Assessing the shape of the viscoplastic iron-ore zone in a blast furnace

A mathematical model permits assessment of the position and shape of the viscoplastic iron-ore zone (cohesion zone) in a blast furnace and identification of a rational configuration for this zone on the basis of readily available information regarding the blast furnace in the baseline period. The model also permits the solution of design problems with variation in the furnace parameters. Modeling results are outlined for the baseline and design periods. © 2013 Allerton Press, Inc

Simulation of heat-transfer processes and assessment of the viscoplastic parameters of iron ore in blast furnaces

Stages in the development of an information and simulation system for assessing the position and shape of the viscoplastic iron-ore zone (the cohesion zone) in the blast furnace are outlined. This system also permits diagnostics of the zone's optimal configuration on the basis of available operational information for the furnace in the baseline period. In addition, the system proves useful during the design period, with variation in the smelting parameters. The capabilities of the corresponding software are discussed, and its use in blast-furnace control at OAO Magnitogorskii Metallurgicheskii Kombinat is demonstrated. © 2013 Allerton Press, Inc

Conjoint approach of the "residual" prediction and the nonlinear autoregressive neural network increases the forecast precision of the base model

An algorithm based on predicting the residuals of the nonlinear autoregressive neural network model with external input (NARX), which can improve the prediction accuracy, was proposed. Data of the concentration of one of the main greenhouse gases methane (CH4) on the Arctic Island of Belyy, Russia, were used for prediction. A time interval, which was characterized by high daily fluctuations in the CH4 concentration was selected. The forecast accuracy was determined by the mean absolute error (MAE), root mean squared error (RMSE) and root mean squared relative error (RMSRE) errors. The use of the algorithm allowed to increase the forecast accuracy from 11% for RMSE to 20% for RMSRE. © 2020 American Institute of Physics Inc.. All rights reserved

Addressing climate change with behavioral science: A global intervention tournament in 63 countries

International audienceEffectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors