Crushing method and device for preparing biomass particles
By obtaining particle size information of the crushing equipment and water content detection to generate control indicators, and activate the monitoring module for classification control, it solves the problems of low efficiency and unevenness in traditional biomass particle crushing, achieving more stable production and lower costs .
Patent Information
- Application Number
- CN202411161300.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-22
- Publication Date
- 2024-12-31
- Estimated Expiration
- 2044-08-22
AI Technical Summary
Traditional biomass particle crushing methods have problems such as low crushing efficiency, uneven particle size distribution, serious equipment wear and biomass adhesion, resulting in unstable production and increased costs, limiting the large-scale application of biomass particles.
By obtaining the inlet and discharge particle size information of multi-stage crushing equipment, configuring stable control parameters, and combining moisture content detection to generate crushing difficulty, equipment wear and adhesion indicators, activate the crushing monitoring module for classified over-level judgment and crushing control, ensuring each level of crushing Particle uniformity of the equipment.
It improves the uniformity of biomass particles crushing, reduces production costs, enhances production stability, and solves the problems of crushing inhomogeneity and equipment wear in traditional methods.
Smart Images
Figure CN118768074B_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the field of pulverization technology, and in particular to a pulverization method and device for preparing biomass particles. Background Art
[0002] As the global demand for renewable energy continues to grow, biomass energy, as a clean and renewable form of energy, is gradually gaining attention. As an important form of biomass energy, biomass pellets have the advantages of high energy density, high combustion efficiency, easy storage and transportation, and are widely used in heating, power generation and other fields. However, in the preparation process of biomass pellets, the crushing link is a crucial step, and its effect directly affects the quality and production efficiency of the final product. Traditional biomass crushing methods often have problems such as low crushing efficiency, uneven particle size distribution, severe equipment wear, and unstable production due to biomass adhesion. These problems not only increase production costs, but also limit the large-scale application of biomass pellets. Summary of the invention
[0003] The present application provides a pulverizing method and device for preparing biomass particles, which solves the technical problem of uneven pulverization of biomass particles in the prior art.
[0004] In view of the above problems, the present application provides a pulverizing method and device for preparing biomass particles.
[0005] In a first aspect of the present application, a comminution method for preparing biomass particles is provided, the method comprising:
[0006] The method comprises the steps of: obtaining the inlet and outlet particle size information of the multi-stage pulverizing device and configuring the multi-stage stable control parameters, wherein the inlet and outlet particle size information of the multi-stage pulverizing device is inversely proportional to the pulverizing level; detecting the moisture content of the biomass raw material after impurity cleaning, performing a crushing influence analysis based on the moisture content detection result, and generating a crushing difficulty index, an equipment wear influence index, and a biomass adhesion index; judging whether the crushing difficulty index, the equipment wear influence index, and the biomass adhesion index meet the preset index, and if so, feeding the biomass raw material into the multi-stage pulverizing device through a feeding device, performing crushing control according to the multi-stage stable control parameters, and activating a crushing monitoring module; monitoring and identifying the discharge particle size of the first-stage pulverizing device through the crushing monitoring module, and generating a first discharge particle size distribution feature; performing a classified and skipped-level judgment based on the first discharge particle size distribution feature, and classifying the crushed particles output by the first-stage pulverizing device based on the judgment result, generating a first crushed particle sent to the second-stage pulverizing device and a second crushed particle sent to the skipped-level pulverizing device; and sending the first crushed particle and the second crushed particle to the corresponding pulverizing device for crushing.
[0007] The second aspect of the present application provides a comminution device for preparing biomass particles, the device comprising:
[0008] A parameter configuration component, the parameter configuration component is used to obtain the inlet and outlet particle size information of the multi-stage pulverizing equipment and configure the multi-stage stability control parameters, wherein the inlet and outlet particle size information of the multi-stage pulverizing equipment is inversely proportional to the pulverizing level; an analysis component, the analysis component is used to detect the moisture content of the biomass raw material after impurity cleaning, perform crushing influence analysis based on the moisture content detection result, and generate a crushing difficulty index, an equipment wear influence index, and a biomass adhesion index; a judgment component, the judgment component is used to judge whether the crushing difficulty index, the equipment wear influence index, and the biomass adhesion index meet the preset index, and if so, feed the biomass raw material into the multi-stage pulverizing equipment through a feeding device The crushing control is performed according to the multi-stage stable control parameters, and the crushing monitoring module is activated; an identification component is used to monitor and identify the output particle size of the first-stage crushing equipment through the crushing monitoring module, and generate a first output particle size distribution characteristic; a classification component is used to perform classification and skip-level judgment according to the first output particle size distribution characteristic, and classify the crushed particles output by the first-stage crushing equipment according to the judgment result to generate first crushed particles sent to the second-stage crushing equipment and second crushed particles sent to the skip-level crushing equipment; a crushing control component is used to send the first crushed particles and the second crushed particles to the corresponding crushing equipment for crushing.
