细粒煤干法深度筛分技术进展及研究方向

细粒煤干法深度筛分技术在煤炭工业中具有重要作用,已成为当今国内外筛分领域研究热点与难点。目前细粒煤干法筛分粒度界限主要为6和3 mm,尤其对于3 mm动力煤不经分选可直接用于火力发电。因此,实现6或3 mm细粒煤干法深度筛分,不仅能提高粉煤的利用率,也可节约大量分选成本。笔者论述了目前国内外细粒煤干法深度筛分相关理论研究成果,着重阐述了国内外典型细粒煤干法深度筛分设备结构及性能特点,并深入探讨了今后细粒煤干法深度筛分技术的研究方向。目前国外细粒煤干法深度筛分相关理论研究成果主要为概率筛分原理、等厚筛分理论、电磁激振理论,弛张筛分理论、强化筛分理论。论述了黏附细粒物料深度筛分模型,潮湿细粒物料的黏结力计算公式及减少黏结力方法,降低潮湿细粒物料堵孔方法以及双质体共振与网振筛分技术等。国内研究主要为实用概率筛分模型,潮湿细粒物料在筛面上积聚原因和黏附机理及黏附力、黏附厚度的影响因素,克服潮湿细粒原煤深度筛分堵孔、黏孔现象的设计方案,采用筛板振动、筛箱不振动的形式实现难筛物料的筛分,弹性筛面克服堵孔的机理及与刚性筛面筛分效果对比,细粒煤粒度分布模型分布特性研究等。在筛分技术和设备开发上不断创新,目前已开发出的概率筛、等厚筛、博后筛、高幅筛、节肢筛、琴弦筛、弛张筛、抛射筛、谐振式泰勒振动筛、双质体振动筛、电磁高频细筛等细粒煤干法深度筛分设备结构与性能特点各异,且适应于不同用途。基于目前细粒煤干法深度筛分技术发展现状,建议未来应加强筛分理论、关键技术、筛机结构优化、振动参数优化配置、筛板相关技术及多样化等方面研究。     

细粒筛分振动机械专利分析及战略体系构建

概述了细粒筛分振动机械的发展现状,分析了细粒筛分振动机械目前相关专利申请的状况,提出了今后细粒筛分振动机械相关专利战略体系构建的方法与思路。     

德瑞克高频细筛在薛湖选煤厂细粒煤筛分中的应用

从筛分效率、总错配物含量和粒度分配曲线等方面,对比分析了常用的振动弧形筛和德瑞克高频细筛对细粒煤的筛分效果,薛湖选煤厂成功地将德瑞克高频细筛应用到选煤厂细粒煤筛分工艺中,对TBS分选溢流进行筛分,取得了良好的脱泥降灰效果。     

潮湿细粒煤筛分机械大型化的发展前景

分析了潮湿细粒煤筛分设备的发展过程,急待解决的问题和我国目前这一技术领域的现状;指出细胞级粘性物下一步地筛分是当今国内外筛分技术的难点,我们国家非常需要。采用大振幅、大振动强度和较低频率的筛分机械是解决细粒煤筛分问题的关键;建议采用动力学平衡结构使筛面振动,筛箱和机架不参与振动,是实现筛分机械大型化、保护原建筑地基的最佳方案。     

大型潮湿细粒物料筛分机的研制和应用

针对目前潮湿细粒粘性物料的筛分问题提出了一种新思路 ,认为对难筛物料的筛分应该采用大振幅、大振动强度和弹性筛面的工艺参数 ,并采用筛板振动而筛箱不振动的机械结构形式 ,实现难筛物料的筛分 ;研制的大型博后筛用于潮湿细粒物料的筛分 ,取得了良好的效果。     

筛片交叉式筛分设备的研制及应用

论述了筛片交叉式筛分设备——交叉筛的工作原理,利用"动态筛孔"筛分理念,使筛分过程不粘、不堵、不卡,提高了筛分效率,解决了细粒湿粘物料筛分难题;企业应用后取得了很好的经济效益。     

全密度聚乙烯装置振动筛堵孔机理与解决对策

目前,潮湿细粒物料的堵塞筛孔是筛分作业中经常遇到的难题,造成筛孔堵塞的因素有很多。本文论述了国内线性低密度聚乙烯装置挤压机组中振动筛在实际筛分过程中经常出现的多种问题,主要针对筛孔堵塞这一问题作出了详细的分析,由于物料特性、物料表面水分的含量以及筛网结构等原因造成筛分过程中物料容易黏附在筛网上或者一些筛孔被粒径大或形状不规则的物料颗粒堵塞,导致筛分效率下降,甚至有时候需要停机进行清理,严重影响了筛分作业的顺利进行。为了解决这一问题,对振动筛筛网结构进行优化设计,通过提高振动筛的振动强度来减缓筛网的堵塞情况,提高筛分效率,同时采用弹簧装置降低振动筛带来的噪声,体现了环保要求。并指出了标准化、系列化、通用化、低噪声是细粒难筛物料筛分机械的发展趋势。     

弛张筛在潮湿细粒煤筛分领域的应用前景

比较分析了弛张筛与其他筛分机械的工作原理、结构及其技术特点,弛张筛在粘滞细粒煤筛分方面具有处理量大、负载小、噪声低、水及电能消耗少、筛分效果好、运行效率高等特点,可为企业创造较好的经济效益,在煤炭筛分、垃圾处理、常规动力煤发电、非金属及有色金属等行业具有广阔的应用前景。     

