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The storage of CO2 and hydrogen within depleted gas and oil reservoirs holds immense potential for mitigating greenhouse gas emissions and advancing renewable energy initiatives. However, achieving effective storage necessitates a thorough comprehension of the dynamic interplay between interfacial tension and wettability alteration under varying conditions. This comprehensive review investigates the multifaceted influence of several critical parameters on the alterations of IFT and wettability during the injection and storage of CO2 and hydrogen. Through a meticulous analysis of pressure, temperature, treatment duration, pH levels, the presence of nanoparticles, organic acids, anionic surfactants, and rock characteristics, this review elucidates the intricate mechanisms governing the changes in IFT and wettability within reservoir environments. By synthesizing recent experimental and theoretical advancements, this review aims to provide a holistic understanding of the processes underlying IFT and wettability alteration, thereby facilitating the optimization of storage efficiency and the long-term viability of depleted reservoirs as carbon capture and storage or hydrogen storage solutions. The insights gleaned from this analysis offer invaluable guidance for researchers, engineers, and policymakers engaged in harnessing the potential of depleted reservoirs for sustainable energy solutions and environmental conservation. This synthesis of knowledge serves as a foundational resource for future research endeavors aimed at enhancing the efficacy and reliability of CO2 and hydrogen storage in depleted reservoirs. [ABSTRACT FROM AUTHOR]

The rapid growth of industrialization has led to the uncontrolled pollution of the environment, and rapid action is needed. This study synthesized Ag/TiO2/polyvinyl alcohol (PVA) nano photocatalyst for promising light-derived photocatalytic removal of heavy metal ions. The design of experiment (DOE) was used to study the effect of important factors (pH, reaction time, and photocatalyst dosage) to maximize the final performance of the photocatalyst. In the optimized condition, the Ag/TiO2/PVA nano-photocatalyst removed more than 94% of Cr6+ in 180 min, and the efficiency was more than 70% for Cu2+, Zn2+, and Ni2+ metal ions. The adsorption of the heavy metal ions on the photocatalyst was described well with the Langmuir isotherm, while the pseudo-second-order linear kinetic model fitted with the experimental data. The nano-photocatalyst's stability was confirmed after maintaining its performance for five successive runs. The enhanced photocatalytic activity for the heavy metal ions removal can be attributed to the presence of metallic silver nanoparticles (electron transfer and plasmonic fields mechanisms) and PVA, which delayed the recombination of electron–hole. The synthesized ternary Ag/TiO2/PVA nano-photocatalyst showed promising performance for the elimination of heavy metal ions and can be used for environmental remediation purposes. [ABSTRACT FROM AUTHOR]

Discharging untreated dye-containing wastewater gives rise to environmental pollution. The present study investigated the removal efficiency and adsorption mechanism of Acid Red 18 (AR18) utilizing hexadecyl-trimethyl ammonium chloride (HDTMA.Cl) modified Nano-pumice (HMNP), which is a novel adsorbent for AR18 removal. The HDTMA.Cl is characterized by XRD, XRF, FESEM, TEM, BET and FTIR analysis. pH, contact time, initial concentration of dye and adsorbent dose were the four different parameters for investigating their effects on the adsorption process. Response surface methodology-central composite design was used to model and improve the study to reduce expenses and the number of experiments. According to the findings, at the ideal conditions (pH = 4.5, sorbent dosage = 2.375 g/l, AR18 concentration = 25 mg/l, and contact time = 70 min), the maximum removal effectiveness was 99%. The Langmuir (R2 = 0.996) and pseudo-second-order (R2 = 0.999) models were obeyed by the adsorption isotherm and kinetic, respectively. The nature of HMNP was discovered to be spontaneous, and thermodynamic investigations revealed that the AR18 adsorption process is endothermic. By tracking the adsorption capacity of the adsorbent for five cycles under ideal conditions, the reusability of HMNP was examined, which showed a reduction in HMNP's adsorption effectiveness from 99 to 85% after five consecutive recycles. [ABSTRACT FROM AUTHOR]

