Tertiary treatment of coke-oven wastewater using suspended and immobilized whole live cells of constructed bacterial-microalgal consortium: modeling and optimization using ANN-GA hybrid methodology.

Coke-oven wastewater (CW), containing an array of toxic pollutants above permissible limits even after conventional primary and secondary treatment, needs a tertiary (polishing) step to meet the statutory limit. In the present study, a suitable bacterial-microalgal consortium (Culture C) was constructed using bacterial (Culture B: Bacillus sp. NITD 19) and microalgal (Culture A: a consortium of Chlorella sp. and Synechococcus sp.) cultures at different ratios (v/v) and the potential of these cultures for tertiary treatment of CW was assessed. Culture C4 (Culture B:Culture A = 1:4) with inoculum size: 10% (v/v) was selected for the treatment of wastewater since the maximum growth (3.08 ± 0.57 g/L) and maximum chlorophyll content (4.05 ± 0.66 mg/L) were achieved for such culture in PLE-enriched BG-11 medium. During treatment of real secondary treated coke-oven effluent using Culture C4 in a closed photobioreactor, the removal of phenol (80.32 ± 2.76%), ammonium ions (47.85 ± 1.83%), fluoride (65.0 ± 4.12%), and nitrate (39.45 ± 3.42%) was observed after 24 h. In a packed bed bioreactor containing immobilized C4 culture, the maximum removal was obtained at the lowest flow rate (20 mL/h) and highest column bed height (20 cm). Artificial intelligence-based techniques were used for modeling and optimization of the process.

Modeling and multi-objective optimization of commercial ethylene oxide reactor to strike a delicate balance between profit and negative environmental impact.

The present work emphasizes the development of a generic methodology that addresses the core issue of any running chemical plant, i.e., how to maintain a delicate balance between profit and environmental impact. Here, ethylene oxide (EO) production plant has been taken as a case study. The production of EO takes place in a multiphase catalytic reactor, the reliable first principle-based model of which is still not available in the literature. Artificial neural network (ANN) was therefore applied to develop a data-driven model of the complex reactor with the help of actual industrial data. The model successfully built up a correlation between the catalyst selectivity and other operational parameters. This model was used to establish two objective functions, profit and environmental impact. In this paper, the negative environmental impact has been designated by Eco-indicator 99, which considers all the negative health and environmental impacts of a certain product. A recently developed metaheuristic optimization technique, namely multi-objective firefly (MOF) algorithm, was used to develop Pareto diagram of profit vs. Eco-99. The Pareto diagram will help the plant engineers to make strategy on what operating conditions to be maintained to make a delicate balance between profit and environmental impact. It was also found that by applying this modeling and optimization technique, for a 130 kTA EO plant, approximately 7048 t/year of carbon dioxide can be saved from emission into the atmosphere.

Enhancement of nutritional value of fried fish using an artificial intelligence approach.

Frying affects the nutritional quality of fish detrimentally. In this study, using Catla catla and mustard oil, experiments were carried out in varying temperatures (140-240 °C), times (5-20 min), and oil amounts (25-100 ml/kg of fish) which established drastic reduction of 44.97% and 99.40% for polyunsaturated fatty acid (PUFA)/saturated fatty acids (SFA) and index of atherogenicity (IA) profile, respectively. Artificial neural network (ANN) was implemented successfully to provide an association between the independent inputs with dependent outputs (values of R2 were 0.99 and 0.98; RMSE were 0.038 and 0.046; and performance were 0.038 and 0.067 for PUFA/SFA and IA, respectively) by exhaustive search of various algorithms and activation functions available in literature. ANN model-based meta-heuristic, stochastic optimization formalisms, genetic algorithm (GA) and particle swarm optimization (PSO), were applied to optimize the combination of cooking parameters for improving the nutritional quality of food which improved the nutritional value by maximizing the PUFA/SFA profile up to 63.05% and minimizing the IA profile to 99.64%. Multi-objective genetic algorithm (MOGA) was also employed to tune the inputs by maintaining a balance between the contrasting outputs and enhance the overall food value simultaneously with multi-objective (beneficial for health, economic, and environment-friendly) proposal. MOGA was able to improve the PUFA/SFA profile up to 44.76% and reduce the IA profile to 92.94% concurrently with the reduction of wastage of culinary media and energy consumption, following the optimized cooking condition (118.92 °C, 6.06 min, 40 ml oil/kg of fish).

Bioremediation of hexavalent chromium from wastewater using bacteria-a green technology.

Hexavalent chromium has high toxic effect on the ecological system. The aim of the present study is to isolate and characterize the bacteria that can reduce the toxicity of hexavalent chromium from liquid effluent. The bacterial isolate was identified as Bacillus sp. ltds1 after 16 S rRNA gene sequencing, and annotation has been submitted in National Center for Biotechnology Information (NCBI) GenBank. The bacterial strain was found able to grow in Luria Broth medium at 100 mg/L Cr6+ concentration. A maximum Cr6+ bioremediation (95.24 ± 2.08 %) could be achieved using the said isolate at 40 mg/L, pH 7, and inoculum concentration 4 % at 24 h. The residual chromium was found in the form of less toxic trivalent chromium (Cr3+), which confirms that the bacterial isolate can transform toxic Cr6+ to non-toxic Cr3+. Fourier Transform Infra-Red (FTIR) study was performed to analyze the functional groups and overall nature of chemical bonds involved in the remediation process, whereas, Energy-Dispersive Spectroscopy (EDS) studies of native and treated cells showed the changes in elemental composition in response to metal stress. Artificial Neural Network (ANN) based prediction model is developed based on experimental points. The developed model was found to predict the bioremediation of Cr6+ at various operating conditions. Particle Swarm Optimization (PSO) is used to optimize the variables like the initial concentration of metal, pH, temperature, and inoculum concentration for the said bacterial strain. The results showed that the isolate could be applied as a potential bioremediation agent for Cr6+ removal.