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The paper presents an experimental study on the formation process of burden surface texture on the blast furnace throat and its influence on the radial distribution of gas flow. The study was performed with the application of blast furnaces equipped with a bell-type charging device using radio-isotope means for the control of burden surface texture (profile) and burden surface level, i.e., gamma locators for burden surface texture. The study was carried out under the conditions of an operating blast furnace in an iron and steel plant using a unique GEOTAPS system for automated control of geometric and temperature parameters of burden material surface on the blast furnace throat. The influence of the surface texture on the gas flow distribution was also investigated. The possibility of a self-stabilization effect for burden surface texture and gas flow in an operating blast furnace under suitable conditions was experimentally proven. As a result of the experimental study performed, four ways of energy-saving technology implementation were determined for the control of blast furnace melting based on the data on the burden surface texture and previously unknown regularities of surface layer formation of burden material on the throat of an operating blast furnace with a bell-type charging device. The main idea of the paper is the development of automated control for the radial distribution of burden material and gas flow using actual or predicted surface texture parameters as important intermediate factors that both describe the process and have a significant simultaneous influence on it.

The paper presents an experimental study on the formation process of burden surface texture on the blast furnace throat and its influence on the radial distribution of gas flow. The study was performed with the application of blast furnaces equipped with a bell-type charging device using radio-isotope means for the control of burden surface texture (profile) and burden surface level, i.e., gamma locators for burden surface texture. The study was carried out under the conditions of an operating blast furnace in an iron and steel plant using a unique GEOTAPS system for automated control of geometric and temperature parameters of burden material surface on the blast furnace throat. The influence of the surface texture on the gas flow distribution was also investigated. The possibility of a self-stabilization effect for burden surface texture and gas flow in an operating blast furnace under suitable conditions was experimentally proven. As a result of the experimental study performed, four ways of energy-saving technology implementation were determined for the control of blast furnace melting based on the data on the burden surface texture and previously unknown regularities of surface layer formation of burden material on the throat of an operating blast furnace with a bell-type charging device. The main idea of the paper is the development of automated control for the radial distribution of burden material and gas flow using actual or predicted surface texture parameters as important intermediate factors that both describe the process and have a significant simultaneous influence on it.

Abstract The effect of the thermal environment on performance and productivity has been a focus of interest among indoor environmental researchers for nearly a century, but most of that work has been conducted in relative isolation from the cognate disciplines of human performance evaluation. The present review examines thermal environmental effects on cognitive performance research conducted across multiple disciplines. After differentiating performance from productivity, we compare the two dominant conceptual models linking thermal stress to performance; (1) the inverted-U concept and (2) the extended-U relationship. The inverted-U specifies a single optimum temperature (or its corresponding subjective thermal sensation) at which performance is maximised. In contrast, the extended-U model posits a broad central plateau across which there is no discernible thermal effect on cognitive performance. This performance plateau is bounded by regions of progressive performance decrements in more extreme thermal conditions. The contradictions between these opposing conceptual models might derive from various confounding factors at play in their underlying research bases. These include, inter alia, environment-related, task-related, and performer-related factors, as well as their associated two-way and three-way interaction effects. Methodological discrepancies that might also contribute to the divergence of these conceptual models are evaluated, along with the proposed causal mechanisms underlying the two models. The weight of research evidence reviewed in this paper suggests that the extended-U hypothesis fits the relationship between moderate thermal environments and cognitive performance. In contrast to the inverted-U relationship, implemention of the extended-U in indoor climate control implies substantial reductions in building energy demand, since it permits the heating and cooling setpoint deadband to expand across the full width of the thermal comfort zone, or even slightly further during emergencies such as peak demand events on the electricity grid. Use of personal comfort systems can further extend the thermostat setpoint range beyond the comfort zone.

Abstract The effect of the thermal environment on performance and productivity has been a focus of interest among indoor environmental researchers for nearly a century, but most of that work has been conducted in relative isolation from the cognate disciplines of human performance evaluation. The present review examines thermal environmental effects on cognitive performance research conducted across multiple disciplines. After differentiating performance from productivity, we compare the two dominant conceptual models linking thermal stress to performance; (1) the inverted-U concept and (2) the extended-U relationship. The inverted-U specifies a single optimum temperature (or its corresponding subjective thermal sensation) at which performance is maximised. In contrast, the extended-U model posits a broad central plateau across which there is no discernible thermal effect on cognitive performance. This performance plateau is bounded by regions of progressive performance decrements in more extreme thermal conditions. The contradictions between these opposing conceptual models might derive from various confounding factors at play in their underlying research bases. These include, inter alia, environment-related, task-related, and performer-related factors, as well as their associated two-way and three-way interaction effects. Methodological discrepancies that might also contribute to the divergence of these conceptual models are evaluated, along with the proposed causal mechanisms underlying the two models. The weight of research evidence reviewed in this paper suggests that the extended-U hypothesis fits the relationship between moderate thermal environments and cognitive performance. In contrast to the inverted-U relationship, implemention of the extended-U in indoor climate control implies substantial reductions in building energy demand, since it permits the heating and cooling setpoint deadband to expand across the full width of the thermal comfort zone, or even slightly further during emergencies such as peak demand events on the electricity grid. Use of personal comfort systems can further extend the thermostat setpoint range beyond the comfort zone.

Abstract With promising benefits such as traffic emission reduction, traffic congestion alleviation, and parking problem solving, Electric Vehicle (EV)-sharing systems have attracted large attentions in recent years. Different from other business modes, customers in sharing economy systems are usually price sensitive. Therefore, it is possible to shift the usage of shared EVs through a well-designed Dynamic Pricing Scheme (DPS), with the objective of maximizing the system operator's total profit. In this study, we propose a novel DPS for a large-scale EV-sharing network to address the EV unbalancing issue and satisfy the vehicle-grid-integration (VGI) service based on accurate station-level demand prediction. The proposed DPS is formulated as a complex optimization problem, which includes two Price Adjustment Level (PAL) decision variables for every origin-destination pair of stations. The two PALs are employed to affect the EV-sharing demand and travel time between each station pair, respectively. Physical and operational constraints from both EV demand and VGI service aspects are also included in the proposed model. Two case study are conducted to validate the effectiveness of the proposed method.

Abstract With promising benefits such as traffic emission reduction, traffic congestion alleviation, and parking problem solving, Electric Vehicle (EV)-sharing systems have attracted large attentions in recent years. Different from other business modes, customers in sharing economy systems are usually price sensitive. Therefore, it is possible to shift the usage of shared EVs through a well-designed Dynamic Pricing Scheme (DPS), with the objective of maximizing the system operator's total profit. In this study, we propose a novel DPS for a large-scale EV-sharing network to address the EV unbalancing issue and satisfy the vehicle-grid-integration (VGI) service based on accurate station-level demand prediction. The proposed DPS is formulated as a complex optimization problem, which includes two Price Adjustment Level (PAL) decision variables for every origin-destination pair of stations. The two PALs are employed to affect the EV-sharing demand and travel time between each station pair, respectively. Physical and operational constraints from both EV demand and VGI service aspects are also included in the proposed model. Two case study are conducted to validate the effectiveness of the proposed method.

Low-salinity water injection emerges to be a cost-effective and environmentally friendly enhanced oil recovery technique. Furthermore, additives, such as the surfactant and nanoparticles in combina...

Low-salinity water injection emerges to be a cost-effective and environmentally friendly enhanced oil recovery technique. Furthermore, additives, such as the surfactant and nanoparticles in combina...