[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0010] First, the particle size information of the input and output of the multi-stage pulverizing equipment is obtained, and the multi-stage stability control parameters are configured, wherein the particle size information of the input and output of the multi-stage pulverizing equipment is inversely proportional to the pulverizing level. Next, the water content of the biomass raw material after impurity cleaning is detected, and the crushing impact analysis is performed according to the water content detection result to generate the crushing difficulty index, equipment wear impact index and biomass adhesion index. Next, it is determined whether the crushing difficulty index, equipment wear impact index and biomass adhesion index meet the preset index. If so, the biomass raw material is fed into the multi-stage pulverizing equipment through the feeding device for crushing control according to the multi-stage stability control parameters, and the crushing monitoring module is activated; the discharge particle size of the first-stage pulverizing equipment is monitored and identified through the crushing monitoring module to generate the first discharge particle size distribution characteristics. Then, a classified leapfrog judgment is performed according to the first discharge particle size distribution characteristics, and the crushed particles output by the first-stage pulverizing equipment are classified according to the judgment result to generate the first crushed particles sent to the second-stage pulverizing equipment and the second crushed particles sent to the leapfrog pulverizing equipment. Finally, the first crushed particles and the second crushed particles are sent to the corresponding pulverizing equipment for crushing. The invention solves the technical problem of uneven crushing of biomass particles in the prior art, and achieves the technical effect of improving crushing uniformity through graded crushing control. BRIEF DESCRIPTION OF THE DRAWINGS
[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0012] Figure 1 A schematic flow chart of a comminution method for preparing biomass particles provided in an embodiment of the present application.
[0013] Figure 2 A schematic diagram of the structure of a pulverizing device for preparing biomass particles provided in an embodiment of the present application.
[0014] Explanation of reference numerals: parameter configuration component 11 , analysis component 12 , judgment component 13 , identification component 14 , classification component 15 , crushing control component 16 . DETAILED DESCRIPTION
[0015] The present application solves the technical problem of uneven pulverization of biomass particles in the prior art by providing a pulverization method and device for preparing biomass particles.
[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0017] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.
[0018] Embodiment 1, as Figure 1 As shown, the present application provides a pulverizing method for preparing biomass particles, wherein the method comprises:
[0019] The particle size information of the inlet and outlet of the multi-stage pulverizing equipment is obtained, and the multi-stage stability control parameters are configured, wherein the particle size information of the inlet and outlet of the multi-stage pulverizing equipment is inversely proportional to the pulverizing level.
[0020] The multi-stage crushing equipment is a device that gradually refines the biomass raw materials through multiple crushing cascades. Among them, the multiple crushing levels include primary crushing, secondary crushing and tertiary crushing. The primary crushing is to crush the biomass raw materials into 10-20mm particles by a hammer mill, the secondary crushing is to further crush the particles after the primary crushing to 2-4mm by a roller mill, and the tertiary crushing is to grind the particles after the secondary crushing to 80-200 mesh by a ball mill. The inlet and outlet particle size information of the multi-stage crushing equipment is inversely proportional to the crushing level, that is, as the crushing level increases, the outlet particle size gradually decreases to meet the crushing requirements of different levels. The inlet and outlet particle size information of the multi-stage crushing equipment is obtained by interacting with the multi-stage crushing equipment. The inlet and outlet particle size information refers to the particle size of the feed and outlet of the raw materials of the multi-stage crushing equipment at different crushing levels. According to the obtained inlet and outlet particle size information, the corresponding stable control parameters (such as speed, feed rate, etc.) are configured for the multi-stage crushing equipment to ensure the stability of the crushing process.
[0021] Furthermore, multi-level stability control parameters are configured, including:
[0022] The historical crushing records of any device in the multi-stage crushing equipment are collected based on the input and output particle size information to generate multiple groups of historical crushing records, wherein any group of historical crushing records includes crushing parameters and particle size distribution information after crushing; abnormality calculation is performed based on the particle size distribution information after crushing and the input and output particle size information to generate multiple groups of abnormality indicators; the crushing parameters corresponding to the minimum abnormality indicator are extracted according to the group of abnormality indicators, and added into the multi-stage stability control parameters.
