Optimization studies on a coal preparation plant

Coal washing and preparation is a critical process for cleaner and more efficient coal utilization, while safety performance in coal preparation plants is often challenged by harsh operating conditions, tightly coupled equipment, and limited effectiveness of manual supervision. In this study, a coal preparation plant was used as an engineering scenario to investigate equipment safety risk grading and a deployable workflow for risk-based monitoring and on-site warning. Hazardous factors in key processes were identified through accident analysis and field investigation, and a multi-dimensional risk assessment model was established by integrating Fault Tree Analysis (FTA) and the Analytic Hierarchy Process (AHP). To enhance real-time supervision in high-risk areas, an optimized YOLOv10-based object detection model was developed, improving robustness under heavy dust, low illumination, and complex backgrounds. On this basis, a personnel safety monitoring system integrating monocular vision-based ranging and audible–visual alarms was implemented to enable real-time warning and traceable logging. Experimental and field results show that, under the reported deployment conditions, the proposed system achieved. These results suggest that risk grading can be combined with vision-based monitoring to support risk-informed warning and traceable logging in coal preparation plants.

Coal preparation can remove most of the impurities in raw coal, reduce ash, and improve coal quality. Reducing the ineffective transportation of vermiculite, reducing the environmental pollution caused by burning coal, and providing the basic guarantee for the further clean and efficient use of coal are the foundation of clean coal technology. The domestic status of the automation technology of the coal preparation plant is introduced, and the issues needing attention in the automation technology of the coal preparation plant in China are introduced. Briefly describe the main contents of the current coal preparation plant automation technology and the issues needing attention in the control loop, and the possible consequences of an unreasonable design of the coal preparation plant automation system. By analyzing the development trend of the automation technology of the coal preparation plant, the broader development prospects of optimal control and video surveillance substation technology are expected. The scientific development of the coal preparation plant is to make full use of advanced coal preparation technologies, processes and equipment, improve the raw coal selection rate, continuously improve and improve the process, develop and introduce new efficient and high-yield washing equipment, and improve automation, professionalism, and informationization. And intelligence

Coal preparation is an integral step in the process of mining, preparing, transporting and utilizing coal and should be employed to gain maximum benefits from our coal resource. The desired degree of coal preparation depends on the estimated costs and potential benefits associated with the entire process. Three hypothetical coal preparation plants have been designed to provide a basis for preliminary cost estimates. These are a crushing and screening plant, a Baum jig plant, and a heavy-media plant. In addition, hypothetical facilities for raw coal receiving and breaking, coal stockpiling and blending, prepared coal loadout, and support facilities are included. The design of a preparation plant is based on detailed knowledge of the properties of run-of-mine coal and the feedstock specifications for the conversion plant. Little published data is available for either area to facilitate forecasting the range of future coal preparation plant designs. Based on the estimated capital cost of the hypothetical plants, factors were developed that can be used to estimate the capital costs of plants of differing production capacity. 44 references. (auth)

Sources of air pollution due to coal mining and their impacts in Jharia coalfield

The increased cleaning required to provide coal which will meet present-day market requirements has greatly expanded the use of processing equipment in coal preparation plants. This places increased emphasis on the selection of the electric equipment to obtain the utmost in safety, reliability, and efficiency. The various phases of the electric system should be considered early in the planning stages so that the location of the mechanical and electric equipment may be properly co-ordinated.

Most of the geostatistical studies and applications in coal to date have focused on raw coal and clean coal at a single float gravity (generally 1.50 s.g.). In addition, most of the academic research has been done on ash/recovery prediction from core hole washabilities using geostatistics; sulfur prediction is still a problem.Recently interest has developed in combining geostatistics and coal preparation plant simulation to predict clean coal qualities (Armstrong et al. 1989). The basic concept is that geostatistics would provide statistically realistic models of recovery, ash content, sulfur content, etc. of the coal deposit for each washing (separation) density. These washing characteristics could then be fed into a preparation plant simulator to forecast the clean coal quality. The combining of geostatistics with preparation simulation shows great promise, but the procedure described above in mixing these two technologies would be very difficult, if not impossible, to apply on a regular basis to predict clean coal qualities. The difficulty arises from the fact that the coal is cleaned by size and by gravity. The geostatistical models must consider all the cleaning gravities of the plant, as well as the size fractions of the run-of-mine coal, which could be different than the size consist of the core hole data.This paper proposes a modification to the above approach that would make the clean coal prediction more practical and applicable. Using this method, the preparation plant simulation would first be run using the core hole washabilities data to estimate clean coal parameters at each core hole location; geostatistics would then be applied on the estimated clean coal parameters to predict qualities of a small coal block. This proposed technique is being applied in an underground coal mine operated by the Island Creek Coal Company and the results are discussed here.KeywordsCore HoleUnderground Coal MineClean CoalPreparation PlantSeam ThicknessThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The Potential for Automation and Process Control in Coal Preparation

Development history of coal mines and advanced mining technology in Japan

A review of state-of-the-art processing operations in coal preparation

Analysis of a coal preparation plant. Part 2. Effect of water quality on flotation performance

Kuwait to lead the Middle Eastern refinery capacity growth by 2024.

