mining processes

data mining process: models, process steps & challenges involved

data mining process: models, process steps & challenges involved

Data Mining, which is also known as Knowledge Discovery in Databases is a process of discovering useful information from large volumes of data stored in databases and data warehouses. This analysis is done for decision-making processes in the companies.

Data Mining is a process of discovering interesting patterns and knowledge from large amounts of data. The data sources can include databases, data warehouses, the web, and other information repositories or data that are streamed into the system dynamically.

With the advent of Big Data, data mining has become more prevalent. Big data is extremely large sets of data that can be analyzed by computers to reveal certain patterns, associations, and trends that can be understood by humans. Big data has extensive information about varied types and varied content.

Thus with this amount of data, simple statistics with manual intervention would not work. This need is fulfilled by the data mining process. This leads to change from simple data statistics to complex data mining algorithms.

The data mining process will extract relevant information from raw data such as transactions, photos, videos, flat files and automatically process the information to generate reports useful for businesses to take action.

Any business problem will examine the raw data to build a model that will describe the information and bring out the reports to be used by the business. Building a model from data sources and data formats is an iterative process as the raw data is available in many different sources and many forms.

CRISP-DM is a reliable data mining model consisting of six phases. It is a cyclical process that provides a structured approach to the data mining process. The six phases can be implemented in any order but it would sometimes require backtracking to the previous steps and repetition of actions.

#2) Data Understanding: This step will collect the whole data and populate the data in the tool (if using any tool). The data is listed with its data source, location, how it is acquired and if any issue encountered. Data is visualized and queried to check its completeness.

#4) Modeling: Selection of the data mining technique such as decision-tree, generate test design for evaluating the selected model, building models from the dataset and assessing the built model with experts to discuss the result is done in this step.

#5) Evaluation: This step will determine the degree to which the resulting model meets the business requirements. Evaluation can be done by testing the model on real applications. The model is reviewed for any mistakes or steps that should be repeated.

#6) Deployment: In this step a deployment plan is made, strategy to monitor and maintain the data mining model results to check for its usefulness is formed, final reports are made and review of the whole process is done to check any mistake and see if any step is repeated.

SEMMA makes it easy to apply exploratory statistical and visualization techniques, select and transform the significant predicted variables, create a model using the variables to come out with the result, and check its accuracy. SEMMA is also driven by a highly iterative cycle.

The data mining process is divided into two parts i.e. Data Preprocessing and Data Mining. Data Preprocessing involves data cleaning, data integration, data reduction, and data transformation. The data mining part performs data mining, pattern evaluation and knowledge representation of data.

There are many factors that determine the usefulness of data such as accuracy, completeness, consistency, timeliness. The data has to quality if it satisfies the intended purpose. Thus preprocessing is crucial in the data mining process. The major steps involved in data preprocessing are explained below.

Binning is done by smoothing by bin i.e. each bin is replaced by the mean of the bin. Smoothing by a median, where each bin value is replaced by a bin median. Smoothing by bin boundaries i.e. The minimum and maximum values in the bin are bin boundaries and each bin value is replaced by the closest boundary value.

When multiple heterogeneous data sources such as databases, data cubes or files are combined for analysis, this process is called data integration. This can help in improving the accuracy and speed of the data mining process.

Different databases have different naming conventions of variables, by causing redundancies in the databases. Additional Data Cleaning can be performed to remove the redundancies and inconsistencies from the data integration without affecting the reliability of data.

This technique is applied to obtain relevant data for analysis from the collection of data. The size of the representation is much smaller in volume while maintaining integrity. Data Reduction is performed using methods such as Naive Bayes, Decision Trees, Neural network, etc.

In this process, data is transformed into a form suitable for the data mining process. Data is consolidated so that the mining process is more efficient and the patterns are easier to understand. Data Transformation involves Data Mapping and code generation process.

Data Mining is a process to identify interesting patterns and knowledge from a large amount of data. In these steps, intelligent patterns are applied to extract the data patterns. The data is represented in the form of patterns and models are structured using classification and clustering techniques.

This step involves identifying interesting patterns representing the knowledge based on interestingness measures. Data summarization and visualization methods are used to make the data understandable by the user.

Relational Database management systems such as Oracle support Data mining using CRISP-DM. The facilities of the Oracle database are useful in data preparation and understanding. Oracle supports data mining through java interface, PL/SQL interface, automated data mining, SQL functions, and graphical user interfaces.

