Review of Reactors with Potential Use in Thermochemical Energy Storage in Concentrated Solar Power Plants

The aim of this study is to perform a review of the state-of-the-art of the reactors available in the literature, which are used for solid-gas reactions or thermal decomposition processes around 1000 ºC that could be further implemented for thermochemical energy storage in CSP (concentrated solar power) plants, specifically for SPT (solar power tower) technology. Both direct and indirect systems can be implemented, with direct and closed systems being the most studied ones. Among direct and closed systems, the most used configuration is the stacked bed reactor, with the fixed bed reactor being the most frequent option. Out of all of the reactors studied, almost 70% are used for solid-gas chemical reactions. Few data are available regarding solar efficiency in most of the processes, and the available information indicates relatively low values. Chemical reaction efficiencies show better values, especially in the case of a fluidized bed reactor for solid-gas chemical reactions, and fixed bed and rotary reactors for thermal decompositions.The work is partially funded by the Spanish government (ENE2015-64117-C5-1-R (MINECO/FEDER) and ENE2015-64117-C5-2-R (MINECO/FEDER)). The authors would like to thank the Catalan Government for the quality accreditation given to their research groups GREA (2017 SGR 1537) and DIOPMA (2017 SGR 118). GREA and DIOPMA are certified agents TECNIO in the category of technology developers from the Government of Catalonia. Dr. Aran Solé would like to thank Ministerio de Economía y Competitividad de España for Grant Juan de la Cierva, FJCI-2015-25741

Improving Precipitation Estimation Using Convolutional Neural Network

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach

Study of a static screen, jig, spiral, and a compound water cyclone in a placer gold recovery plant

During the 1986 mining season both laboratory and field test work were conducted to study the performance efficiencies of a wedge-wire static screen, a Pan-American jig, a Reichert Mark VII spiral, and a 12" compound water cyclone. This work was conducted at EVECO, Inc.'s placer gold operations near Fox, Alaska, and funded by the State of Alaska Department of Natural Resources. The Mineral Industry Research Laboratory of the University of Alaska-Fairbanks perfomed the test work.Funded by the State of Alaska Department of Natural Resources

Rapid methods of landslide hazard mapping : Fiji case study

A landslide hazard probability map can help planners (1) prepare for, and/or mitigate against, the effects of landsliding on communities and infrastructure, and (2) avoid or minimise the risks associated with new developments. The aims of the project were to establish, by means of studies in a few test areas, a generic method by which remote sensing and data analysis using a geographic information system (GIS) could provide a provisional landslide hazard zonation map. The provision of basic hazard information is an underpinning theme of the UN’s International Decade for Natural Disaster Reduction (IDNDR). It is an essential requirement for disaster preparedness and mitigation planning. This report forms part of BGS project 92/7 (R5554) ‘Rapid assessment of landslip hazards’ Carried out under the ODA/BGS Technology Development and Research Programme as part of the British Government’s provision of aid to developing countries. It provides a detailed technical account of work undertaken in a test area in Viti Levu in collaboration with Fiji Mineral Resources Department. The study represents a demonstration of a methodology that is applicable to many developing countries. The underlying principle is that relationships between past landsliding events, interpreted from remote sensing, and factors such as the geology, relief, soils etc provide the basis for modelling where future landslides are most likely to occur. This is achieved using a GIS by ‘weighting’ each class of each variable (e.g. each lithology ‘class’ of the variable ‘geology’) according to the proportion of landslides occurring within it compared to the regional average. Combinations of variables, produced by summing the weights in individual classes, provide ‘models’ of landslide probability. The approach is empirical but has the advantage of potentially being able to provide regional scale hazard maps over large areas quickly and cheaply; this is unlikely to be achieved using conventional ground-based geotechnical methods. In Fiji, landslides are usually triggered by intense rain storms commonly associated with tropical cyclones. However, the regional distribution of landslides has not been mapped nor is it known how far geology and landscape influence the location and severity of landsliding events. The report discusses the remote sensing and GIS methodology, and describes the results of the pilot study over an area of 713 km2 in south east Viti Levu. The landslide model uses geology, elevation, slope angle, slope aspect, soil type, and forest cover as inputs. The resulting provisional landslide hazard zonation map, divided into high, medium and low zones of landslide hazard probability, suggests that whilst rainfall is the immediate cause, others controls do exert a significant influence. It is recommended that consideration be given in Fiji to implementing the techniques as part of a national strategic plan for landslide hazard zonation mapping

