生物脱氮外加碳源发展趋势研究

碳源是异养生物反硝化过程的重要影响因素,文章结合国内外对于反硝化外加碳源的研究成果,论述了生物脱氮外加碳源的研究进展,并对反硝化碳源的发展趋势进行了分析,为后续碳源的研究提供参考。     

反硝化脱氮新型外加碳源研究进展

为应对更加严格的氨氮排放标准,城市污水处理厂普遍通过向生物营养物去除工艺(BNR)中投加外碳源以强化反硝化脱氮.常规的外加碳源包括甲醇、乙醇及糖类等.结合国内外对于反硝化补充碳源的研究成果,综述了以天然纤维素物质及人工合成高聚物为主的固体碳源、工业废水、污泥水解上清液、垃圾渗滤液和气体碳源甲烷等新型碳源,并总结了这几种新型碳源的研究现状及在反硝化中存在的问题.     

基于A/O工艺的己内酰胺废水脱氮技术研究

生物法是石化行业废水处理中的常用方法之一,A/O工艺是常见的污水处理方法,两级A/O工艺则可以强化硝化和反硝化过程,对高含氮有机废水来说是较为理想的水处理手段。针对某化工企业的己内酰胺废水处理工艺,研究了A/O工艺和两级A/O工艺对己内酰胺废水的脱氮率影响。研究结果表明,A/O工艺对己内酰胺废水的脱氮效率仅为53.6%,投加甲醇碳源后,脱氮率可提升至85.0%,出水总氮可降至40 mg/L。两级A/O串联工艺对己内酰胺废水的脱氮率在投加甲醇碳源后,出水总氮可降至11.2 mg/L,脱氮率可达96.2%,两级A/O工艺可以有效提高总氮去除率。     

减少外加碳源强化脱氮在A2/O氧化沟的应用

针对广东省某生活污水厂进水总氮浓度高、COD偏低、碳氮比低、碳源投加量大的现状。为实现在少加、甚至不外加碳源的情况下,低碳氮比生活污水厂出水总氮稳定达标的目标,在现有一体化A²/O改良氧化沟的基础上,拟采取严控缺氧区溶解氧、利用出水氨氮控制风机风量等简便、易操作的措施强化脱氮。实践表明,在出水总氮达标的前提下,缺氧区溶解氧由0.56 mg/L降至0.36 mg/L,外加碳源的投加量可节省23%;保证出水氨氮不超标的情况下,最大限度地降低风机风量,可以不外加碳源,并确保总氮达标,达到了节省碳源和降低能耗的目的,降低了运营成本。     

缓释碳源应用于低碳氮比污水脱氮研究进展

处理低碳氮比（C/N）污水时,生物法脱氮反硝化进程中碳源不足易导致脱氮效率偏低,需通过额外投加碳源进行补偿。外加碳源在污水脱氮过程中应用广泛,其投加量不易控制的缺陷可以通过利用缓释技术进行弥补。归纳了碳源缓释的特点及其在低C/N污水处理中的应用现状,总结了固体缓释碳源的不同类型及其在生物反硝化脱氮中的强化效果,对比分析不同缓释碳源在脱氮方面的优缺点。提出了目前缓释碳源应用的局限性,并对缓释碳源在生物反硝化脱氮领域的发展方向进行了展望。     

强化反硝化脱氮湿地外加碳源研究进展

人工湿地作为一种新型污水处理技术已被广泛应用,碳源是影响其反硝化脱氮的主要因素之一,针对污水中C/N比较低的问题,需向系统中额外投加碳源。在大量相关文献研究基础上,对强化反硝化脱氮人工湿地的外加碳源进行了总结,并比较了外加碳源的种类和及其优缺点。并对人工湿地外加碳源的研究进行了讨论和展望,为今后强化脱氮人工湿地外加碳源的深入研究提供了建议。     

生活污水脱氮的A/O/N工艺和A/O工艺对比试验研究

为提高传统生物脱氮工艺的脱氮水平,在分析传统生物脱氮工艺局限性的基础上提出了传统A/O工艺的改良工艺即A/O/N工艺。通过采取相同能耗条件下最佳工艺参数的对比试验研究,试验结果表明两工艺对CODCr去除效果均较好,A/O/N工艺对NH3-N和TN的去除效果明显优于A/O工艺,且耐NH3-N冲击负荷更强。     

