Ammonia recovery from wastewater has positive environmental benefits, avoiding eutrophication and reducing production energy consumption, which is one of the most effective ways to manage nutrients in wastewater. Specifically, ammonia recovery by membrane distillation has been gradually adopted due to its excellent separation properties for volatile substances. However, the global optimization of direct contact membrane distillation (DCMD) operating parameters to maximize ammonia recovery efficiency (ARE) has not been attempted. In this work, three key operating factors affecting ammonia recovery, i.e., feed ammonia concentration, feed pH, and DCMD running time, were identified from eight factors, by a two-level Plackett-Burman Design (PBD). Subsequently, Box-Behnken design (BBD) under the response surface methodology (RSM) was used to model and optimize the significant operating parameters affecting the recovery of ammonia though DCMD identified by PBD and statistically verified by analysis of variance (ANOVA). Results showed that the model had a high coefficient of determination value (R2 = 0.99), and the interaction between NH4Cl concentration and feed pH had a significant effect on ARE. The optimal operating parameters of DCMD as follows: NH4Cl concentration of 0.46 g/L, feed pH of 10.6, DCMD running time of 11.3 h, and the maximum value of ARE was 98.46%. Under the optimized conditions, ARE reached up to 98.72%, which matched the predicted value and verified the validity and reliability of the model for the optimization of ammonia recovery by DCMD process.

The effect of freeze and hot air drying methods on the retention of total phenolics, antioxidant activity (AA), and color of different cultivars of rose petals was analyzed. Both methods similarly preserved the phenolic content and AA, while freeze drying showed better red color retention. Furthermore, the conditions of total phenolics and AA extraction from two rose cultivars, Lovely Red and Malu, were optimized by response surface methodology through a Box-Behnken design. The solvent exhibited a major effect on the total phenolic content (TPC) and AA. The selected parameters were ethanol 38%, 75 °C, and 30 min. Under these conditions, the predicted values for Lovely Red were 189.3 mg GA/g dw (TPC) and 535.6 mg Trolox/g dw (AA), and those for Malu were 108.5 mg GA/g dw (TPC) and 320.7 mg Trolox/g dw (AA). The experimental values were close to the predicted values, demonstrating the suitability of the model. Ultrasound-assisted extraction increased the AA of the extracts but not the TPC. Fifteen compounds were identified in the Lovely Red cultivar, with no differences between the two drying methods. The results obtained suggest that the analyzed cultivars, particularly the red ones, can be considered a natural source of powerful antioxidant compounds.

In recent years, the power sector of Bangladesh has seen a major development in terms of generation capacity. But as before, it is heavily dependent on fossil fuels overlooking the potential of renewable energy resources. The scope for grid-connected renewable energy systems has not been explored too far and in terms of solar thermal energy and concentrating solar power (CSP), it is even less. This study focuses on assessing the techno-economic feasibility of solar-driven Dish Stirling system for large-scale grid-connected power generation in Bangladesh. Detailed modeling and optimization of a 100 MW Dish Stirling power plant have been carried out in Cox\'s Bazar, Bangladesh, a location suitable for solar energy harnessing due to favorable climatic conditions. The modeling parameters and weather data have been collected from relevant literature, various solar data providers, and specific plant parameters have been optimized for the Bangladeshi climatic condition. Simulation of the modeled plant carried out by the System Advisor Model (SAM) shows that, it can supply 129.856 GWh electricity annually operating at an overall efficiency of 24.91% which is much higher than the values reported in similar literature for the South-Asian regions. The levelized cost of electricity (LCOE) has been determined to be 10.18 cents/kWh, which is highly competitive and promising. The insights obtained from this study can be a perfect starting point for the policymakers and concerned authorities of Bangladesh to further explore the viability of this technology for renewable and sustainable power.