All Canadian coal destined for export abroad or to distant domestic markets must be beneficiated to remove mineral matter and to produce a high-quality, uniform, saleable, thermal or coking coal product. The geological setting in which coal is mined in Canada varies in the extreme; from steep, thick, often contorted seams in the mountains of western Canada to more uniform seams in eastern Canada, comparable to those found in Europe, except that most eastern Canadian production comes from undersea mines. Coal preparation problems in Canada, which are likewise complex, centre on fine coal losses in western Canada and sulphur in eastern Canada. The 14 coal preparation plants described in the report have been grouped into four geographical regions: the Wolverine area in northeastern British Columbia, the East Kootenay area in southeastern British Columbia, the Yellowhead area in west central Alberta, and Cape Breton in Nova Scotia (Fig. 1). Individual plants are described in terms of their geographical setting and transportation facilities (Fig. 2), special technological features, and a flowsheet subdivided into four parts: pretreatment of feed coal, cleaning, subsequent treatment of products, and storage and loading.

Coal preparation is effective in controlling primary mercury emissions in coal combustion systems; however, the combustion of coal preparation byproducts may cause secondary emissions. The inconsistent coal preparation statistics, unclear mercury distribution characteristics during coal preparation, and limited information regarding the byproduct utilization pathways lead to great uncertainty in the evaluation of the effect of coal preparation in China. This study elucidated the mercury distribution in coal preparation based on the activity levels of 2886 coal preparation plants, coal mercury content database, tested mercury distribution factors of typical plants, and then traced the mercury flows and emissions in the downstream sectors using a cross-industry mercury flow model. We found that coal preparation altered the mercury flows by reducing 68 tonnes of mercury to sectors such as coking and increasing the flows to byproduct utilization sectors. Combusting cleaned coal rather than raw coal reduced the mercury emissions by 47 tonnes; however, this was offset by secondary mercury emissions. Coal gangue spontaneous combustion and the cement kiln coprocessing process were dominant secondary emitters. Our results highlight the necessity of whole-process emission control of atmospheric mercury based on flow maps. Future comprehensive utilization of wastes in China should fully evaluate the potential secondary mercury emissions.

Coal-separating is the most important procedure in the coal industry with the highest value-added rate.It will have vital significance to the coal industry tooptimize coal preparation plant’s value chain, control its cost and improve its efficiency.Based on this,this article first establishes the coal preparation plant’s internal value chain model using the quantitative analysis method;Then the author deduces comprehensive weights of each index in the model by the analytic hierarchy process (ahp);Nextthe article establishes a dynamic evaluation mathematical model of coal preparation plant’s e internal value chain combining with fuzzy linear programming and the time factor;At last the optimization model of coal preparation plant’s e internal value chain is calculated,which provide a quantitative and dynamic cost control strategy for coal preparation plant. Research shows that the quantitative analysis of enterprise internal value chain, provides managers with a quantitative analysis of the enterprise cost control from the perspective of value chain of the basis, and scientifically builds a fuzzy linear programming model, and achieves the overall interests of the value chain optimization. Keywords-Cost control; value chain; analytic hierarchy process(ahp); fuzzy linear programming

The appropriate selection and optimization of flotation reagents are essential for enhancing the quality of flotation coal, reducing reagent consumption, and lowering production costs in coal preparation plants. Currently, most coal preparation plants utilize compound reagents in their production processes. However, these compound reagents often fail to optimize both bubble formation and collection efficiency simultaneously, resulting in excessive reagent consumption during production. This study uses the compound reagent (XZ) employed by Xinzheng Clean Coal Company as a case study to thoroughly investigate the causes of its high reagent consumption and to perform optimization experiments. First, the differences in flotation performance between the XZ compound agent and the kerosene-sec-octanol system were compared. Next, the causes of high XZ agent consumption were identified using infrared spectral analysis, and finally, the response surface methodology (RSM) was applied to optimize the reagent system. Experimental results indicated that the effectiveness of the XZ agent primarily depended on its ester and ether groups, although its foaming performance was inadequate. The addition of sec-octanol significantly enhanced its foaming performance. Response surface optimization revealed that when the XZ agent dosage was 1000 g/t, sec-octanol dosage was 275 g/t, and slurry concentration was 90 g/L, the recovery of combustible matter peaked at 98.95%. When the XZ agent dosage was 997.33 g/t, sec-octanol dosage was 191.27 g/t, and slurry concentration was 60 g/L, the flotation perfection index reached its highest value of 58.12%. Further analysis revealed that a moderate froth layer height improves flotation performance, whereas excessively high or low froth layers negatively impact the flotation performance.