Ammonia recovery via direct contact membrane distillation: Modeling and performance optimization.

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.

A comprehensive study on the impact of human hair fiber and millet husk ash on concrete properties: response surface modeling and optimization.

Revolutionizing construction, the concrete blend seamlessly integrates human hair (HH) fibers and millet husk ash (MHA) as a sustainable alternative. By repurposing human hair for enhanced tensile strength and utilizing millet husk ash to replace sand, these materials not only reduce waste but also create a durable, eco-friendly solution. This groundbreaking methodology not only adheres to established structural criteria but also advances the concepts of the circular economy, representing a significant advancement towards environmentally sustainable and resilient building practices. The main purpose of the research is to investigate the fresh and mechanical characteristics of concrete blended with 10-40% MHA as a sand substitute and 0.5-2% HH fibers by applying response surface methodology modeling and optimization. A comprehensive study involved preparing 225 concrete specimens using a mix ratio of 1:1.5:3 with a water-to-cement ratio of 0.52, followed by a 28 day curing period. It was found that a blend of 30% MHA and 1% HH fibers gave the best compressive and splitting tensile strengths at 28 days, which were 33.88 MPa and 3.47 MPa, respectively. Additionally, the incorporation of increased proportions of MHA and HH fibers led to reductions in both the dry density and workability of the concrete. In addition, utilizing analysis of variance (ANOVA), response prediction models were created and verified with a significance level of 95%. The models' R2 values ranged from 72 to 99%. The study validated multi-objective optimization, showing 1% HH fiber and 30% MHA in concrete enhances strength, reduces waste, and promotes environmental sustainability, making it recommended for construction.

Numerical Study of Thin-Walled Polymer Composite Part Quality When Manufactured Using Vacuum Infusion with Various External Pressure Controls.

The article presents the results of modeling various modes of vacuum infusion molding of thin-walled polymer-composite structures of arbitrary geometry. The small thickness of the manufactured structures and the fixation of their back surface on the rigid surface of the mold made it possible to significantly simplify the process model, which takes into account the propagation of a thermosetting resin with changing rheology in a compressible porous preform of complex 3D geometry, as well as changes in boundary conditions at the injection and vacuum ports during the post-infusion molding stage. In the four modes of vacuum-infusion molding studied at the post-infusion stage, the start time, duration and magnitude of additional pressure on the open surface of the preform and in its vacuum port, as well as the state of the injection gates, were controlled (open-closed). The target parameters of the processes were the magnitude and uniformity of the distribution of the fiber volume fraction, wall thickness, filling of the preform with resin and the duration of the process. A comparative analysis of the results obtained made it possible to identify the most promising process modes and determine ways to eliminate undesirable situations that worsen the quality of manufactured composite structures. The abilities of the developed simulation tool, demonstrated by its application to the molding process of a thin-walled aircraft structure, allow one to reasonably select a process control strategy to obtain the best achievable quality objectives.

Development and modeling of an integrated fixed-film activated sludge (IFAS) system for simultaneous nitrogen and carbon removal from an industrial estate wastewater.

The potential of an integrated fixed film activated sludge (IFAS) bioreactor for developing simultaneous aerobic and anoxic micro-zones under continuous aeration regime to promote carbon and nitrogen removal from Faraman industrial estate wastewater was evaluated in the present research. The effects of three independent variables on carbon and nitrogen removal were assessed. Overall, the optimum condition with 94 %, 77 %, and 2 NTU of COD (chemical oxygen demand) removal, Total nitrogen (TN) removal, and effluent turbidity has been specified with hydraulic retention time (HRT) of 11 h, air flow rate (AFR) of 3.5 L/min, and filling ratio (FR) of 50 %. To assess the stability of treating processes in the system, the IFAS system was operated in this optimal condition. Moreover, the simulation of the bioreactor was accomplished via calibration and verification of GPS-X model. GPSX simulation results and experimental data were compared using an independent sample T-test, which the T-test result confirmed that there was no significant difference between them.

Solar-driven Dish Stirling System for sustainable power generation in Bangladesh: A case study in Cox's Bazar.

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.

Effect of the Drying Method and Optimization of Extraction on Antioxidant Activity and Phenolic of Rose Petals.

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.

Cellulose from the banana stem: optimization of extraction by response surface methodology (RSM) and charaterization.

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.

Ultrasonic-Assisted Rapid Preparation of Sulfonated Polyether Ether Ketone (PEEK) and Its Testing in Adsorption of Cationic Species from Aqueous Solutions.