#1) Financial Data Analysis: Data Mining is widely used in banking, investment, credit services, mortgage, automobile loans, and insurance & stock investment services. The data collected from these sources is complete, reliable and is of high quality. This facilitates systematic data analysis and data mining.

#2) Retail and Telecommunication Industries: Retail Sector collects huge amounts of data on sales, customer shopping history, goods transportation, consumption, and service. Retail data mining helps to identify customer buying behaviors, customer shopping patterns, and trends, improve the quality of customer service, better customer retention, and satisfaction.

#3) Science and Engineering: Data mining computer science and engineering can help to monitor system status, improve system performance, isolate software bugs, detect software plagiarism, and recognize system malfunctions.

#4) Intrusion Detection and Prevention: Intrusion is defined as any set of actions that threaten the integrity, confidentiality or availability of network resources. Data mining methods can help in intrusion detection and prevention system to enhance its performance.

Data Mining is an iterative process where the mining process can be refined, and new data can be integrated to get more efficient results. Data Mining meets the requirement of effective, scalable and flexible data analysis.

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process mining from theory to practice | sap blogs

process mining from theory to practice | sap blogs

My name is Gabriela and I am a SAP Profitability and Performance Management Content Developer. I am mainly occupied with contents belonging to process mining topic, as well with finance tasks. The idea behind this blog post was inspired by high-profile topics driving the world such as new technologies, trending analytical and process management techniques, digital transformation, innovations, and growth strategies. How did you equip your organization in order to be prepared for all these challenges? How fast can you adapt to those changes? Are you competitive? Does your organization deliver satisfying customer experience? If you have any doubts answering these questions, then read my blog post. I first start with Big Data term and how process mining and SAP Profitability and Performance Management are connected with it, then how companies can benefit from process mining and how this technique evolves from theory to practice, and finally how we apply our solution functionalities to leverage process mining.

Lets see how we ended up with the need for process mining. Process mining brings the data science in action. Big Data was introduced due to intensive increase of global data. It describes large, in most cases unstructured datasets which need analysis in order to discover new values and understand better the hidden ones. Big Data also brings to our attention the question how can we benefit from it? In order to answer this question, we developed SAP Profitability and Performance Management. Calculation of massive volumes of data in seconds, improved decision making and integration of calculated results in process activities, planning cycles, visualizations and execution, are just small part of its capabilities.

SAP Profitability and Performance Management can help your organization to gain real-time actionable information and to align your processes with profitability and performance. Here is where the solution converges with process mining technique. Process mining measures business alignment. It points out the deviations in processes owned to discrepancies between real and expected behavior. This intersection with process mining immediately draw our attention and we created predefined contents for process mining with integration into SAP S/4HANA.

Growing digitalization put companies around the world in a competition wherein operational excellence is essential to survival. Our incentive to develop such a content was that process mining is a core element of achieving operational optimization and is essential for staying ahead of the competition. Its focus is to discover, monitor and improve real processes.

Before answering this question, we need to know what process itself means. The recurrence of sequential order of different set of events by their timestamp builds up a given process instance or case. Each event corresponds to an activity executed in the process. Processes have defined start and end activities and are performed with the aim to achieve a certain goal (see Fig. 3). One process normally has many variants or specific sequence of activities (see Fig. 4). Process mining techniques obtain knowledge and retrieve actionable insights from processes by the means of real-time event logs extracted from companys information systems and allow us to perform more specific types of analysis. Each event log collection represents an activity (action on a case) which has a unique activity ID. A case represents an instance of a given process, in the example below handling of one credit application is one case, which is studied by an activity. Event timestamps tell us when a given activity took place, and when is available provide us with more details about an activity. Usually, a process related data is scattered in various data sources. Data collection allow us to review in depth the history of a process. In our example below a case is related to one credit application. Each case runs through a certain process variant. Please see the overall picture of event data for process mining on Fig. 5.

After the retrieval of this basic statistic information stored in event logs, which is starting point for process mining, it continues further with understanding of the organizational process. As you know, business processes are usually performed by various people who are involved just in a part of the overall process and are situated in different company departments. These circumstances make the understanding of the overall process a challenging task. Building main process flows in a process diagram gives us general knowledge of the process which each person involved in must have. The process diagram allows fast process discovery and easy detection of deviations to standard activity sequences(see Fig. 6). Process monitoring aids informed decision making, by telling us how we have performed against our expectations, as well as uncovering previously unknown process-related information.

