S Mohan

Cellular Automata (CA) is an evolutionary computing technique that makes discrete idealizations of differential equations and represents the physical system at the mesoscopic scale. A novel CA approach for predicting the temporal and spatial variations of chlorine in Water Distribution Systems (WDSs) is presented in this paper. Random Walk Particle Tracking (RWPT), a stochastic Lagrangian technique, is used to represent the advection and dispersion processes. A one-dimensional CA-based reactive-transport model for chlorine, named as RWPT_CA model, incorporating advective-dispersive transport mechanism is developed and demonstrated. The significance of the cell dimension in the model algorithm is ascertained, and a deterministic approach is formulated for its selection. An indirect numerical solution technique is developed to improve the computational efficiency of the CA algorithm and to minimize the restrictions in the process of discretization of mass into equivalent particles. The numerical accuracy of the proposed RWPT_CA model is verified by applying it on to a benchmark problem. The RWPT_CA model provided excellent representations of the chlorine concentration profiles for low to medium range dispersion in WDSs. The model testing on a benchmark problem from the literature, well tested by researchers, revealed its effectiveness to derive the chlorine concentration patterns under dynamic hydraulic conditions. The dispersion mechanism was found significant in controlling the temporospatial distribution of chlorine at the nodes farther from the source nodes. The models which consider only advective transport mechanism were found over-predicting the chlorine concentrations, and thereby, establishing untrue representations of the quality of the delivered water.

Reuse of wastewater for irrigation is a common practice adopted worldwide. Nearly 90% of such activity is done without proper treatment of wastewater. As a result, 10 % of the world population consuming foods produced from such reuse are exposed to a high risk of trace metal accumulation. In addition to human health risk, the lack of proper treatment before reuse may affect the environment polluting soil and groundwater. The study's objective is to evaluate the effect of wastewater irrigation on the transfer of metals from soil to plants and their toxic effect. The soil and plant samples were collected from the wastewater irrigation region, where such practice is done for more than five decades. The Daily Intake level of Metals (DIM) were found in order Cu> Mn> Cr> Sr> Al> Zn> Ba> Ni> Se> Cd> Co> Mo> As representing potential accumulation of these trace elements in human food chain. The BCF (Bioconcentration Factor) of Cu was critical, ranging from 2.5 to 11, and PLI (Pollution Load Index) was found higher in order Cu>Ni>Pb>Se>Zn. The concentration of certain metals (Al, Cu, Mn) was higher in the leaf than in the stem. Among six varieties of plant samples, Alternanthera sessile was found to exhibit good bioaccumulation capacity, followed by Moringa oleifera. The concentration of Cu and Pb was higher than the standards in all the plants.

Triclosan (TCS) is extensively used in healthcare and personal care products as an antibacterial agent. Due to the persistent and toxic nature of TCS, it is not completely degraded in the biological wastewater treatment process. In this research work, identification of TCS degrading bacteria from municipal wastewater sludge and applying the same as bioaugmentation treatment for wastewater have been reported. Based on the 16S rRNA analysis of wastewater sludge, it was found that Providencia rettgeri MB-IIT strain was active and able to grow in higher TCS concentration. The identified bacterial strain was able to use TCS as carbon and energy source for its growth. The biodegradation experiment was optimized for the operational parameters viz. pH (5−10), inoculum size (1–5% (v/v)) and different initial concentration (2, 5, and 10 mg/L) of TCS. During the TCS degradation process, manganese peroxidase (MnP) and laccase (LAC) enzyme activity and specific growth rate of P. rettgeri strain were maximum at pH=7% and 2% (v/v) inoculum size, resulting in 98% of TCS removal efficiency. A total of six intermediate products were identified from the Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analysis, and the two mechanisms responsible for the degradation of TCS have been elucidated. The study highlights that P. rettgeri MB-IIT strain could be advantageously used to degrade triclosan present in the wastewater.

A novel way of performing nondominated sorting in a multiobjective optimization problem is proposed using a modified directional Bat algorithm. Unlike NSGA-II, where the solutions of two generations are merged and then sorted for elitism, in the proposed algorithm, the solution is generated and compared with all the previous solutions one by one. Hence, this method reduces the computational time by avoiding the comparison of solutions of two generations, at the same time, generates a diverse solution. A unique way of sorting the solutions is proposed using a Nondomination matrix, which can easily be updated if a new solution is accepted. The Nondomination matrix serves as an archiving strategy to preserve elitism. Detailed criteria are proposed for the selection of a new solution. We have tested the proposed algorithm on some of the standard benchmark optimization problems. The results show that the proposed algorithm is very competitive and outperforms other algorithms in terms of efficiency and other performance metrics for most problems. The algorithm also provides a standard platform for nondomination sorting, which can be applied to any other metaheuristic algorithm.

