Activation of persulfate (PS) and peroxymonosulfate (PMS) and application for the degradation of emerging contaminants

Sulfate radical-based advanced oxidation processes (SR-AOPs) employing heterogeneous catalysts to generate sulfate radical (SO4 −) from peroxymonosulfate (PMS) and persulfate (PS) have been extensively employed for organic contaminant removal in water. This article aims to provide a state–of–the–art review on the recent development in heterogeneous catalysts including single metal, mixed metal, and nonmetal carbon catalysts for organic contaminants removal, with particular focus on PMS activation. The hybrid heterogeneous catalyst/PMS systems integrated with other advanced oxidation technologies is also discussed. Several strategies for the identification of principal reactive radicals in SO4 −–oxidation systems are evaluated, namely (i) use of chemical probe or spin trapping agent coupled with analytical tools, and (ii) competitive kinetic approach using selective radical scavengers. The main challenges and mitigation strategies pertinent to the SR-AOPs are identified, which include (i) possible formation of oxyanions and disinfection byproducts, and (ii) dealing with sulfate produced and residual PMS. Potential future applications and research direction of SR-AOPs are proposed. These include (i) novel reactor design for heterogeneous catalytic system based on batch or continuous flow (e.g. completely mixed or plug flow) reactor configuration with catalyst recovery, and (ii) catalytic ceramic membrane incorporating SR-AOPs.

Generation of sulfate radical through heterogeneous catalysis for organic contaminants removal: Current development, challenges and prospects

Application of peroxymonosulfate and its activation methods for degradation of environmental organic pollutants: Review

Recently sulfate radical-based advanced oxidation processes (SR-AOPs) attract increasing attention due to their capability and adaptability in decontamination. The couple of cobalt and peroxymonosulfate (PMS) is an efficient way to produce reactive sulfate radicals. This article reviews the state-of-the-art progress on various heterogeneous cobalt-based catalysts for PMS activation, including cobalt oxides, cobalt-ferrite and supported cobalt by diverse substrates. We summarize the intrinsic properties of these catalysts and their fundamental behaviors in PMS activation, as well as synthetic approaches. In addition, influencing factors and synergistic techniques of Co/PMS systems in organic degradation and possible environmental applications are also discussed. Finally, we propose perspectives on challenges related to cobalt-based catalysts, heterogeneous Co/PMS systems and their potential applications in practical environmental cleanup.

Cobalt-catalyzed sulfate radical-based advanced oxidation: A review on heterogeneous catalysts and applications

Adsorption of heavy metals on conventional and nanostructured materials for wastewater treatment purposes: A review

Abstract In this paper, a Chattering Free Sliding Mode Control (CFSMC) with observer based adaptive Radial Basis Function Neural Network (RBFNN) has been designed for first-order transfer function model of temperature trajectory in a fixed bed reactor. The steady-state behavior and effect of different operating parameters such as feed velocity and temperature on the operation of the fixed bed reactor have been discussed. Due to RBFNN’s capability to map the nonlinear dynamics online through self-learning ability, it is combined with CFSMC to reduce the chattering behavior. The adaptive RBFNN has been used to approximate the nonlinear dynamic behavior of the fixed bed reactor. To predict the states of the system, high gain observer based on adaptive RBFNN has been used. Design parameter of the observer has been estimated using Hurwitz polynomial. The effect of neuron number on the mapping error and the effect of space discretization step on modeling error have also been discussed. To decrease the chattering generated by the Sliding Mode Controller (SMC) in the temperature trajectory tracking, an equivalent control term is neglected from the final controller. It has two main advantages: one is the reduction in chattering behavior which is the main drawback of SMC and the second is the reduction of the high gain requirement. The SMC is used to overcome against external disturbance, load variation, variation in key parameters and model mismatch. To make the simulation realistic, constraints have been applied to control input and input rate. For guaranteeing the system stability, Lyapunov theorem has been applied. To show the suitability of the hybrid controller, a comparison has been carried out between the hybrid and PID controller. To quantify the performance, Integral Time Weighted Absolute Error (ITAE) has been estimated. Under the condition of existing model errors and external disturbances, simulation study of the control of the fixed bed reactor shows that the hybrid control algorithm consisting of sliding mode control and observer-based adaptive RBFNN performs well both for tracking the temperature trajectory and reducing the chattering.

