Thermal error compensation of a 5-axis machine tool using indigenous temperature sensors and CNC integrated Python code validated with a machined test piece

Achieving high workpiece accuracy is the long-term goal of machine tool designers. There are many causes for workpiece inaccuracy, with thermal errors being the most common. Indirect compensation (using prediction models for thermal errors) is a promising strategy to reduce thermal errors without increasing machine tool costs. The modelling approach uses transfer functions to deal with this issue; it is an established dynamic method with a physical basis, and its modelling and calculation speed are suitable for real-time applications. This research presents compensation for the main internal and external heat sources affecting the 5-axis machine tool structure including spindle rotation, three linear axes movements, rotary C axis and time-varying environmental temperature influence, save for the cutting process. A mathematical model using transfer functions is implemented directly into the control system of a milling centre to compensate for thermal errors in real time using Python programming language. The inputs of the compensation algorithm are indigenous temperature sensors used primarily for diagnostic purposes in the machine. Therefore, no additional temperature sensors are necessary. This achieved a significant reduction in thermal errors in three machine directions X, Y and Z during verification testing lasting over 60 hours. Moreover, a thermal test piece was machined to verify the industrial applicability of the introduced approach. The results of the transfer function model compared with the machine tool’s multiple linear regression compensation model are discussed

Global food efficiency of climate change mitigation in agriculture

Concerns exist regarding potential trade-offs between climate change mitigation in agriculture and food security. Against this background, the Global Biosphere Management Model (GLOBIOM) is applied to a range of scenarios of mitigation of emissions from agriculture to assess the implications of climate mitigation for agricultural production, prices and food availability. The “food efficiency of mitigation” (FEM) is introduced as a tool to make statements about how to attain desired levels of agricultural mitigation in the most efficient manner in terms of food security. It is applied to a range of policy scenarios which contrast a climate policy regime with full global collaboration to scenarios of fragmented climate policies that grant exemptions to selected developing country groups. Results indicate increasing marginal costs of abatement in terms of food calories and suggest that agricultural mitigation is most food efficient in a policy regime with global collaboration. Exemptions from this regime cause food efficiency losse

Dynamic Merge of the Global and Local Models for Sustainable Land Use Planning with Regard for Global Projections from GLOBIOM and Local Technical–Economic Feasibility and Resource Constraints

In order to conduct research at required spatial resolution, we propose a model fusion involving interlinked calculations of regional projections by the global dynamic model GLOBIOM (Global Biosphere Management Model) and robust dynamic downscaling model, based on cross-entropy principle, for deriving spatially resolved projections. The proposed procedure allows incorporating data from satellite images, statistics, expert opinions, as well as data from global land use models. In numerous case studies in China and Ukraine, the approach allowed to estimate local land use and land use change projections corresponding to real trends and expectations. The disaggregated data and projections were used in national models for planning sustainable land use and agricultural development

Climate-induced severe water scarcity events as harbinger of global grain price

The severe water scarcity (SWS) concept allows for consistent analysis of the supply and demand for water sourced grain production worldwide. Thus, the primary advantage of using SWS is its ability to simultaneously accommodate the spatial extent and temporal persistence of droughts using climatic data. The SWS concept was extended here to drivers of global grain prices using past SWS events and prices of three dominant grain crops: wheat, rice and maize. A significant relation between the SWS affected area and the prices of wheat was confirmed. The past price–SWS association was then used to project future wheat prices considering likely climate change scenarios until 2050 and expected SWS extent. The projected wheat prices increase with increasing SWS area that is in turn a function of greenhouse gas emissions. The need to act to reduce greenhouse gas emissions is again reinforced assuming the SWS-price relation for wheat is unaltered

A low energy demand scenario for meeting the 1.5 °C target and sustainable development goals without negative emission technologies

Scenarios that limit global warming to 1.5 °C describe major transformations in energy supply and ever-rising energy demand. Here, we provide a contrasting perspective by developing a narrative of future change based on observable trends that results in low energy demand. We describe and quantify changes in activity levels and energy intensity in the global North and global South for all major energy services. We project that global final energy demand by 2050 reduces to 245 EJ, around 40% lower than today, despite rises in population, income and activity. Using an integrated assessment modelling framework, we show how changes in the quantity and type of energy services drive structural change in intermediate and upstream supply sectors (energy and land use). Down-sizing the global energy system dramatically improves the feasibility of a low-carbon supply-side transformation. Our scenario meets the 1.5 °C climate target as well as many sustainable development goals, without relying on negative emission technologies