[0023] Preferably, based on the particle size information of the input and output materials, the historical crushing records of any device in the multi-stage crushing equipment are collected to obtain multiple groups of historical crushing records, wherein any group of historical crushing records includes crushing parameters and particle size distribution information after crushing, the crushing parameters include rotation speed, feeding rate, etc., and the particle size distribution information after crushing refers to the particle size of the particles after crushing; based on the collected historical crushing records, the particle size distribution information after crushing of each group of records is compared with the expected or standard particle size distribution information to calculate the abnormality and generate multiple groups of abnormality indicators. The abnormality indicator is used to measure the degree of deviation between the actual crushing effect and the expected effect. The lower the indicator value, the closer the crushing effect is to the expected state; among all the calculated abnormality indicators, the crushing parameters corresponding to the minimum abnormality indicator are extracted. This group of parameters represents the control parameter combination that can achieve the best crushing effect under the current equipment conditions, and the extracted crushing parameters are added to the multi-stage stable control parameters.
[0024] Furthermore, based on the particle size distribution information after crushing and the particle size information of the input and output materials, the abnormality is calculated to generate multiple groups of abnormality indicators, including:
[0025] Based on the particle size distribution information after crushing and the input and output particle size information, the output particle size deviation is calculated to generate multiple groups of particle size deviation sets; based on the multiple groups of particle size deviation sets, the proportion of particles whose output particle size deviation is greater than the preset deviation is counted to generate the multiple groups of abnormality indicators.
[0026] Preferably, based on the particle size distribution information after crushing and the input and output particle size information, the deviation between the particle size after crushing and the output particle size is calculated to generate multiple groups of particle size deviation sets; by traversing multiple groups of particle size deviation sets, the proportion of particles whose output particle size deviation is greater than the preset deviation is counted, and the calculated proportion is used as an abnormality index, thereby generating multiple groups of abnormality indicators.
[0027] The moisture content of the biomass raw materials after impurity cleaning is tested, and the crushing impact analysis is performed based on the moisture content test results to generate the crushing difficulty index, equipment wear impact index and biomass adhesion index.
[0028] Use moisture content detection equipment to detect the moisture content of biomass raw materials that have been cleaned of impurities, and perform crushing impact analysis based on the moisture content detection results, that is, analyze the possible impact of the moisture content of biomass raw materials on the crushing process. Generally speaking, biomass raw materials with higher moisture content may increase crushing difficulty, equipment wear, and biomass adhesion during crushing. Based on the crushing impact analysis results, generate crushing difficulty index, equipment wear impact index and biomass adhesion index. Among them, the crushing difficulty index is used to measure the difficulty of crushing biomass raw materials, the equipment wear impact index is used to evaluate the degree of equipment wear during the crushing process, and the biomass adhesion index is used to quantify the adhesion between biomass particles and between biomass and equipment surfaces.
[0029] Furthermore, the crushing impact analysis is performed based on the moisture content test results to generate crushing difficulty index, equipment wear impact index and biomass adhesion index, including:
[0030] A moisture content test sample set is constructed, wherein the moisture content test sample set includes multiple moisture contents, and the distance between the multiple moisture contents meets a preset distance; historical crushing data is collected based on the multiple moisture contents to generate multiple crushing sample sets, and any crushing sample set includes a crushing efficiency sample, an equipment wear degree sample, and an equipment inner wall biomass adhesion ratio sample; a central trend analysis is performed on the crushing efficiency samples, equipment wear degree samples, and equipment inner wall biomass adhesion ratio samples in the multiple crushing sample sets to generate multiple crushing difficulty benchmark values, multiple equipment wear impact benchmark values, and biomass adhesion benchmark values; with the multiple moisture contents as independent variables and the multiple crushing difficulty benchmark values, the multiple equipment wear impact benchmark values, and the biomass adhesion benchmark values as dependent variables, relationship fitting is performed to generate an influence relationship fitting model; the moisture content detection result is input into the influence relationship fitting model to generate the crushing difficulty index, the equipment wear impact index, and the biomass adhesion index.
[0031] By collecting the moisture content of multiple different biomass raw materials, a moisture content test sample set is constructed. The moisture content test sample set includes multiple moisture contents to cover the range that biomass raw materials may encounter, and the distance between multiple moisture contents (i.e., the difference between two adjacent moisture content values) meets the preset distance. For example, it can be set to select a moisture content value every certain percentage (such as 5%). For each moisture content, the corresponding historical crushing data is collected, including crushing efficiency, equipment wear degree, and the proportion of biomass adhesion on the inner wall of the equipment, and then multiple crushing sample sets are obtained. Each moisture content value corresponds to a crushing sample set, and any crushing sample set includes a crushing efficiency sample, an equipment wear degree sample, and a sample of the proportion of biomass adhesion on the inner wall of the equipment. The crushing efficiency, equipment wear, and equipment adhesion data of each moisture content are subjected to a central trend analysis (such as calculating the average, median, etc.), and according to the analysis results, the crushing difficulty benchmark value, equipment wear impact benchmark value, and biomass adhesion benchmark value are determined for each moisture content. A machine learning algorithm is used to fit the relationship between moisture content and crushing difficulty, equipment wear and biomass adhesion. The moisture content detection results are input into the impact relationship fitting model to generate corresponding crushing difficulty index, equipment wear impact index and biomass adhesion index.
