基于选择性集成核学习算法的固废焚烧过程二噁英排放浓度软测量

城市固废焚烧(MSWI)过程排放的二噁英(DXN)是被称为"世纪之毒"的持续性污染物。该过程的多阶段、多温度区间的物理化学特性导致DXN排放浓度的机理模型难以构建。工业实际中通常以月或季为周期耗时近1周时间在实验室以离线化验方式滞后检测。针对这些问题,提出了基于选择性集成(SEN)核学习算法的DXN排放浓度软测量方法。首先,基于先验知识给出候选核参数集和候选惩罚参数集,采用核学习算法构建基于这些超参数的候选子子模型;然后,耦合优化和加权算法对相同核参数的候选子子模型进行选择与合并,进而得到基于不同核参数的候选SEN子模型集合;最后,再次采用优化和加权算法获得结构与超参数自适应的多层SEN软测量模型。采用UCI平台水泥抗压强度和焚烧过程DXN数据验证了所提方法的有效性。     

城市固废炉排炉焚烧过程二英排放浓度数值仿真

城市固废焚烧（MSWI）过程产生的二英（DXN）是至今机理仍复杂不清的剧毒污染物，获悉DXN在炉排炉内的生成、燃烧和再生成等过程的边界条件对降低污染排放极为重要。对此，本文提出了城市固废炉排炉焚烧过程DXN排放浓度数值仿真方法。首先，依据面向DXN的典型炉排炉MSWI工艺流程，描述焚烧炉内固相燃烧、气相燃烧、高温换热和低温换热等与DXN相关反应的机理。接着，依据上述所划分区域，结合实际MSWI过程相关参数构建DXN数值仿真模型。最后，基于烟气分流分率所表征的反应物浓度和不同区域的反应温度进行单因素分析，以获取G1处DXN浓度的边界条件，并基于正交实验分析分流分率和反应温度对G1处DXN浓度的影响，进而获得最优参数组合。基于北京某MSWI电厂实际数据的数值仿真分析与验证，表明了该数值仿真模型的有效性，为后续优化控制G1处的DXN排放浓度提供了支撑。     

基于改进大趋势扩散和隐含层插值的虚拟样本生成方法及应用

针对获取复杂工业过程的难以检测质量或环境污染指标数据的时间和经济成本高导致有标记建模样本稀缺的问题，提出了基于改进大趋势扩散和隐含层插值的虚拟样本生成（VSG）方法，并将其应用于城市固废焚烧过程的二英（DXN）排放预测。首先，采用基于子区域欧氏距离改进大趋势扩散（MTD）方法对真实样本输入/输出空间进行扩展；接着，采用等间隔插值方式生成虚拟样本输入，再结合映射模型和删减机制获得虚拟样本输出；然后，采用基于正则化改进的随机权神经网络隐含层插值依次得到虚拟样本输出和输入，再结合扩展空间对虚拟样本进行删减；最后，将上述具有互补性的虚拟样本与原始真实样本进行混合，实现建模数据容量扩充。通过基准数据集和工业过程DXN数据验证了所提方法的有效性和合理性。     

基于STA-K均值聚类的电化学废水处理过程离子浓度软测量

针对电化学废水处理过程出口离子浓度无法在线检测的问题,提出了一种基于状态转移的K均值聚类算法的软测量建模方法。在分析内部反应机理的基础上,结合物料平衡和吸附动力学定理建立电化学过程的机理模型;由于单一的软测量模型难以满足实际的精度要求,提出一种基于状态转移的K均值聚类算法将原始数据集进行聚类,应用状态转移算法对K均值算法的初始聚类中心进行优化,同时,引入离群值矩阵动态迭代同时实现数据聚类和异常值检测;最后,对聚类后的不同训练子集分别建立子模型,综合各子模型得到基于多模型切换方法的软测量模型。通过某废水处理厂的现场数据进行实例验证,结果证明了所建立的电化学废水处理过程离子浓度软测量模型合理有效。     

多尺度小波核支持向量回归及其对丙烯浓度的估计与应用

多尺度小波核支持向量回归方法具有较强的鲁棒性和较好的泛化能力,而模型选择是其获得良好泛化性能的关键,其中采用多尺度核方法参数选择的复杂度比单个核方法的参数选择大得多。这里提出了一种构造多尺度Morlet小波核的支持向量回归机的方法,它采用量子聚类方法划分样本类别以确定多尺度核的尺度个数,依赖支持向量数据描述方法对相应样本计算其核宽度,然后用文化算法优化剩下的少量模型参数。结果表明所得到的多尺度小波核模型的泛化能力明显优于单小波核或高斯核情形。分别用3个标准回归数据集Bostonhous-ing、Bodyfat和Santa作仿真,结果表明,相对于高斯核方法,多尺度小波核支持向量回归方法的测试集均方差分别减小了6.8%、62.0%和91.3%。同时,该方法对丙烯精馏塔的塔釜丙烯浓度预估表现出较好的泛化能力。它不仅使丙烯浓度训练集模型输出与实际输出基本吻合,而且使丙烯浓度测试集相对误差为0.211,与其他方法相比,其预测误差是最小的。     