高氮废水分段进水A/O工艺脱氮效果研究

采用5段进水A/O工艺处理高氮废水,进行了小试试验研究,试验时采用自配的高氮废水作为进水原料,考察分水比、回流比对每段出水氨氮、硝氮和总氮去除率的影响。结果表明:1Ⅰ、Ⅱ段由于进水中有机物含量较高,反硝化反应不完全,造成出水中氨氮及硝氮含量较高,后续段数随着污水有机物含量的降低,出水中氨氮及硝氮去除效果愈加明显;2该工艺中氨氮的去除主要是通过硝化细菌的硝化作用完成,且污泥有机负荷越小,氨氮去除效果越明显;3经5段进水A/O工艺处理后,出水可满足GB 18918—2002的一级B排放标准。研究结果可为国内外广泛推广生物脱氮除磷技术提供理论基础。     

高氮废水分段进水A/O工艺脱氮效果研究

采用5段进水A/O工艺处理高氮废水,进行了小试试验研究,试验时采用自配的高氮废水作为进水原料,考察分水比、回流比对每段出水氨氮、硝氮和总氮去除率的影响。结果表明:1Ⅰ、Ⅱ段由于进水中有机物含量较高,反硝化反应不完全,造成出水中氨氮及硝氮含量较高,后续段数随着污水有机物含量的降低,出水中氨氮及硝氮去除效果愈加明显;2该工艺中氨氮的去除主要是通过硝化细菌的硝化作用完成,且污泥有机负荷越小,氨氮去除效果越明显;3经5段进水A/O工艺处理后,出水可满足GB 18918—2002的一级B排放标准。研究结果可为国内外广泛推广生物脱氮除磷技术提供理论基础。     

污染水体脱氮工艺中外加碳源的研究进展

碳源是传统生物脱氮过程中的一个重要因素,当水体中碳氮质量比过低时,往往需要投加外加碳源作为电子供体,以保证反硝化反应的顺利进行.作者主要对污水脱氮过程中添加不同外加碳源后,对氮的去除率、工艺的优缺点及成本等方面进行了总结.     

Early identification of process deviation based on convolutional neural network

A novel process monitoring method based on convolutional neural network(CNN) is proposed and applied to detect faults in industrial process. By utilizing the CNN algorithm, cross-correlation and autocorrelation among variables are captured to establish a prediction model for each process variable to approximate the first-principle of physical/chemical relationships among different variables under normal operating conditions. When the process is operated under pre-set operating conditions, predic...     

Predicting coal ash fusion temperature with a back-propagation neural network model

A novel technique, the back-propagation (BP) neural network, is presented for predicting the ash fusion temperature from ash compositions for some Chinese coals instead of the traditional techniques, such as the ternary equilibrium phase diagrams and regression relationships. In the applications of the BP networks, some modifications to the original BP algorithm are adopted to speed up the BP learning algorithm, and some useful advice is put forward for the choice of some key parameters in the BP model. Compared to the traditional techniques, the BP neural network method is much more convenient and direct, and can always achieve a much better prediction effect.     

基于神经网络模型的对二甲苯氧化过程优化

根据PTA工厂生产操作数据,用人工神经网络的反向传播算法建立了氧化过程尾氧和尾气二氧化碳生成速率的预测模型。在此基础上,利用逐步二次规划（SQP）法对预测模型进行了寻优,获得了优化的工艺条件。工业应用表明,它实现了氧化产品质量稳定条件下最低的燃烧消耗量。     

基于人工神经网络的大型浇注型聚氨酯弹性体收缩规律的模拟

收缩率的选取是决定大型浇注型聚氨酯弹性体（CPUE）模具设计是否成功的关键因素之一.以大量的实验为基础,系统地研究了各种工艺参数对大型聚氨酯弹性体制品收缩率的影响,并建立了制品的结构模型以及基于BP网的神经网络模型.通过对实验数据的学习,利用该神经网络模型可以实现以工艺参数为输入,制品收缩率为输出的大型聚氨酯弹性体收缩率的预测.预测结果和实验数据的对比表明此方法可以较为准确地对不同工艺条件下的弹性体收缩率进行预测,从而减少修模次数,降低生产成本.     

采用BP神经网络预测乳聚丁苯橡胶的门尼黏度

在工业生产乳液聚合丁苯橡胶配方的基础上,于实验室聚合釜中考察了增加引发剂和乳化剂用量对聚合速率的影响以及补加相对分子质量调节剂及其加入时机对聚合产物门尼黏度的影响。结果表明,增加引发剂和乳化剂用量可以加快丁苯乳液聚合的速率,配合补加相对分子质量调节剂的手段可以使单体转化率达到70%时丁苯橡胶生胶的门尼黏度达到国家标准的要求。同时以原配方及其调整数据为基础,采用Levenberg-Marquardt算法对所建立的BP神经网络模型进行训练,仿真结果显示该网络的仿真数据与实验数据的误差小于1%,具有较好的一致性,可以用于判断丁苯乳液聚合不同配方在特定反应条件下产物的门尼黏度。     