Discharging untreated dye-containing wastewater gives rise to environmental pollution. The present study investigated the removal efficiency and adsorption mechanism of Acid Red 18 (AR18) utilizing hexadecyl-trimethyl ammonium chloride (HDTMA.Cl) modified Nano-pumice (HMNP), which is a novel adsorbent for AR18 removal. The HDTMA.Cl is characterized by XRD, XRF, FESEM, TEM, BET and FTIR analysis. pH, contact time, initial concentration of dye and adsorbent dose were the four different parameters for investigating their effects on the adsorption process. Response surface methodology-central composite design was used to model and improve the study to reduce expenses and the number of experiments. According to the findings, at the ideal conditions (pH = 4.5, sorbent dosage = 2.375 g/l, AR18 concentration = 25 mg/l, and contact time = 70 min), the maximum removal effectiveness was 99%. The Langmuir (R2 = 0.996) and pseudo-second-order (R2 = 0.999) models were obeyed by the adsorption isotherm and kinetic, respectively. The nature of HMNP was discovered to be spontaneous, and thermodynamic investigations revealed that the AR18 adsorption process is endothermic. By tracking the adsorption capacity of the adsorbent for five cycles under ideal conditions, the reusability of HMNP was examined, which showed a reduction in HMNP's adsorption effectiveness from 99 to 85% after five consecutive recycles. [ABSTRACT FROM AUTHOR]

This paper introduces a new bio-inspired metaheuristic algorithm called Walrus Optimization Algorithm (WaOA), which mimics walrus behaviors in nature. The fundamental inspirations employed in WaOA design are the process of feeding, migrating, escaping, and fighting predators. The WaOA implementation steps are mathematically modeled in three phases exploration, migration, and exploitation. Sixty-eight standard benchmark functions consisting of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, CEC 2015 test suite, and CEC 2017 test suite are employed to evaluate WaOA performance in optimization applications. The optimization results of unimodal functions indicate the exploitation ability of WaOA, the optimization results of multimodal functions indicate the exploration ability of WaOA, and the optimization results of CEC 2015 and CEC 2017 test suites indicate the high ability of WaOA in balancing exploration and exploitation during the search process. The performance of WaOA is compared with the results of ten well-known metaheuristic algorithms. The results of the simulations demonstrate that WaOA, due to its excellent ability to balance exploration and exploitation, and its capacity to deliver superior results for most of the benchmark functions, has exhibited a remarkably competitive and superior performance in contrast to other comparable algorithms. In addition, the use of WaOA in addressing four design engineering issues and twenty-two real-world optimization problems from the CEC 2011 test suite demonstrates the apparent effectiveness of WaOA in real-world applications. The MATLAB codes of WaOA are available in https://uk.mathworks.com/matlabcentral/profile/authors/13903104. [ABSTRACT FROM AUTHOR]

4-Chlorophenol pollution is a significant environmental concern. In this study, powdered activated carbon modified with amine groups is synthesized and investigated its efficiency in removing 4-chlorophenols from aqueous environments. Response surface methodology (RSM) and central composite design (CCD) were used to investigate the effect of different parameters, including pH, contact time, adsorbent dosage, and initial 4-chlorophenol concentration, on 4-chlorophenol removal efficiency. The RSM-CCD approach was implemented in R software to design and analyze the experiments. The statistical analysis of variance (ANOVA) was used to describe the roles of effecting parameters on response. Isotherm and kinetic studies were done with three Langmuir, Freundlich, and Temkin isotherm models and four pseudo-first-order, pseudo-second-order, Elovich, and intraparticle kinetic models in both linear and non-linear forms. The synthesized adsorbent was characterized using X-ray diffraction (XRD), Fourier transforms infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) analyses. The results showed that the synthesized modified activated carbon had a maximum adsorption capacity of 316.1 mg/g and exhibited high efficiency in removing 4-chlorophenols. The optimal conditions for the highest removal efficiency were an adsorbent dosage of 0.55 g/L, contact time of 35 min, initial concentration of 4-chlorophenol of 110 mg/L, and pH of 3. The thermodynamic study indicated that the adsorption process was exothermic and spontaneous. The synthesized adsorbent also showed excellent reusability even after five successive cycles. These findings demonstrate the potential of modified activated carbon as an effective method for removing 4-chlorophenols from aqueous environments and contributing to developing sustainable and efficient water treatment technologies. [ABSTRACT FROM AUTHOR]

