Optimizing methanol synthesis from CO2 using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability.

Assessment of the performance of linear and nonlinear regression-based methods for estimating in situ catalytic CO2 transformations employing TiO2/Cu coupled with hydrogen exfoliation graphene (HEG) has been investigated. The yield of methanol was thoroughly optimized and predicted using response surface methodology (RSM) and artificial neural network (ANN) model after rigorous experimentation and comparison. Amongst the different types of HEG loading from 10 to 40 wt%, the 30 wt% in the HEG-TiO2/Cu assisted photosynthetic catalyst was found to be successful in providing the highest conversion efficiency of methanol from CO2. The most influencing parameters, HEG dosing and inflow rate of CO2, were found to affect the conversion rate in the acidic reaction regime (at pH of 3). According to RSM and ANN, the optimum methanol yields were 36.3 mg g-1 of catalyst and 37.3 mg g-1 of catalyst, respectively. Through the comparison of performances using the least squared error analysis, the nonlinear regression-based ANN showed a better determination coefficient (overall R2 > 0.985) than the linear regression-based RSM model (overall R2 ∼ 0.97). Even though both models performed well, ANN, consisting of 9 neurons in the input and 1 hidden layer, could predict optimum results closer to RSM in terms of agreement with the experimental outcome.

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.

Cetylpyridinium bromide assisted micellar-enhanced ultrafiltration for treating enrofloxacin-laden water.

The presence of a fluoroquinolone base veterinary antibacterial drug enrofloxacin in aqueous media poses a major threat due to its ecotoxicity on aquatic microbiota. Hence, for the first time, an attempt was made to remove enrofloxacin (ENX) from its aqueous solution by employing micellar-enhanced ultrafiltration (MEUF) where cetylpyridinium bromide (CPB), a cationic surfactant was used for micellization. Response surface methodology (RSM) with central composite design (CCD) approach was applied to design the experiment, and to optimize the process parameters, namely, ENX concentration (3-15 mg/L), transmembrane pressure (2-6 kgf/cm2), recirculation flow rate (5.5-7.5 L/min) and CPB concentration (1.4-4.2 mM). The objective of this study was to maximize the permeate flux and rejection coefficient and to find out the optimal process condition for the removal of enrofloxacin from aqueous solution. Though maximum 68.23 L/m2 h of permeate flux and 94.20% of rejection coefficient were achieved at different process conditions, the optimization study reveals that the predicted optimal values of permeate flux and rejection coefficient are 67.53 L/m2 h and 89.67% respectively. Modelling was also carried out with the aid of artificial neural network (ANN) to validate the prediction of RSM. The predictability of the model by RSM and ANN was compared statistically by evaluating root-mean-square error (RMSE), absolute average deviation (AAD) and mean absolute error (MAE), where ANN exhibited better predictability. The following set of parameters was proposed for industrial scale up: ENX concentration of 8.4 mg/L, TMP of 5 kgf/cm2, recirculation flow rate of 6 L/min and CPB concentration of 2.1 mM.

Comprehensive analysis on sorptive uptake of enrofloxacin by activated carbon derived from industrial paper sludge.

The current investigation deals with how chemically activated carbon derived from industrial paper sludge (ACPS) performs on sorptive removal of enrofloxacin (ENF), an antibacterial drug from its water solution. Thermogravimetric (TGA) and proximate analysis of raw paper sludge (RPS) were conducted. ACPS was characterized with proximate analysis, XRD, FT-IR, SEM and BET. The influence of five operational parameters viz. adsorbate concentration (initial), dose of adsorbent, pH, temperature, and contact time on the adsorption of ENF onto ACPS has been conducted using batch experiments. The process of adsorption was optimized through ANN (artificial neural network) in addition to RSM (response surface methodology). The maximum percentage removal (95.85%) was achieved at initial ENF concentration 12 mg/g, adsorbent dose 1.2 g/L, contact duration of 18 h and temperature 20 °C. Kinetic data were best fitted into pseudo-second order kinetic model and adsorption equilibrium study indicates that the adsorption process follows Langmuir isotherm model. Adsorption capacity was noted to have a highest value of 44.44 mg/g. A study on thermodynamics of the adsorption process suggests that it exhibits spontaneity, being essentially exothermic. Cost analysis and reusability study confirm that adsorbent produced from industrial paper sludge is cost-effective and reusable. Therefore, ACPS as adsorbent has potency for removing ENF from aqueous solution.

Ground state charge transfer complex formation of some metalloporphyrins with aromatic solvents: Further theoretical and experimental investigations.

In our earlier work (Chem. Phys. Letts. 592 (2014) 149-154), a new broad band was observed in the near infrared region (700-900nm) of the steady state absorption spectra of some metalloporphyrins (zinc tetraphenylporphyrin, zinc octaethylporphyrin and magnesium octaethylporphyrin) in aromatic solvents (chlorobenzene, 1,2-dichlorobenzene, benzonitrile, benzene and toluene) at high concentrations (~10-4molL-1). The band was ascribed to be due to ground state charge transfer complexation between solute and solvent molecules. In the present work, density functional theory calculations are carried out to study the possibility of such ground state charge transfer complex formation between zinc tetraphenylporphyrin and four aromatic solvents viz., benzene, toluene, chlorobenzene and benzonitrile with 1:1 and 2:1 solvent-solute stoichiometries. Also, we determined the association constants for the ground state charge transfer complex formation of zinc tetraphenylporphyrin and zinc octaethylporphyrin with two aromatic solvents (benzene and benzonitrile) by Benesi-Hildebrand method.