Analysis of Energy Sustainability in Ore Slurry Pumping Transport Systems

The mining industry is characterized by a high consumption of energy due to the wide diversity of processes involved, specifically the transportation of ore slurry via pipeline systems. This study investigates the relationship among the variables that define the slurry transportation system to minim...

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Main Authors: Yunesky Masip Macía, Jacqueline Pedrera, Max Túlio Castro, Guillermo Vilalta
Format: Article
Language:English
Published: MDPI AG 2019-06-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/11/11/3191
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spelling doaj-285fe5c2736045d0a2358e3795a932062020-11-24T20:57:58ZengMDPI AGSustainability2071-10502019-06-011111319110.3390/su11113191su11113191Analysis of Energy Sustainability in Ore Slurry Pumping Transport SystemsYunesky Masip Macía0Jacqueline Pedrera1Max Túlio Castro2Guillermo Vilalta3Escuela de Ingeniería Mecánica, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, ChileThermal Sciences and Fluids Department, Federal University of São João del-Rei, São João del-Rei-Minas Gerais 36307-352, BrazilThermal Sciences and Fluids Department, Federal University of São João del-Rei, São João del-Rei-Minas Gerais 36307-352, BrazilThermal Sciences and Fluids Department, Federal University of São João del-Rei, São João del-Rei-Minas Gerais 36307-352, BrazilThe mining industry is characterized by a high consumption of energy due to the wide diversity of processes involved, specifically the transportation of ore slurry via pipeline systems. This study investigates the relationship among the variables that define the slurry transportation system to minimize the power requirements and increase energy sustainability. The energy indicator (<i>I</i>), the criterion used for the energy assessment of three different pumping system layouts, was computed via numerical simulation. Optimization of response <i>I</i> was carried out through a statistical technique in the design of the experiment. In the study, four variables were defined to describe the slurry transportation systems, two of which are associated with the piping system (length <i>L</i> and diameter <i>D</i>); the other two are related to the slurry pattern (the volumetric concentration <i>Cv</i> and granulometry <i>D</i><sub>50</sub>). The results show that all variables are statistically significant relative to the indicator <i>I</i>, with <i>L</i> having the greatest amplitude of variation in the response, increasing the energy indicator by approximately 60%. Likewise, the decrease of the <i>D</i><sub>50</sub> from 300 &#181;m to 100 &#181;m produces an average decrease of <i>I</i> of 24%. Moreover, the interaction among the factors indicates that two pairs of factors are correlated, namely <i>D</i><sub>50</sub> with <i>L</i> and <i>D</i> with <i>L</i>. Finally, a predictive model obtained a fit that satisfactorily relates with the numerical data, allowing, in a preliminary way, to identify the minimum power requirement in iron ore slurry pipeline systems.https://www.mdpi.com/2071-1050/11/11/3191energy sustainabilityslurry transportationdesign of experimentsnumerical simulationpredictive model
collection DOAJ
language English
format Article
sources DOAJ
author Yunesky Masip Macía
Jacqueline Pedrera
Max Túlio Castro
Guillermo Vilalta
spellingShingle Yunesky Masip Macía
Jacqueline Pedrera
Max Túlio Castro
Guillermo Vilalta
Analysis of Energy Sustainability in Ore Slurry Pumping Transport Systems
Sustainability
energy sustainability
slurry transportation
design of experiments
numerical simulation
predictive model
author_facet Yunesky Masip Macía
Jacqueline Pedrera
Max Túlio Castro
Guillermo Vilalta
author_sort Yunesky Masip Macía
title Analysis of Energy Sustainability in Ore Slurry Pumping Transport Systems
title_short Analysis of Energy Sustainability in Ore Slurry Pumping Transport Systems
title_full Analysis of Energy Sustainability in Ore Slurry Pumping Transport Systems
title_fullStr Analysis of Energy Sustainability in Ore Slurry Pumping Transport Systems
title_full_unstemmed Analysis of Energy Sustainability in Ore Slurry Pumping Transport Systems
title_sort analysis of energy sustainability in ore slurry pumping transport systems
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2019-06-01
description The mining industry is characterized by a high consumption of energy due to the wide diversity of processes involved, specifically the transportation of ore slurry via pipeline systems. This study investigates the relationship among the variables that define the slurry transportation system to minimize the power requirements and increase energy sustainability. The energy indicator (<i>I</i>), the criterion used for the energy assessment of three different pumping system layouts, was computed via numerical simulation. Optimization of response <i>I</i> was carried out through a statistical technique in the design of the experiment. In the study, four variables were defined to describe the slurry transportation systems, two of which are associated with the piping system (length <i>L</i> and diameter <i>D</i>); the other two are related to the slurry pattern (the volumetric concentration <i>Cv</i> and granulometry <i>D</i><sub>50</sub>). The results show that all variables are statistically significant relative to the indicator <i>I</i>, with <i>L</i> having the greatest amplitude of variation in the response, increasing the energy indicator by approximately 60%. Likewise, the decrease of the <i>D</i><sub>50</sub> from 300 &#181;m to 100 &#181;m produces an average decrease of <i>I</i> of 24%. Moreover, the interaction among the factors indicates that two pairs of factors are correlated, namely <i>D</i><sub>50</sub> with <i>L</i> and <i>D</i> with <i>L</i>. Finally, a predictive model obtained a fit that satisfactorily relates with the numerical data, allowing, in a preliminary way, to identify the minimum power requirement in iron ore slurry pipeline systems.
topic energy sustainability
slurry transportation
design of experiments
numerical simulation
predictive model
url https://www.mdpi.com/2071-1050/11/11/3191
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