Optimization of various process parameters for biodegradation of 4-chlorophenol using Taguchi methodology

Nanophotonic structure inverse design for switching application using deep learning

Switching functionality is pivotal in advancing communication systems, serving as a paramount mechanism. Despite numerous innovations in this field, optical switch design, fabrication, and characterization have traditionally followed an iterative approach. Within this paradigm, the designer formulates an informed conjecture regarding the switch's structural configuration and subsequently resolves Maxwell's equations to ascertain its performance. Conversely, the inverse problem, which entails deriving a switch geometry to achieve a targeted electromagnetic response, continues to pose formidable challenges and necessitates substantial time and effort, particularly under the constraints of specific assumptions. In this work, we propose a deep neural network-based method to approximate the spectral transmittance of all-optical switches. The findings substantiate the efficacy of deep learning in the design of all-optical plasmonic switches, which are renowned as the fastest switches at the nanoscale. The nonlinear Kerr effect in square resonators is leveraged to demonstrate the switching performance. Juxtaposed with conventional simulations, the proposed model showcases a remarkable improvement in computational efficiency. Furthermore, deep learning can resolve nanophotonic inverse design problems without reliance on trial-and-error or empirical strategies. Compared to simulations, the mean squared error for both forward and inverse models is meager, with values of around 0.03 and 0.02, respectively. The deep learning-proposed switches exhibit excellent suitability for integration into photonic integrated circuits, substantially influencing the progression of all-optical signal processing.

Kinetic Study of 4-Chlorophenol Biodegradation by Acclimated Sludge in a Packed Bed Reactor

In this study, batch experiments were conducted to evaluate the degradation of 4-CP using acclimated sludge. The Monod and Haldane models were employed to fit the specific growth rate with various initial 4-CP concentrations of 67–412 mg/L in the batch experiments. Haldane kinetics showed a better fit to experimental results than Monod kinetics. The kinetic parameters were obtained from a comparison of Monod and Haldane kinetics with batch experimental data. The values of μm and KS were found to be 0.691 d−1 and 5.62 mg/L, respectively, for Monod kinetics. In contrast, the values of μm, KS, and KI were 1.30 d−1, 8.38 mg/L, and 279.4 mg/L, respectively, for Haldane kinetics. The kinetic parameters in Haldane kinetics were used as input parameters for the kinetic model system of the packed bed reactor (PBR). The continuous flow PBR was conducted to validate the kinetic model system. The model-simulated results agreed well with experimental data in the PBR performance operation. At the steady-state stage, the removal efficiency of 4-CP was 70.8–96.1%, while the hydraulic retention time (HRT) was 2.5 to 12.4 h. The corresponding removal of 4-CP was assessed to be 94.6 and 96.1% when the inlet 4-CP loading rate was increased from 0.11 to 0.51 kg/m3-d. The approaches of kinetic models and experiments presented in this study can be applied to design a PBR for 4-CP treatment in wastewater from the effluents of various industries.

Downstream Process Development for Extraction of Prodigiosin: Statistical Optimization, Kinetics, and Biochemical Characterization

Prodigiosin is natural red colourant derived from Serratia marcescens. However, the high cost of prodigiosin restricts its use in food and pharmaceutical industries, which can be addressed with the design of a suitable extraction procedure. Therefore, the present study aims to use Taguchi methodology to optimize various process parameters during ultrasound-assisted extraction (UAE) to get a higher prodigiosin extraction yield. The most significant contribution comes from the solid-to-liquid ratio (36.66%), followed by sonication of duty cycle (34.82%), medium pH (15.7%), and acoustic intensity (12.82%). The Taguchi technique predicts the highest optimal yield using the solid-liquid ratio (0.3 g/mL), duty cycle sonication (75%), acoustic intensity (12.5 w/cm²), and medium pH (3) as parameters. When the extraction conditions were optimized, the yield of prodigiosin increased by 4166.89 mg/L. In the future, the above extraction conditions determined using Taguchi approach will be applied for large-scale extraction of prodigiosin. Finally, a second-order kinetic model is used to suit the batch extraction investigation and the second-order rate constant (k) has a value of 4 × 10^-5 L/mg/min. In the future, the rate constant, which is reported for the first time, will be used to create a batch extractor for commercial extraction of prodigiosin. Prodigiosin has also been shown to have substantial antioxidant and scavenging properties, which increase in a dose-dependent way with prodigiosin concentration. Because of its antioxidant and scavenging properties, prodigiosin can be used as food additives or pharmaceutical ingredients in industries.

Biodegradation Kinetics of Phenol and 4-Chlorophenol in the Presence of Sodium Salicylate in Batch and Chemostat Systems

The biodegradation of phenol, sodium salicylate (SA), and 4-chlorophenol (4-CP) by Pseudomonas putida (P. putida) was evaluated by batch and chemostat experiments in single and binary substrate systems. The Haldane kinetics model for cell growth was chosen to describe the batch kinetic behavior to determine kinetic parameters in the single or binary substrates system. In the single phenol and SA system, the kinetic constants of μm,P = 0.423 h−1, μm,A = 0.247 h−1, KS,P = 48.1 mg/L, KS,A = 71.7 mg/L, KI,P = 272.5 mg/L, and KI,A = 3178.2 mg/L were evaluated. Experimental results indicate that SA was degraded more rapidly by P. putida cells compared to phenol because SA has a much larger KI value than phenol, which makes the cells less sensitive to substrate inhibition even though the μm,P value is larger compared to μm,A. The ratio of inhibition of phenol degradation due to the presence of SA (IA1) to the inhibition of SA degradation due to the presence of phenol (IA2) is 2.3, indicating that SA has a higher uncompetitive inhibition on phenol biodegradation compared to that of phenol on SA biodegradation in the binary substrate system. In the ternary substrate system, the time required for the complete degradation of SA and phenol was 14 and 11.5 d and an approximately 90% removal efficiency for 4-CP was achieved within 14 d. In the chemostat system, the removal rates of phenol and SA were 96.6 and 97.0%, while those of SA and 4-CP were 91.4% and 95.2%, respectively. The model prediction agreed satisfactorily with the experimental results of the chemostat system.