基于STA-K均值聚类的电化学废水处理过程离子浓度软测量

针对电化学废水处理过程出口离子浓度无法在线检测的问题,提出了一种基于状态转移的K均值聚类算法的软测量建模方法。在分析内部反应机理的基础上,结合物料平衡和吸附动力学定理建立电化学过程的机理模型;由于单一的软测量模型难以满足实际的精度要求,提出一种基于状态转移的K均值聚类算法将原始数据集进行聚类,应用状态转移算法对K均值算法的初始聚类中心进行优化,同时,引入离群值矩阵动态迭代同时实现数据聚类和异常值检测;最后,对聚类后的不同训练子集分别建立子模型,综合各子模型得到基于多模型切换方法的软测量模型。通过某废水处理厂的现场数据进行实例验证,结果证明了所建立的电化学废水处理过程离子浓度软测量模型合理有效。     

基于动态多核相关向量机的软测量建模研究

针对污水处理过程中存在的多变量耦合、强非线性以及参数时变等问题，提出基于多核学习相关向量机的软测量建模方法，并采用粒子群算法对多核权重以及核参数进行优化。同时，引入时间差分(time difference)方法改进多核相关向量机的动态特性。为了验证所提模型的有效性，通过一仿真案例与单核相关向量机、多层前馈神经网络和基于遗传算法的支持向量机进行对比研究。结果表明，所提模型具有更好的预测效果。最后，对模型的鲁棒性在数据漂移和异常的场景下进行了讨论。     

基于加权模糊聚类方法的多模型建模

针对化工过程软测量模型的多样性,提出基于一种加权模糊聚类方法的多模型建模方法。将输入向量与输出的相关性作为加权系数,构建加权模糊聚类算法,对样本空间的输入数据进行聚类,然后用与输入变量对应的子模型进行输出估计,子模型输出作为系统模型的最终输出。该方法能够实现对输入数据更加合理的划分,提高软测量模型的精度。将该方法应用于双酚A生产过程的质量指标软测量建模,仿真结果表明了该方法的可行性和有效性。     

单光照条件变化的镨/钕元素组分含量软测量

针对镨/钕稀土萃取工业生产现场光照条件变化导致具有颜色特征的镨/钕组分含量难以准确检测的问题，提出了一种基于单光照条件变化的组分含量软测量方法。首先，采用参数优化的Grey Edge算法，将不同光照条件下的稀土溶液图像校正到标准光照下；然后，以镨/钕溶液图像HSI颜色空间中的H、S、I分量一阶矩为模型的输入变量，利用加权最小二乘支持向量机（WLSSVM）建立组分含量软测量模型；最后采用工业数据对所提方法进行仿真实验，结果表明所提方法在不同光照条件下均能满足稀土萃取过程组分含量检测的准确度和快速性要求。     

基于群智能算法优化LSTM的催化裂化预测研究

针对长短期记忆神经网络(LSTM)预测模型中许多网络参数过分依赖于经验设置，人工参数设置导致模型的精度低、泛化能力弱等问题，采用搜索范围广、收敛速度快的粒子群算法(PSO)和布谷鸟算法(CS)对LSTM的一些超参数进行优化，构建PSO-LSTM模型和CS-LSTM模型，寻找到LSTM的最优参数集，从而更好地提高了模型预测精度。将优化后的模型应用于催化裂化吸收稳定系统主要控制变量解吸塔再沸器返塔温度预测中，验证了模型的有效性。     

生活垃圾智慧焚烧的研究现状及展望

对生活垃圾收运、存储、焚烧处置和污染物排放控制等过程的智能化监测、控制和管理等国内外研究现状进行了梳理,分析了物联网、焚烧状态诊断、污染物在线监测和人工智能算法等技术在生活垃圾焚烧处置过程中应用的优缺点,并对未来生活垃圾智慧焚烧技术的发展做出了展望,建议对现有技术进行系统化耦合,并通过大数据分析及云计算平台,构建智能化反馈和优化模型,进一步开发智慧焚烧技术与装备。     

Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills

Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volumeratio. Latent features are first extracted fromdifferent vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposedmodeling approach has better prediction performance than previous ones.     

Virtual sample generation method based on improved megatrend diffusion and hidden layer interpolation with its application

The time and economic cost of obtaining difficult-to-measure quality or environmental pollution indices data for complex industrial processes are very high, which leads to the scarcity of labeled modeling samples. Aimed at this problem, a new virtual sample generation method based on improved megatrend diffusion and hidden layer interpolation is proposed. It is applied to the dioxin (DXN) emissions prediction of municipal solid waste cineration process. First, the true sample input/output sample space is expanded by using improved megatrend diffusion technology (MTD) based on the sub-regional Euclidean distance. Next, the virtual sample input is generated using an equal interval interpolation method, and the virtual sample output is obtained by combining the mapping model and the pruning mechanism. Then, the hidden layer interpolation method based on the improved random weight neural network with regularization mechanism is used to obtain the virtual sample output and input, and the virtual sample is deleted by combining with the expansion space. Finally, the above-mentioned complementary input/output virtual samples are mixed with the original true samples to realize the expansion of the modeling data capacity. The validity and rationality of the proposed method are verified by benchmark data set and industrial process DXN data.     