Process mining can be applied in multiple business areas, including loans management, premium collection, customer service management or internal audit. SAP Profitability and Performance Management contains different process mining topics, please refer to Fig.7. The heart of our predefined contents is the integration into the SAP ERP and SAP S/4HANA system. From there, extensive detailed information is obtained automatically, converted into valuable knowledge about the different industry and cross-industry processes and then made available for analysis and what-if simulation of optimization scenarios.

Event log forms the factual basis for the discovery and analysis of the processes. It records in individual events which information about which application was created, changed or deleted by which process and user. Depending on the event, internal and external business partner information, additional documents or even compliance violations can be looked up to better understand the context. Further, case specific information is used, for example invoices, contracts, policies, business partner data, loans data, claims data and so on. On this basis, the entire enrichment, calculation and determination of KPIs is carried out (see Fig. 8).

Afterwards, all information is available for analysis, for example to examine the most frequent or slowest occurring process variant, the automation rate, the compliance rate, or the happy path. You can read more about these standard and industry specific KPI reports in the approaching process mining blog posts. Report management offers capability to drill down through any dimension down to the individual case and activity level. With the help of the what-if simulation, the financial and temporal effects of possible process optimizations can be determined, for example by increasing the automation for certain activities or blocking undesired activities.

Okay, dear readers, that was just an introduction of process mining topic. I hope you find this information useful. If I have captured your interest stay tuned with the upcoming blogs on this topic! Feel free to like, share or ask questions.

mining legacy processes setting the industry behind in sustainability targets report

mining legacy processes setting the industry behind in sustainability targets report

A report commissioned by Glasgow-based engineering company the Weir Group states that the total amount of power used in hard rock mining is equal to 12 exajoules per year, or close to 3.5% of global energy use and that, therefore, the industry has to move away from legacy systems and processes if it wants to meet sustainability targets.

After analyzing mine energy data from over 40 studies published from 2007 to 2020 and which focused on five commodities copper, gold, iron ore, nickel and lithium comminution was identified as the single biggest user of energy at mine sites, typically accounting for 25% of minings final energy consumption.

Extended across all hard rock mining, this is equivalent to the power used by 221 million typical UK homes, or c.1% of total consumption globally, the report reads. Comminution is, therefore, a natural target for the most impactful energy savings opportunities.

In the gold mining sector, most studies reviewed for the report estimated that the overall average energy consumption is approximately 134 GJ/kg of gold for underground mines and average energy consumption of 372 GJ/kg of gold for open-pit operations.

According to the document, by comparing what the literature shows with a database of 400 mine sites it is possible to conclude that copper production has an average energy intensity of approximately 25 GJ/t of copper.

Energy use in the mining operations is 40% electricity (primarily ventilation) and 60% diesel (for loading and hauling). In the processing plant, the majority of energy consumption occurs in grinding (90%) with the remaining energy consumption in this study being attributed to flotation, the report reads.

Nickel sulphide processing, on the other hand, averages an energy intensity for concentrators of 31 GJ/t of nickel produced. Of this, the split between mining and processing is approximately 50:50with then 90% of the processing plant energy consumption being used in comminution.

Under the premise that hard rock lithium mining is likely to dominate in coming years due to the trend towards low/no cobalt batteries which tend to use lithium hydroxide rather than lithium carbonate, the review indicates that energy requirements for a lithium concentrator are approximately 15 GJ/t lithium produced. Of this, 60% of energy consumption is electricity in the concentrator with the remaining 40% in the mining operations.

In the processing plant, the largest portion of energy consumption was identified to be in the crushing area, followed by dense media separation. Crushing accounts for about 20% of site energy consumption, with a further 7% in the regrind mill.

In hematite operations, the report found that energy intensity averages less than 0.15 GJ/t of iron ore, taking into account both diesel consumption in the mine and electricity production for processing plants.

Approximately 90% of the energy consumption was determined to be diesel consumption in mobile equipment, while the remaining energy consumption is electricity in processing plantsmostly crushing and conveyors.

In magnetite operations, the paper shows that energy intensity for processing is 0.23 GJ/t of shipped ore, including the additional separation and concentration equipment. Mining operations add up to an additional 0.16 GJ/t of shipped ore giving a total site energy intensity of 0.3 GJ/t of shipped ore.

