaBoost regression, multiple linear regression (MLR), linear regression (LR). and multilayer perceptron (MLP), are used for feedstock yield estimation, biodiesel productivity prediction, and biodiesel quality prediction. Among these, RF and ANN were identified as the most appropriate algorithms, providing high prediction accuracy. This finding will help engineers and managers understand concepts of machine learning technologies so they can use appropriate technology to solve operational problems in supply chain management. [ABSTRACT FROM AUTHOR]

Building-integrated photovoltaics (BIPVs) entail the use of photovoltaics as building materials, such as windows, roofs, and walls. Owing to their electricity-generation ability, BIPVs have become popular building materials for green buildings. This study involves an economic feasibility analysis of BIPVs for an agricultural research and development center in Fiji. The computerized relative allocation of facilities technique (CRAFT) is extended for cost-efficient facility design, and the levelized cost of electricity (LCOE) of BIPVs is evaluated to identify feasible alternatives from among three options, namely roof-integrated, window, and rooftop BIPVs. From the experiments, the LCOE values of the roof-integrated, window, and rooftop BIPVs were USD 0.13/kWh, 0.17/kWh, and 0.09/kWh, respectively. Moreover, the profit for the window BIPV was negative when the discount rate was greater than 0.08 due to its inefficient productivity. Contrarily, the roof-integrated BIPV showed a reasonable LCOE even though it required the highest investment cost. Nevertheless, further efforts are needed to reduce the cost of BIPVs for practical implementation. [ABSTRACT FROM AUTHOR]

Ethanol, a common renewable energy resource, can reduce greenhouse gas (GHG) emissions to resolve the problem of global warming worldwide. Various feedstocks such as corn, sugarcane, maize stover, and wheat straw can be utilized for ethanol production. They determine production operations and relevant costs. Although there are monetary incentives and government policies in different countries to increase ethanal use, it is still challenging to make its sales price competitive due to the inefficient supply chain of ethanol. Unlike fossil fuels such as coal, oil, and natural gas using a well-designed supply chain in the long history of mankind, additional efforts are needed to organize and stabilize the supply chain of ethanol efficiently. The goal of this study is to investigate how simulation modeling techniques can be applied to various supply chain management issues of ethanol. Particularly, application cases of three major simulation paradigms such as discrete-event simulation, system dynamics, and agent-based simulation are investigated by conducting a scientific literature review. The findings of this study will contribute to the expansion of simulation use in the field of biofuel supply chain management. [ABSTRACT FROM AUTHOR]

Various types of photovoltaic (PV) modules have been developed to exploit solar energy, a major renewable energy resource. One of the popular types of PV modules is building-integrated photovoltaics (BIPV), which are PV modules used as building materials. The goal of this study is to conduct an economic feasibility analysis of BIPV on the rooftop of the Keith D. McFarland Science Building at Texas A&M University, Commerce, Texas. To this end, a polynomial regression (PR) model is developed to estimate the electricity generation quantity of solar energy in the subject area, where the maximum temperature is 104 °F (40 °C) in summer. The developed PR models are used to estimate the potential profit of BIPV on the rooftop of the subject building, and the results are compared with the profit of a green roof system at the subject building. The economic feasibility analyses show that the levelized cost of electricity (LCOE) of the green roof system is approximately 39.77% higher than that of the BIPV system at a discount rate of 5%. Thus, the BIPV system is more profitable than the green roof system; consequently, this research will contribute to the implementation of BIPV on building rooftops and the expansion of renewable energy use in preference to fossil fuel. [ABSTRACT FROM AUTHOR]

APV systems producing both crops and electricity are becoming popular as an alternative way of producing renewable energy in many countries with land shortage issues (e.g., South Korea). This study aims at developing a hybrid performance model of an Agrophotovoltaic (APV) system that produces crops underneath the PV modules. In this study, the physical model used to estimate solar radiation is integrated with a polynomial regression approach to forecast the amount of electricity generation and crop production in the APV system. The model takes into account not only the environmental factors (i.e., daily temperature, precipitation, humidity, and wind speed) but also physical factors (i.e., shading ratio of the APV system) related to the performance of the APV system. For more accurate modelling, the proposed approach is validated based on field experiment data collected from the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. As a result, the proposed approach can predict the electricity generation quantity in the APV system with an R2 of 80.4%. This will contribute to the distribution of the APV system, which will increase farmers' income as well as the sustainability of our society. [ABSTRACT FROM AUTHOR]

The Agrophotovoltaic (APV) system is a novel concept in the field of Renewable Energy Systems. This system enables the generation of solar energy via photo-voltaic (PV) modules above crops, to mitigate harmful impact on food production. This study aims to develop a performance evaluation model for an APV system in a temperate climate region, such as South Korea. To this end, both traditional electricity generation models (solar radiation-based model and climate-based model) of PV modules and two major machine learning (ML) techniques (i.e., polynomial regression and deep learning) have been considered. Electricity generation data was collected via remote sensors installed in the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. Moreover, economic analysis in terms of cost and benefit of the subject APV system was conducted to provide information about the return on investment to farmers and government agencies. As a result, farmers, agronomists, and agricultural engineers can easily estimate performance and profit of their APV systems via the proposed performance model. [ABSTRACT FROM AUTHOR]

This study aims at introducing a modeling and simulation approach for a green roof system which can reduce energy cost of a building exposed to high temperatures throughout the summer season. First, to understand thermal impact of a green roof system on a building surface, a field-based study has been conducted in Commerce, Texas, U.S., where the average maximum temperature in summer is 104 °F (40 °C). Two types of analyses were conducted: (1) comparison of temperature between different plant type via Analysis of variance (ANOVA) and (2) polynomial regression analysis to develop thermal impact estimation model based on air temperature and presence of a green roof. In addition, an agent-based simulation (ABS) model was developed via AnyLogic® University 8.6.0 simulation software, Chicago, IL, U.S., in order to accurately estimate energy cost and benefits of a building with a photovoltaic-green roof system. The proposed approach was applied to estimate energy reduction cost of the Keith D. McFarland Science Building at Texas A&M University, Commerce, Texas (33.2410° N, 95.9104° W). As a result, the proposed approach was able to save $740,325.44 in energy cost of a heating, ventilation, and air conditioning (HAVC) system in the subject building. The proposed approach will contribute to the implementation of a sustainable building and urban agriculture. [ABSTRACT FROM AUTHOR]