Integration of iron ore deposit evaluation and mine plan for selecting cost effective beneficiation operations towards improved resource utilisation: Experience at Tata Steel

In the recent past a paradigm shift in terms of resource utilization and beneficiation practices is experienced by the Indian mining and mineral sector. Depleting iron ore reserves coupled with increasing demand for low-alumina iron ore fines to improve blast furnace performance in terms of productivity and reduced slag rate necessitate intensive beneficiation of iron ore. The task is even difficult for Indian iron ores which are generally high in iron content and at the same time characterized by aluminous gangue and thereby calls for capital investment of higher order for beneficiation. In another direction, any beneficiation means rejecting some mined ore as waste for obtaining the desired product grade and involves costs. Hence, beneficiation option has to be used judiciously. Towards this, the enabler available is characterization and assessment of geological resource to explore the options of segregation, scheduling, sequencing and blending. Joda East Iron Mines (JEIM), which caters to approximately 45% of iron ore requirement of Tata Steel plant in Jamshedpur, and has its ambitious plan for upgrading existing beneficiation facilities in the form of ‘Total Beneficiation’. The paper describes the judicious beneficiation and effective utilization efforts made, in the light of characterization and assessment of deposit, mine planning over last 10 years as experienced by JEIM plant and also discuss the importance of blending of processed ores in achieving despatchable product grades to meet customer requirement while optimizing overall ore resource utilization. JEIM has modified to earlier only washing plant to present two beneficiation circuits, namely Wet and Dry Processing plants, to take care of off - and high grade ores, respectively. The Dry processing plant is quite simple, comprising crushing and dry screening with no tailings/wastes. Off grade material is concentrated through primary and secondary crushing followed by screening and oversize crushing and re-screening, washing and sizing, scrubbing, classification in classifier and hydrocyclone, with a overall weight recovery of ~87%. The journey in improving product quality while optimizing plant production in JEIM relates to changes in operational philosophy which basically could be related as “identifying correct methods of ore dressing for correct ore” in line with deposit ratio

Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

Environmental optimisation of mine scheduling through life cycle assessment integration

This is the final version. Available on open access from Elsevier via the DOI in this recordLife cycle assessments (LCA) are useful to quantify the environmental costs of mining projects, however the application of LCA is often a retrospective environmental measurement of operating mines. This paper presents a novel methodology of carrying out a LCA to generate life cycle impact assessment data that can form an environmental block model of a deposit. These spatially explicit data can then be used as a constraint within long-term mine scheduling simulations. The results indicate that significant reductions in global warming impact can be achieved at a small economic cost. For example using an environmental constraint it was possible to achieve 91.9% of the global warming impact whilst achieving 95.9% of the net present value compared to the baseline. Different constraints and economic scenarios are explored and multi-criteria decision analysis is carried out. This approach enables environmental considerations to be included in strategic mine planning. This is important because mining will continue to form an important part of our society for the foreseeable future. Integrating environmental considerations into the earliest stages of mine planning can assist in driving environmentally responsible raw material extraction

PRIMJENA STOHASTIČKOGA MODELA NA ODLAGALIŠTU JALOVINE KAO POTPORA DUGOROČNOJ PROIZVODNJI U OTVORENOME KOPU S CILJEM POVEĆANJA POSTOTKA RUDE U OBRADBI