According to the document, small improvements in comminution technologies can lead to relatively large savings in both energy consumption and greenhouse gas emissions. As an example, the report presents the idea of a 5% incremental improvement in energy efficiency across comminution, which could result in greenhouse gas emissions reductions of more than 30m tonnes of CO2e.

The replacement of traditional comminution equipment with new grinding technology also reduces indirect emissions in the mining value chain, for example by removing the need for the manufacture of emission-intensive steel grinding balls. Of the remaining energy consumption by the mining industry, diesel in varied forms of mobile equipment accounts for 46%, electricity in mining (ventilation) 15% and other electricity 14%, the review states.

Advancements in high pressure grinding rolls, high intensity grinding and stirred mills/vertical mills mean that traditional semi-autogenous grinding/ball mill applications could be replaced and the same outcomes achieved subject, of course, to the amenability of the particular ore type and processing requirements to these comminution circuits, the report suggests.

The paper notes that additional throughput in grinding circuits as a result of energy savings unlocking additional energy capacity should form part of business cases when exploring the potential of equipment and circuit changes.

Similarly, the report found that a good case could be built around automating operations so that computing technology and big data are able to provide high-fidelity block models showing mineral concentration and composition throughout mining areas.

Further, becoming a zero-emissions industry is also presented as a possibility if zero-emissions energy sources are deployed for mining equipment. Renewable energy, energy storage and alternative fuels are mentioned as viable options which, in turn, would leave a relatively small role for offsets and carbon credits to play.

The report comes as the mining industry is under ever-greater pressure to produce essential minerals that support some of the biggest global structural trends, the study reads. Copper, nickel, steel and lithium are core components of electricity transmission and storage, electric vehicles and renewable energy infrastructure. The move to a decarbonized economy will result in increased primary consumption of these mined commodities, even after factoring for recycling, so it is important that mining itself becomes more sustainable.

five barite mining processes and common barite mining equipment - xinhai

five barite mining processes and common barite mining equipment - xinhai

Barite is a common mineral of barium, and the elements of barite are barium sulfate, the BaO accounts for 65.7% and SO3 accounts for 34.3%. It is an important industrial raw mineral material for making barium and barium compounds.

Taking the barite deposit in China as an example, it can be divided into four types: sedimentary deposit, volcano-sedimentary deposit, hydrothermal deposit, and residual deposit. According to ore types, raw ore properties, mine scale and application, the common barite mining processes mainly include handpicking, gravity separation, flotation, magnetic separation and combined barite mining process.

In general, the residual barite ore can be separated by the gravity separation method. The sedimentary barite ore and hydrothermal barite ore associated with sulfide ore and fluorite can be extracted by the gravity separation and flotation method. Below, we will explain to you one by one each barite mining process and barite mining equipment.

After the raw ore is extracted, simple handpicking is a common barite mining process for many small barite mines. Some barite mines have a high geological grade and stable quality, so the qualified barite products can be selected by handpicking according to the difference in the color and density between barite and associated minerals. For the rich barite ore selected by hand, the grain size shall be 30-150mm, and the BaSO4 shall be larger than 95%, generally larger than 92%. In general, the simple handpicking process is easy to operate and can be carried out without barite mining equipment. This barite mining process is suitable for the small-scale barite mining plant, but it also has the disadvantages of low productivity, high ore grade requirements and serious resource waste.

This barite mining process is mainly based on the density difference between barite and associated minerals, including ore washing, screening, desliming, jigging, shaking table, which is mostly used to treat residual barite ore.

After the raw ore is treated with washing, screening, crushing, classification and desliming, the high-quality barite concentrate can be obtained by jigging and shake table. The crushing stage adopts the jaw crusher and impact crusher, the fine crushing stage adopts the double roll crusher. The separation stage adopts a heavy medium rotary drum separator, cone classifier, jig or shake table. The heavy medium separation and jigging separation can be used when the particle size of barite is larger than 2mm, but the upper limit of particle size of heavy medium separation is 50mm, and the upper limit of wet and dry jigging separation is about 20mm. The shake table can be used for separation when the particle size of the barite is less than 2mm, but he hydrocyclone must be used to remove the mud before the separation, so as to improve the separation effect.

The specific gravity of the barite is large, generally 4-4.6, and it also has a good floatability. The barite flotation process is mainly used to separate the sedimentary barite and hydrothermal barite ore associated with sulfide ore and fluorite based on the difference of surface physical and chemical properties between barite and associated minerals.

