Grade and Tonnage Model of Contact Metasomatic Copper Deposit in China

Grade-tonnage model is one of the research frontiers of systematical exploration theory. Based on the “Reserve Database of Mineral Resources in China (1997)”, this paper establishes the geological model, grade model, tonnage model, grade-tonnage model and tonnage-sequence model of contact metasomatic copper deposits in China. The mathematical properties of these models are described in detail.

Grade-Tonnage Model of Porphyry Copper Deposits of China

A grade-tonnage model is established according to the analysis of 72 porphyry copper deposits recorded in 'The Mineral Resources Data Base of China'. Based on the analysis of frequency histogram, the cumulative frequency distributing graph and the theoretical model with double logarithmic coordinates of copper deposits, the typical mathematical characteristics of grade-tonnage model of porphyry copper deposits are clarified.

Grade-Tonnage, Ore Value-Tonnage, andEnrichment Ratio-Tonnage Models forResource Assessment

According to grade-tonnage diagrams of nickel and zinc deposits, their critical grades are 0.4 % and 3. 4 %, respectively, and hence the former resources can be considered optimistic and the latter pessimistic. The grade-tonnage diagram of gold deposits is convex downwards suggesting that the critical grade is 1 X 10-6 in the low-grade part. The ore value (OV)-tonnage diagram of all deposits In the world consists of three parts: high, middle and low vain f classes. The enrichment ratio (ER)-tonnage diagram of all deposits in the world ho consists of three parts: high, middle and low ratio classes.Nine quality categories defined by ER and OV are characterized by some keywords indicating deposit types as follows: category RH (high ER-high OV: 0. 7 %) by 'unconformity' and 'Mississippi Val-ley', category HM (high ER-middle: OV: 0.7 %) by 'vein', category ML (middle ER-low OV: 0 %) by 'sandstone', 'stockwork' and' dissemination', category LM by 'orthomagmatic',' laterite',komatiite and ' chemical', and category LL by 'porphyry', 'dissemination' and 'placer'. Category MM is not characterized by any keyword. If the commodities of a deposit are defined by both the enrichment ratio and the ore value, the defined commodities are relatively coincident for gold and nickel,but different for copper, silver and zinc, and greatly different for molybdenum and lead. Deposits containing lead and/or zinc are complimentary. If the commodity Ph+Zn is applied, most lead or zinc deposits are classified as Ph+Zu by both definitions. Accessory metals are commonly expected for deposits of kuroko-type zinc, epithermal silver, massive sulfide-type zinc and volcanogenic zinc, but uncommon for deposits of orthomagmatic chromium, chemically precipitated copper and sandstone-type uranium.

Maize Cultivation Techniques in Tonnage Farmlands in Tunliu County of Shanxi

In recent years,benefiting from the unique natural conditions and the promotion and application of agricultural science and technology,the level of maize production in Tunliu County continues to increase,and many tonnage farmlands are emerging. East Ligao Village in Ligao Town is famous for its tonnage farmlands throughout the county. Through a lot of research,maize cultivation techniques in tonnage farmlands are summed up in this paper from the aspects of plot selection,intensive cultivation,high-quality manure application,chemical fertilizers scientific application,disease-resistant high-yield superior varieties selection,reasonable close planting and strengthening field management,etc,which has certain guiding significance for the high yield of maize.

Some Suggested Future Directions ofQuantitative Resource Assessments

Future quantitative assessments will be expected to estimate quantities, values, and locations of undiscovered mineral resources in a form that conveys both economic viability and uncertainty associated with the resources. Historically, declining metal prices point to the need for larger deposits over time. Sensitivity analysis demonstrates that the greatest opportunity for reducing uncertainty in assessments lies in lowering uncertainty associated with tonnage estimates. Of all errors possible in assessments, these affecting tonnage estimates are by far the most important. Selecting the correct deposit model is the most important way of controlling errors because of the dominance of tonnage-deposit models are the best known predictors of tonnage. Much of the surface is covered with apparently barren rocks and sediments in many large regions. Because many exposed mineral deposits are believed to have been found, a prime concern is the presence of possible mineralized rock under cover. Assessments of areas with resources under cover must rely on extrapolation from surrounding areas, new geologic maps of rocks under cover, or analogy with other well-explored areas that can be considered training tracts. Cover has a profound effect on uncertainty and on methods and procedures of assessments because geology is seldom known and geophysical methods typically have attenuated responses. Many earlier assessment methods were based on relationships of geochemical and geophysical variables to deposits learned from deposits exposed on the surface-these will need to be relearned based on covered deposits. Mineral-deposit models are important in quantitative resource assessments for two reasons: (1) grades and tonnages of most deposit types are significantly different, and (2) deposit types are present in different geologic settings that can be identified from geologic maps. Mineral-deposit models are the keystone in combining the diverse geoscience information on geology, mineral occurrences, geophysics, and geochemistry used in resource assessments and mineral exploration. Grade and tonnage models and development of quantitative descriptive, economic, and deposit density models will help reduce the uncertainty of these new assessments.

