Correction model for CO detection in the coal combustion loss process in mines based on GWO-SVM

Accurate detection of CO gas is crucial to the prevention of coal combustion. Tuneable diode laser absorption spectroscopy (TDLAS) is a reliable method for CO detection during coal combustion. The influences of temperature and pressure cause changes in the line strength and linewidth of the index gases’ absorption spectra, leading to sizable measurement errors. To correct the distortion of the CO absorption spectrum caused by temperature and pressure fluctuation, a compensation model based on the grey wolf optimizer–support vector machine (GWO–SVM) was proposed. The results were compared with those of the single SVM, the back propagation neural network (BPNN), and multiple regression analysis (MRA). MRA was revealed to result in the lowest accuracy, which indicated that MRA is not ideal for compensation in TDLAS. The hyperparameter selection of the SVM had the disadvantages of randomness and blindness, which led to instability and large errors. The BPNN achieved better correction in the training stage, but severe overfitting occurred in the testing stage. The modified results revealed that the GWO–SVM model had higher accuracy and stability than the other models. It effectively inhibited the effects of temperature and pressure on the measured concentration and greatly improved the measurement accuracy. The equipment is thus suitable for CO gas detection with the aim to preventing coal combustion loss, and it can be further applied to loss prevention in other process fields.     

SVM与ANN在湖泊富营养化评价中的对比研究

支持向量机是由Vapnik等提出的建立在统计学习理论基础上的一种新的机器学习方法,由于其使用结构风险最小化原则代替经验风险最小化原则,又由于其应用了核函数思想,它可以较好地解决非线性问题;人工神经网络(ANN)已经较成功解决模式识别和任意非线性函数回归问题,但是存在训练样本不足,并可能出现过拟合现象。SVM的结构风险最小化算法引起了科学界的关注,对传统基于经验风险最小化的神经网络算法提出了挑战,文章介绍了SVM和ANN的基本原理,并对二者在巢湖富营养化水平评价上做对比研究,结果表明,ANN比较容易陷入局部最优,支持向量机评价结果更加符合实际。     

A framework to minimise total energy consumption and total tardiness on a single machine

A great amount of energy is wasted in industry by machines that remain idle due to underutilisation. A way to avoid wasting energy and thus reducing the carbon print of an industrial plant is to consider minimisation of energy consumption objective while making scheduling decisions. To minimise energy consumption, the decision maker has to decide the timing and length of turn off/turn on operation (a setup) and also provide a sequence of jobs that minimises the scheduling objective, assuming that all jobs are not available at the same time. In this paper, a framework to solve a multiobjective optimisation problem that minimises total energy consumption and total tardiness is proposed. Since total tardiness problem with release dates is an NP‐hard problem, a new greedy randomised multiobjective adaptive search metaheuristic is utilised to obtain an approximate pareto front (i.e. an approximate set of non‐dominated solutions). Analytical Hierarchy Process is utilised to determine the ‘best’ alternative among the solutions on the pareto front. The proposed framework is illustrated in a case study. It is shown that a wide variety of dispersed solutions can be obtained via the proposed framework, and as total tardiness decreases, total energy consumption increases.     

基于随机森林模型的辽河高时间分辨率氮、磷浓度模拟与预测

随着环境信息获取技术的飞速发展,水质监测数据逐渐呈现出高时间分辨率的特点.针对传统方法在模拟高频检测数据时的不足,本文基于随机森林建立了河流氮、磷逐日浓度预测模型,识别了影响辽河干流马虎山断面氮、磷浓度变化的主要因素,并预测了未来不同情景下氮、磷浓度变化特征.结果表明：①马虎山断面氮、磷浓度变化的主要影响因素是上游珠尔山断面的来水水质和本断面的流量;②随机森林模型在高时间分辨率的水质指标模拟中具有误差低和拟合优度高的特点,其中,TN浓度预测模型的RMSE为0.40 mg·L^-1,R²为0.95,TP浓度预测模型的RMSE为0.01 mg·L^-1,R²为0.96;③在不同水文、污染控制和来水水质的变化情景下,未来马虎山断面氮、磷浓度变化主要取决于上游 来水水质,加强全流域营养盐控制是确保断面水质稳定达标的重要基础.     

Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.     

基于三坐标测量仪测量角度及不确定度分析

通过三坐标测量仪对机械加工零件的外锥和内锥进行测量分析。对一些通用量具无法测量的角度提供准确的数值。利用不确定度评定标准公式计算出三坐标测量角度的不确定度分析。经过三坐标测量仪的先进技术改变现代通用量具的测量方法,提高通用量具无法达到的高精度测量时代,为高精度测量提供了一种新的测量手段。     

天津钢铁行业烧结烟气脱硫治理工程实践

天津两大钢铁企业分别采用LJS-FGD技术和石灰-石膏湿法工艺进行烧结烟气脱硫。通过分析两种脱硫方法的工程实践数据,从其脱硫效果、除尘脱硝效果、能耗技术指标等方面对两种脱硫方法进行比较:LJS-FGD技术在保证脱硫脱硝效果的基础上,具有总投资低、占地面积小、能耗低、污染小、运行成本低等特点;石灰-石膏湿法脱硫脱硝效果好,但能耗高,占地面积大,设备易于腐蚀。     

基于KPCA-LSSVM的矿井工作面动力环境安全评价模型

为实现对采前工作面所处动力环境的客观、准确评价,选取9个直接影响工作面 动力环境的指标因素构建安全评价指标体系,建立基于核主成分分析(KPCA)和最小二乘 支持向量机(LSSVM)的工作面动力环境多因素耦合安全评价智能模型。首先根据KPCA理 论对评价指标施行简约化处理,剔除冗余信息,得出6个简约后的评价指标并输入LSSVM 模型中训练学习,最后得到评价模型。选取从平顶山矿区和大同矿区搜集到的30组工作 面历史数据,按照20∶10的比例对模型进行训练和测试,并将测试结果与其他四种模型 结果进行了对比,结果表明:KPCA方法可有效减少数据信息冗余,利用KPCA优化的 LSSVM模型可准确评价工作面动力环境,误判率为0。     

烧结机分导式半干法脱硫技术研究

介绍了烧结烟气的特性,列举了目前烧结脱硫存在的主要问题,并对常用的烧结脱硫技术进行分析。提出一种经济性更好的烧结机分导式半干法脱硫技术。     

表面处理行业废水治理及中水回用工程实践

针对电镀等表面处理行业废水的水质特征,以浙江台州某表面处理有限公司为例,介绍了高压脉冲电凝机处理电镀综合废水与膜法反渗透中水回用工艺,经过近年的运行及实践证明,该工艺先进可行,效果好,不仅确保出水达标,而且75%中水回用,操作性强,工程投资和运行费用较低,无二次污染,是一种较实用的治理电镀综合废水与中水回用新技术。