Taking the barite ore in China as an example, the barite deposits are featured with more lean ore and less rich ore. More than 80% of the proven reserves are associated with other minerals. The flotation method must be used for the separation of fine barite ore and gravity separation tailings. The main barite flotation processes are positive flotation process and reverse flotation process, in which the purpose of the reverse flotation process is to remove the alkali sulfide.

As a common salt mineral, the barite flotation process can be divided into two types according to the form of adsorption: one is to use fatty acid alkyl sulfate, alkyl sulfonate and other anion collectors to adsorb on the surface of barite mineral in the form of chemical adsorption, so as to separate from the other associated minerals. Another is to use an amine collector to float barite in the form of physical adsorption. The amines collector has low efficiency and is very sensitive to the influence of slime, so it is suggested to adopt the anionic collector in this barite mining process. In general, add the NaOH to the ball mill, adjust the pH value to 8-10, and add the sodium silicate to the slurry as the regulator, then use the oleic acid collector in the barite flotation process under the condition of 40%-50% solid concentration.

The barite magnetic separation process is mainly based on the surface magnetic difference between barite and iron oxide minerals, which is widely used to separate the iron-containing barite. The magnetic separation process is often used as the combined process with gravity separation, which can produce the barite a material of barium-based drugs requiring very low iron content.

The combined barite mining process of gravity separation and flotation is often used for the barite associated with sulfide ores. For the flotation-gravity separation process used to treat the barite-quartz-calcite ore, add the sodium silicate and collector into the flotation tank of flotation machine for removing the quartz, and obtain the barite-calcite mixed ore, and adopt jig, shake table to obtain the barite concentrate based on the density difference between gangue minerals (such as calcite) and barite.

The early mining barite ore is mostly high-grade ore, which is usually separated by the low-cost and low technical handpicking or gravity separation process. However, with the low grade of barite ore and the complex ore properties, magnetic separation, flotation and combined barite mining processes have been widely used in barite mining. It is suggested that each mine owner should make a scientific and reasonable barite mining process according to the beneficiation test report, avoiding unnecessary economic losses.

what are the quartz mining processes? - xinhai

what are the quartz mining processes? - xinhai

Quartz stone, also known as silica sand, is a common non-metallic material, which can be made into high-purity quartz sand after separation and purification, widely used in glass, ceramics, metallurgy, casting and refractories and other industries. So, what are the common quartz mining processes at present?

Usually, there are iron oxide, clay, mica, organic impurities, etc., in the quartz stone except for SiO2. The purpose of the quartz mining process is to remove a small amount or trace impurities in quartz stone, then obtain the refined quartz stone. At present, the common quartz mining processes mainly include the physical quartz mining process and the chemical quartz mining process. Among them, the physical quartz mining process includes washing, classifying and desliming process, scrubbing process, magnetic separation process and flotation process. The chemical quartz mining process is the acid leaching process.

The grade of SiO2 in the quartz stone is reduced with the thinner of quartz grain size, while the grade of mineral impurities (such as iron and aluminum impurities) is increased, this kind of phenomenon is especially obvious in the quartz stone containing a lot of the clay minerals. Therefore, the spiral washing machine, drum sieve, hydrocyclone, desliming bucket and hydraulic classifier are often used to the water concentration of quartz stone, it is very necessary to carry out the classification and desliming in the quartz processing plant. As a pretreatment method before the ore separation, the washing, classifying and desliming are applied earlier and widely in the quartz washing plant, but this quartz mining process doesn't have the obvious removal effect for the thin-film iron and adhesive impurity minerals on the surface of quartz stone.

Scrubbing process is mainly to remove the thin film iron, bond and muddy impurity mineral on the surface of quartz stone with the help of mechanical force and the grinding force among sand particles, and further wipe up the non-monolithic mineral aggregation, and then achieve the further quartz processing effect through the classification operation.

At present, the quartz stone scrubbing process mainly includes rod friction washing and mechanical scrubbing. For the mechanical scrubbing method, the factors affecting the scrubbing effect are mainly the structural characteristics and configuration form of the mineral scrubber, followed by technological factors (scrubbing time and scrubbing concentration).

