Global distribution of material consumption: Nickel, copper, and iron

Nakajima, Kenichi; Daigo, Ichiro; Nansai, Keisuke; Matsubae, Kazuyo; Takayanagi, Wataru; Tomita, Makoto; Matsuno, Yasunari

doi:10.1016/j.resconrec.2017.08.029

Cited by 92 publications

(42 citation statements)

References 19 publications

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“…The design of another effective parameter (neuron number) on the GMDH model needs another parametric study. To do this, considering the previous studies (e.g., [21]), values of 2,4,6,8,10,12,14,16,18, and 20 were selected to be used as number of neurons in the parametric study and results of GMDH models based on R 2 are shown in Table 4. Among the obtained results, as it can be seen in Table 4, GMDH model number 8 with 16 neurons shows the best prediction performance and due to that neuron number of 16 was selected in the rest of modelling process.…”

Section: Group Methods Of Data Handlingmentioning

confidence: 99%

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Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

Moghaddam

Monjezi

et al. 2020

Applied Sciences

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Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques.

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Section: Group Methods Of Data Handlingmentioning

confidence: 99%

“…Iron ore has previously confirmed itself to be a substantial resource in the economy and society [3]. More than half of the worldwide extraction of metal ores is related to iron ore and copper ore [4]. Recently, the expansive substructure buildings in the world increased its demand for iron ore [5].…”

Section: Introductionmentioning

confidence: 99%

Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

Moghaddam

Monjezi

et al. 2020

Applied Sciences

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“…Global demand for metals has rapidly increased as the global economy has grown to create huge amounts of social material stocks [16,17]. Thinking in terms of a circular economy and "closing the loop" [18] would help to significantly reduce pressure on natural resources and lead a transition to a sustainable economic system that encompasses the concept of decoupling resource use and economic growth.…”

Section: Circulation System Designed To Retain Added Valuementioning

confidence: 99%

Development of multi-value circulation based on remanufacturing

Nakajima

Matsumoto

Murakami

et al. 2019

Matériaux & Techniques

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Remanufacturing is an industrial process that turns used products into new ones with the same quality, functionality, and warranty as new products; it is a critical element for realizing a resource-efficient manufacturing industry and a circular economy. Remanufacturing may involve adding new and better functionality to used products, such as adding more wear-resistant materials to the surface or new sensor systems. Remanufacturing has been undertaken for products such as: automobile parts, machinery, photocopiers, single-use cameras, furniture, and turbine components, etc. It is generally superior to material recycling in terms of energy and material savings. Our project aims to develop technologies necessary for the promotion of remanufacturing and to establish a cooperative network related to remanufacturing. As technical development items, our aim is to develop methods to assess the reliability of parts/components, develop technologies to restore deteriorated metal surfaces of used products, introduce production management methods for remanufacturing, and design a circulation system to retain the added values of products. In this paper, we introduce an outline of the project and present some preliminary results. This paper shows the possibility to quantitatively evaluate the carbide distribution (size and density) of the carburized surface of a gear, and also shows the potential to repair materials exposed to a high-temperature oxidative atmosphere by Pr-Ir coating technology.

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“…Rapid expansion of these industries, especially stainless-steel manufacture, has dramatically increased the demand for nickel in recent years. Of all the land nickel reserves, 30% exist as sulfide ores, with the balance comprised of oxide ores [1]. Because of the low grade of nickel ore mined from mines, most of the nickel concentrate can be obtained after the beneficiation process before smelting.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Nickel from Garnierite Laterite Ore Using Roasting and Magnetic Separation with Calcium Chloride and Iron Concentrate

et al. 2020

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In this study, segregation roasting and magnetic separation are used to extract nickel from a garnierite laterite ore. The garnierite laterite ore containing 0.72% Ni, 0.029% Co, 8.65% Fe, 29.66% MgO, and 37.86% SiO 2 was collected in the Mojiang area of China. Garnierite was the Ni-bearing mineral; the other main minerals were potash feldspar, forsterite, tremolite, halloysite, quartz, and kaolinite in the garnierite laterite ore. The iron phase transformations show that nickel is transformed from (Ni,Mg)O·SiO 2 ·nH 2 O to a new nickel mineral phase dominated by [Ni]Fe solid solution; and iron changed from Fe 2 O 3 and FeOOH to a new iron mineral phase dominated by metal Fe and Fe 3 O 4 after segregation roasting. Ferronickel concentrate with Ni of 16.16%, Fe of 73.67%, and nickel recovery of 90.33% was obtained under the comprehensive conditions used: A roasting temperature of 1100 • C, a roasting time of 90 min, a calcium chloride dosage of 15%, an iron concentrate dosage of 30%, a coke dosage of 15%, a coke size of −1 + 0.5 mm, a magnetic separation grinding fineness of <45 µm occupying 90%, and a magnetic separation magnetic field intensity of H = 0.10 T. The main minerals in ferronickel concentrate are Fe, [Ni]Fe, Fe 3 O 4 , and a small amount of gangue minerals, such as CaO·SiO 2 and CaO·Al 2 O 3 ·SiO 2 .

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Global distribution of material consumption: Nickel, copper, and iron

Cited by 92 publications

References 19 publications

Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price

Development of multi-value circulation based on remanufacturing

Extraction of Nickel from Garnierite Laterite Ore Using Roasting and Magnetic Separation with Calcium Chloride and Iron Concentrate

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