Pharmaceutical industries are known for their batch operations using wide varieties of solvents, reactants, and catalysts. To study the efficiency of production process within a pharmaceutical industry, synthesis of an active pharmaceutical ingredient (API) namely, aliskiren hemifumarate (AH) was analyzed, which is a blood-pressure-lowering medicine. The process mass index, a metric to assess the efficiency of AH production, was calculated. It was found that the process mass index for the AH synthesis was 109 kg raw materials/kg of product, which showed the amount of waste generated during its synthesis. In addition, a life cycle assessment (LCA) study was performed on AH synthesis to understand the overall impacts throughout the life cycle of the product. The results of the LCA revealed that among the various raw materials required, the metal catalyst palladium and solvent dichloromethane were found to have the highest impact on the environment as well as on human health. Both the metal catalyst and solvent play important roles in improving the sustainability of the production. Therefore, the study was extended by comparing the base case with two scenarios of process modification, replacing the toxic palladium with other catalysts and replacing dichloromethane with ethanol, acetone, and benzene. The results of the modified case showed a reduction in the impacts on human health by 97.7%, on the ecosystem by 98.3%, and on resources by 74.1%, thus enhancing the sustainability of the overall process.

We have examined the effect of bifunctional group in the same cross-linkage degree on the Li isotope fractionation in the cation exchange reaction in the aqueous solutions ranging in temperature from 278 to 333 K. For this purpose, the sulfonated pyridine-styrene-divinylbenzene resin with the cross-linkage degree of 50 wt%, embedded in porous silica beads was successfully synthesized by a typical polymerization method. The isotope separation coefficients (ε) per unit mass (ε/ΔMass) was 8.1 × 10–4 at 298 K. Therefore, the effect of bifunctional group against the ε/ΔMass value has been discussed, compared with those of previous works.

This paper describes a novel cellular automata-based mesoscale multispecies reactive-transport (CA-MSRT) model for predicting microbial regrowth and trihalomethanes (THMs) formation in water distribution systems (WDSs) under different loading and temperature conditions. The kinetic models to analyze the degradation of chlorine, natural organic matter (NOM), and microbial biomass and formation of THMs during secondary chlorination applications are also proposed. The CA-MSRT model was applied to a benchmark WDS with two water sources (river and lake), a problem well tested by many researchers. A 60% reduction in total organic carbon (TOC) combined with chlorine application of 1 mg/L at the lake water source has resulted in regulating the microbiological and chemical quality of the delivered water at the outlet points. Results indicate that supplementing the chlorine levels through secondary disinfection stimulates formation of THMs and thereby compromises the chemical quality of the delivered water. It was found that the fractions of THMs formed from the carbon content associated with attached and suspended microbial biomass were only <2%, and the remaining fractions were derived from NOM. Overall, the study confirmed the benefits of reducing the organic matter content and optimal selection of the chlorine dose at the source point in contriving the water quality at the outlet points of WDSs.

Effluent treatment plants act as the last line of defense against the discharge of pollutants from industrial effluents. The higher resource consumption, variety of dyes, and auxiliary chemicals used for textile manufacturing classify it as a highly polluting industry. Standards for color, organics, and dissolved solids are becoming more stringent with time, and local regulators are forced to insist on the establishment of zero liquid discharge (ZLD) units. However, the application of the ZLD concept has the major limitation of high energy consumption when compared with the conventional wastewater treatment plant. The application of carbon footprint analysis to both units would provide a comprehensive solution to the carbon footprint computation and bring out the advantages of the ZLD. The carbon footprint of a typical ZLD treatment facility in south India was found to be 10,598 tons of carbon dioxide equivalent per year (tCO2eq/year), which is only one-third more than that of a conventional treatment plant. The carbon footprint of a given ZLD treatment facility can effectively be used as a performance indicator to limit the overall energy consumption.

The Penman-Monteith evapotranspiration (ET) model has superior predictive ability to other methods, but it is challenging to apply in several Indian stations, owing to the need for a large number of climatic variables. The study investigated an artificial neural network (ANN) model for calculating ET for various agro-climatic regions of India. Sensitivity analysis showed that the overall average changes in ET0 values for 25% change in the climatic variables were 18, 16, 14, 7, 5, and 4%, respectively, for Tmax, RHmean, Rn, wind speed, Tmin, and sunshine hours. The dominant climatic variables were identified from the principal component analysis (PCA) and ET0 was computed using an ANN with dominant climatic variables. The ANN architecture with backpropagation technique had one hidden layer and neurons ranging from 10 to 30 for all climatic variables and from 5 to 10 for PCA variables. The new ET models were statistically compared with Penman-Monteith ET estimate, and found reliable. PCA variables guaranteed an estimate of ET0 accounting for 98% of the variability. The average values of coefficient of determination, standard error of estimate, and percentage efficiency were observed as 0.96, 0.24, and 94%, respectively.