A new generic open pit mine planning process with risk assessment ability

Conventionally, mining industry relies on a deterministic view, where a unique mine plan is determined based on a single resource model. A major shortfall of this approach is the inability to assess the risk caused by the well-known geological uncertainty, i.e. the in situ grade and tonnage variability of the mineral deposit. Despite some recent attempts in developing stochastic mine planning models which have demonstrated promising results, the industry still remains sceptical about this innovative idea. With respect to unbiased linear estimation, kriging is the most popular and reliable deterministic interpolation technique for resource estimation and it appears to remain its popularity in the near future. This paper presents a new systematic framework to quantify the risk of kriging-based mining projects due to the geological uncertainties. Firstly, conditional simulation is implemented to generate a series of equally-probable orebody realisations and these realisations are then compared with the kriged resource model to analyse its geological uncertainty. Secondly, a production schedule over the life of mine is determined based on the kriged resource model. Finally, risk profiles of that production schedule, namely ore and waste tonnage production, blending grade and Net Present Value （NPV）, are constructed using the orebody realisations. The proposed model was applied on a multi-element deposit and the result demonstrates that that the kriging-based mine plan is unlikely to meet the production targets. Especially, the kriging-based mine plan overestimated the expected NPV at a magnitude of 6.70% to 7.34% （135 M$ to 151 M$）. A new multivariate conditional simulation framework was also introduced in this paper to cope with the multivariate nature of the deposit. Although an iron ore deposit is used to prove the concepts, the method can easily be adapted to other kinds of mineral deposits, including surface coal mine.

Standardization of the Main Dimensions of Design Container Ships in Ports—A Case Study

Ports are crucial element in the export and import chains of each country. Therefore, sufficient and efficient designing of ports is of great importance to increase the safety of operations and to lower the construction costs. Their design procedure is iterative and specifications of the design ship are of the most important inputs in this regard. The design ship is characterized with its length (L), breadth (B), and draft (d). In this research a regression analysis has been carried out in the southern coasts of Iran (Persian Gulf) to obtain these specifications. The results have shown under- and over-estimations of the main dimensions. Hence, this supports the idea that it is worth performing individual regression analysis, especially in restricted waterways and in the regions with specific fleet for the sake of verification of recommended values by standard technical guidelines.

The Supportive Shipping Policies in Late Qing Dynasty： A Comparative Study between China and Foreign Countries

This study makes a comparison between China and foreign countries about the ＂supportive shipping policies＂ in the period of late Qing Dynasty, from 1840 to 1911. After the first opium war, China was forced to open the gate of the old oriental country to the world. As more treaty ports opened, the import and export of goods increased the growing trade that brought about the increased demands of shipping transportation. In the same period, British, France, America, Germany and Japan governing bodies instated various kinds of laws, regulations and other policies encouraging their national shipping companies to grab shipping market share. As a result, foreign steamship companies monopolized Chinese shipping market quickly. Faced with this situation, in 1872, the China Merchants＇ Steam Navigation Company was the fisst steamship company of China to be formed. It was a government-supervised and merchant-managed company, and the Qing Dynasty government took a series of measures to support it under the fierce competition, these measures were different from foreign countries＇.

Remote condition monitoring of rail tracks using distributed acoustic sensing(DAS):A deep CNN-LSTM-SW based model

Railroad condition monitoring is paramount due to frequent passage through densely populated regions.This significance arises from the potential consequences of accidents such as train derailments,hazardous materials leaks,or collisions which may have far-reaching impacts on communities and the surrounding areas.As a solution to this issue,the use of distributed acoustic sensing(DAS)-fiber optic cables along railroads provides a feasible tool for monitoring the health of these extended infrastructures.Nevertheless,analyzing DAS data to assess railroad health or detect potential damage is a challenging task.Due to the large amount of data generated by DAS,as well as the unstructured patterns and substantial noise present,traditional analysis methods are ineffective in interpreting this data.This paper introduces a novel approach that harnesses the power of deep learning through a combination of CNNs and LSTMs,augmented by sliding window techniques(CNN-LSTM-SW),to advance the state-of-the-art in the railroad condition monitoring system.As well as it presents the potential for DAS and fiber optic sensing technologies to revolutionize the proposed CNN-LSTM-SW model to detect conditions along the rail track networks.Extracting insights from the data of High tonnage load(HTL)-a 4.16 km fiber optic and DAS setup,we were able to distinguish train position,normal condition,and abnormal conditions along the railroad.Notably,our investigation demonstrated that the proposed approaches could serve as efficient techniques for processing DAS signals and detecting the condition of railroad infrastructures at any remote distance with DAS-Fiber optic cable setup.Moreover,in terms of pinpointing the train's position,the CNN-LSTM architecture showcased an impressive 97%detection rate.Applying a sliding window,the CNN-LSTM labeled data,the remaining 3%of misclassified labels have been improved dramatically by predicting the exact locations of each type of condition.Altogether,these proposed models exhibit promising potential for accurately identifying various railroad conditions,including anomalies and discrepancies that warrant thorough exploration.

Optimization and Simulation of Operation Performance in Crushing Plants Using Fuzzy Modelling

This research includes optimization of aggregate production of the stone crushing plant using fuzzy modelling.The investigation includes onsite aggregate testing and fuzzy logic implementation.Fuzzy modelling is a type of computerized reasoning used to simulate the real plant operation.In this work,a lot of agent degree information for crushers were reproduced using fuzzy logic.Fuzzy logic was then used to shape the information after a crusher process.Fuzzy logic is created to improve the final product gradation for the client.Strategies for utilizing the created fuzzy model is sketched out and could be utilized as a part of a representative preparing program or for informative purposes.About the tonnages anticipated by fuzzy,it is evident that the program does great in predicting the final product tonnage with an average of 13.7 % for just four samples.