快速测定COD最佳催化消解体系研究

通过正交试验 ,确定了测定环境水样中COD的最佳回流消解条件。以CuSO4作催化剂 ,KAl(SO4) 2 Na2 MoO4作助催化剂 ( 0 1 8gCuSO4·5H2 O 0 1 0gKAl(SO4) 2 ·1 2H2 O 0 1 0gNa2 MoO4·2H2 O) ,在H2 SO4 H3PO4体系中可很好地实现对样品的消解。该体系将回流时间由国标法的 2h缩短至 2 0min ,实现了快速测定的目的且大幅度降低测定成本。样品 1 0次平行测定的相对标准偏差为 1 1 % ;加标回收率为 97 7%～ 1 0 3 3% ,均与国标法无显著性差异     

生化需氧量(BOD_5)测定中稀释倍数的简捷确定法

在生化需氧量 (BOD5)的测定中 ,稀释倍数的估计是否适当至关重要。该文根据废水生物处理可行性的判断标准———BOD5/CODcr比值的波动范围 ,将其分为三个区间 ,经过数学推导 ,得出各区间的稀释倍数 ,即n1=0 .57COD/d ,n2 =1.3COD/d ,n3=1.2 3COD/d ,其中d为当天稀释水的溶解氧。这三个稀释倍数可覆盖BOD5整个波动范围。由此 ,根据化学需氧量 (CODcr)可以简捷地确定出稀释倍数 ,从而提高了BOD5测定的有效性和准确性     

Characterization of a Stable Adaptive Calibration Model Using Near-Infrared Spectroscopy and Partial Least Squares with a Kalman Filter

The calibration model of near-infrared (NIR) spectra established using the Kalman filter-partial least square (partial least squares combined with a Kalman filter) method can be adapted to outdated equipment, environmental changes, external samples, and other applications. However, the variance of the measurement noise estimation for NIR spectrum measurements cannot be easily obtained using Kalman filter-partial least squares; therefore, the variance in the measurement noise is often assumed to be zero for the Kalman filter-partial least square calibration model, which affects the stability of the model. In this study, the measured input and output data were used effectively, and the gamma test method for estimating the measurement noise variance was used to improve the stability of the Kalman filter-partial least square calibration model. First, an accurate estimation of the measurement noise variance was obtained, and accurate modeling was then performed using Kalman filter-partial least squares. Finally, 600 abandoned drilling fluid samples were used to confirm the validity of the proposed method. The Kalman filter-partial least square and gamma test-Kalman filter-partial least square methods are compared. Testing of external samples 401–600 demonstrated that the stability of the Kalman filter-partial least square model decreased. The root mean square error of the prediction of the Kalman filter-partial least square model was 27.135, which was worse than that of the gamma test-Kalman filter-partial least square model (20.307). The validation results show that the proposed method has better stability in tracking the evolution of the NIR spectrometer’s measurement state.     

Support vector machines in water quality management

Support vector classification (SVC) and regression (SVR) models were constructed and applied to the surface water quality data to optimize the monitoring program. The data set comprised of 1500 water samples representing 10 different sites monitored for 15 years. The objectives of the study were to classify the sampling sites (spatial) and months (temporal) to group the similar ones in terms of water quality with a view to reduce their number; and to develop a suitable SVR model for predicting the biochemical oxygen demand (BOD) of water using a set of variables. The spatial and temporal SVC models rendered grouping of 10 monitoring sites and 12 sampling months into the clusters of 3 each with misclassification rates of 12.39% and 17.61% in training, 17.70% and 26.38% in validation, and 14.86% and 31.41% in test sets, respectively. The SVR model predicted water BOD values in training, validation, and test sets with reasonably high correlation (0.952, 0.909, and 0.907) with the measured values, and low root mean squared errors of 1.53, 1.44, and 1.32, respectively. The values of the performance criteria parameters suggested for the adequacy of the constructed models and their good predictive capabilities. The SVC model achieved a data reduction of 92.5% for redesigning the future monitoring program and the SVR model provided a tool for the prediction of the water BOD using set of a few measurable variables. The performance of the nonlinear models (SVM, KDA, KPLS) was comparable and these performed relatively better than the corresponding linear methods (DA, PLS) of classification and regression modeling.     