[0032] Determine whether the crushing difficulty index, the equipment wear impact index and the biomass adhesion index meet the preset index. If so, feed the biomass raw material into the multi-stage crushing equipment through the feeding equipment, perform crushing control according to the multi-stage stable control parameters, and activate the crushing monitoring module.
[0033] Based on production experience, equipment specifications, safety standards, product quality requirements and other factors, preset indicators are determined to determine whether the crushing difficulty indicator, equipment wear impact indicator and biomass adhesion indicator meet the preset indicators; if all indicators meet the preset indicators, that is, the crushing difficulty is within an acceptable range, the equipment wear impact is within an allowable range, and the biomass adhesion ratio is low, then the crushing operation can be carried out safely, and then the biomass raw materials are fed into the multi-stage crushing equipment through the feeding equipment, and the crushing equipment is controlled according to the pre-set multi-stage stable control parameters, and the crushing monitoring module is activated at the same time to monitor the equipment's operating status, crushing efficiency, equipment wear and biomass adhesion in real time.
[0034] Furthermore, judging whether the crushing difficulty index, the equipment wear impact index and the biomass adhesion index meet preset indexes further includes:
[0035] If not, reverse the moisture content based on the preset index to generate a target moisture content; perform dehumidification parameter analysis on the dehumidification equipment based on the moisture content detection result and the target moisture content; send the biomass raw material into the dehumidification equipment and perform dehumidification control according to the dehumidification parameters.
[0036] If the crushing difficulty index, equipment wear impact index or biomass adhesion index do not meet the preset index, then the moisture content of the raw material needs to be adjusted. Specifically, analyze which indicators do not meet the preset requirements and the relationship between these indicators and the moisture content. Based on the preset indicators and the indicator values under the current moisture content, reversely deduce the target moisture content that meets all the preset indicators. According to the difference between the target moisture content and the current moisture content (moisture content test results), combined with the performance parameters of the dehumidification equipment (such as dehumidification rate, energy consumption, processing capacity, etc.), analyze and determine the appropriate dehumidification parameters, including dehumidification time, dehumidification temperature, wind speed, etc. The biomass raw material is sent to the dehumidification equipment, and the biomass raw material is dehumidified according to the dehumidification parameters obtained by the analysis. When the moisture content of the raw material reaches the target moisture content, the dehumidification process is stopped and the raw material is taken out of the dehumidification equipment.
[0037] The crushing monitoring module monitors and identifies the discharge particle size of the first-stage crushing equipment to generate a first discharge particle size distribution feature.
[0038] During the biomass pulverization process, the discharge particle size of the first-stage pulverization equipment is monitored and identified through the pulverization monitoring module to generate a first discharge particle size distribution characteristic, which refers to a particle size measurement result.
[0039] According to the first output particle size distribution characteristics, classification and skip-level judgment are performed, and according to the judgment result, the crushed particles output by the first-level crushing equipment are classified to generate first crushed particles sent to the second-level crushing equipment and second crushed particles sent to the skip-level crushing equipment.
[0040] In the biomass crushing process, a classification and skip-level judgment is performed based on the first output particle size distribution characteristics output by the first-level crushing equipment. The classification and skip-level judgment means that if the particle size of the particles is small enough (such as less than a preset threshold), it may cause over-crushing in the next-level crushing, and then it can be directly sent to the next-level crushing equipment; according to the classification and skip-level judgment result, the crushed particles output by the first-level crushing equipment are classified to generate first crushed particles sent to the second-level crushing equipment and second crushed particles sent to the skip-level crushing equipment.
[0041] Further, the method comprises: performing a classification and skipping judgment according to the first output particle size distribution characteristics, classifying the crushed particles output by the first-stage crushing device according to the judgment result, and generating first crushed particles sent to the second-stage crushing device and second crushed particles sent to the skipping-stage crushing device, including:
[0042] The second-stage pulverizing equipment is a device adjacent to the first-stage pulverizing equipment, and the feed particle size of the second-stage pulverizing equipment is obtained; based on the feed particle size and the first output particle size distribution characteristics, a particle size difference distribution characteristic is generated; based on the particle size difference distribution characteristic and the stability control parameters of the second-stage pulverizing equipment, a pulverizing quality impact analysis is performed to generate a pulverizing quality impact index; if the pulverizing quality impact index is greater than the preset quality impact index, a classified skip-level pulverizing instruction is generated, and the pulverized particles output by the first-stage pulverizing equipment are classified according to the feed particle size to generate the first pulverized particles and the second pulverized particles; the second pulverized particles are matched with the feed and output particle size information of the multi-stage pulverizing equipment, and the equipment is marked to generate the skip-level pulverizing equipment.