FORMATION MECHANISMS AND CONTROL STRATEGIES FOR DIOXINS IN INCINERATION PROCESS OF MUNICIPAL SOLID WASTES

Dioxins, which are of the most toxic materials on the earth, are principal emitted from waste incineration process. The molecular structures, toxicity parameters, such as toxicity equivalency factor, tolerable daily intake and physic-chemical properties of dioxins are briefly summarized. Three formation mechanisms of dioxins in waste incineration process, namely as de novo synthesis, mechanisms involving small organic molecular as precursors and homogenous gas phase reaction mechanism are alto reviewed. The influencing factors for dioxins formation during waste incineration process are also discussed. Three major methods for reducing dioxins emission from waste incineration process are discussed based upon the formation mechanisms and influencing factors. A new waste incineration process with low dioxins emission and low hydrogen chloride corrosion has been proposed based on multi- stage unit operation principal according to formation mechanisms of dioxins and potential production location in waste incinerators.     

Illumination correction model with sine-cosine algorithm to optimize gray wolf population for least-squares support vector regression

The color of an object appears different from its true color when illuminated with light sources of different hues. To solve this problem, this article proposes a combination algorithm (SCA-GWO-LSSVR) based on the sine-cosine algorithm (SCA) and the gray wolf optimization (GWO) algorithm to optimize the regression prediction model of the least-squares support vector regression (LSSVR) algorithm. The performance of the traditional LSSVR is significantly affected by the penalty parameter (gamma) and the sig2 kernel function parameter. The proposed method uses the improved GWO algorithm to search the population to find the best LSSVR parameter solution. The proposed algorithm uses the SCA to create multiple random candidate solutions in population initialization to avoid blind initialization of the GWO algorithm. In the process of iterative optimization, the SCA is infiltrated, and its sine-cosine wave mathematical model is used to quickly identify the best outward or inward position of the gray wolf. Finally, the LSSVR combines the optimal sig2 kernel function parameters and penalty parameters (gamma) to obtain a highly versatile illumination correction model. The experimental results show that the fitting accuracy of the proposed method reaches 86.8%, which is 5% higher than that of the LSSVR algorithm alone.     

基于改进差分进化和最小二乘支持向量机的铝酸钠溶液浓度软测量

针对氧化铝蒸发过程铝酸钠溶液浓度难以在线检测问题,提出了改进差分进化和最小二乘支持向量机的铝酸钠溶液浓度软测量建模方法。首先基于灰色关联分析和核主成分分析确定模型的输入变量,再用改进差分进化算法的最小二乘支持向量机构建软测量模型。并与DE-LSSVM软测量模型进行比较;最后应用蒸发过程生产数据进行验证,结果表明,新模型具有更好的学习能力和泛化性能且预测精度更高,可为蒸发过程操作优化提供必要的指导。     

工业废水焚烧处理工艺综述

笔者讨论了焚烧法处理工业废水的机理以及废水焚烧效果的影响因素。介绍了国内外主要的废水焚烧炉型,并分析了各种焚烧炉的特点。介绍了废水焚烧工艺在国内外的应用情况。并指出优化焚烧炉的操作参数,从而达到较高的焚烧效率又保证较低的污染物排放浓度是研究废水焚烧工艺的发展方向。     

Operational optimization of industrial steam systems under uncertainty using data-Driven adaptive robust optimization

This article addresses the operational optimization of industrial steam systems under device efficiency uncertainty using a data-driven adaptive robust optimization approach. A semiempirical model of steam turbine is first developed based on process mechanism and operational data. Uncertain parameters of the proposed steam turbine model are further derived from the historical process data. A robust kernel density estimation method is then used to construct the uncertainty sets for modeling these uncertain parameters. The data-driven uncertainty sets are incorporated into a two-stage adaptive robust mixed-integer linear programming (MILP) framework for operational optimization of steam systems to minimize the total operating cost. Integer variables are introduced to model the on/off decisions of the steam turbines and electrical motors, which are the major energy consumers of the steam system. By applying the affine decision rule, the proposed multilevel optimization model is transformed into its robust counterpart, which is a single-level MILP problem. The proposed framework is applied to the steam system of a real-world ethylene plant to demonstrate its applicability. © 2018 American Institute of Chemical Engineers AIChE J, 65: e16500 2019     

生活垃圾焚烧烟气脱硝超低排放技术经济比较

介绍了生活垃圾焚烧烟气成分及特点,针对垃圾焚烧烟气温度偏低及二噁英类、重金属、HCl、Hg、H2O等杂质含量偏高、SO2浓度低等特点,提出垃圾焚烧烟气脱硝超低排放的4种技术方案,并进行技术经济比较。分析结果表明:固体脱硝剂SNCR脱硝技术投资及运行成本最低,脱硝效率可达90%以上,是一种生活垃圾焚烧烟气处理优选的脱硝技术。