Volume 18, Issue 5

This paper theoretically studied pressure drop variation in microchannels having different cross sections (circular, rectangular, square, trapezoidal, triangular, elliptical, parallel plate, co-centric circles, hexagonal, wavy, smoothed or rounded corners cross sections, and rhombus) for single phase Newtonian fluid (gas and liquid) flow. Based on 41 years (approximately) prior literature (1981–till now), 249 articles were studied and number of correlations of pressure drop calculation in microchannels with or without friction factor equation for four cross sections i.e., rectangular, square, circular, trapezoidal, wavy and triangular is collected and also mentioned their limitations at one place. Other than these four cross sections, there is very few experimental/numerical works was present in the literature. A comparable study was performed for laminar as well as turbulent friction factor to calculate the pressure drop with the help of classical theory for gas and liquid flow in microchannels with circular and rectangular cross sections. Results show wonderful outcomes i.e., correlations of laminar pressure drop study can be extendable for transition and turbulent regime in both types (circular and rectangular) of cross sections of microchannels. In different types of flow regime, it is suggested that for each type of cross section (circular and rectangular) we can go for single correlation for gas/liquid system. It is also investigated that the macro channels pressure drop equations can be used for microchannels up to the certain values of Reynolds number. Basically, this paper provides all possible equations of friction factor related to the microchannels that helps to calculate the pressure drop, is collected at one platform also compared their deviation with conventional channels.

Reactive orange 16 (RO16) is the most widely used azo dye in Textile industry. Complex aromatic structures and resistivity to biological decay caused the dye pollutants incompletely treated by the conventional oxidative methods. The current study presents the electro-Fenton-based advanced oxidation treatment of RO16 dye and the process optimization by Taguchi-based design of experiment (DOE). Using a 500 mL volume lab-scale experimental setup, the process was first studied for the principal operational parameters (initial dye concentration ( q ); [H 2 O 2 ]/[Fe +2 ] ( R ); current density ( ρ ); and temperature ( T )) effect on decolourization ( D R ) and COD removal ( C R ). Then, by means of the L 16 (4 4 ) orthogonal array (OA) formation, standard mean and signal-to-noise (S/N) ratio, the process was optimized for the response variables. The result showed the optimized result at q  = 100 mg/L, R  = 100, ρ  = 8 mA/cm 2 , and T  = 32 °C; with D R and C R as 90.023 and 84.344%, respectively. It was found that the current density affects the process most, followed by [H 2 O 2 ]/[Fe +2 ] ratio, initial dye concentration, and temperature i.e.,  ρ  >  R  >  q  >  T . Also, with the analysis of variance (ANOVA), model equations for D R and C R were developed and its accuracy was verified for experimental results. At optimized conditions, the first order removal rate constants ( k a ) were found from batch results. Additionally, the thermodynamic constants (Δ H e , Δ S e , and Δ G b ) were also calculated for the nature of heat-energy involved and temperature effect study on dye degradation. The results showed that the process was thermodynamically feasible, endothermic, and non-spontaneous with a lower energy barrier ( E A  = 46.7 kJ mol −1 ).

Many methods have been developed for the synthesis of silver nanoparticles (Ag-NPs), yet disadvantages are there to declined their catalytic activity due to the large size with small surface area. Hence, herein, the fungus mediated synthesis of Ag-NPs has been reported. The synthesized Ag-NPs were further characterized by XRD, SEM, EDS, and UV–Vis spectroscopy to study the particle size, surface, crystalline nature, phase formation of Ag-NPs and the produced particles were found to be 41.9 nm. The antibacterial efficiency of synthesized Ag-NPs was examined on various bacteria including Streptococcus pyrogenes, Staphylococcus aureus, Bacillus coagulans, Klebsiella pneumoniae and Corynibacterium glutamicum. The Ag-NPs could be considered as excellent broad-spectrum antibacterial agent. More prominently, the Ag-NPs produced by Aspergillus flavus exhibited potent antibacterial activity against certain pathogens. Bacillus coagulans exhibited maximum zone of inhibition 25.16 ± 0.80 at 80 μg/mL with respective to the standard antibiotic 26.66 ± 1.22 at 30 μg/mL concentration.