环境水中化学需氧量的FI分光光度法自动在线检测

本文将流动注射分光光度法用于环境废水中化学需氧量（ＣＯＤ）的自动在线检测，方法线性范围在０－１００ｍｇ／Ｌ之间，相对标准偏差＜２％以葡萄糖和苯二甲酸氢钾混合液制备标准溶液，对标准参考水样ＣＯＤ含量（Ｃｒ值）的测定结果表明两者有良好的相关性（Ｒ＝０．９８８０）对环境水样中ＣＯＤ含量的动态变化连续１０ｈ模拟试验，自制的在线过滤装置稳定，未发生堵塞现象；该系统可以较好地跟踪水样中ＣＯＤ浓度的实际变化     

Simulation of aerated lagoon using artificial neural networks and multivariate regression techniques

The aim of this study was to develop an empirical model that provides accurate predictions of the biochemical oxygen demand of the output stream from the aerated lagoon at International Paper of Brazil, one of the major pulp and paper plants in Brazil. Predictive models were calculated from functional link neural networks (FLNNs), multiple linear regression, principal components regression, and partial least-squares regression (PLSR). Improvement in FLNN modeling capability was observed when the data were preprocessed using the PLSR technique. PLSR also proved to be a powerful linear regression technique for this problem, which presents operational data limitations.     

丙烯酸生产外排废水化学需氧量与总有机碳相关性研究

化学需氧量（COD_Cr）与总有机碳（TOC）指标均可用来表征废水的有机物污染程度.不过,以COD_Cr表征有机物污染程度一般只能反映水中部分有机物的量,而TOC能更全面地反映废水中有机物的含量.以丙烯酸生产外排废水为研究对象,分析结果表明：在一定范围内,废水中COD_Cr与TOC满足关系式y=1.147 4x+74.75（R²=0.955 26）,回收率范围98%~115%,TOC测定结果的精密度高、可靠、准确,在一定条件下,可使用TOC来间接换算COD_Cr.     

水环境中有机污染物生化需氧量的测定

本文从理论和实践上讨论了水质监测中有机污染物生化需氧量(BOD)测定中的有关问题。其中包括BOD测定中发生的生化反应过程，好氧生化反应速率及速率常数，影响BOD测定结果的各种因素，BOD测定技术，以及BOD与COD、TOC的相关性。     

A sensitive, rapid ferricyanide-mediated toxicity bioassay developed using Escherichia coli

A need for rapid toxicity techniques has seen recent research into developing new microbiological assays and characterising their toxicity responses using a range of substances. A microbiological bioassay that determines changes in ferricyanide-mediated respiration for toxicity measurement (FM-TOX) shows particular promise. The development and optimisation of an improved FM-TOX method, incorporating novel features, is described using Escherichia coli as the biocatalyst. Omission of an exogenous carbon source, used in previously described FM-TOX assays, substantially improves the assay sensitivity. In addition, the development of a two-step procedure (toxicant exposure followed by determination of microbial respiratory activity) was found to enhance the inhibition of E. coli by 3,5-dichlorophenol and four other toxicants, compared to single-step procedures. Other assay parameters, such as the ferricyanide concentration, exposure times and optical density of the biocatalyst were also optimised, sometimes based on practical aspects. Toxicity tests were carried out using the adopted technique on both organic and inorganic toxicants, with classic sigmoid-shaped dose-response curves observed, as well as some non-standard responses. IC₅₀ data is presented for a number of common toxicants. The optimised assay provides a good foundation for further toxicity testing using E. coli, as well as the potential for expanding the technique to utilise other bacteria with complementary toxicity responses, thereby allowing use of the assay in a range of applications.