European and Italian incentive schemes promote small-scale biogas plant distribution using different types of biological and agricultural wastes as feedstock. A feed in tariff system is used in most of the European Union countries, and the incentive is paid on top of the market price capped at a maximum amount sold.\ud The proposed study explores the feasibility of two-stage biogas plants for small-scale CHP, based on a two-phase bio-film process partially tested during the Biowalk4Biofuels (B4B) FP7 project implementing an Anaerobic Digestion (AD) based on a rotating biological contactor thus able to combine significant yields and reduced volume. The project developed a small pre-industrial biogas plant implementing a recovered 45 kWel CHP unit with 95 kWth thermal power. In the two-stage process, a high-temperature hydrolysis phase was followed by a continuously stirred methanogenesis bioreactor equipped with a rotating biological contactor. Main process performances were related to Organic Load Rate (OLR) up to 15 kg VS/m3; the overall reactor volume was 70 m3 for expected biogas production of 25 Nm3/h.\ud Specifically, the aim of the present article is to address the use of the results and outcomes from some laboratory tests verified by the B4B system to model an overall feasibility evaluation. This allows to explore theoretical and economic feasibility of two ideal plants characterized by a 50 and 150 Nm3/h biogas production based on the overall system performances implementing a fixed biofilm for enhancing methanogenesis process. The feasibility study for the 50 Nm3/h biogas plants (equivalent to 100 kWel) shows profitable results, as well as evaluation of the 150 Nm3/h plants (300 kWe), that represent the biggest size for Italian incentives aimed at “small size” biogas plants
Life cycle assessment (LCA) is a fundamental tool for evaluating the environmental and energy load of a production cycle. Its application to renewable energy production systems offers the possibility of identifying the environmental benefits of such processes—especially those related to the by-products of production processes (i.e., digestion or biochar). Biochar has received worldwide interest because of its potential uses in bioenergy production, due to its coproducts (bio-oil and syngas), as well as in global warming mitigation, sustainable agriculture, pollutant removal, and other uses. Biochar production and use of soil is a strategy for carbon sequestration that could contribute to the reduction of emissions, providing simultaneous benefits to soil and opportunities for bioenergy generation. However, to confirm all of biochar’s benefits, it is necessary to characterize the environmental and energy loads of the production cycle. In this work, soil carbon sequestration, nitrous oxide emissions, use of fertilizers, and use of water for irrigation have been considered in the biochar’s LCA, where the latter is used as a soil conditioner. Primary data taken from experiments and prior studies, as well as open-source available databases, were combined to evaluate the environmental impacts of energy production from biomass, as well as the biochar life cycle, including pre- and post-conversion processes. From the found results, it can be deduced that the use of gasification production of energy and biochar is an attractive strategy for mitigating the environmental impacts analyzed here—especially climate change, with a net decrease of about −8.3 × 103 kg CO2 eq. Finally, this study highlighted strategic research developments that combine the specific characteristics of biochar and soil that need to be amended.
Question Difficulty Estimation from Text (QDET) is the application of Natural Language Processing techniques to the estimation of a value, either numerical or categorical, which represents the difficulty of questions in educational settings. We give an introduction to the field, build a taxonomy based on question characteristics, and present the various approaches that have been proposed in recent years, outlining opportunities for further research. This survey provides an introduction for researchers and practitioners into the domain of question difficulty estimation from text, and acts as a point of reference about recent research in this topic to date.
The main objective of exams consists in performing an assessment of students' expertise on a specific subject. Such expertise, also referred to as skill or knowledge level, can then be leveraged in different ways (e.g., to assign a grade to the students, to understand whether a student might need some support, etc.). Similarly, the questions appearing in the exams have to be assessed in some way before being used to evaluate students. Standard approaches to questions' assessment are either subjective (e.g., assessment by human experts) or introduce a long delay in the process of question generation (e.g., pretesting with real students). In this work we introduce R2DE (which is a Regressor for Difficulty and Discrimination Estimation), a model capable of assessing newly generated multiple-choice questions by looking at the text of the question and the text of the possible choices. In particular, it can estimate the difficulty and the discrimination of each question, as they are defined in Item Response Theory. We also present the results of extensive experiments we carried out on a real world large scale dataset coming from an e-learning platform, showing that our model can be used to perform an initial assessment of newly created questions and ease some of the problems that arise in question generation.
Statistical models such as those derived from Item Response Theory (IRT) enable the assessment of students on a specific subject, which can be useful for several purposes (e.g., learning path customization, drop-out prediction). However, the questions have to be assessed as well and, although it is possible to estimate with IRT the characteristics of questions that have already been answered by several students, this technique cannot be used on newly generated questions. In this paper, we propose a framework to train and evaluate models for estimating the difficulty and discrimination of newly created Multiple Choice Questions by extracting meaningful features from the text of the question and of the possible choices. We implement one model using this framework and test it on a real-world dataset provided by CloudAcademy, showing that it outperforms previously proposed models, reducing by 6.7% the RMSE for difficulty estimation and by 10.8% the RMSE for discrimination estimation. We also present the results of an ablation study performed to support our features choice and to show the effects of different characteristics of the questions' text on difficulty and discrimination.
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