Air pollution is one of the most challenging issues poses serious threat to human health and environment. The increasing influx of population in metropolitan cities has further worsened the situation. Quantifying the air pollution experimentally is quite a challenging task as it depends on many parameters viz., wind speed, wind temperature, relative humidity, temperature etc. It requires the investment of huge money and manpower for controlling air pollution. Machine learning technique-based computer modelling reduces both of the parameters. In the present work, the dependence of air pollution level on wind speed and temperature has been taken up using machine learning in the form of ANN and LSTM model. The recorded data of air pollution level (PM 2.5 ) is collected from a measurement station of Lucknow city situated at Central School, CPCB. The data is used in an Artificial Neural based network and in an LSTM model to predict suitably the level of air pollution for a known value of average wind speed and temperature without experimental measurements. LSTM model is found to predict the pollution level better than ANN for the developed ANN networks.

In this study, the degree of reduction has been proposed to evaluate the heat of combustion in eight structural groups. The degree of reduction is commonly used in microbiology as a valuable tool to calculate the stoichiometry of process reactions. The degree of reduction model provides a simple, direct, and single-step technique for calculating the heat of combustion. The results from the degree of reduction model revealed that predicted values are in good agreement with results obtained using bond energies, with an average error of less than 2 %. Also, the computational method applied in this study can calculate the heat of combustion for other organic compounds and even unknown chemical compounds by measuring chemical oxygen demand (COD).

In this work, a proportional–integral (PI) controller with a set point filter is designed using the direct synthesis method for unstable plus time delay process. The Suggested method involves design parameters whose suitable values are recommended based on robust stability and robust performance constraints. The absence of derivative term makes PI controllers less sensitive to noise and, therefore, PI controllers are more preferable than PID in industrial applications. Despite a simple control architecture, the proposed method gives improved or comparable performance to previously presented approaches, which are comparatively complex. Four case studies are considered to evaluate the suitability and superiority of the suggested control technique. Proposed controller may be applied to the integrating plus time delay plants after some elementary transformations in the process model.

This study invested the impact of the operational parameters (pH, Current Density (CD) and Electrode Gap (EG)) on the sludge generated after electrocoagulation treatment of textile dyeing effluent (TDE) by studying sludge settleability and filterability characteristics of EC generated sludge. Electrocoagulation treatment evaluated at laboratory scale with aluminum as electrode material at operating condition of parameters of pH, CD and EG. (pH-5.5, CD-105.12 A/m 2 and EG-2.5 cm). Under these operating conditions, maximum COD reduction was achieved 88.45 % and maximum color reduction was about 66.92 %. Settling characteristics of TDE was studied by sludge volume index (SVI) and Centrifugal settleability index (CSI). From disposal point of view, filterability characteristic was also studied by simple gravity filtration method. Specific cake resistance, α (m/kg), filter medium resistance, R m (m −1 ) was also studied. The specific cake resistance was found in the order of 38.4 × 10 13 >34.13 × 10 13 >22.25 × 10 13 >11.19 × 10 13 >7.30 × 10 13 (m/kg) at the pH 9.5, 7.5, 1.5, 5.5 and 3.5 respectively. While filter medium resistance was found to be 111.22 × 10 9 , 92.28 × 10 9 , 82.33 × 10 9 , 41.38 × 10 9 and 13.87 × 10 9  m −1 at pH 7.5, 9.5, 3.5, 5.5 and 1.5 respectively.