[0043] The second-stage crushing equipment is a device adjacent to the first-stage crushing equipment. The feed particle size of the second-stage crushing equipment is obtained based on the design specifications of the second-stage crushing equipment. The particle size difference between the two is calculated based on the first output particle size distribution characteristics of the first-stage crushing equipment (i.e., the distribution of particles of different particle sizes) and the feed particle size requirements of the second-stage crushing equipment. This difference can be quantified by statistical methods (such as calculating the average particle size difference, the overlap of the particle size distribution, etc.), and the particle size difference distribution characteristics are generated. The particle size difference distribution characteristics and the stable control parameters of the second-stage crushing equipment (such as rotation speed, feed rate, crushing gap, etc.) are used to perform a crushing quality impact analysis. This analysis aims to evaluate whether the crushing quality will be adversely affected if the output of the first-stage crushing equipment is directly fed into the second-stage crushing equipment, such as over-crushing, increased energy consumption, and equipment Increased wear, etc. The result of the analysis will generate a crushing quality impact index to quantify the size of this adverse effect; if the crushing quality impact index is greater than the preset quality impact index (that is, the adverse effect exceeds the acceptable range), measures need to be taken to mitigate this impact. At this time, a classified skip-level crushing instruction can be generated to classify the crushed particles output by the first-level crushing equipment according to the feed particle size of the second-level crushing equipment, so that those particles with too large particle size that are not suitable for directly entering the second-level crushing equipment can be screened out to form second-level crushing particles, while the remaining particles that meet the requirements continue to enter the second-level crushing equipment as first-level crushing particles; for the classified second-level crushed particles, according to their particle size characteristics, they are matched with the feed and discharge particle size information of the multi-stage crushing equipment, and the matched equipment is marked to generate a skip-level crushing equipment.
[0044] Furthermore, the second crushed particles are matched with the inlet and outlet particle size information of the multi-stage crushing equipment, and the equipment is marked to generate the skip-level crushing equipment, including:
[0045] A particle size difference analysis is performed on the second pulverized particles to generate a concentrated particle size feature and a particle size uniformity index; if the particle size uniformity index meets a preset uniformity threshold, equipment matching is performed based on the concentrated particle size feature; if the particle size uniformity index does not meet the preset uniformity threshold, a multi-classification instruction is generated, the second pulverized particles are multi-classified, and multiple cross-level pulverization equipment is matched.
[0046] By analyzing the particle size difference of the second crushed particles, that is, measuring the particle size of the particles to determine the size distribution of the second crushed particles, the concentrated particle size characteristics and particle size uniformity index are generated. The concentrated particle size characteristics refer to the particle size range where most particles in the particle group are located, while the particle size uniformity index reflects the uniformity of the particle size distribution of the particle group. The particle size uniformity index is compared with the preset uniformity threshold. If the particle size uniformity index meets the preset uniformity threshold, it means that the particle size distribution of the particles is relatively uniform. At this time, a suitable processing equipment can be selected based on the concentrated particle size characteristics, that is, a crushing equipment suitable for the particle size range is selected. If the particle size uniformity index does not meet the preset uniformity threshold, it means that the particle size distribution of the particles is relatively dispersed, and a multi-classification instruction is generated. The multi-classification instruction is used to divide the particles into multiple categories according to their particle size, and each category contains particles of similar particle size; for each classified particle group, the corresponding leapfrog crushing equipment can be matched according to its particle size characteristics. In short, if the particle uniformity is good, the equipment is matched directly; if the uniformity is poor, the uniformity is improved by multi-classification and leapfrog crushing.
[0047] The first crushed particles and the second crushed particles are sent to corresponding crushing equipment for crushing.
[0048] The classified first crushed particles and the second crushed particles are sent to corresponding crushing equipment for crushing, which can ensure the smooth progress of the crushing process and the stable improvement of product quality.