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.

Modeling a sustainable vaccine supply chain for a healthcare system.

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.

Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito.

Weather Normalized Models (WNMs) are modeling methods used for assessing air contaminants under a business-as-usual (BAU) assumption. Therefore, WNMs are used to assess the impact of many events on urban pollution. Recently, different approaches have been implemented to develop WNMs and quantify the lockdown effects of COVID-19 on air quality, including Machine Learning (ML). However, more advanced methods, such as Deep Learning (DL), have never been applied for developing WNMs. In this study, we proposed WNMs based on DL algorithms, aiming to test five DL architectures and compare their performances to a recent ML approach, namely Gradient Boosting Machine (GBM). The concentrations of five air pollutants (CO, NO2, PM2.5, SO2, and O3) are studied in the city of Quito, Ecuador. The results show that Long-Short Term Memory (LSTM) and Bidirectional Recurrent Neural Network (BiRNN) outperform the other algorithms and, consequently, are recommended as appropriate WNMs to quantify the effects of the lockdowns on air pollution. Furthermore, examining the variable importance in the LSTM and BiRNN models, we identify that the most relevant temporal and meteorological features for predicting air quality are Hours (time of day), Index (1 is the first collected data and increases by one after each instance), Julian Day (day of the year), Relative Humidity, Wind Speed, and Solar Radiation. During the full lockdown, the concentration of most pollutants has decreased drastically: -48.75%, for CO, -45.76%, for SO2, -42.17%, for PM2.5, and -63.98%, for NO2. The reduction of this latter gas has induced an increase of O3 by +26.54%.

Multi-Criteria Decision Approach to Design a Vacuum Infusion Process Layout Providing the Polymeric Composite Part Quality.

The increasingly widespread use of vacuum assisted technologies in the manufacture of polymer-composite structures does not always provide the required product quality and repeatability. Deterioration of quality most often appears itself in the form of incomplete filling of the preform with resin as a result of the inner and outer dry spot formation, as well as due to premature gelation of the resin and blockage of the vacuum port. As experience shows, these undesirable phenomena are significantly dependent on the location of the resin and vacuum ports. This article presents a method for making a decision on the rational design of a process layout. It is based on early forecasting of its objectives in terms of quality and reliability when simulating its finite element model, on the correlation analysis of the preliminary and final quality assessments, as well as on the study of the cross-correlation of a group of early calculated sub-criteria. The effectiveness of the proposed method is demonstrated by the example of vacuum infusion of a 3D thin-walled structure of complex geometry.

Hybrid support vector regression and crow search algorithm for modeling and multiobjective optimization of microalgae-based wastewater treatment.

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.

Modeling, Optimization and Performance Evaluation of TiC/Graphite Reinforced Al 7075 Hybrid Composites Using Response Surface Methodology.

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.

The superior adsorption capacity of 2,4-Dinitrophenol under ultrasound-assisted magnetic adsorption system: Modeling and process optimization by central composite design.

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.

An Analytical Model for Interference Alignment in Broadcast Assisted VANETs.

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.

Statistical modeling and optimization of Toluidine Red biodegradation in a synthetic wastewater using Halomonas strain Gb.

BACKGROUND: Synthetic dye wastewater is a group of environmental pollutants that are widely used in some industries like textile, printing, dyeing and etc. Traditional treatment methods for wastewaters containing synthetic dyes are considered as expensive and time consuming approaches due to the chemical stability of these pollutants. Therefore, in recent years, biodegradation by means of capable microorganisms has been considered as an effective way to remove these pollutants. Hence, the present study has aimed at examining the decolorization of Toluidine Red (C.I. no.12120), which is an oil soluble azo dye, as the sole sources of carbon and energy from a synthetic dye wastewater by the halophilic Halomonas strain Gb bacterium. In order to model, optimize, and investigate the individual factors affecting the biodegradation capacity of this dye by Halomonas strain Gb, for the first time response surface methodology (RSM) and central composite design (CCD) were applied. METHODS: In this research, statistical modeling and optimization were performed by Design Expert software version 10 and the degradation capacity was considered by carrying out 30 tests using RSM method. For this purpose, the effect of 4 variables included dye concentration (10-30 ppm), salt concentration (2-10%), pH (5.5-9.5), and temperature (20-40) at different times of 2nd, 4th, and 10th days have been studied. Then, a second-order function was presented for the amount of dye removal in terms of the four selected variables, based on statistical modeling. RESULTS: According to the obtained results and analysis of variance, all main variables were found to be significantly effective on the biodegradation capacity. With regard to the results, the highest amount of biodegradation between different days was 81% and observed at the 4th day, while the optimum conditions for the maximum biodegradation of this time has been determined at pH of 6.5, temperature of 35 °C, and salt and dye concentrations were equivalent to 4% and 25 ppm, respectively. There is 11% relative error between the experimental and predicted results in the selected experiments, which confirms the reliability of the obtained correlation for calculating the decolorization capacity. CONCLUSION: In accordance with the results, the proposed model can provide a good prediction of the effect of different conditions on the biodegradation of Toluidine Red, and the optimization results in this study have been consistent with the previous studies conducted with the IP8 and D2 strains by the OFAT method. Moreover, the proposed model may help in better understanding the impact of main effects and interaction between variables on the dye removal. Overall, the results indicated that the halophilic bacterium used in dye removal can be more effective in high-salinity environments.