Phase change material (PCM) has high latent heat on one hand albeit low thermal conductivity on the other hand which restricts its utilization in thermal energy storage applications. Therefore, to improve thermal performance of PCM, various techniques have been employed. This numerical work intends to estimate the effect of copper metal foam (MF) in the seven various configurations (M1–M7) of triple-tube heat exchanger (TTHX) under simultaneous charging and discharging (SCD) conditions using heat transfer fluids (HTF) both the sides. Five distinct configurations with equal volumes of PCM and composite PCM (CPCM) have been considered for optimization standpoint. RT55 (melting temperature = 327 K) is taken as PCM. Based on thermo-physical properties of PCM and thermal boundary conditions on the heated tube, the dimensionless controlling parameters such as the Rayleigh number ( Ra ), Prandtl number ( Pr ), and Stefan number ( Ste ) were taken as 1.79 × 10 5 , 30, and 0.21, respectively. Typical results on melt fraction, latent heat storage, temperature contours, and steady-state melt fraction and corresponding melting time have been reported. Performance yielded by all the configurations was compared for a fixed duration of 2 h. The positioning of MF largely affects the heat transfer mechanism in the latent heat storage unit. Results show that the bottom-side positioning of MF can boost the heat storage due to enhanced buoyancy-induced convection. Among all the models, M3 predicts the highest steady-state melt fraction (λ$\lambda $ ss  ≈ 0.62) in the shortest steady-state melting time ( t ss  ≈ 66 min), followed by model M6 ( λ ss  ≈ 0.58, t ss  ≈ 65 min). The optimized design (model M3) shows ∼75 % latent heat storage enhancement than pure PCM (M1) case. Interestingly, one may also achieve ∼17.2 % higher enhancement using model M3 than M2 but with only half of the mass of MF than that used in full porous configuration (M2).

The paper proposes an approach to the optimal design of pressure swing adsorption (PSA) units for hydrogen recovery under uncertainty, which provides a reasonable margin of the potential resource of the PSA hydrogen unit and compensates for the negative impact of a random change in uncertain parameters within specified limits. A heuristic iterative algorithm is proposed to solve the design problem with a profit criterion, which is guaranteed to provide the technological requirements for the PSA unit, regardless of the values that take uncertain parameters from the specified intervals of their possible change. An experimental verification of the approach with the root-mean-square error of 19.43 % has been carried out. Optimization problems of searching for a combination of mode and design parameters under uncertainty for a range of 4-bed 4-step VPSA units with a capacity of 100–2000 L/min STP have been solved taking into account the requirements for hydrogen purity of 99.99+ %, gas inlet velocity of 0.2 m/s, and bed pressure drop (no more than 1 atm). It has been established that taking into account uncertainties leads to an increase in energy costs by 8–10 %, a decrease in profit by 10–15 %, and a decrease in hydrogen recovery by 4–5 %, which is a payment for the uninterrupted operation of the PSA unit. The effect of uncertain parameters (percentage composition of the gas mixture; gas temperature; the diameter of adsorbent particles) on the key indicators of the PSA process (recovery, profit, hydrogen purity, unit capacity) has been established and trends in adsorption duration, adsorption and desorption pressure, P / F ratio, valve capacity, bed length, adsorber diameter for design of hydrogen PSA unit, which are necessary for subsequent design and scaling of units.

In this study, we investigate the thermal stabilities, thermo-kinetic, and thermodynamic behaviours of Corn Cob (CC), Husk (CH), Leaf (CL), and Stalk (CS) during pyrolysis using the Thermogravimetric Analysis (TGA) at a single heating rate of 10 °C/min. Thermo-kinetics and thermodynamic parameters were evaluated for two temperature regions, region I (100–350 °C) and region II (350–500 °C) by employing the Coats–Redfern (CR) integral method to fit the TGA data to sixteen kinetic models. Results showed that diffusion models (D1, D1, D3, and D1) best suited the decomposition of CC, CH, CL, and CS in region I with Ea values of 109.90, 186.01, 129.4, and 78.7 kJ/mol respectively. Similarly, D1, third order model (F3), D3, and nucleation model (P4) with Ea values of 68.50 (CC), 177.10 (CH), 62.10 (CL), and 127.70 (CS) kJ/mol respectively best described residues’ decomposition in region II. Furthermore, kinetic parameters were used to compute the thermodynamic parameters; change in enthalpy (∆ H ), Gibbs free energy (∆ G ), and change in entropy (∆ S ) values for both regions. To study the pyrolytic behaviours of the residues, Artificial Neural Network (ANN) was employed to develop models to predict weight losses in samples by determining the coefficient of determination ( R 2 ) and minimum Mean Square Error (MSE). Results showed ANN as a very important tool for predicting the pyrolytic behaviours of corn residues and other biomass samples.