[0049] In summary, the embodiments of the present application have at least the following technical effects:
[0050] First, the particle size information of the input and output of the multi-stage pulverizing equipment is obtained, and the multi-stage stability control parameters are configured, wherein the particle size information of the input and output of the multi-stage pulverizing equipment is inversely proportional to the pulverizing level. Next, the water content of the biomass raw material after impurity cleaning is detected, and the crushing impact analysis is performed according to the water content detection result to generate the crushing difficulty index, equipment wear impact index and biomass adhesion index. Next, it is determined whether the crushing difficulty index, equipment wear impact index and biomass adhesion index meet the preset index. If so, the biomass raw material is fed into the multi-stage pulverizing equipment through the feeding device for crushing control according to the multi-stage stability control parameters, and the crushing monitoring module is activated; the discharge particle size of the first-stage pulverizing equipment is monitored and identified through the crushing monitoring module to generate the first discharge particle size distribution characteristics. Then, a classified leapfrog judgment is performed according to the first discharge particle size distribution characteristics, and the crushed particles output by the first-stage pulverizing equipment are classified according to the judgment result to generate the first crushed particles sent to the second-stage pulverizing equipment and the second crushed particles sent to the leapfrog pulverizing equipment. Finally, the first crushed particles and the second crushed particles are sent to the corresponding pulverizing equipment for crushing. The invention solves the technical problem of uneven crushing of biomass particles in the prior art, and achieves the technical effect of improving crushing uniformity through graded crushing control.
[0051] Example 2, based on the same inventive concept as the pulverization method for preparing biomass particles in the previous example, Figure 2 As shown, the present application provides a pulverizing device for preparing biomass particles, wherein the device comprises:
[0052] A parameter configuration component 11, the parameter configuration component 11 is used to obtain the inlet and outlet particle size information of the multi-stage pulverizing equipment and configure the multi-stage stability control parameters, wherein the inlet and outlet particle size information of the multi-stage pulverizing equipment is inversely proportional to the pulverizing level; an analysis component 12, the analysis component 12 is used to detect the moisture content of the biomass raw material after impurity cleaning, perform crushing influence analysis based on the moisture content detection result, and generate a crushing difficulty index, an equipment wear influence index, and a biomass adhesion index; a judgment component 13, the judgment component 13 is used to judge whether the crushing difficulty index, the equipment wear influence index, and the biomass adhesion index meet the preset index, and if so, feed the biomass raw material into the multi-stage pulverizing equipment through a feeding device The crushing control is performed according to the multi-stage stable control parameters, and the crushing monitoring module is activated; the identification component 14, the identification component 14 is used to monitor and identify the discharge particle size of the first-stage crushing equipment through the crushing monitoring module, and generate a first discharge particle size distribution characteristic; the classification component 15, the classification component 15 is used to perform classification and skip-level judgment according to the first discharge particle size distribution characteristic, and classify the crushed particles output by the first-stage crushing equipment according to the judgment result, to generate first crushed particles sent to the second-stage crushing equipment and second crushed particles sent to the skip-level crushing equipment; the crushing control component 16, the crushing control component 16 is used to send the first crushed particles and the second crushed particles to the corresponding crushing equipment for crushing.
[0053] Furthermore, the parameter configuration component 11 is used to execute the following method:
[0054] The historical crushing records of any device in the multi-stage crushing equipment are collected based on the input and output particle size information to generate multiple groups of historical crushing records, wherein any group of historical crushing records includes crushing parameters and particle size distribution information after crushing; abnormality calculation is performed based on the particle size distribution information after crushing and the input and output particle size information to generate multiple groups of abnormality indicators; the crushing parameters corresponding to the minimum abnormality indicator are extracted according to the group of abnormality indicators, and added into the multi-stage stability control parameters.
[0055] Furthermore, the parameter configuration component 11 is used to execute the following method:
[0056] Based on the particle size distribution information after crushing and the input and output particle size information, the output particle size deviation is calculated to generate multiple groups of particle size deviation sets; based on the multiple groups of particle size deviation sets, the proportion of particles whose output particle size deviation is greater than the preset deviation is counted to generate the multiple groups of abnormality indicators.
[0057] Furthermore, the analysis component 12 is used to perform the following method:
[0058] A moisture content test sample set is constructed, wherein the moisture content test sample set includes multiple moisture contents, and the distance between the multiple moisture contents meets a preset distance; historical crushing data is collected based on the multiple moisture contents to generate multiple crushing sample sets, and any crushing sample set includes a crushing efficiency sample, an equipment wear degree sample, and an equipment inner wall biomass adhesion ratio sample; a central trend analysis is performed on the crushing efficiency samples, equipment wear degree samples, and equipment inner wall biomass adhesion ratio samples in the multiple crushing sample sets to generate multiple crushing difficulty benchmark values, multiple equipment wear impact benchmark values, and biomass adhesion benchmark values; with the multiple moisture contents as independent variables and the multiple crushing difficulty benchmark values, the multiple equipment wear impact benchmark values, and the biomass adhesion benchmark values as dependent variables, relationship fitting is performed to generate an influence relationship fitting model; the moisture content detection result is input into the influence relationship fitting model to generate the crushing difficulty index, the equipment wear impact index, and the biomass adhesion index.