Biodegradation of 4-chlorophenol in batch and continuous packed bed reactor by isolated Bacillus subtilis

In present work, biodegradation of 4-Chlorophenol (4-CP) has been successfully achieved using bacteria i.e. Bacillus subtilis (MF447841.1), which was isolated from the wastewater of a nearby drain of Hyundai Motor Company service centre, Agartala, Tripura (India). Geonomic identification was carried out by 16 S rDNA technique and phylogenetic processes. Both, batch and column mode of experiments were performed to optimize various parameters (initial concentration, contact time, dosages etc.) involved in the significant biodegradation of 4-CP. Based on R² value (0.9789), the Levenspiel's model was found to be best fit than others. The kinetic parameters; specific growth rate (μ), yield of cell mass (Y_X/S), and saturation constant (KS), were obtained as 0.6383 (h^-1), 0.35 (g/g), and 0.006884 (g/L), respectively. The isolated strain has shown the ability of degrading 4-CP up to 1000 mg/L initial concentration within 40 h. Bacterial strain was immobilized via developing calcium alginate beads along by optimizing weight proportion of calcium chloride and sodium alginate and size of the bead for further experiments. Various process parameters i.e. initial feed concentration, bed height, rate of flow of were optimized during packed bed reactor (PBR) study. Maximum biodegradation efficiency of 4-CP was observed as 45.39% at initial concentration of 500 mg/L within 105 min, using 2 mm size of immobilized beads which were formed using 3.5% w/v of both calcium chloride and sodium alginate within. Thus, Bacillus subtilis (MF447841.1) could be used for biological remediation of 4-CP pollutant present in wastewater. Moreover, because of affordable and eco-friendly nature of water treatment, relatively it has the better scope of commercialization.

Enzymatic pretreatment of algal biomass has different optimal conditions for biogas and bioethanol routes

Enzymatic pretreatment is emerging as an efficient tool for the extraction of biofuel precursors from algal biomass. However, yardsticks for end-use directed selection of optimal pretreatment conditions are not yet identified. The present study, for the first time, reveals different optimal conditions for algal biomass solubilization and sugar release. Algal biomass pretreatment optimization was carried out using the Taguchi method. Crude enzyme from Aspergillus fischeri was found effective for pretreatment of Chlorella pyrenoidosa. Maximum sugar yield (190 mg g^-1 biomass) from algal biomass was observed at a substrate concentration of 4 g L^-1, with a 5% enzyme load at temperature 60°C, pH 5.5, and shaking speed of 80 rpm. In contrast, maximum sCOD (1350 mg g^-1 biomass) was obtained at 2 g L^-1 substrate concentration with enzyme load of 20% v/v, at 60°C, pH 4, and shaking speed of 100 rpm. Hence, the first set of conditions would be more beneficial for bioethanol production. Whereas another set of conditions would improve the biofuel production that requires maximum solubilization of algal biomass, such as fermentative methane production. Overall, the present observations established that process conditions required for enzymatic pretreatment of algal biomass should be selected according to the desired biofuel type.

4-chlorophenol removal by air lift packed bed bioreactor and its modeling by kinetics and numerical model (artificial neural network)

4-chlorophenol (4-CP) is a hazardous contaminant that is hardly removed by some technologies. This study investigated the biodegradation, and physical 4-CP removal by a mixed microbial consortium in the Airlift packed bed bioreactor (ALPBB) and modeling by an artificial neural network (ANN) for first the time. The removal efficiency of ALPBB was investigated at 4-CP(1-1000 mg/L) and hydraulic retention time (HRT)(6-96 hr) by HPLC. The results showed that removal efficiency decreased from 85 at 1 to 0.03% at 1000 mg/L, with increasing 4-CP concentration and HRT decreasing. BOD₅/COD increased with increasing exposure time and concentration decreasing, from 0.05 at 1000 to 0.96 at 1 mg/L. With time increasing, the correlation between COD and 4-CP removal increased (R² = 0.5, HRT = 96 h). There was a positive correlation between the removal of 4-CP and SCOD by curve fitting was R² = 0.93 and 0.96, respectively. Moreover, the kinetics of 4-CP removal follows the first-order and pseudo-first-order equation at 1 mg/L and other concentrations, respectively. 4-CP removal modeling has shown that the 2:3:1 and 2:4:1 were the best structures (MSE: physical = 0.126 and biological = 0.9)(R²_allphysical = 0.999 and R²_testphysical = 0.999) and (R²_allbiological = 0.71, and R²_testbiological = 0.997) for 4-CP removal. Also, the output obtained by the ANN prediction of 4-CP was correlated to the actual data (R²_physical = 0.9997 and R²_biological = 0.59). Based on the results, ALPBB with up-flow submerged aeration is a suitable option for the lower concentration of 4-CP, but it had less efficiency at high concentrations. So, physical removal of 4-CP was predominant in biological treatment. Therefore, the modification of this reactor for 4-CP removal is suggested at high concentrations.