Modeling Bromate Formation During Ozonation

Bromate formation has been identified as a significant barrier in the application of ozone during water treatment for water sources that contain high levels of bromide. Bromate has been identified as a possible human carcinogen and bromate levels in drinking water are strictly controlled at 10 μg/L in most developed countries. Various models have been proposed to model bromate formation during ozonation based on raw water quality, ozone dose and contact time. Two main approaches for modeling have been used: an empirical regression modeling methodology and kinetic-based methodology. Currently, the benefit of the bromate models lies in their ability to show how process parameters may impact on the amount bromate formed.     

Modeling Oil Content of Sesame (Sesamum indicum L.) Using Artificial Neural Network and Multiple Linear Regression Approaches

Sesame (Sesamum indicum L.) is an important ancient oilseed crop with high oil content (OC) and quality. The direct selection to improve OC of sesame (OCS) due to low heritability leads to a low profit. The OCS modeling and indirect selection through high‐heritable characters associated with OCS using advanced modeling techniques is a beneficial approach to overcome this limitation that allows breeder to get a better idea of the plant properties that should be monitored during breeding experiments. This study, carried out in 2013 and 2014, compared the potential of artificial neural network (ANN) and multilinear regression (MLR) to predict OCS in the Imamzadeh Jafar plain of Gachsaran, Iran. Principal component analysis (PCA) and stepwise regression (SWR) were used to evaluate 18 input variables. Based on PCA and SWR, the 6 traits of number of capsules per plant (NCP), number of days from flowering to maturity (NDFM), plant height (PH), thousand seed weight, capsule length, and seed yield were chosen as input variables. The network with the sigmoid axon transfer function and 2 hidden layers was selected as the final ANN model. Results showed that the ANN predicted the OCS with more accuracy and efficacy (R² = 0.861, root mean square error [RMSE] = 0.563, and mean absolute error [MAE] = 0.432) compared with the MLR model (R² = 0.672, RMSE = 0.742, and MAE = 0.552). These results showed the potential of the ANN as a promising tool to predict OCS with good performance. Based on sensitivity tests, NCP followed by NDFM and PH, respectively, were the most influential factors in predicting OCS in both models. It seems that a breeding program to select or create long sesame genotypes with a long period from flowering to maturity can be a good approach to address OCS in the future.     

Survey of Seawater Intrusion in the Pearl River Basin and Modeling Bromate Formation during Ozonation of the Raw Water

Bromate formation has been identified as a significant barrier in the application of ozone during water treatment the downstream region of the Pearl River Basin that contains high levels of bromide. Seawater intrusion will increase bromide concentration in the inshore surface water. In this study, seawater intrusion in the Pearl River Basin was surveyed and modeling bromate formation during ozonation of the raw water affected by seawater intrusion was studied. Bromate formation models were developed to simulate the effects of the characteristics of water quality and the operating parameters of treatment processes on bromate formation during preozonation process and postozonation process. The results show that the downstream of the Pearl River Basin is affected seriously by seawater intrusion and the bromide mainly comes from seawater. Some empirical models were developed to estimate the concentration of bromate in ozonated surface raw water affected by seawater intrusion during the treatment process.     

纳米金属氧化物催化臭氧氧化抑制溴酸盐形成的效能研究

通过纯水的模拟研究,探讨了纳米SnO2和纳米TiO2催化臭氧氧化抑制溴酸盐形成的情况及不同条件下纳米TiO2的抑制效能.结果表明,纳米SnO2催化及纳米TiO2催化均能有效抑制溴酸盐的形成,相对单独臭氧氧化条件溴酸盐生成量分别降低了45.81％和74.10％;光照对纳米TiO2催化抑制溴酸盐形成的效能影响不大;反应温度在10～26℃之间时,随着温度的升高,纳米TiO2催化抑制溴酸盐形成的作用越明显;纳米TiO2催化抑制溴酸盐生成的效能随着Br-初始质量浓度的增加和pH的上升而降低.     