Valorization of waste materials and byproducts as adsorbents is a sustainable approach for water treatment systems. Pottery Granules (PG) without any chemical and thermal modification were used as a low-cost, abundant, and environmentally benign adsorbent against Pb(II), the toxic metal in drinking water. The porous structure and complex mineral composition of PG made it an efficient adsorbent material for Pb(II). The effect of key physicochemical factors was investigated to determine the significance of contact time, PG dose, pH, solution temperature, and coexisting ions, on the process. Pb(II) removal increased by PG dose in the range of 5–15 g/L, and agitation time from 5 to 60 min. Increasing Pb(II) concentration led to a drop in Pb(II) removal, however, adsorption capacity increased significantly as concentration elevated. Pb(II) removal also increased significantly from ~ 45% to ~ 97% by pH from 2 to 12. A ~ 20% improvement in Pb(II) adsorption after rising the solution temperature by 30˚C, indicated the endothermic nature of the process. The sorption was described to be a favorable process in which Pb(II) was adsorbed in a multilayer onto the heterogeneous PG surface. The qmax of 9.47 mg/g obtained by the Langmuir model was superior among many reported low-cost adsorbents. The Pb(II) adsorption was described well by the Pseudo- first-order kinetic model. Na+, Mg2+, Ca2+, Cd2+, and Zn2+ showed a negligible effect on Pb(II) adsorption. However, the presence of Mn2+ and Fe2+ significantly hindered the process efficacy. In conclusion, the use of waste material such as PG against Pb(II) is a viable option from the economic and effectiveness points of view. [ABSTRACT FROM AUTHOR]

In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications. [ABSTRACT FROM AUTHOR]

In this paper, a new optimization algorithm called hybrid leader-based optimization (HLBO) is introduced that is applicable in optimization challenges. The main idea of HLBO is to guide the algorithm population under the guidance of a hybrid leader. The stages of HLBO are modeled mathematically in two phases of exploration and exploitation. The efficiency of HLBO in optimization is tested by finding solutions to twenty-three standard benchmark functions of different types of unimodal and multimodal. The optimization results of unimodal functions indicate the high exploitation ability of HLBO in local search for better convergence to global optimal, while the optimization results of multimodal functions show the high exploration ability of HLBO in global search to accurately scan different areas of search space. In addition, the performance of HLBO on solving IEEE CEC 2017 benchmark functions including thirty objective functions is evaluated. The optimization results show the efficiency of HLBO in handling complex objective functions. The quality of the results obtained from HLBO is compared with the results of ten well-known algorithms. The simulation results show the superiority of HLBO in convergence to the global solution as well as the passage of optimally localized areas of the search space compared to ten competing algorithms. In addition, the implementation of HLBO on four engineering design issues demonstrates the applicability of HLBO in real-world problem solving. [ABSTRACT FROM AUTHOR]

This paper introduces a new human-based metaheuristic algorithm called Sewing Training-Based Optimization (STBO), which has applications in handling optimization tasks. The fundamental inspiration of STBO is teaching the process of sewing to beginner tailors. The theory of the proposed STBO approach is described and then mathematically modeled in three phases: (i) training, (ii) imitation of the instructor's skills, and (iii) practice. STBO performance is evaluated on fifty-two benchmark functions consisting of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. The optimization results show that STBO, with its high power of exploration and exploitation, has provided suitable solutions for benchmark functions. The performance of STBO is compared with eleven well-known metaheuristic algorithms. The simulation results show that STBO, with its high ability to balance exploration and exploitation, has provided far more competitive performance in solving benchmark functions than competitor algorithms. Finally, the implementation of STBO in solving four engineering design problems demonstrates the capability of the proposed STBO in dealing with real-world applications. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms have a wide range of applications in handling optimization problems. In this study, a new metaheuristic algorithm, called the chef-based optimization algorithm (CBOA), is developed. The fundamental inspiration employed in CBOA design is the process of learning cooking skills in training courses. The stages of the cooking training process in various phases are mathematically modeled with the aim of increasing the ability of global search in exploration and the ability of local search in exploitation. A collection of 52 standard objective functions is utilized to assess the CBOA's performance in addressing optimization issues. The optimization results show that the CBOA is capable of providing acceptable solutions by creating a balance between exploration and exploitation and is highly efficient in the treatment of optimization problems. In addition, the CBOA's effectiveness in dealing with real-world applications is tested on four engineering problems. Twelve well-known metaheuristic algorithms have been selected for comparison with the CBOA. The simulation results show that CBOA performs much better than competing algorithms and is more effective in solving optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications. [ABSTRACT FROM AUTHOR]