Cellulose was extracted from the banana stem by chemical method and the factors affecting the extraction process such as concentration of NaOH and H2O2, as well as the assisted microwave time were investigated. Design-Expert software with Response Surface Methodology was used in the modeling and optimization of the cellulose extraction process. The results of XRD, FT-IR, SEM were also used to determine the physicochemical properties of cellulose obtained from the banana stem. The results of the modeling and optimization process of cellulose extraction showed the efficiency of the process and the high applicability of cellulose from the banana stem to create valuable industrial products.

Herein, we report a new approach for the sulfonation of polyether ether ketone (PEEK) following a shorter path of reaction undertaken at 60 °C under ultrasonication. The application of this method enabled the reduction of the reaction time from several hours to less than one hour, achieving a relevant sulfonation degree. The sulfonated-PEEK (SPEEK) was characterized by advanced chemical and physical instrumental methods. According to 1H-NMR analysis, the degree of sulfonation of the polymer was equal to 70.3%. Advanced microscopy (SEM) showed that the fabricated SPEEK beads (2-4 mm) were porous inside with a log-normal distribution of pore sizes within the range 1.13-151.44 μm. As an application, the SPEEK polymer was tested for the adsorption of a cationic organic pollutant (Methylene blue, MB) from aqueous solutions. The equilibrium studies (isotherms) disclosed maximum adsorption capacities of 217 mg/g, 119 mg/g, and 68 mg/g at temperatures of 323 K, 313 K, and 300 K, respectively. The thermodynamic calculations indicated an endothermic effect (ΔHad = +11.81 kJ/mol) of the investigated adsorption process. The maximum removal efficiency of 99.14% was established by process optimization using the design of experiments strategy and data-driven modeling. Additionally, molecular docking simulations were performed to disclose the mechanism of interaction at the molecular level between the adsorbent (SPEEK) and pollutant.

This study develops a vaccine supply chain (VSC) to ensure sustainable distribution during a global crisis in a developing economy. In this study, a multi-objective mixed-integer programming (MIP) model is formulated to develop the VSC, ensuring the entire network\'s economic performance. This is achieved by minimizing the overall cost of vaccine distribution and ensuring environmental and social sustainability by minimizing greenhouse gas (GHG) emissions and maximizing job opportunities in the entire network. The shelf-life of vaccines and the uncertainty associated with demand and supply chain (SC) parameters are also considered in this study to ensure the robustness of the model. To solve the model, two recently developed metaheuristics-namely, the multi-objective social engineering optimizer (MOSEO) and multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) methods-are used, and their results are compared. Further, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model has been integrated into the optimization model to determine the best solution from a set of non-dominated solutions (NDSs) that prioritize environmental sustainability. The results are analyzed in the context of the Bangladeshi coronavirus disease (COVID-19) vaccine distribution systems. Numerical illustrations reveal that the MOSEO-TOPSIS model performs substantially better in designing the network than the MOFEPSO-TOPSIS model. Furthermore, the solution from MOSEO results in achieving better environmental sustainability than MOFEPSO with the same resources. Results also reflect that the proposed MOSEO-TOPSIS can help policymakers establish a VSC during a global crisis with enhanced economic, environmental, and social sustainability within the healthcare system.

Microalgae-based wastewater treatment (and biomass production) is an environmentally benign and energetically efficient technique as compared to traditional practices. The present study is focused on optimization of the major treatment variables such as temperature, light-dark cycle (LD), and nitrogen (N)-to-phosphate (P) ratio (N/P) for the elimination of N and P from tertiary municipal wastewater utilizing Chlorella kessleri microalgae species. In this regard, a hybrid support vector regression (SVR) technique integrated with the crow search algorithm has been applied as a novel modeling/optimization tool. The SVR models were formulated using the experimental data, which were furnished according to the response surface methodology with Box-Behnken Design. Various statistical indicators, including mean absolute percentage error, Taylor diagram, and fractional bias, confirmed the superior performance of SVR models as compared to the response surface methodology (RSM) and generalized linear model (GLM). Finally, the best SVR model was hybridized with the crow search algorithm for single/multi-objective optimizations to acquire the global optimal treatment conditions for maximum N and P removal efficiencies. The best-operating conditions were found to be 29.3°C, 24/0 h/h of LD, and 6:1 of N/P, with N and P elimination efficiencies of 99.97 and 93.48%, respectively. The optimized values were further confirmed by new experimental data.