Data-driven prediction model of indoor air quality in an underground space

Several data-driven prediction methods based on multiple linear regression (MLR), neural network (NN), and recurrent neural network (RNN) for the indoor air quality in a subway station are developed and compared. The RNN model can predict the air pollutant concentrations at a platform of a subway station by adding the previous temporal information of the pollutants on yesterday to the model. To optimize the prediction model, the variable importance in the projection (VIP) of the partial least squares (PLS) is used to select key input variables as a preprocessing step. The prediction models are applied to a real indoor air quality dataset from telemonitoring systems data (TMS), which exhibits some nonlinear dynamic behaviors show that the selected key variables have strong influence on the prediction performances of the models. It demonstrates that the RNN model has the ability to model the nonlinear and dynamic system, and the predicted result of the RNN model gives better modeling performance and higher interpretability than other data-driven prediction models.     

Bromate Surveys in French Drinking Waterworks

Two bromate surveys were made recently in order to evaluate the frequency of bromate appearance in drinking waters issued from waterworks including one or two ozonation steps. The First survey was carried out on 47 waterworks. Two sampling campaigns were analyzed in cool and warm seasons. The objective of the second survey was to follow, during 4 to 10 months, at 12 selected waterworks.

The aim of this paper is to present the data obtained and to try to model for some waterworks the bromate formation by means of some important parameters (Br, O₃/DOC, T° and pH) of water to ozonate.

The main conclusion is that the bromate presence in distributed drinking waters is a reality for waterworks using ozonation steps, especially in warm period of the year. In the case of some waterworks, disinfection by sodium hypochlorite increased bromate levels in distributed water.

As shown by others on a laboratory-scale level, a multi-linear regression allows us the prediction of the bromate formation from some determining parameters, for some waterworks. However, the poor values of the linear regression lead us to have some doubts about its universal application in the real situation of an operating waterworks. A better evaluation of “C.t” will be required in the future in order to get a better prediction by the use of multi linear regression.     

Octane number prediction for gasoline blends

Artificial Neural Network (ANN) models have been developed to determine the Research Octane Number (RON) of gasoline blends produced in a Greek refinery. The developed ANN models use as input variables the volumetric content of seven most commonly used fractions in the gasoline production and their respective RON numbers. The model parameters (ANN weights) are presented such that the model can be easily implemented by the reader. The predicting ability of the models, in the multi-dimensional space determined by the input variables, was thoroughly examined in order to assess their robustness. Based on the developed ANN models, the effect of each gasoline constituent on the formation of the blend RON value, was revealed.     

Estimation of free-swelling index based on coal analysis using multivariable regression and artificial neural network

The effects of proximate, ultimate and elemental analysis for a wide range of American coal samples on Free-swelling Index (FSI) have been investigated by multivariable regression and artificial neural network methods (ANN). The stepwise least square mathematical method shows that variables of ultimate analysis are better predictors than those from proximate analysis. The non linear multivariable regression, correlation coefficients (R²) from ultimate analysis inputs was 0.71, and for proximate analysis input variables was 0.49. With the same input sets, feed-forward artificial neural network (FANN) procedures improved accuracy of predicted FSI with R² = 0.89, and 0.94 for proximate and ultimate analyses, respectively. The ANN based prediction method, as a first report, shows FSI is a predictable variable, and ANN can be further employed as a reliable and accurate method in the free-swelling index prediction.     

Analysis and prediction of indoor air pollutants in a subway station using a new key variable selection method

A new key variable selection and prediction model of IAQ that can select key variables governing indoor air quality (IAQ), such as PM₁₀, CO₂, CO, VOCs and formaldehyde, are suggested in this paper. The essential problem of the prediction model is the question of which of the original variables are the most important for predicting IAQ. The next issue is determining the number of key variables that should be ranked. A new index of discriminant importance in the projection (DIP) of Fisher??s linear discriminant (FLD) is suggested for selecting key variables of the prediction models with multiple linear regression (MLR) and partial least squares (PLS), as well as for ranking the importance of input measurement variables on IAQ prediction. The prediction models were applied to a real IAQ dataset from telemonitoring data (TMS) in a metro system. The prediction results of the model using all variables were compared with the results of the model using only key variables of DIP. It shows that the use of our new variable selection method cannot only reduce computational effort, but will also enhance the prediction performances of the models.     

基于人工神经网络的重组毕赤酵母表达期菌体浓度模型

针对生物反应过程控制关键变量难以测量的问题,提出一种基于人工神经网络的重组毕赤酵母高密度发酵表达期的细胞菌量软测量模型,并对该模型的拓扑结构以及训练参数进行了初步探讨.当选取合适的模型结构和输入参数,模型的预测值最大误差为3.12%,表明该模型的计算值与菌体浓度实验值基本一致.因此,在毕赤酵母的高密度培养过程中采用基于神经网络的软测量模型具有较高的准确度,可以应用于发酵过程中菌体浓度的实时预测.     