[0059] Furthermore, the classification component 15 is used to perform the following method:
[0060] The second-stage pulverizing equipment is a device adjacent to the first-stage pulverizing equipment, and the feed particle size of the second-stage pulverizing equipment is obtained; based on the feed particle size and the first output particle size distribution characteristics, a particle size difference distribution characteristic is generated; based on the particle size difference distribution characteristic and the stability control parameters of the second-stage pulverizing equipment, a pulverizing quality impact analysis is performed to generate a pulverizing quality impact index; if the pulverizing quality impact index is greater than the preset quality impact index, a classified skip-level pulverizing instruction is generated, and the pulverized particles output by the first-stage pulverizing equipment are classified according to the feed particle size to generate the first pulverized particles and the second pulverized particles; the second pulverized particles are matched with the feed and output particle size information of the multi-stage pulverizing equipment, and the equipment is marked to generate the skip-level pulverizing equipment.
[0061] Furthermore, the classification component 15 is used to perform the following method:
[0062] A particle size difference analysis is performed on the second pulverized particles to generate a concentrated particle size feature and a particle size uniformity index; if the particle size uniformity index meets a preset uniformity threshold, equipment matching is performed based on the concentrated particle size feature; if the particle size uniformity index does not meet the preset uniformity threshold, a multi-classification instruction is generated, the second pulverized particles are multi-classified, and multiple cross-level pulverization equipment is matched.
[0063] Furthermore, the judgment component 13 is used to execute the following method:
[0064] If not, reverse the moisture content based on the preset index to generate a target moisture content; perform dehumidification parameter analysis on the dehumidification equipment based on the moisture content detection result and the target moisture content; send the biomass raw material into the dehumidification equipment and perform dehumidification control according to the dehumidification parameters.
[0065] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0066] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.
[0067] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.
Claims
1. A comminution method for preparing biomass particles, characterized in that: The method comprises: Obtaining the particle size information of the input and output materials of the multi-stage pulverizing equipment, and configuring the multi-stage stability control parameters, wherein the particle size information of the input and output materials of the multi-stage pulverizing equipment is inversely proportional to the pulverizing level; Test the moisture content of the biomass raw materials that have been cleaned of impurities, and perform crushing impact analysis based on the moisture content test results to generate crushing difficulty indicators, equipment wear impact indicators, and biomass adhesion indicators; Determine whether the crushing difficulty index, the equipment wear impact index and the biomass adhesion index meet the preset index; if so, feed the biomass raw material into the multi-stage crushing device through the feeding device, perform crushing control according to the multi-stage stable control parameters, and activate the crushing monitoring module; The crushing monitoring module monitors and identifies the particle size of the first-stage crushing equipment to generate a first particle size distribution feature of the crushing equipment; According to the first output particle size distribution characteristics, a classification and skipping judgment is performed, and according to the judgment result, the crushed particles output by the first-stage crushing device are classified to generate first crushed particles sent to the second-stage crushing device and second crushed particles sent to the skipping-stage crushing device; Sending the first crushed particles and the second crushed particles to corresponding crushing equipment for crushing; According to the moisture content test results, the crushing impact analysis is carried out to generate the crushing difficulty index, equipment wear impact index and biomass adhesion index, including: Constructing a moisture content test sample set, wherein the moisture content test sample set includes multiple moisture contents, and the distances between the multiple moisture contents meet a preset distance; Collecting historical crushing data based on the multiple moisture contents to generate multiple crushing sample sets, wherein any crushing sample set includes a crushing efficiency sample, an equipment wear degree sample, and an equipment inner wall biomass adhesion ratio sample; Performing a central trend analysis on the crushing efficiency samples, equipment wear degree samples, and equipment inner wall biomass adhesion ratio samples in the plurality of crushing sample sets to generate a plurality of crushing difficulty benchmark values, a plurality of equipment wear impact benchmark values, and a biomass adhesion benchmark value; Taking the multiple moisture contents as independent variables and the multiple crushing difficulty benchmark values, the multiple equipment wear impact benchmark values and the biomass adhesion benchmark values as dependent variables, performing relationship fitting to generate an impact relationship fitting model; The moisture content detection result is input into the influence relationship fitting model to generate the crushing difficulty index, the equipment wear influence index and the biomass adhesion index.