The tenacious thirst for fuel-saving and desirable physical and mechanical properties of the materials have compelled researchers to focus on a new generation of aluminum hybrid composites for automotive and aircraft applications. This work investigates the microhardness behavior and microstructural characterization of aluminum alloy (Al 7075)-titanium carbide (TiC)-graphite (Gr) hybrid composites. The hybrid composites were prepared via the powder metallurgy technique with the amounts of TiC (0, 3, 5, and 7 wt.%), reinforced to Al 7075 + 1 wt.% Gr. The microstructural characteristics were investigated by optical microscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDS) elemental mapping. A Box Behnken design (BBD) response surface methodology (RSM) approach was utilized for modeling and optimization of density and microhardness independent parameters and to develop an empirical model of density and microhardness in terms of process variables. Effects of independent parameters on the responses have been evaluated by analysis of variance (ANOVA). The density and microhardness of the Al 7075-TiC-Gr hybrid composites are found to be increased by increasing the weight percentage of TiC particles. The optimal conditions for obtaining the highest density and microhardness are estimated to be 6.79 wt.% TiC at temperature 626.13 °C and compaction pressure of 300 Mpa.

2,4-Dinitrophenol (DNP) was listed as a priority pollutant; accordingly, DNP-contaminated effluent must be treated before discharging to the receiving resources. In the present study, the hybrid ultrasound-assisted GO-Fe3O4 system was employed to decontaminate DNP solution. Ultrasound irradiation makes the mass transfer of adsorbate improved and Fe3O4 enables GO separation from liquid phase under external magnetic field. The as-synthesized GO-Fe3O4 composite was characterized by SEM, TEM, XRD, FTIR, BET and VSM. A response surface methodology based central composite design (RSM-CCD) was used to estimate and optimize of various variables on DNP removal percentage. Under optimal conditions (pH: 4.45, adsorbent dose: 0.178 g/L, ultrasound frequency: 40.02 kHz and DNP concentration: 50.10 mg/L, maximum adsorption capacity was calculated to be 425.58 mg/g for the ultrasound system, higher than the simple system 309.40 mg/g, indicating the importance of synergistic effect between the ultrasound waves and the adsorption process. The ultrasound-assisted adsorption system showed the better agreement with the Langmuir isotherm (R2 > 0.997), while the results of the stirring system were more consistent with the Freundlich model (R2 > 0.991). The experimental results indicated that the pseudo-second-order kinetic model well fitted by experiment data and rate constant was calculated to be 0.000148 min-1 and 0.000002 min-1 under ultrasound and silent systems, respectively. The rate of desorption under ultrasound was more favorable and reuse of the adsorbent in both systems after 10th consecutive cycles reduced by about 22%. Thermodynamic calculations also confirmed the endothermicity and spontaneity of both systems. Electrostatic attraction, hydrogen bonding, and π -π interactions played key roles during the adsorption of DNP onto the MGO. In conclusion, the outcomes of this study provide valuable information of the ultrasound-assisted GO-Fe3O4 system for practical applications.

Application of safety-related information interaction among vehicles has always been a research frontier in Vehicular Ad-hoc NETworks (VANETs). These messages require high real-time performance. There is a lot of research dependant on creating optimization model for communication task scheduling or routing protocols to reduce communication delay. In this paper, we analyze characteristics of safety-related information and introduce Interference Alignment (IA) technology in VANETs. To further improve routing efficiency, a data-driven assisted transmission routing and broadcast model framework for Vehicle to Vehicle(V2V) and Vehicle to Infrastructure (V2I) communication are constructed which are the basis for IA. Depending on the proposed model, we propose an optimization problem of minimizing total number of time slots required for safety information sharing in VANETs. Then a clustering algorithm is designed to narrow feasible solution space. Simulation results show that the approach can effectively reduce the number of time slots required and improve link use by 20% percent compared with no IA applied.