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This paper proposes a technique for the probabilistic wind power forecasting (WPF) of a newly built wind farm (NWF) using a limited amount of historical data. First, the state-of-the-art Transformer network is employed to capture the power generation pattern of different wind farms (WFs) based on abundant historical training samples. Then, the Bayesian averaging regression method is applied to transfer the learned power generation pattern to the NWF by assigning proper weights to the WPF results of different WFs. This enables the proposed method to yield accurate NWF power predictions utilizing a limited amount of historical data. The Bayesian characteristics further enable the quantification of multiple uncertainties in forecasting results that may be essential for the NWF operator when the input is uncertain. Comprehensive tests were also performed by employing other deterministic and probabilistic WPF methods using field data. By comparing the results, the proposed method is demonstrated to produce accurate forecasting results with sparse historical data. Moreover, the uncertainties of outcomes are quantified, and acceptable performance is achieved.

AbstractSustainable biorefineries have a critical role to play in our common future. The need to provide more goods using renewable resources, combined with advances in science and technology, has provided a receptive environment for biorefinery systems development. Biorefineries offer the promise of using fewer non‐renewable resources, reducing CO2 emissions, creating new employment, and spurring innovation using clean and efficient technologies. Lessons are being learned from the establishment of first‐generation biofuel operations. The factors that are key to answering the question of biorefinery sustainability include: the type of feedstock, the conversion technologies and their respective conversion and energy efficiencies, the types of products (including coproducts) that are manufactured, and what products are substituted by the bioproducts. The BIOPOL review of eight existing biorefineries indicates that new efficient biorefineries can revitalize existing industries and promote regional development, especially in the R&D area. Establishment can be facilitated if existing facilities are used, if there is at least one product which is immediately marketable, and if supportive policies are in place. Economic, environmental, and social dimensions need to be evaluated in an integrated sustainability assessment. Sustainability principles, criteria, and indicators are emerging for bioenergy, biofuels, and bioproducts. Practical assessment methodologies, including data systems, are critical for both sustainable design and to assure consumers, investors, and governments that they are doing the ‘right thing’ by purchasing a certain bioproduct. If designed using lifecycle thinking, biorefineries can be profitable, socially responsible, and produce goods with less environmental impact than conventional products … and potentially even be restorative!. Copyright © 2010 Society of Chemical Industry and John Wiley & Sons, Ltd

This study assesses the socio-economic impacts in terms of value added, imports and employment of sugarcane-derived bioethanol production in Northeast (NE) Brazil. An extended inter-regional Input–Output (IO) model has been developed and is used to analyse three scenarios, all projected for 2020: a business-as-usual scenario (BaU) which projects current practices, and two scenarios that consider more efficient agricultural practices and processing efficiency (scenario A) and in addition an expansion of the sector into new areas (scenario B). By 2020 in all scenarios, value added and imports increase compared to the current situation. The value added by the sugarcane–ethanol sector in the NE region is 2.8 billion US$ in the BaU scenario, almost 4 billion US$ in scenario A, and 9.4 billion US$ in scenario B. The imports in the region will grow with 4% (BaU scenario), 38% (scenario A) and 262% (scenario B). This study shows that the large reduction of employment (114,000 jobs) due to the replacement of manual harvesting by mechanical harvesting can be offset by additional production and indirect effects. The total employment in the region by 2020 grows with 10% in scenario A (around 12,500 jobs) and 126% in scenario B (around 160,000 jobs). The indirect effects of sugarcane production in the NE are large in the rest of Brazil due to the import of inputs from these regions. The use of an extended inter-regional IO model can quantify direct and indirect socio-economic effects at regional level and can provide insight in the linkages between regions. The application of the model to NE Brazil has demonstrated significant positive socio-economic impacts that can be achieved when developing and expanding the sugarcane–ethanol sector in the region under the conditions studied here, not only for the NE region itself but also for the economy of the rest of Brazil.