Quantification of uncertainties in global grazing systems assessments

Livestock systems play a key role in global sustainability challenges like food security and climate change, yet, many unknowns and large uncertainties prevail. We present a systematic, spatially explicit assessment of uncertainties related to grazing intensity (GI), a key metric for assessing ecological impacts of grazing, by combining existing datasets on a) grazing feed intake, b) the spatial distribution of livestock, c) the extent of grazing land, and d) its net primary productivity (NPP). An analysis of the resulting 96 maps implies that on average 15% of the grazing land NPP is consumed by livestock. GI is low in most of worlds grazing lands but hotspots of very high GI prevail in 1% of the total grazing area. The agreement between GI maps is good on one fifth of the world's grazing area, while on the remainder it is low to very low. Largest uncertainties are found in global drylands and where grazing land bears trees (e.g., the Amazon basin or the Taiga belt). In some regions like India or Western Europe massive uncertainties even result in GI > 100% estimates. Our sensitivity analysis indicates that the input-data for NPP, animal distribution and grazing area contribute about equally to the total variability in GI maps, while grazing feed intake is a less critical variable. We argue that a general improvement in quality of the available global level datasets is a precondition for improving the understanding of the role of livestock systems in the context of global environmental change or food security

Leaching efficiency and kinetics of the recovery of palladium and rhodium from a spent auto-catalyst in HCl/CuCl2 media

The recycling of scarce elements such as platinum-group metals is becoming crucial due to their growing importance in current and emerging applications. In this sense, the recovery of palladium and rhodium from a spent auto-catalyst by leaching in HCl/CuCl2 media was studied, aiming at assessing the kinetic performance as well as the influence of some processing factors, and the behaviour of contaminant metals. Based on a kinetic model developed for the present case, the influence of temperature was evaluated and the corresponding values of activation energy were estimated as 60.1 ± 4.1 kJ mol-1 for Pd and 44.3 ± 7.3 kJ mol-1 for Rh, indicating the relevance of the chemical step rather than diffusion. This finding was corroborated by the non-significant influence of the stirring velocity. The reaction orders were estimated for each leaching reagent: for HCl, values of 2.1 ± 0.1 for Pd and 1.0 ± 0.3 for Rh were obtained; for Cu2+, the obtained values were 0.42 ± 0.04 for Pd and 0.36 ± 0.06 for Rh. Without any significant loss of efficiency, solutions with higher metal concentrations were obtained using lower liquid/solid ratios, such as 5 L/kg. The main contaminant in solution was aluminum, and its leaching was found to be very dependent on the temperature and acid concentration.Portuguese national funds through FCT – Fundação para a Ciência e a Tecnologia (Lisbon, Portugal), under the projects reference numbers PTDC/QUI-QUI/109970/2009, UID/Multi/04326/2019 and UID/MULTI/00612/2013.info:eu-repo/semantics/publishedVersio

Two new triterpene and a new nortriterpene glycosides from Phlomis viscosa

The isolation and structure elucidation of two new oleanane-type triterpene glycosides, 29-(β-D-glucopyranosyloxy)-2α,3β,23-trihydroxyolean-12-en-28-oic acid (=(2α,3β,4α,29α)-29-(β-D-glucopyranosyloxy)-2,3,23-trihydroxyolean-12-en-28-oic acid; 1) and its C(20)-epimer, 30-(β-D-glucopyranosyloxy)-2α,3β,23-trihydroxyolean-12-en-28-oic acid (=(2α,3β,4α,29β)-29-β-D-glucopyranosyloxy)-2,3,23-trihydroxyolean-12-en-28-oic acid; 2), and a novel nortriterpene glycoside, (17S)-2α,18β,23-trihydroxy-3,19-dioxo-19(18[RIGHTWARDS ARROW]17)- abeo-28-norolean-12-en-25-oic acid β-D-glucopyranosyl ester (=(1R,2S,4aS,4bR,6aR,7R,9R,10aS,10bS)-3,4,4a,4b,5,6,6a,7,8,9,10,10a,10b,11-tetradecahydro-1-hydroxy-7-(hydroxymethyl)-3′,4′,4a,4b,7-pentamethyl-2′,8- dioxospiro[chrysene-2(1H),1′-cyclopentane]-10a-carboxylic acid β-D-glucopyranosyl ester; 3) from Phlomis viscosa (Lamiaceae) are reported. The structures of the compounds were asigned by means of spectroscopic (IR, 1D- and 2D-NMR, and LC-ESI-MS) and chemical (acetylation) methods