Evaluation of neural network models and quality forecasting based on process time-series data

Complex industrial process modelling is critically important within the context of industrial intelligence. In recent years, soft sensor techniques based on neural networks have become increasingly popular for modelling nonlinear industrial processes. This paper proposes an integrated framework of neural network modelling and evaluation for nonlinear dynamic processes. This framework achieves an integrated solution for modelling, prediction, evaluation, and network structure parameter selection. It can be applied to noisy sensors and dense data in the time domain. The framework's proposed evaluation mechanism employs two novel evaluation metrics, the variational auto-encoder (VAE)-based Kullback–Leibler (KL) divergence metric and the maximum likelihood estimation-based J metric, which both evaluate the model by mining the statistical properties of the residuals. The framework models the dynamic process with a model order based-gated recurrent units (MOb-GRU) neural network and a modified transformer model. Numerical experiments demonstrate that the evaluation mechanism functions properly in scenarios with multiple signal-to-noise ratios and multiple noise statistical properties and that the framework produces accurate modelling results.     

Batch-to-batch optimal control of a batch polymerisation process based on stacked neural network models

A neural network based batch-to-batch optimal control strategy is proposed in this paper. In order to overcome the difficulty in developing mechanistic models for batch processes, stacked neural network models are developed from process operational data. Stacked neural networks have enhanced model generalisation capability and can also provide model prediction confidence bounds. However, the optimal control policy calculated based on a neural network model may not be optimal when applied to the true process due to model plant mismatches and the presence of unknown disturbances. Due to the repetitive nature of batch processes, it is possible to improve the operation of the next batch using the information of the current and previous batch runs. A batch-to-batch optimal control strategy based on the linearisation of stacked neural network model is proposed in this paper. Applications to a simulated batch polymerisation reactor demonstrate that the proposed method can improve process performance from batch to batch in the presence of model plant mismatches and unknown disturbances.     

Discoloration process modeling by neural network

The photo-oxidation of acid orange 52 dye was performed in the presence of H₂O₂, utilizing UV light, aiming the discoloration process modeling and the process variable influence characterization. The discoloration process was modeled by the use of feedforward neural network. Each sample was characterized by five independent variables (dye concentration, pH, hydrogen peroxide volume, temperature and time of operation) and a dependent variable (absorbance). The neural model has also provided, through Garson Partition coefficients and the Pertubation method, the independent variable influence order determination. The results indicated that the time of operation was the predominant variable and reaction mean temperature was the lesser influent variable. The neural model obtained presented coefficients of correlation on the order 0.98, for sets of trainability, validation and testing, indicating the power of prediction of the model and its character of generalization.     

Deep neural network based recursive feature learning for nonlinear dynamic process monitoring

The data collected from modern industrial processes always have nonlinear and dynamic characteristics. The recently developed deep neural network method, stacked denoising auto-encoder (SDAE), can extract robust nonlinear latent variables from data against noise. However, it leaves the dynamic relationship unconsidered. To solve this problem, a novel algorithm named the recursive stacked denoising auto-encoder (RSDAE) is proposed. To learn the dynamic relationship, the RSDAE focuses on the predictability of the latent variables in the recurrence to contain the most dynamic variations. After the dynamic variations are extracted by the RSDAE, there is little autocorrelation left in the residuals. Then, the residuals can be monitored by principal component analysis (PCA). For the purpose of process monitoring, corresponding fault detection statistics are developed based on the RSDAE. Finally, a numerical case and the Tennessee Eastman process benchmark are used to demonstrate the effectiveness of the proposed algorithm.     

基于BP神经网络的乳酸脱水制丙烯酸仿真模拟

通过生物质发酵产生的乳酸（2-羟基丙酸）在食品和医药领域具有广泛的商业应用价值,作为可再生资源的生物质乳酸已成为备受关注的化学原料。本文采用BP神经网络对乳酸脱水制丙烯酸的催化过程进行了仿真研究。采用正交实验设计确定实验点,主要考察原料液pH、原料液流速、载气流量和温度4个因素对丙烯酸产率的影响。针对正交实验的缺陷,将正交实验设计和神经网络结合起来,采用基于DOS界面并能方便调节BP神经网络计算的程序包对正交实验结果进行训练,用训练好的网络模拟催化反应体系的动态过程。结果表明,由神经网络仿真模拟出的三维图可以直观地体现各个反应条件对丙烯酸产率的影响,并用穷举法求出最佳反应条件,在该条件下的神经网络模拟产率为27.45%,与实验结果较吻合,相对误差约为－0.4391%。