Micropollutants in freshwater, e.g., pharmaceuticals such as contraceptives, are a source of increased concern for human health and wildlife. Even after excretion, some of these compounds are pharmaceutically active in aquatic environment and they are found to cause endocrine disruption in both human and wildlife populations. In this study we analyzed a membrane system, coated with enzymes, which removes endocrine disrupting chemicals or micropollutants from surface water used for drinking. In order to help a membrane manufacturer in product development, we conducted a cradle-to-grave life cycle assessment. Water purification with two membrane systems, based on membrane coating covalent binding versus adsorption, were analyzed and compared with granulated activated carbon made from coal and wood. It was found that the membrane with covalent binding can have much lower environmental impacts than activated carbon made from coal. A sensitivity analysis showed that operational electricity use, the source of electricity and membrane coating frequency influence the results significantly. Scenario analysis indicates that a membrane system with covalent binding which uses operational electricity lower than 0.2 kWh per m3 of filtered water and with monthly enzyme coating frequency can perform better than conventional activated carbon systems irrespective of the electricity source. These findings can be used to guide the optimization of the membrane parameters. This study provided an understanding of the membrane modification for micropollutant removal and its impacts on environment. Finally, we describe how environmental sustainability can be integrated into business decisions, such as process and material selection and design optimization, with the help of life cycle assessment.

Abstract With drastic fossil fuel depletion and environmental deterioration concerns, a move towards a more sustainable bioenergy-based economy is essential. Lately, the application of microwave (MW) irradiation for waste processing has been attracting interest globally. MW-assisted heating possesses several advantages such as the provision of high microwave energy into dielectric materials with deeper penetration for internal heat generation, showing beneficial features in improving the heating rate and reducing the reaction time. Consequently, the most recent literature regarding the applications of MW-assisted heating for biomass pretreatment as well as biofuel and bioenergy production was reviewed and consolidated in this study. An impressive increase in the product yield and improvement of the product properties are reported, with the use of MW-assisted heating in several conversion routes to produce biofuels. Despite being a promising technology for biofuel production, some major fundamental data of MW-assisted heating have not been comprehensively identified. Therefore, the feasibility of this technology for large-scale implementation is still subpar. Understanding the interaction between the feedstock and the microwave electromagnetic field, and the optimization of several operational and mechanical parameters are the two main keystones that would propel the industrialization of MW heating in the near future. This provides key insights leading to increased feasibility and more advanced application of MW heating.

Consider a situation where spatial heterogeneity leads to a cline, a gradual transition in dominance of two competing species. We first prove, in the context of a simplified competition–diffusion model, that there exists a stationary solution showing that the two species coexist in a transition zone. What happens then if, owing to climate change, the environmental profile moves with constant speed in space? We show here that, when the speed with which the environmental condition shifts exceeds the Fisher invasion speed of the advancing species, an expanding gap will form. We raise the question of whether such a phenomenon has been or can be observed.

This study evaluates quality function deployment-based innovation pathways for renewable energy projects. For this purpose, a 2-stage novel model is suggested. Firstly, the weights of the factors are calculated by considering Pythagorean fuzzy decision-making trial and evaluation laboratory methodology based on 2-tuple linguistic values. With the help of the impact-relation map, the activities, and immediate predecessors are identified. Within this context, five different paths are determined for this situation. Secondly, the duration pathways and innovation costs are calculated for renewable energy projects. The findings indicate that the activities A (customer expectations) and E (production requirement) play the most critical role because they take place in all different paths. Therefore, it is strongly recommended that renewable energy investment companies should take necessary actions to satisfy customer expectations, such as offering affordable products and providing necessary information for the use of these projects.

Abstract The study presents recent developments in the Turkish power market and introduces an Analytic Hierarchy Process model to evaluate and compare the relative overall attractiveness of power plant options for Turkey. The developed model incorporates technical characteristics, resource availability, socio-economic, environmental, cost, political, legal and organisational aspects, for evaluating and prioritising power plant types (biomass, coal, geothermal, hydro, natural gas, nuclear, petroleum, solar and wind). The study incorporates perspectives of different experts that represent various stakeholders of the Turkish power sector. The study reveals that supply reliability, investment costs and contribution to national economy are perceived as most important factors, whereas waste disposal and decommissioning costs are perceived as least important factors. Considering the overall weights, the most attractive power plant types for the Turkish power market are coal, hydro and natural gas power plants. The study indicates that Turkey should drastically decrease the installed capacity share of traditionally dominant power plants (to 58% from 89% in 2016) and fossil fuel power plants (to 40% from 56% in 2016), and increase the share of renewable power plants (to 52% from 44% in 2016), indigenous resource based power plants (to 67% from 56% in 2016) and nuclear power plants.