The study shows that the scrubbing concentration of quartz stone shall be between 50% and 60%, but it also increases the difficulty of quartz processing to some extent. In principle, the scrubbing time shall be based on the requirements of preliminary product quality, not too long. Too long scrubbing time will increase the equipment wear, improve energy consumption, and increase the cost of beneficiation. For some quartz minerals, the mechanical scrubbing and wiping effect are not ideal, adding the reagents when necessary can increase the electrical repulsion on the surface of impurity minerals and quartz particles, enhance the separation effect between impurity minerals and quartz particles.

The magnetic separation process can remove the weakly magnetic impurity minerals as possible, such as hematite, limonite and biotite, etc. High-intensity magnetic separation usually adopts the wet high-intensity magnetic separator or high gradient magnetic separator. Generally speaking, the wet high-intensity magnetic separator (large than 10000 Oe) can deal with the quartz containing the weakly magnetic impurity minerals (such as limonite, hematite and biotite). It is better to use a weak magnetic separator or a medium magnetic separator to separate the quartz containing the strong magnetic impurity minerals (magnetite).

The studies show that the frequency of magnetic separation and magnetic field strength has an important effect on the iron removal effect of the magnetic separation process. With the increase of magnetic separation times, the iron content is gradually decreased. Under a certain magnetic field strength, most of the iron can be removed, while the iron removal rate has a little change even if the magnetic field strength is larger than the certain limit. In addition, the finer the particle size of quartz sand, the better the iron removal effect. The reason is that the fine quartz sand contains a high amount of iron impurity minerals. When there are more impurity minerals in the quartz sand, it is impossible to purify the quartz sand into high purity sand only by scrubbing, desliming and magnetic separation.

The flotation process is mainly to remove the non-magnetic associated impurities in quartz sand, such as feldspar, mica. The quartz sand flotation process mainly includes fluorine flotation and fluorine-free flotation process. Among them, the fluoride flotation process is carried out in the acidic pH range with the cationic collector and hydrofluoric acid activator. But the fluorine-containing wastewater causes serious environmental pollution, which needs to be discharged after treatment. The fluorine-free flotation process is to take advantage of the differences in quartz and feldspar structure, rationally mix the ratio and dosage of anion and anion mixed collector, and make use of their different Zeta potentials to preferentially float the feldspar and achieve the separation.

The acids commonly used in the acid leaching process mainly include sulfuric acid, hydrochloric acid, nitric acid and hydrofluoric acid, the reducing agents mainly include sulfurous acid and its salts. The study shows that these acids have a good removal effect on the non-metallic impurities of quartz mineral. But the acid type and concentration have a significant effect on the different metal impurity. Generally, a variety of dilute acid has a significant effect on the removal of Fe and Al, while the relatively concentrated sulfuric acid and aqua regia or HF are often used in the removal of Ti and Cr. The mixed acid composed of the above acids is usually used for the acid leaching removal of impurity minerals. Since the HF has the dissolution effect on the quartz, the HF concentration is generally not more than 10%. In addition to the acid concentration, the acid amount, leaching time, temperature and slurry agitation can also affect the effect of acid leaching effect of quartz. The control of various factors shall be determined based on the final grade requirements of quartz, like reduce the concentration, temperature and dosage of acid as far as possible, decrease the time of acid leaching, so as to realize the quartz processing at a lower cost.

Due to the different amount, type and occurrence of impurities contained in quartz sand and different product quality requirements, a single quartz mining process may not be able to achieve the purpose of quartz sand purification, and sometimes the several quartz mining processes are required to form a combined beneficiation process. The common quartz mining processes are as follows:

In the weathering sedimentary and mineralization process of quartz sand, a large number of clayey minerals and iron form the cementation or adhesion minerals on the surface of the quartz. It is a common quartz mining process to adopt the scrubbing-classifying- desliming process to remove clay impurity minerals, argillaceous iron, and some thin-film iron. This quartz mining process is generally used as a pretreatment process before raw ore separation, which can effectively remove the argillaceous impurities.

Generally, the common impurity minerals in the quartz (such as the limonite, tourmaline, hematite and biotite and other weakly magnetic minerals, magnetite and other strongly magnetic minerals) can only be removed by a magnetic separation process. In the actual production, the wet strong magnetic separator with 13000 Oe of magnetic field strength is mostly used for separation.