Treatment of end-of-life concrete in an innovative heating-air classification system for circular cement-based products

A stronger commitment towards Green Building and circular economy, in response to environmental concerns and economic trends, is evident in modern industrial cement and concrete production processes. The critical demand for an overall reduction in the environmental impact of the construction sector can be met through the consumption of high-grade supplementary raw materials. Advanced solutions are under development in current research activities that will be capable of up-cycling larger quantities of valuable raw materials from the fine fractions of End-of-Life (EoL) concrete waste. New technology, in particular the Heating-Air classification System (HAS), simultaneously applies a combination of heating and separation processes within a fluidized bed-like chamber under controlled temperatures (±600 °C) and treatment times (25–40 s). In that process, moisture and contaminants are removed from the EoL fine concrete aggregates (0–4 mm), yielding improved fine fractions, and ultrafine recycled concrete particles (<0.125 mm), consisting mainly of hydrated cement, thereby adding value to finer EoL concrete fractions. In this study, two types of ultrafine recycled concrete (either siliceous or limestone EoL concrete waste) are treated in a pilot HAS technology for their conversion into Supplementary Cementitious Material (SCM). The physico-chemical effect of the ultrafine recycled concrete particles and their potential use as SCM in new cement-based products is assessed by employing substitutions of up to 10% of the conventional binder. The environmental viability of their use as SCM is then evaluated in a Life Cycle Assessment (LCA). The results demonstrated accelerated hydration kinetics of the mortars that incorporated these SCMs at early ages and higher mechanical strengths at all curing ages. Optimal substitutions were established at 5%. The results suggested that the overall environmental impact could be reduced by up to 5% when employing the ultrafine recycled concrete particles as SCM in circular cement-based products, reducing greenhouse gas emissions by as much as 41 kg CO2 eq./ton of cement (i.e. 80 million tons CO2 eq./year). Finally, the environmental impacts were reduced even further by running the HAS on biofuel rather than fossil fuel.The authors of the present paper, prepared in the framework ofthe Project VEEP "Cost-Effective Recycling of C&DW in High AddedValue Energy Efficient Prefabricated Concrete Components forMassive Retrofitting of our Built Environment", wish to acknowl-edge the European Commission for its support. This project hasreceived funding from the European Union’s Horizon 2020 researchand innovation programme under grant agreement No 723582.This paper reflects only the author’s view and the European Com-mission is not responsible for any use that may be made of theinformation it contains.The authors are also grateful to the Spanish Ministry of Science,Innovation and Universities (MICIU) and the European RegionalDevelopment Fund (FEDER) for funding this line of research(RTI2018-097074-B-C21)

Conceptual design study of a coal gasification combined-cycle powerplant for industrial cogeneration

A conceptual design study was conducted to assess technical feasibility, environmental characteristics, and economics of coal gasification. The feasibility of a coal gasification combined cycle cogeneration powerplant was examined in response to energy needs and to national policy aimed at decreasing dependence on oil and natural gas. The powerplant provides the steam heating and baseload electrical requirements while serving as a prototype for industrial cogeneration and a modular building block for utility applications. The following topics are discussed: (1) screening of candidate gasification, sulfur removal and power conversion components; (2) definition of a reference system; (3) quantification of plant emissions and waste streams; (4) estimates of capital and operating costs; and (5) a procurement and construction schedule. It is concluded that the proposed powerplant is technically feasible and environmentally superior