Detailed study on fusion characteristics of rigid poly(vinyl chloride) nanocomposites: The comparison of using multiple regression analysis and artificial neural network

Fusion behavior of poly(vinyl chloride) (PVC) compounds plays an important role in the development of physical properties of processed material. The fusion characteristics in PVC processing are governed by material variables that affect the fusion with some interactions. In this research, the aim was to characterize the effects of formulation ingredients on fusion characteristics of PVC. Four material parameters, including the contents of nanoclay (NC), azodicarbonamide, calcium stearate, and processing aid, are proposed as affecting variables. The fusion time (FT) as well as fusion factor (FF) are considered fusion indicators and are experimentally determined in some different levels of affecting parameters. The multivariable regression analysis (MRA) and the Artificial Neural Network (ANN) modeling are considered as two analytical methods. The regression analysis result for the FT denotes, in part, significant linear and quadratic effects of NC and also its significant interactions with azodicarbonamide and calcium stearate, whereas that of FF indicates only a linear effect of NC. ANN modeling is performed with a three‐layer (input, hidden, and output) neural network. The results of the comparison of the MRA and ANN predictions with experimental values are reported as the correlation coefficient (R²), mean‐square error, and mean absolute percentage error for both FF and FT parameters. The obtained values clearly denote that the ANN results are more precise and especially more general than those of MRA. However, in the case of FT, improvement of the ANN modeling is much greater than that of FF. J. VINYL ADDIT. TECHNOL., 21:147–155, 2015. © 2014 Society of Plastics Engineers     

Parameters Affecting the Formation of Bromate Ion During Ozonation

Batch type ozone experiments conducted on aquatic humic substances solutions spiked with bromide ion were developed to evaluate the importance of various parameters that may affect the formation of bromate ion during ozonation. The nature of the NOM, the alkalinity, the bromide ion content and the presence of ammonia were found to significantly affect the bromate ion production. Temperature and pH can be considered as minor factors. The ozonation of a clarified surface water using a continuous flow ozone contactor have shown that the addition of a low quantity of ammonia (0.05 to 0.1 mg/L NNH₄ ⁺) appeared to be an interesting option for controlling the bromate formation. On the contrary, the addition of hydrogen peroxide may enhance or reduce the bromate ion production, depending on the applied hydrogen peroxide/ozone ratio.     

Prediction of Bromate Removal in Drinking Water Using Artificial Neural Networks

In treatment of natural water resources, bromide transforms into carcinogenic bromate, especially during the ozonation process. Adsorption was used in the experimental part of this study to remove this harmful compound from drinking water. For this purpose, technically, HCl-, NaOH-, and NH₃-modified activated carbons were used. Scanning Electron Microscopy (SEM) and Brunauer–Emmett–Teller (BET) analyses were carried out within the characterization study. Moreover, the effects of diameters and heights of adsorption columns, flowrate, and particle size of adsorbent were investigated on the removal amounts of bromate. Optimum conditions were obtained from the experiments, and regional/real samples were collected and analyzed. After the experiments, an artificial neural network (ANN) was used to predict bromate removal percentage by using the observed data. Within this context, a feed-forward back-propagation ANN was chosen in this study. Additionally, the transfer function was selected as tangent sigmoid and 3 neurons were used in the hidden layer. Particle size and amount of the activated carbon, height and diameter of the column, volumetric flowrate, and initial concentration were selected as the input variables. Bromate removal percentage was selected as the output. It was found that the model an R value of 0.988, RMSE value of 3.47 and mean absolute percentage error (MAPE) of 5.19% in the test phase.     

Bromate Formation during Ozonation of Bromide Containing Drinking Water- a Pilot Scale Study

The effect of bromide ion concentration, pH, temperature, alkalinity, and hydrogen peroxide content on bromate formation was studied. Increase in pH was found to give the greatest increase in bromate formation. Also increase in the ozonation temperature, bromide ion concentration and hydrogen peroxide content increased the observed bromate concentration. Only increased alkalinity decreased the bromate formation during the ozonation experiments. Bromate formation exceeded the EU limit value for bromate ion, 10 μg/l, when the initial bromide ion concentration was around 100 μg/l, except for the alkalinity of 1.4 mmol/1, when the bromate formation was 9.4 μg/l.     