After the washing, magnetic separation and flotation separation of raw ore, the impurity mineral particles with low occurrence (including monomer, aggregate) are basically cleared, the silica purity can generally reach 99.5%-99.9%, basically can meet the majority of industrial requirements of quartz sand. However, in order to obtain the ultra-high purity quartz, the impurities continuously adhere to the surface of quartz particles in the form of spots and inclusions must be treated with acid leaching. The mixed acid leaching with different concentrations and matching must be carried out according to the different requirements of impurity minerals (Fe, AL, Ti, Cr) in the different industries. The high purity quartz with silica content of 99.99% or more can be obtained by the rod milling washing - desliming - magnetic separation - flotation - acid leaching process.

By improving the structure of existing mineral scrubber, optimizing the technical parameters, add reagents for high-efficiency and strong scrubbing classifying and desliming can remove more than 80% of impurity iron and aluminum ore. The magnetic separation process is mainly to remove the iron-contained impurity minerals. Through this quartz mining process, the high-quality refined quartz sand (silicon dioxide 99.8%, iron oxide 0.023%, aluminum oxide 0.05%, titanium dioxide 0.02%) can be obtained, which has reached the requirements of primary optical glass sand, and the yield of fine quartz sand is as high as 73%, while the yield of rod friction washing is only 49%. After further flotation and acid leaching process, the high-purity quartz (silica 99.9%, ferric oxide 0.005%, aluminum oxide 0.05%, titanium dioxide 0.02%) can be obtained. This quartz mining process overcomes the shortcomings of secondary contamination of iron caused by rod friction washing and low yield.

In the practice, the selection of the quartz mining processes and flows is often determined based on the nature of quartz stone, conditions of the quartz processing plant, investment budget. It is suggested to choose a single or joint quartz mining process through the mineral processing test report, thus striving for the ideal technical and economic benefits.

7 different types of mining processes newsmag online

7 different types of mining processes newsmag online

Mining is used to extract many highly used resources in the world today. Mining unveils valuable minerals like coal, which is a source of electricity and heat, as well as in the manufacturing of cement and steel. As such, these minerals must be mined to supply the high demand for this resource, and there are many ways to do so.

Most mining operations combine different methods to extract minerals most efficiently. Initially, the mineral closer to the surface would be mined using a surface mining method. After the minerals have been exhausted that way, the coal found deeper in the seam would be mined using an underground mining method. This allows for the most minerals in an area to be extracted, using the most efficient methods.

The type of mining is based off of a variety of factors. The thickness and depth of the coal seam are very important in deciding which method will be used, as well as the terrain that is found on the surface above the coal. Economic factors also come into play, as some methods are more or less costly than others.

There are two main types of mining every method can be divided into either surface or underground. Logically, surface mining is used to gain access to minerals located closer to the earths surface. The place where minerals can be found is often called a seam. A seam is considered shallow if it is located closer to the earths surface, and deep if it is located further away.

Underground mining can be used for minerals in more shallow seams, but is more often used for deeper deposits.In order to access the minerals underneath, blasting is done. Blasting is an key part of surface mining, as it helps to prepare the terrain for mining. The rocks are broken up using explosives, and then cleared away using machinery.

The shaft may be an exact vertical or a near vertical drop into the mine, and an elevator or lift is installed to bring workers into and out of the mine. The mine will extend from this initial spot in all directions.

Also known as open pit mining, this is a method of surface mining where a large pit is made in the ground, and the coal is mined directly from that pit. This is a great method to use for a shallow coal seam and it is extremely productive.

Using specialized machines, longwall mining is an underground mining method that cuts out panels of the coal, which are then sheared down and brought to the surface. This method is highly mechanized, and is therefore a very safe type of mining.

It is a highly productive method as well, with a very low cost per ton as compared to some other methods. However, longwall mining does require highly specialized machinery, which can be very costly to start off. It also doesnt allow for very great selection of the grade of the ore.

The title given to room and pillar mining is very fitting, as it is an underground method of clearing out coal by creating roomlike spaces. Some material is left in pillars, which helps to support the roof so that ore around it can be removed.

These types of mining offer good ventilation a sought after feature in underground mining and high productivity. Its not a very flexible method, and doesnt offer a lot of selectivity, however it is better than some other methods. The room and pillar method does, however, limit the depth at which the ore can be mined.

With hydraulic coal mining, water is pushed at high pressure against the ores surface, breaking away the chunks bit by bit. The water, with the bits of coal in tow, are then pushed through a pipe to the surface, where the coal is filtered out of the water.

One of the major advantages of hydraulic coal mining is that the water minimizes the chances of fire and explosion, and it reduces the amount of dust, making it a safer and more healthy working environment. However, power consumption for these types of mining is very high.

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