This paper presents a chance-constrained integer programming approach based on the linear method to solve the longterm open pit mine production scheduling problem. Specifically, a single stockpile has been addressed for storing excess low-grade material based on the availability of processing capacity and for possible future processing. The proposed scheduling model maximizes the project NPV while respecting a series of physical and economic constraints. Differently from common practice, where deterministic models are used to calculate the average grade for material in the stockpiles, in this work a stochastic approach was performed, starting from the time of planning before the stockpile realization. By performing a probability analysis on two case studies (on iron and gold deposits), it was proven that the stockpile attributes can be treated as normally distributed random variables. Afterwards, the stochastic programming model was formulated in an open pit gold mine in order to determine the optimum amount of ore dispatched from different bench levels in the open pit and at the same time a low-grade stockpile to the mill. The chance-constrained programming was finally applied to obtain the equivalent deterministic solution of the primary model. The obtained results have shown a better feed grade for the processing plant with a higher NPV and probability of grade blending constraint satisfaction, with respect to using the traditional stockpile deterministic model.Rad prikazuje uporabu vjerojatnosnoga cjelobrojnog programiranja, temeljenoga na linearnome algoritmu, za dugoročno rješavanje proizvodnje u rudniku otvorenoga kopa. Obrađeno je jedno odlagalište jalovine sa „siromašnom” koncentracijom rude u cilju aktiviranja toga materijala u budućoj preradbi korisne sirovine. Takav projekt maksimizira trenutačnu vrijednost rudarenja uzimajući u obzir niz fizičkih i ekonomskih varijabli. Posebnost u odnosu na determinističke modele koji se danas uglavnom koriste za izračun granične prosječne vrijednosti koncentracije rude prije odlaganja kao jalovine izražena je stohastikom. Ona je uključila vjerojatnosnu analizu dvaju slučajeva, tj. za ležište željeza i zlata. U obama je dokazano kako se varijable određene na odlagalištu mogu opisati normalnom razdiobom. Stohastički model programiran je za rudnik zlata te je uzeta u obzir optimalna vrijednost rude razvrstane na različitim rudničkim razinama, a prije slanja na obradbu (mljevenje). Optimizirani model zatim je primijenjen za dobivanje usporednoga determinističkog modela. Rezultati su upozorili na to da je konačno rješenje pokazalo znatno bolji odabir granične koncentracije rude koja se mogla poslati na daljnju obradbu. Time je uvećana i ukupna vrijednost rudnika/ležišta

The application of a stockpile stochastic model into long-term open pit mine production scheduling to improve the feed grade for the processing plant

This paper presents a chance-constrained integer programming approach based on the linear method to solve the long-term open pit mine production scheduling problem. Specifically, a single stockpile has been addressed for storing excess low-grade material based on the availability of processing capacity and for possible future processing. The proposed scheduling model maximizes the project NPV while respecting a series of physical and economic constraints. Differently from common practice, where deterministic models are used to calculate the average grade for material in the stockpiles, in this work a stochastic approach was performed, starting from the time of planning before the stockpile realization. By performing a probability analysis on two case studies (on iron and gold deposits), it was proven that the stockpile attributes can be treated as normally distributed random variables. Afterwards, the stochastic programming model was formulated in an open pit gold mine in order to determine the optimum amount of ore dispatched from different bench levels in the open pit and at the same time a low-grade stockpile to the mill. The chance-constrained programming was finally applied to obtain the equivalent deterministic solution of the primary model. The obtained results have shown a better feed grade for the processing plant with a higher NPV and probability of grade blending constraint satisfaction, with respect to using the traditional stockpile deterministic model.

Auction-based approach to resolve the scheduling problem in the steel making process

Steel production is an extremely complex process and determining coherent schedules for the wide variety of production steps in a dynamic environment, where disturbances frequently occur, is a challenging task. In the steel production process, the blast furnace continuously produces liquid iron, which is transformed into liquid steel in the melt shop. The majority of the molten steel passes through a continuous caster to form large steel slabs, which are rolled into coils in the hot strip mill. The scheduling system of these processes has very different objectives and constraints, and operates in an environment where there is a substantial quantity of real-time information concerning production failures and customer requests. The steel making process, which includes steel making followed by continuous casting, is generally the main bottleneck in steel production. Therefore, comprehensive scheduling of this process is critical to improve the quality and productivity of the entire production system. This paper addresses the scheduling problem in the steel making process. The methodology of winner determination using the combinatorial auction process is employed to solve the aforementioned problem. In the combinatorial auction, allowing bidding on a combination of assets offers a way of enhancing the efficiency of allocating the assets. In this paper, the scheduling problem in steel making has been formulated as a linear integer program to determine the scheduling sequence for different charges. Bids are then obtained for sequencing the charges. Next, a heuristic approach is used to evaluate the bids. The computational results show that our algorithm can obtain optimal or near-optimal solutions for combinatorial problems in a reasonable computation time. The proposed algorithm has been verified by a case study

A scheduling model for production in a hot strip mill

M.Ing.This research dissertation highlights the important role of scheduling in a production environment. The functioning of an integrated iron and steel works is discussed. The importance of production scheduling in this environment is shown, followed by a literature survey of strip mill production scheduling models. Thereafter a model is introduced that aids in the production scheduling of plate via coil in a hot strip mill. Finally the benefits of the scheduling model are shown

