Electrocoagulation using Ti/Ti for the remediation and reuse of aqueous Dispersive Blue-79.

The entire ecology is contaminated by the synthetic dyes that are widely utilised in the textile industries. They can be handled using a variety of technologies, but an eco-friendly method called electrocoagulation has been used to prevent additional contamination. Textile wastewater containing disperse dyes are successfully treated in Electrocoagulation (EC) utilizing Al, Fe, and Stainless Steel (SS), but it is not cost effective, also the treated water contains certain mg/L of the metals used, along with dye components, which obstructs the reuse of the same. The effects of initial pH, applied voltage, dye concentration, supporting electrolyte, and treatment time on the colour removal efficiency (CRE) and consumption of energy were examined in EC process followed by activated charcoal filtration (hybrid process) with a monopolar Ti/Ti electrode on the remediation of aqueous solution of Dispersive Blue-79 (dye 3G). The maximum CREobtained was 99.4%, chemical oxygen demand (COD) 93%, and biological oxygen demand (BOD) 85%, under the following optimized operating conditions, applied voltage 15 V, pH = 7, concentration of dye, electrolyte 110 mg/L, 0.2 g/L and time = 15 min. The overall operating cost for the treatment of aqueous dye 3G was 0.455US/m3. The mechanism of EC was studied using XPS analysis in the sludge obtained. For the purpose of the reuse, FTIR, AAS, and ICP-OES analysis were done and compared with the aqueous dye 3G, after EC and hybrid process to ensure the maximum removal of the degraded dye components and metal. ICP-OES results showed that there were no traces of metal in the treated aqueous dye 3G using this method. Throughout the study, the experimental outcomes indicated that the hybrid process upgraded the quality of the treated aqueous dye 3G.

Electrochemical treatment of textile wastewater using copper electrodes.

The conventional electrode aluminum used in electrocoagulation (EC) for the textile wastewater undergoes pitting type of corrosion, so dissolution of the same is very high during electrolysis. This research focuses on the treatment of real-time textile effluent with copper electrodes that corrode uniformly during electrolysis, with optimizing operating parameters for high color removal efficiency (CRE%). The sludge acquired was analyzed by XPS and XRD to study the mechanism of dye removal. The treated effluent was subjected to phytotoxicity analysis using Vigna radiata to study the toxicity effect of the intermediary products. 98.6% of CRE was attained in treating the effluent with copper electrodes. XPS and XRD results showed that both Cu(OH)2 and CuO served as coagulants in the dye removal. The phytotoxicity results showed that the percentage of germination, shoot and root lengths of Vigna radiata in the treated effluent were similar to the results obtained for the control.

Elucidation of electrocoagulation mechanism in the removal of Blue SI dye from aqueous solution using Al-Al, Cu-Cu electrodes - A comparative study.

This work presents the research on the treatment of an anthraquinone derivatives of disperse dye Blue SI from aqueous solution using aluminium for the optimization of operational parameters like pH, current density, addition of electrolyte, contact time for the color removal efficiency (CRE) and the results are compared with the performance of copper electrodes in electrocoagulation (EC). The parameters for maximum CRE was found with Al at current density 40 Am-2, time 10 min at pH 7, and for Cu at 60 Am-2 15 min, at pH 6 were optimized. The characterization of the treated water using HPLC, MS studies revealed intermediate compounds. From the XPS analysis of the sludge obtained, the mechanism of EC was deduced. Treated aqueous solution was studied for its phytotoxicity with Vigna radiata and ecotoxicity studies were conducted on Artemia salina to study the toxicity effect of the intermediatory products in the treated dye solution. Blue SI dye aqueous solution treated with aluminium electrodes shows no or lesser toxicity in plants as well as in ecotoxic study compared with copper electrodes.

Simulation of anaerobic digestion processes using stochastic algorithm.