Chattering Free Sliding Mode Control with Observer Based Adaptive Radial Basis Function Neural Network for Temperature Tracking in a Fixed Bed Reactor

Macrophages play an important role in the early development of type 1 diabetes (T1D). Based on the phenotype, macrophages can be classified into pro-inflammatory (M1) and anti-inflammatory (M2) macrophages. Despite intensive research in the field of macrophages and T1D, the kinetic response of M1/M2 ratio has not been studied in T1D. Thus, herein, we studied the M1 and M2 macrophages in the early development of T1D using the multiple low dose streptozotocin (MLDSTZ) mouse model. We determined the proportions of M1 and M2 macrophages in thymic glands, pancreatic lymph nodes and spleens on days 3, 7 and 10 after the first injection of STZ. In addition, we investigated the effect of IL-35 in vivo on the M1/M2 ratio and IL-35+ plasmacytoid dendritic cells in diabetic mice and in vitro on the sorted macrophages. Our results revealed that the M1/M2 ratio is higher in STZ-treated mice but this was lowered upon the treatment with IL-35. Furthermore, IL-35 treated mice had lower blood glucose levels and a higher proportion of IL-35+ cells among pDCs. Macrophages treated with IL-35 in vitro also had a higher proportion of M2 macrophages. Together, our data indicate that, under diabetic conditions, pro-inflammatory macrophages increased, but IL-35 treatment decreased the pro-inflammatory macrophages and increased anti-inflammatory macrophages, further suggesting that IL-35 prevents hyperglycemia by maintaining the anti-inflammatory phenotype of macrophages and other immune cells. Thus, IL-35 should be further investigated for the treatment of T1D and other autoimmune disorders.

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Interleukin-35 Prevents the Elevation of the M1/M2 Ratio of Macrophages in Experimental Type 1 Diabetes

A macroscopic mass transfer model based on the unsteady-state liquid film mass transfer mechanism for a single spherical bubble was formulated. Analytical solution of the model equation was obtained in Laplace transform using surface renewal rates based on Danckwerts' surface age distribution function. The mass transfer coefficient, kL, in a slurry bubble column under different operating conditions of temperature, pressure, gas flow rate, and solid concentration has been simulated using a program code “BUBBLESIM” in MATLAB®, developed by the authors. The proposed model has been validated using secondary data for a slurry system under a wide range of operating conditions. The predicted values of kL show very good agreement with the experimental data within an average deviation of ±2%. The results show that the mass transfer coefficient, kL, increased with increasing superficial gas velocity and temperature and decreased with increase in slurry concentration, while it changed slightly with pressure. Based on the present work, empirical correlations have been proposed for the prediction of δ in terms of dimensionless groups for H2–, CO–, and CO2–paraffin–quartz sand systems under elevated temperatures (298–423 K) and elevated pressures (1–3 MPa) in a slurry bubble column.

Estimation of liquid-side mass transfer coefficient and liquid film thickness in a bubble column using single spherical bubble model

To cope up with the strict emission and product quality standards the process industries are stern for the maintenance of the product quality and waste emission. For process industries, online hardware analyzers are costly alternate for analyzing the product quality, they are switching to online soft sensors which are cheap and efficient in use. In this work, soft sensor based on artificial neural network (ANN) has been developed. The motive of the sensor is to measure the immeasurable primary variables of the reactive distillation column i.e. product concentration, using the data of easily measurable secondary variables i.e. tray temperatures. ANN soft sensor has been trained using Levenberg-Marquardt algorithm. The predictions of the soft sensor are compared with the MATLAB mathematical model outputs.

https://accentsjournals.org/PaperDirectory/Journal/IJATEE/2016/11/2.pdf

Kailash Singh

Papers

Chattering Free Sliding Mode Control with Observer Based Adaptive Radial Basis Function Neural Network for Temperature Tracking in a Fixed Bed Reactor

Interleukin-35 Prevents the Elevation of the M1/M2 Ratio of Macrophages in Experimental Type 1 Diabetes

Estimation of liquid-side mass transfer coefficient and liquid film thickness in a bubble column using single spherical bubble model

ANN based soft sensor model for reactive distillation column

Vacuum Membrane Distillation for Water Desalination