Binder弛张筛在张集选煤一厂的应用

介绍了Binder弛张筛的工作原理和技术特点,通过与博后筛进行筛分效果对比,证明了该筛分机对细粒煤的筛分效率高,筛孔不易堵塞,日常维护少,筛上物带细粒煤少等特点;张集选煤一厂应用后,有效降低了煤泥水系统的压力,改善了作业环境和劳动强度,提高了经济效益。     

脱泥筛工业性试验分析与评述

新峪选煤厂一期工程系统的脱泥筛工业性试验分析表明,粗粒物正配效率为98.05%,细粒物正配效率为76.78%,筛分效率74.83%;虽然筛分效果良好,但脱泥筛设计处理能力严重不足,每年损失在混煤中的粗精煤量较高,在筛分设计过程中应仔细核算。     

Design of Natural-Product-Inspired Multitarget Ligands by Machine Learning

A virtual screening protocol based on machine learning models was used to identify mimetics of the natural product (−)-galantamine. This fully automated approach identified eight compounds with bioactivities on at least one of the macromolecular targets of (−)-galantamine, with different polypharmacological profiles. Two of the computer-generated hits possess an expanded spectrum of bioactivity on targets relevant to the treatment of Alzheimer's disease and are suitable for hit-to-lead expansion. These results advocate multitarget drug design by advanced virtual screening protocols based on chemically informed machine learning models.     

Deep Learning in Virtual Screening: Recent Applications and Developments

Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.     

Grade efficiency for sieve classification processes

Modelling of the screening performance for classification processes is important to obtain a first estimate for a new process in the planning phase. In this work especially the grade efficiency curves of sieve classifications with vibrating screens were examined. A sensitivity study was performed by changing the operating parameters of the sieving machine and the parameters of the charging material (i.e. mass flow, particle size, etc.). The aim was to correlate the input parameters with the grade efficiency curve of the classification process. The main aspect of the presented work is to find an appropriate method to adjust four screening parameters in a way that the measured grade efficiency curve is modelled correctly. Several methods for this adjustment step are reviewed. A sensitivity study using a tumbling screen was performed previously. It is apparent that for that study, different methods and models for the parameter adjustment need to be used. Furthermore it is shown that data reconciliation is necessary, since the mass balance of the particle streams may not be closed correctly. In summary this work is the first step to predict the screening performance of a sieving machine without material‐ and time‐consuming experiments.     

Machine learning corrected alchemical perturbation density functional theory for catalysis applications

Alchemical perturbation density functional theory (APDFT) has promise for enabling computational screening of hypothetical catalyst sites. Here, we analyze errors in first order APDFT calculation schemes for binding energies of CH_x, NH_x, OH_x, and OOH adsorbates over a range of different coverages on hypothetical alloys based on a Pt(111) reference system. We then train three different support vector regression machine learning models that correct systematic APDFT prediction errors for each of the three classes of carbon, nitrogen, and oxygen based adsorbates. While uncorrected first order APDFT alone approximates accurate adsorbate binding energies on up to 36 hypothetical alloys based on a single Kohn–Sham DFT calculation on a 3 × 3 unit cell for Pt(111), the machine learning-corrected APDFT extends this number to more than 20,000 and provides a recipe for developing other machine learning-based APDFT models.     

提高注射用硫酸链霉素成品率

通过优化生产工艺流程、根据粉体学性质对原粉进行判断并将适合现有分装设备分装生产的无菌原粉集中生产、分装机新备件的制作和使用,我们将100万单位注射用硫酸链霉的成品率提高到98.12%,降低了生产成本.     

Siebklassieren

Sizing by sifting with screens. Viewed physically, screening comprises superposition of segregation, diffusion, and passage through the screen. Frictional and cohesive forces have to be overcome and the coarse feed material transported over the screen, primarlily by circular, elliptical, or linear vibration. The specific screening performance and the sharpness of separation prove to be a measure for the quality of screening. There are three uses for screening: protective screening with mean particle size D_m ? mesh w with a simultaneous wish for high screen performance; classification with D_m ≈ w with a desire for greater sharpness of separation; and dust removal with D_m ? w and the problem of deagglomeration, i. e. separation of fines from the screened material. The maximum screen performance can be calculated for the coarse range; it is attainable with most screening machines. A problem encountered in the medium to fine size range is prevention of blinding of the screening machine and deagglomeration of sieved material, with attrition phenomena deserving attention. A range of machines is available: casting, planar, tumble, agitator screening machines are available, as are those with deforming screens, those utilizing centrifugal forces, and, as the latest addition, flow screening. New developments concentrate on fine screening, e. g. screening machines with direct activation of the screen fabric, tumble screening machines, combinations of mechanical and pneumatic screening extending through to pure flow screening. Plastic screens made of polyurethane are of advantage for wet screening; they exhibit greatly reduced wear. Safety requirements are met by silenced and explosion-proof-screening machines.     

垃圾分选系统及其在中转站中的应用

垃圾分选系统纳入中转站的总体设计,将其分选设备分成破碎、筛分和传送等几部分。垃圾分选系统设置需要解决的工艺问题主要包括：（1）垃圾机械分选与垃圾破碎分选：（2）纸张、金属、塑料等可回收废品的分选装要。     

小型选煤厂的煤泥处理方式

随着煤炭及焦炭市场竞争的加剧,小型选煤厂要取得更好的经济效益,必须对煤泥进行分选处理;灰分较低的粗煤泥宜选用沉淀槽,反之则选用螺旋分选机,细煤泥处理设备有浮选旋流器、圆形离心浮选机和浮选柱,应根据条件选择;煤泥产品的脱水可采用筛分设备加沉淀池工艺。