BACKGROUND: The Anaerobic Digestion (AD) processes involve numerous complex biological and chemical reactions occurring simultaneously. Appropriate and efficient models are to be developed for simulation of anaerobic digestion systems. Although several models have been developed, mostly they suffer from lack of knowledge on constants, complexity and weak generalization. The basis of the deterministic approach for modelling the physico and bio-chemical reactions occurring in the AD system is the law of mass action, which gives the simple relationship between the reaction rates and the species concentrations. The assumptions made in the deterministic models are not hold true for the reactions involving chemical species of low concentration. The stochastic behaviour of the physicochemical processes can be modeled at mesoscopic level by application of the stochastic algorithms. METHOD: In this paper a stochastic algorithm (Gillespie Tau Leap Method) developed in MATLAB was applied to predict the concentration of glucose, acids and methane formation at different time intervals. By this the performance of the digester system can be controlled. The processes given by ADM1 (Anaerobic Digestion Model 1) were taken for verification of the model. RESULTS: The proposed model was verified by comparing the results of Gillespie's algorithms with the deterministic solution for conversion of glucose into methane through degraders. At higher value of 'τ' (timestep), the computational time required for reaching the steady state is more since the number of chosen reactions is less. When the simulation time step is reduced, the results are similar to ODE solver. CONCLUSION: It was concluded that the stochastic algorithm is a suitable approach for the simulation of complex anaerobic digestion processes. The accuracy of the results depends on the optimum selection of tau value.

ANN application for prediction of atmospheric nitrogen deposition to aquatic ecosystems.

The occurrences of increased atmospheric nitrogen deposition (ADN) in Southeast Asia during smoke haze episodes have undesired consequences on receiving aquatic ecosystems. A successful prediction of episodic ADN will allow a quantitative understanding of its possible impacts. In this study, an artificial neural network (ANN) model is used to estimate atmospheric deposition of total nitrogen (TN) and organic nitrogen (ON) concentrations to coastal aquatic ecosystems. The selected model input variables were nitrogen species from atmospheric deposition, Total Suspended Particulates, Pollutant Standards Index and meteorological parameters. ANN models predictions were also compared with multiple linear regression model having the same inputs and output. ANN model performance was found relatively more accurate in its predictions and adequate even for high-concentration events with acceptable minimum error. The developed ANN model can be used as a forecasting tool to complement the current TN and ON analysis within the atmospheric deposition-monitoring program in the region.

Simulation of ammonium and chromium transport in porous media using coupling scheme of a numerical algorithm and a stochastic algorithm.

The migration of the species of chromium and ammonium in groundwater and their effective remediation depend on the various hydro-geological characteristics of the system. The computational modeling of the reactive transport problems is one of the most preferred tools for field engineers in groundwater studies to make decision in pollution abatement. The analytical models are less modular in nature with low computational demand where the modification is difficult during the formulation of different reactive systems. Numerical models provide more detailed information with high computational demand. Coupling of linear partial differential Equations (PDE) for the transport step with a non-linear system of ordinary differential equations (ODE) for the reactive step is the usual mode of solving a kinetically controlled reactive transport equation. This assumption is not appropriate for a system with low concentration of species such as chromium. Such reaction systems can be simulated using a stochastic algorithm. In this paper, a finite difference scheme coupled with a stochastic algorithm for the simulation of the transport of ammonium and chromium in subsurface media has been detailed.

A probabilistic cellular automaton for two dimensional contaminant transport simulation in ground water.

In recent years evolutionary computing algorithms have been proposed to solve many engineering problems. Genetic algorithms, Neural Networks, and Cellular Automata are the branches of evolutionary computing techniques. In this study, it is proposed to simulate the contaminant transport in porous media using a Cellular Automaton. The physical processes and chemical reactions occurring in the ground water system are intricately connected at various scales of space, time, transport coefficients and molecular concentration. The validity of continuous approach for the simulation of chemical systems with low concentration of species and intracellular environments has become subtle. Due to the difference in scales of various processes that occur in the ground water system, the description of the system can be well defined in the intermediate scale called mesoscopic scale, which is in between microscopic and macroscopic description. Mesoscopic models provide the relationship between various parameters and their evolvement in time, thus establishing the contact between modeling at various scales at the interface. In this paper, a Probabilistic Cellular Automaton (PCA) model has been developed based on the transport and reaction probability values. The developed model was verified and validated for one, two dimensional transport systems and also for the simulation of BTEX transport in two dimensional system in ground water.