含酚废水处理的吸附和臭氧氧化动力学模型

A three phase fluidized bed reactor was used to investigate the combined effect of adsorption and oxidation for phenolic wastewater treatment.Aqueous solutions containing 10 mg·L 1of phenol and ozone were continuously fed co-currently as upward flow into the reactor at constant flow rate of 2 and 1 L·min1,respectively.The phenolic treatment results in seven cases were compared:(a)O3 only,(b)fresh granular activated carbon(GAC),(c) 1st reused GAC,(d)2nd reused GAC,(e)fresh GAC enhanced with O3,(f)1st reused GAC enhanced with O3,and (g)2nd reused GAC enhanced with O3.The phenolic wastewater was re-circulated through the reactor and its concentration was measured with respect to time.The experimental results revealed that the phenolic degradation using GAC enhanced with O3 provided the best result.The effect of adsorption by activated carbon was stronger than the effect of oxidation by ozone.Fresh GAC could adsorb phenol better than reused GAC.All cases of adsorption on GAC followed the Langmuir isotherm and displayed pseudo second order adsorption kinetics.Finally,a differential equation for the fluidized bed reactor model was used to describe the phenol concentration with respect to time for GAC enhanced with O3.The calculated results agree reasonably well with the experimental results.     

Dynamic composition estimation for a ternary batch distillation

A novel Kalman estimator has been proposed to provide the estimates of dynamic composition in a ternary batch distillation process operated in an optimal-reflux policy. The estimator is formulated based on a sequence of reduced-order process models representing a whole batch behavior. Therefore, the full-order models are first developed around different pseudo-steady-state operating conditions along batch optimal profiles. Then they reduce their orders to achieve all state observability and controllability by a balanced truncation method. In the estimator scheme, the reduced models as well as relevant covariance matrices of process noise are pre-scheduled and switched according to any desired periods. Four important issues have been studied including selection of a sensor frequency, effects of an integrating step size, a state initialization and a measurement noise. The performances of the reduced estimator have been investigated and compared with those of a conventional nonlinear estimator. Simulation results have demonstrated that the performances of the novel linear estimator are reasonably good and almost identical to the nonlinear estimator in all cases, though the linear estimator performs rather sensitively to the effect of high measurement noise. Nevertheless, it has been found to be applicable to implement in real plants with much lower computation effort, easier state initialization and unrequired a priori knowledge of thermodynamics.     

Integrated data reconciliation with generic model control for the steel pickling process

To implement an advanced control algorithm, measurements of process outputs are usually used to determine control action to a process. Nevertheless, measurements of process outputs are often subjected to measuring and signal errors as well as noise. Therefore, in this work, Generic Model Control (GMC), an advanced control technique, with data reconciliation technique has been applied to control the pH of the pickling process consisting of three pickling and three rinsing baths. Here, the data reconciliation problem involves six nodes and fourteen streams. The presence of errors in the data set is determined and identified via measurement test, In addition, the measurement error covariance is initially assumed to be a known variance matrix and is updated every iteration. Simulation results have shown that the reconciled process data give a better view of the true states of the process than raw measuring data. With these reconciled process data, the GMC controller can control the process at a desired set point with great success.     

Human Hazard Assessment Using Drosophila Wing Spot Test as an Alternative In Vivo Model for Genotoxicity Testing—A Review

Genomic instability, one of cancer’s hallmarks, is induced by genotoxins from endogenous and exogenous sources, including reactive oxygen species (ROS), diet, and environmental pollutants. A sensitive in vivo genotoxicity test is required for the identification of human hazards to reduce the potential health risk. The somatic mutation and recombination test (SMART) or wing spot test is a genotoxicity assay involving Drosophila melanogaster (fruit fly) as a classical, alternative human model. This review describes the principle of the SMART assay in conjunction with its advantages and disadvantages and discusses applications of the assay covering all segments of health-related industries, including food, dietary supplements, drug industries, pesticides, and herbicides, as well as nanoparticles. Chemopreventive strategies are outlined as a global health trend for the anti-genotoxicity of interesting herbal extract compounds determined by SMART assay. The successful application of Drosophila for high-throughput screening of mutagens is also discussed as a future perspective.