PREDICTION OF BOD AND COD OF A REFINERY WASTEWATER USING MULTILAYER ARTIFICIAL NEURAL NETWORKS

In the recent past, artificial neural networks (ANNs) have shown the ability to learn and capture non-linear static or dynamic behaviour among variables based on the given set of data. Since the knowledge of internal procedure is not necessary, the modelling can take place with minimum previous knowledge about the process through proper training of the network. In the present study, 12 ANN based models were proposed to predict the Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) concentrations of wastewater generated from the effluent treatment plant of a petrochemical industry. By employing the standard back error propagation (BEP) algorithm, the network was trained with 103 data points for water quality indices such as Total Suspended Solids (TSS), Total Dissolved Solids (TDS), Phenol concentration, Ammoniacal Nitrogen (AMN), Total Organic Carbon (TOC) and Kjeldahl’s Nitrogen (KJN) to predict BOD and COD. After appropriate training, the network was tested with a separate test data and the best model was chosen based on the sum square error (training) and percentage average relative error (% ARE for testing). The results from this study reveal that ANNs can be accurate and efficacious in predicting unknown concentrations of water quality parameters through its versatile training process

Reduction of Color and COD of Anaerobically Treated Distillary Wastewater by Electrochemical Method

Abstrac

Prediction of Water Quality Indices by Regression Analysis and Artificial Neural Networks

The quality of wastewater generated in any process industry is generally indicated by performance indices namely BOD, COD and TOC, expressed in mg/L. The use of TOC as an analytical parameter has become more common in recent years especially for the treatment of industrial wastewater. In this study, several empirical relationships were established between BOD and COD with TOC using regression analysis, so that TOC can be used to estimate the accompanying BOD or COD. A new, the use of Artificial Neural Networks has been explored in this study to predict the concentrations of BOD and COD, well in advance using some easily measurable water quality indices. The total data points obtained from a refinery wastewater (143) were divided into a training set consisting of 103 data points, while the remaining 40 were used as the test data. A total of 12 different models (A1-A12) were tested using different combinations of network architecture. These models were evaluated using the % Average Relative Error values of the test set. It was observed that three models gave accurate and reliable results, indicating the versatility of the developed models

Studies on Biosorption of Methylene Blue from Aqueous Solutions by Powdered Palm Tree Flower (Borassus flabellifer)

Biosorption experiments were carried out for the removal of methylene blue (MB) using palm tree male flower (PTMF) as the biosorbent at various pH, temperature, biosorbent, and adsorbate concentration. The optimum pH was found to be 6.0. The kinetic data were fitted in pseudofirst-order and second-order models. The equilibrium data were well-fitted in Langmuir isotherm and the maximum equilibrium capacities of the biosorbent were found to be 143.6, 153,9, 157.3 mg/g at 303, 313, and 323 K, respectively. Thermodynamic data for the adsorption system indicated spontaneous and endothermic process. The enthalpy and entropy values for adsorption were obtained as 15.06 KJ/mol and 0.129 KJ/mol K, respectively, in the temperature range of 303–323 K. A mathematical model for MB transported by molecular diffusion from the bulk of the solution to the surface of PTMF was derived and the values of liquid phase diffusivity and external mass transfer coefficient were estimated

Adsorption of Phenol on Granular Activated Carbon from Nutrient Medium: Equilibrium and Kinetic Study

This paper presents the adsorption of phenol on granular activated carbon (GAC) from nutrient medium suitable for microorganisms’ growth and also the subsequent biodegradation. Two parameter Langmuir and Freundlich adsorption isotherm models were studied using large range of phenol concentration (50-1000 mg/L). In low range of phenol concentration (50-300 mg/L), correlation coefficient, normalized deviation "g% and separation factor were 0.9989, 2.18% and 0.38- 0.78 respectively, while for higher concentration range (400-1000 mg/L), the corresponding values were 0.9719, 1.9% and 0.45- 0.67. Freundlich isotherm gave correlation coefficient of 0.9984, 1/n. value of 0.7269 and normalized deviation of 4.55%. Comparison based on R2, adjusted R2, normalized deviation and root mean square deviation (RMSD) showed that the Redke-Prausnitz isotherm model gives better prediction compared to other models. Adsorption of phenol follows pseudo second order kinetics with correlation coefficient closer to one. Biodegradation study using immobilized cells of Nocardia hydrocarbonoxydans on GAC showed that, biodegradation begins well before GAC reaches the saturation period

ARPN Journal of Agricultural and Biological Science UREA HYDROLYSIS IN SATURATED LOAM SOIL

ABSTRACT In order to evaluate the extent of loss of applied fertilizer urea due to hydrolysis catalyzed by the enzyme urease and assist in the development of mathematical model for the spread of urea from the source of application, it is necessary to conduct hydrolysis studies in wet soils. Agricultural loam soil, with 13% clay content from west coast region of India, was taken up for this purpose. Maximum particle size of the soil was restricted to 2 mm in the study. Batch trials were conducted with different urea concentrations maintained in the soil which was previously incubated at 27 0 C for 48 hours under saturated condition. Evolved gases were allowed to escape to prevent build up of alkali and the subsequent deactivation of the enzyme at high pH. Urea estimation was carried out by colorimetric method. Results indicate that for soil solution urea concentration up to 43.6 mg/mL, the rate of hydrolysis increased with increasing initial urea concentration. For concentrations in the range of 43.6 to 243 mg/mL, rate of hydrolysis decreased with increasing initial urea concentration. The urease induced hydrolysis was completely deactivated at concentration of 305 mg/mL and beyond, due to the substrate induced inhibition. The experimental data could be fitted to a substrate inhibition model

Solar photocatalytically active, engineered silver nanoparticle synthesis using aqueous extract of mesocarp of Cocos nucifera (Red Spicata Dwarf)

Silver nanoparticles synthesised using aqueous extract of Cocos nucifera (CN) mesocarp were evaluated for their photocatalytic activity under solar irradiation. The silver nanoparticles were synthesised by a green method of harnessing bioactive phytocomponents from the mesocarp of Cocos nucifera. Large-scale application of this process necessitates the manoeuvering of the process parameters for increasing the conversion of silver ions to nanoparticles. Process parameters influencing the morphological characteristics of silver nanoparticles such as precursor salt concentration and pH of the synthesis mixture were studied. The crystalline nanoparticles were characterised using UV-vis spectroscopy, XRD, FTIR, SEM and EDX analysis. CN extract and 5 mM silver nitrate solution at a ratio of 1:4 (v/v) in the synthesis mixture was found to be the optimum. Alkaline initial pH of the synthesis mixture was found to favour the synthesis of smaller sized monodispersed silver nanoparticles. Solar energy was harnessed for the photocatalytic degradation of Malachite green dye using silver nanoparticles obtained through the green synthesis method. Overall process aims at utilisation of naturally available resource for the synthesis of silver nanoparticles as well as the degradation of dyes using these nanoparticles, making it useful in the treatment of wastewater