Responsible sourcing of rare earth elements

Rare earth elements (REE) are considered to be critical raw materials due to the combination of their high importance in a range of low-carbon technologies and the concentration of supply, which is dominated in China. The REE industry has a legacy of environmental damage and the mining, processing, and separating out of the REE requires a significant quantity of energy and chemicals. Life cycle assessment (LCA) is a method to quantify the environmental impacts of a product or process and can be applied to the raw materials production sector. This thesis presents how LCA can be applied for REE projects in development. The results can help identify environmental hotspots for a project, and analyse alternatives to help reduce the environmental impacts of REE production. Mineral processing simulation are commonly used in REE project development and data generated from these studies can be used to carry out a LCA. This approach was presented with the Songwe Hill REE project in Malawi. The mineral processing simulation output data which includes energy and chemical flows is used as the life cycle inventory data (LCI) and calculated with characterization factors to generate life cycle impact assessment (LCIA) results such as global warming potential. This data can inform future engineering studies or process simulations. REE projects, like all mining projects, can last decades and extract different ore compositions throughout this life-time. A method is presented to generate tempo- rally explicit LCA results. The Bear Lodge REE project, which is in the prefeasibility stage of development and located in the United States, is used as a case study. LCIA results highlight that grade and mineralogy can influence the LCIA results. The relationships between environmental impacts and grade and mineralogy are explored. Thirdly, a method is presented to include LCA data in the mine scheduling pro- cess. LCIA data can form an environmental block model alongside the economic block model for a deposit. These spatially explicit data can then be used as a constraint within long-term mine scheduling simulations. The results indicate that significant reductions in global warming impact can be achieved at a small economic cost. Finally advances to the current resource depletion impact categories are achieved, advancing the previous methods which neglect socio-economic, regulatory and geopolitical aspects, nor do they include functionalities such as material recycling or reuse that control the supply of raw materials. I examine the economic scarcity potential (ESP) method and make advances based on recent developments in material criticality. ESP criticality scores for 15 REE with the addition of Au, Cu, platinum-group metals (PGM), Fe and Li are measured and a case study is presented to for the inclusion of REE ESP scores for the materials that form a NdFeB permanent magnet. This thesis has a focus on utilising LCA in a proactive manner and incorporating it into the planning stages of REE projects to encourage responsible production of REE

LINEARNO PROGRAMIRANJE KAO ALAT ZA PROJEKTIRANJE SIROVINSKE SMJESE U TVORNICI CEMENTA

This study uses linear programming to develop a methodology for selecting the best raw material mix in an ASCOM cement plant in Egypt. In cement factories, this type adheres to Egyptian chemical composition criteria for raw feed (e.g. 82.5% calcium carbonate, 14.08% silica, 2.5% alumina and 0.92% iron oxide). Furthermore, the model is bound by industry-specific characteristics (e.g. lime saturation factor, silica modulus, alumina modulus and loss of ignition). The results reveal that the model is able to accurately reproduce the mixing of high-quality feed with varying constituent percentages. It is also capable of determining the combining limitations of each ingredient. Furthermore, it demonstrates optimality for additive sourcing short-term planning and capping limestone quality to meet changeable component combinations. Additionally, improving the raw mix reduces limestone feed quality from 51 to 50.6%, resulting in the inclusion of extra limestone reserves.Studija prikazuje metodu linearnoga programiranja uporabljenu sa svrhom odabira najbolje sirovinske smjese u tvornici cementa ASCOM (Egipat). Takva smjesa poštuje egipatske standarde kemijskoga sastava sirovine (npr. 82,5 % kalcijeva karbonata, 14,08 % silikata, 2,5 % aluminijeva oksida, 0,92 % željeznoga oksida). Također, model je uvjetovan industrijskim standardima (npr. faktorom zasićenja vapnom, silikatnim i aluminatnim modulom te gubitkom (oksida) žarenjem). Modelom se mogla točno izračunati visokokvalitetna mješavina različitih (postotnih) komponenti te je dokazan kao optimalan za brz izračun raznih aditiva i postizanje najveće kvalitete vapnenačke sirovine uz doziranje ostalih komponenti. Time je udjel vapnenca bilo moguće smanjiti na 50,6 – 51 %, što je otvorilo put eksploataciji dodatnih rezervi te sirovine

The responsive reply chain: the influence of the positioning of decoupling points

Manufacturing supply chains have been challenged by high competition, dynamic, and stochastic conditions. They have to be constantly responsive in today’s ever-changing manufacturing environment. The proper positioning of decoupling points for material flow and information flow has a significant potential for increasing responsiveness in a supply chain. Positioning the material decoupling point as close to the end consumer as possible whilst the information decoupling point is positioned upstream is the key to the industries’ ability to reduce lead time and enhance performance in the dynamic behaviour of the supply chain. [Continues.