Comparative Assessment of Bromate Control Options

Various bromate control options were assessed through an intensive pilot testing program, performed with a four-cell bubble contactor, and focused on intermediate ozonation of conventionally-treated sand-filtered water. Both acid addition and ammonia addition independently provided good bromate reduction, with their combinational addition providing no further reduction. On a CT basis, the use of a static mixer did not increase bromate formation, while staged ozonation enhanced bromate formation over single stage application.     

A novel chemometric method for the prediction of human oral bioavailability

Orally administered drugs must overcome several barriers before reaching their target site. Such barriers depend largely upon specific membrane transport systems and intracellular drug-metabolizing enzymes. For the first time, the P-glycoprotein (P-gp) and cytochrome P450s, the main line of defense by limiting the oral bioavailability (OB) of drugs, were brought into construction of QSAR modeling for human OB based on 805 structurally diverse drug and drug-like molecules. The linear (multiple linear regression: MLR, and partial least squares regression: PLS) and nonlinear (support-vector machine regression: SVR) methods are used to construct the models with their predictivity verified with five-fold cross-validation and independent external tests. The performance of SVR is slightly better than that of MLR and PLS, as indicated by its determination coefficient (R(2)) of 0.80 and standard error of estimate (SEE) of 0.31 for test sets. For the MLR and PLS, they are relatively weak, showing prediction abilities of 0.60 and 0.64 for the training set with SEE of 0.40 and 0.31, respectively. Our study indicates that the MLR, PLS and SVR-based in silico models have good potential in facilitating the prediction of oral bioavailability and can be applied in future drug design.     

How Bromate and Ozone Concentrations Can be Modeled at Full-Scale Based on Lab-Scale Experiments – A Case Study

This article presents a full-scale modeling study of an industrial ozonation unit for practical application. The modeling framework combines an integrated hydraulic model (systematic network) with a quasi-mechanistic chemical model. Dealing with natural water, the chemical model has to be parameterized, and the parameters calibrated. This was done based on lab-scale experiments. The calibration results showed that the chemical model is able to account for changes in contact time with ozone, pH, temperature, ozone dose, NOM concentration, bromide concentration. Comparison of residence time distributions showed that the hydraulic model accurately reproduces flow conditions. Six sampling points were installed along an industrial ozonation unit of 487 m³ consisting of two baffled tanks in series. Bromate and ozone concentrations were monitored under varying operational process conditions. After the selection of a value for the k_La, simulations were run. Using the lab-scale calibrated models, simulated and experimental data were found in close agreement: 84% of the simulated concentrations for ozone matched measurements (±experimental error), 60 % for bromate. A readjustment of the kinetics of a single reaction (out of 65) showed that seasonal changes in NOM activity may easily be taken into account based on regular concentration measurements (90% of the bromate concentrations were then modeled accurately).     

Neural network (ANN) approach to biodiesel analysis: Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy

The use of ethanol and biodiesel, which are alternative fuels or biofuels, has increased in the last few years. Modern official standards list 25 parameters that must be determined to certify biodiesel quality, and these analyses are expensive and time-consuming. Near infrared (NIR/NIRS) spectroscopy (4000-12,820 cm⁻¹) is a cheap and fast alternative to analyse biodiesel quality, when compared with infrared, Raman, or NMR methods, and quality control can be done in realtime (on-line).We compared the performance of linear and non-linear calibration techniques - namely, multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLS), polynomial and Spline-PLS versions, and artificial neural networks (ANN) - for prediction of biodiesel properties from near infrared spectra. The model was created for four important biodiesel properties: density (at 15 °C), kinematic viscosity (at 40 °C), water content, and methanol content. We also investigated the influence of different pre-processing methods (Savitzky-Golay derivatives, orthogonal signal correction) on the model prediction capability. The lowest root mean squared errors of prediction (RMSEP) of ANN for density, viscosity, water percentage, and methanol content were 0.42 kg m⁻³, 0.068 mm² s⁻¹, 45 ppm, and 51 ppm, respectively. The artificial neural network (ANN) approach was superior to the linear (MLR, PCR, PLS) and “quasi”-non-linear (Poly-PLS, Spline-PLS) calibration methods.