2. The pulverizing method for preparing biomass particles according to claim 1, characterized in that: Configure multi-level stability control parameters, including: Collecting historical crushing records of any device in the multi-stage crushing equipment based on the input and output particle size information to generate multiple groups of historical crushing records, wherein any group of historical crushing records includes crushing parameters and particle size distribution information after crushing; Based on the particle size distribution information after crushing and the particle size information of the input and output materials, abnormality calculation is performed to generate multiple groups of abnormality indexes; The crushing parameter corresponding to the minimum abnormality index is extracted according to the multiple groups of abnormality indexes, and added into the multi-level stability control parameter.
3. The pulverizing method for preparing biomass particles according to claim 2, characterized in that: Based on the particle size distribution information after crushing and the particle size information of the input and output materials, the abnormality is calculated to generate multiple groups of abnormality indicators, including: Based on the particle size distribution information after pulverization and the particle size information of the input and output materials, the output particle size deviation is calculated to generate multiple groups of particle size deviation sets; The multiple groups of abnormality indexes are generated by counting the proportion of particles whose output particle size deviation is greater than a preset deviation based on the multiple groups of particle size deviation sets.
4. The pulverizing method for preparing biomass particles according to claim 1, characterized in that: According to the first output particle size distribution characteristics, a classification and skipping judgment is performed, and according to the judgment result, the crushed particles output by the first-stage crushing device are classified to generate first crushed particles sent to the second-stage crushing device and second crushed particles sent to the skipping-stage crushing device, including: The second-stage pulverizing equipment is a device adjacent to the first-stage pulverizing equipment, and the feed particle size of the second-stage pulverizing equipment is obtained; generating a particle size difference distribution characteristic based on the feed particle size and the first output particle size distribution characteristic; Performing a crushing quality influence analysis based on the particle size difference distribution characteristics and the stability control parameters of the second-stage crushing equipment to generate a crushing quality influence index; If the crushing quality influence index is greater than the preset quality influence index, a classification skip-level crushing instruction is generated, and the crushed particles output by the first-level crushing device are classified according to the feed particle size to generate the first crushed particles and the second crushed particles; The second crushed particles are matched with the input and output particle size information of the multi-stage crushing equipment, and the equipment is marked to generate the skip-stage crushing equipment.
5. The pulverizing method for preparing biomass particles according to claim 4, characterized in that: The second crushed particles are matched with the inlet and outlet particle size information of the multi-stage crushing equipment, and the equipment is marked to generate the skip-level crushing equipment, including: performing a particle size difference analysis on the second crushed particles to generate a concentrated particle size feature and a particle size uniformity index; If the particle size uniformity index meets the preset uniformity threshold, equipment matching is performed based on the concentrated particle size characteristics; If the particle size uniformity index does not meet the preset uniformity threshold, a multi-classification instruction is generated, the second crushed particles are multi-classified, and multiple leapfrogging crushing equipment is matched.
6. The pulverizing method for preparing biomass particles according to claim 1, characterized in that: Determining whether the crushing difficulty index, the equipment wear impact index, and the biomass adhesion index meet preset indicators also includes: If not, reverse the moisture content based on the preset index to generate a target moisture content; Performing a dehumidification parameter analysis on the dehumidification equipment based on the moisture content detection result and the target moisture content; The biomass raw material is sent to a dehumidification device, and dehumidification control is performed according to the dehumidification parameters.
7. A comminution device for preparing biomass particles, characterized in that: The device for implementing the comminution method for preparing biomass particles according to any one of claims 1 to 6 comprises: A parameter configuration component, the parameter configuration component is used to obtain the particle size information of the input and output materials of the multi-stage pulverizing device and configure the multi-stage stability control parameters, wherein the particle size information of the input and output materials of the multi-stage pulverizing device is inversely proportional to the pulverizing level; An analysis component, the analysis component is used to detect the moisture content of the biomass raw material after impurity cleaning, perform crushing impact analysis based on the moisture content detection result, and generate a crushing difficulty index, an equipment wear impact index, and a biomass adhesion index; A judgment component, the judgment component is used to judge whether the crushing difficulty index, the equipment wear impact index and the biomass adhesion index meet the preset index, and if so, feed the biomass raw material into the multi-stage crushing device through the feeding device to perform crushing control according to the multi-stage stable control parameters, and activate the crushing monitoring module; An identification component, the identification component is used to monitor and identify the discharge particle size of the first-stage pulverizing device through the pulverizing monitoring module, and generate a first discharge particle size distribution feature; A classification component, the classification component is used to perform classification and skip-level judgment according to the first output particle size distribution characteristics, and classify the crushed particles output by the first-level crushing device according to the judgment result to generate first crushed particles sent to the second-level crushing device and second crushed particles sent to the skip-level crushing device; A pulverizing control component is used to send the first pulverized particles and the second pulverized particles to corresponding pulverizing equipment for pulverization.