8 results about "Coal chemistry" patented technology

The invention relates to the technical field of thickness measurement, and relates to a coal gelatinous layer thickness measurement system and method based on big data, and the method comprises the steps: obtaining coal chemical characteristic data of a to-be-detected coal sample at different temperatures; inputting the coal chemical characteristic data into a machine learning model to obtain the thickness of the coal sample to be detected; the training process of the machine learning model comprises the following steps: collecting coal chemical characteristic data of a plurality of coal samples at different temperatures; setting a plurality of neighborhood radiuses, and clustering the plurality of coal samples to obtain a cluster under each neighborhood radius; utilizing the improved CH index to evaluate the clustering clusters to obtain corresponding clustering quality indexes, and taking the cluster with the maximum clustering quality index and the corresponding thickness as a training set; and inputting the training set into a machine learning model for training to obtain a trained machine learning model. The detection efficiency of the gelatinous layer of the coal sample and the accuracy of the detection result can be improved.

The invention belongs to the technical field of coal, and particularly discloses an alkaline agent for chemical deep deliming of ultra-pure coal and a preparation method and application of the alkaline agent, and the alkaline agent is prepared from a quaternary ammonium alkali solution, a strong alkali solution, an NH3.H2O solution and a sodium fluoride solution according to the volume ratio of (2-3): (1-2): (1-2): (2-3). Compared with the prior art, the compound agent has high alkalinity and complexing capacity, and after deep deliming is conducted through the compound agent, clean coal with the ash content being 2% + / -0.2% can be prepared into ultra-pure coal with the ash content being lower than 0.2%; the preparation method of the alkaline compound agent is simple and easy to operate, the obtained product is high in quality, the coal ash content of the prepared ultra-pure coal is smaller than 0.2%, deep deashing of the coal is achieved, and further comprehensive utilization of the coal is facilitated.

The invention relates to a high-temperature coal ash oxygen carrier magnetic separation and heat recovery method and system, and belongs to the technical field of coal chemical chain gasification. The system comprises a circulating fluidized bed reactor (a fuel reactor and an air reactor), a gasifying agent supply device, a magnetic separation device, a cyclone separator, an air preheating heat exchanger, a condenser, a gas-liquid separator, a two-way material circulation control device and a heat energy recovery system. The system recycles heat of oxygen carrier particles by taking vapour of a gasifying agent as a fluidizing medium and sends the heat back to the reactor, and the stable self-heating operation of the system is maintained through a double-path material circulation control device. The two bottleneck problems of large mechanical loss of the oxygen carrier and high ash discharge heat loss in the chemical looping process are synchronously solved, heat in the system is recycled, the requirement for an external heat source is remarkably reduced, meanwhile, the oxygen carrier separation efficiency is improved, integrated operation of efficient separation of coal ash, oxygen carrier recovery and heat recovery is achieved, and the industrial production cost is reduced. And continuous and stable self-heating operation of the system is guaranteed under the condition that no extra large amount of heat supply is needed.

The application discloses a coal soluble organic matter molecular category content prediction method based on a solvent domain, relates to the field of coal chemical analysis, and comprises the following steps: S1, acquiring sample data and recording solvent domain identification; S2, performing component identification on each sample to obtain relative content; S3, merging component relative content into a molecular category content candidate set; S4, constructing a feature matrix with component relative content as input and performing data preprocessing; S5, performing unsupervised evaluation on the molecular category content candidate set; and S6, training a prediction model and outputting a content prediction result. The sample is grouped through sequential solvent sub-domain processing, component features are unified, and molecular categories are merged, unsupervised target category screening is performed in combination with principal component analysis and hierarchical cluster analysis, accurate prediction of the coal soluble organic matter molecular category content is realized, and data support is provided for clean and efficient utilization of coal and optimization of a conversion process.

The invention discloses a method for enriching exinite in coal, and relates to the technical field of coal chemistry and chemical engineering. The technical problems that in the prior art, dissociation efficiency on exinite is poor, and separation purity is low are solved. The method comprises the following steps: firstly, pretreating a sample, and crushing the pretreated sample; promoting dissociation of exinite and vitrinite in the coal sample by using a method of ultrasonic vibration, water bath heating and a sodium hydroxide solution with the concentration of 5%-10%; on the basis that the exinite and the vitrinite are completely dissociated, a density solution prepared from benzene and carbon tetrachloride is used for carrying out centrifugal separation on the exinite and the vitrinite; and after the centrifugation is finished, filtering out floats by using a filtering device, and drying the floats, thereby obtaining the product. The method disclosed by the invention has the advantages of simplicity in operation, high dissociation and enrichment efficiency on exinite and high separation purity.

The present invention relates to a tower continuous reaction device and a ash removal method thereof, the device comprises: a tower reactor, a material circulation unit, and a solid-liquid separation unit; the tower reactor comprises a solid raw material inlet, a raw material liquid inlet, a reaction material outlet, a first circulation liquid outlet, a first circulation liquid inlet, and a reuse liquid inlet; the material circulation unit comprises a circulation pipeline and a first circulation pump arranged on the circulation pipeline, the inlet and outlet of the circulation pipeline are respectively connected with the first circulation liquid inlet and the first circulation liquid outlet; the solid-liquid separation unit comprises a solid-liquid separator connected with the reaction material outlet, the liquid outlet of the solid-liquid separator is connected with the reuse liquid inlet through a return liquid pipeline, and a second circulation pump is arranged on the return liquid pipeline. The tower continuous reaction device of the present invention can realize the continuous production of coal chemical ash removal, the chemical ash removal efficiency is high, and the ash removal effect is good.

The present invention relates to the technical field of thickness measurement, and to a system and method for measuring the thickness of a coal colloid layer based on big data, including: obtaining coal chemical characteristic data of a coal sample to be detected at different temperatures; inputting the coal chemical characteristic data into a machine learning model to obtain the thickness of the coal sample to be detected; wherein the training process of the machine learning model includes: collecting coal chemical characteristic data of multiple coal samples at different temperatures; setting multiple neighborhood radii, clustering multiple coal samples, and obtaining clustering clusters under each neighborhood radius; evaluating the clustering clusters using an improved CH index to obtain corresponding clustering quality indicators, and using the cluster with the largest clustering quality indicator and its corresponding thickness as a training set; inputting the training set into a machine learning model for training to obtain a trained machine learning model. The present invention can improve the detection efficiency of the colloid layer of coal samples and the accuracy of the detection results.

The present application aims at providing a simple method for judging whether coal can generate mesophase, belonging to the technical field of coal chemistry, coal chemical industry, carbon material and heavy oil processing, which comprises the following steps: selecting bituminous coal as coal quality raw material, crushing and air drying; performing pyrolysis experiment on the coal to obtain DSC curve; analyzing whether exothermic peak exists in the DSC curve to judge whether mesophase can be generated. Comparing the simple judgment method with the carbonization experiment of coal quality and the picture analysis under the polarizing microscope to prove the feasibility of the method for directly judging whether coal can generate mesophase through simple thermogravimetric DSC experiment curve. The method is simple and convenient, and avoids the complex process of pyrolysis carbonization, tablet preparation and observation of small ball under the polarizing microscope.