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

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CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss

This paper considers contrastive training for cross-modal 0-shot transfer wherein a pre-trained model in one modality is used for representation learning in another domain using pairwise data. The learnt models in the latter domain can then be used for a diverse set of tasks in a zero-shot way, similar to ``Contrastive Language-Image Pre-training (CLIP)'' and ``Locked-image Tuning (LiT)'' that have recently gained considerable attention. Most existing works for cross-modal representation alignment (including CLIP and LiT) use the standard contrastive training objective, which employs sets of positive and negative examples to align similar and repel dissimilar training data samples. However, similarity amongst training examples has a more continuous nature, thus calling for a more `non-binary' treatment. To address this, we propose a novel loss function called Continuously Weighted Contrastive Loss (CWCL) that employs a continuous measure of similarity. With CWCL, we seek to align the embedding space of one modality with another. Owing to the continuous nature of similarity in the proposed loss function, these models outperform existing methods for 0-shot transfer across multiple models, datasets and modalities. Particularly, we consider the modality pairs of image-text and speech-text and our models achieve 5-8% (absolute) improvement over previous state-of-the-art methods in 0-shot image classification and 20-30% (absolute) improvement in 0-shot speech-to-intent classification and keyword classification.Comment: Accepted to Neural Information Processing Systems (NeurIPS) 2023 conferenc

Continuous phenol removal using Nocardia hydrocarbonoxydans in spouted bed contactor: Shock load study

Shock load studies are essential to investigate the suitability of biocontactors in degradation of pollutants. In the present work, the degradation of phenol by immobilized Nocardia hydrocarbonoxydans in a spouted bed contactor was conducted. Granular activated carbon (GAC) and polymer beads were tested for the immobilization of cells of N. hydrocarbonoxydans-NCIM 2386. Initially, batch immobilization study was conducted to know the quantity of immobilized microorganisms per gram of solids and then the immobilized solids were used in the spouted bed contactor for phenol degradation. Also, the shock loading of phenol and hydraulic shock load test was performed to check the stability of operation. The immobilized Nocardia cells sustained the shock load and hydraulic load of phenol. Increase of influent phenol concentration and dilution rates increased the steady state effluent phenol concentration. Almost 95% degradation at maximum phenol loading of 0.73 gL-1h-1 was achieved. GAC has more attached biomass weight compared to polymer beads

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

Improvement in performance of polysulfone membranes through the incorporation of chitosan-(3-phenyl-1h-pyrazole-4-carbaldehyde)

Pure polysulfone membranes are known to exhibit poor permeability, and high fouling. This study was conducted to explore the possibility of improving the permeation characteristics of polysulfone membranes by using a chitosan derivative as an additive. Polysulfone membranes blended with chitosan derivative 3-phenyl-1H-pyrazole-4-carbaldehyde (ChD) were prepared by the method of wet coagulation. The hydroxyl, amine and the imine functional groups present in the ChD evidently increased the hydrophilicity of the surface of the blended membranes which was confirmed by contact angle measurements. The contact angle of the blended membrane having 2 wt.% ChD was 62 ± 1 as compared to 70 ± 1 of neat polysulfone membrane. The SEM analysis of the blended membranes revealed a highly porous structure with a very thin surface skin layer, finger like projections in the sub-layer with a macro void structure at the base. The blended membranes also showed significant improvement in pure water flux of 351 L m−2 h−1 at 0.8 MPa trans membrane pressure (TMP) as compared to 24 L m−2 h−1 of neat polysulfone membrane at the same TMP. The anti-fouling test using bovine serum albumin exhibited improved anti-fouling characteristic of blended membranes with a maximum flux recovery ratio (FRR) of 57%. The heavy metal rejection study revealed that membrane P-1 showed maximum 36 and 29% rejection for Pb and Cu respectively

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