Vacuum Thermoforming Process: An Approach to Modeling and Optimization Using Artificial Neural Networks.

In the vacuum thermoforming process, the group effects of the processing parameters, when related to the minimizing of the product deviations set, have conflicting and non-linear values which make their mathematical modelling complex and multi-objective. Therefore, this work developed models of prediction and optimization using artificial neural networks (ANN), having the processing parameters set as the networks' inputs and the deviations group as the outputs and, furthermore, an objective function of deviation minimization. For the ANN data, samples were produced in experimental tests of a product standard in polystyrene, through a fractional factorial design (2k-p). Preliminary computational studies were carried out with various ANN structures and configurations with the test data until reaching satisfactory models and, afterwards, multi-criteria optimization models were developed. The validation tests were developed with the models' predictions and solutions showed that the estimates for them have prediction errors within the limit of values found in the samples produced. Thus, it was demonstrated that, within certain limits, the ANN models are valid to model the vacuum thermoforming process using multiple parameters for the input and objective, by means of reduced data quantity.

Iohexol degradation in wastewater and urine by UV-based Advanced Oxidation Processes (AOPs): Process modeling and by-products identification.

In this work, an Iodinated Contrast Medium (ICM), Iohexol, was subjected to treatment by 3 Advanced Oxidation Processes (AOPs) (UV, UV/H2O2, UV/H2O2/Fe2+). Water, wastewater and urine were spiked with Iohexol, in order to investigate the treatment efficiency of AOPs. A tri-level approach has been deployed to assess the UV-based AOPs efficacy. The treatment was heavily influenced by the UV transmittance and the organics content of the matrix, as dilution and acidification improved the degradation but iron/H2O2 increase only moderately. Furthermore, optimization of the treatment conditions, as well as modeling of the degradation was performed, by step-wise constructed quadratic or product models, and determination of the optimal operational regions was achieved through desirability functions. Finally, global chemical parameters (COD, TOC and UV-Vis absorbance) were followed in parallel with specific analyses to elucidate the degradation process of Iohexol by UV-based AOPs. Through HPLC/MS analysis the degradation pathway and the effects the operational parameters were monitored, thus attributing the pathways the respective modifications. The addition of iron in the UV/H2O2 process inflicted additional pathways beneficial for both Iohexol and organics removal from the matrix.

Optimization of reactive simulated moving bed systems with modulation of feed concentration for production of glycol ether ester.

In this article, we extend the simulated moving bed reactor (SMBR) mode of operation to the production of propylene glycol methyl ether acetate (DOWANOL™ PMA glycol ether) through the esterification of 1-methoxy-2-propanol (DOWANOL™ PM glycol ether) and acetic acid using AMBERLYST™ 15 as a catalyst and adsorbent. In addition, for the first time, we integrate the concept of modulation of the feed concentration (ModiCon) to SMBR operation. The performance of the conventional (constant feed) and ModiCon operation modes of SMBR are analyzed and compared. The SMBR processes are designed using a model based on a multi-objective optimization approach, where a transport dispersive model with a linear driving force for the adsorption rate has been used for modeling the SMBR system. The adsorption equilibrium and kinetics parameters are estimated from the batch and single column injection experiments by the inverse method. The multiple objectives are to maximize the production rate of DOWANOL™ PMA glycol ether, maximize the conversion of the esterification reaction and minimize the consumption of DOWANOL™ PM glycol ether which also acts as the desorbent in the chromatographic separation. It is shown that ModiCon achieves a higher productivity by 12-36% over the conventional operation with higher product purity and recovery.