In this paper, a new optimization algorithm called hybrid leader-based optimization (HLBO) is introduced that is applicable in optimization challenges. The main idea of HLBO is to guide the algorithm population under the guidance of a hybrid leader. The stages of HLBO are modeled mathematically in two phases of exploration and exploitation. The efficiency of HLBO in optimization is tested by finding solutions to twenty-three standard benchmark functions of different types of unimodal and multimodal. The optimization results of unimodal functions indicate the high exploitation ability of HLBO in local search for better convergence to global optimal, while the optimization results of multimodal functions show the high exploration ability of HLBO in global search to accurately scan different areas of search space. In addition, the performance of HLBO on solving IEEE CEC 2017 benchmark functions including thirty objective functions is evaluated. The optimization results show the efficiency of HLBO in handling complex objective functions. The quality of the results obtained from HLBO is compared with the results of ten well-known algorithms. The simulation results show the superiority of HLBO in convergence to the global solution as well as the passage of optimally localized areas of the search space compared to ten competing algorithms. In addition, the implementation of HLBO on four engineering design issues demonstrates the applicability of HLBO in real-world problem solving. [ABSTRACT FROM AUTHOR]

The need for fresh water is more than before by population growth, and industrial development have affected the quality of water supplies, one of the important reason for water contamination is synthetic dyes and their extensive use in industries. Adsorption has been considered as a common methods for dye removal from waters. In this study, Acid Red18 removal in batch mode by using Granular Ferric Hydroxide (GFH) was investigated. The GFH characterized by XRD, FESEM and FTIR analysis. Experiments were designed using RSM-CCD method. The maximum removal efficiency was obtained 78.59% at pH = 5, GFH dosage = 2 g/l, AR18 concentration = 77.5 mg/l and 85 min of contact time. Optimization with RSM and Genetic Algorithm carried out and is similar together. The non-linear adsorption Isotherm and kinetic fitted with Freundlich (R2 = 0.978) and pseudo-second-order (R2 = 0.989) models, respectively. Thermodynamic studies showed that the AR18 adsorption is endothermic process and GFH nature was found spontaneous. [ABSTRACT FROM AUTHOR]

The performance of ozonation for the removal of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) using Escherichia coli and Pseudomonas aeruginosa carrying ARGs from hospital wastewaters was evaluated in this study. Bacterial inactivation was determined using plate count methods and real time PCR for ARG damage (Sul1, blatem, blactx, blavim and qnrS). The reduction rate of bacterial cells and ARGs was increased by different amounts of transferred ozone dose from 11 to 45 mg/L. The concentration of 108 cfu/ml bacteria was reduced to an acceptable level by ozone treatment after a 5 min contact time, Although the removal rate was much higher for concentrations of 106 cfu/ml and 104 cfu/ml bacteria. Overall, the tendency of gene reduction by ozonation from more to less was 16S rRNA > sul1 > blatem > blactx > qnrS > blavim. Given that plasmid-borne ARGs can potentially be transferred to other bacteria even after the disinfection process, our results can provide important insights into the fate of ARGs during hospital wastewater ozonation. [ABSTRACT FROM AUTHOR]

In the present study, the adsorptive removal of organophosphate diazinon pesticide using porous pumice adsorbent was experimentally investigated in a batch system, modelled and optimized upon response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA), fitted to isotherm, kinetic and thermodynamic models. The quantification of adsorbent elements was determined using EDX. XRD analysis was utilized to study the crystalline properties of adsorbent. The FT-IR spectra were taken from adsorbent before and after adsorption to study the presence and changes in functional groups. The constituted composition of the adsorbent was determined by XRF. Also, the ionic strength and adsorbent reusability were explored. The influences of operational parameters like pH, initial pesticide concentration, adsorbent dosage and contact time were investigated systematically. ANN-GA and RSM techniques were used to identify the optimal process variables that result in the highest removal. Based on the RSM approach, the optimization conditions for maximum removal efficiency is obtained at pH = 3, adsorbent dosage = 4 g/L, contact time = 30 min, and initial pesticide concentration = 6.2 mg/L. To accurately identify the parameters of nonlinear isotherm and kinetic models, a hybrid evolutionary differential evolution optimization (DEO) is applied. Results indicated that the equilibrium adsorption data were best fitted with Langmuir and Temkin isotherms and kinetic data were well described by pseudo-first and second-order kinetic models. The thermodynamic parameters such as entropy, enthalpy and Gibbs energy were evaluated to study the effect of temperature on pesticide adsorption. [ABSTRACT FROM AUTHOR]

In the present study, reactive red 198 (RR198) dye removal from aqueous solutions by adsorption using municipal solid waste (MSW) compost ash was investigated in batch mode. SEM, XRF, XRD, and BET/BJH analyses were used to characterize MSW compost ash. CNHS and organic matter content analyses showed a low percentage of carbon and organic matter to be incorporated in MSW compost ash. The design of adsorption experiments was performed by Box–Behnken design (BBD), and process variables were modeled and optimized using Box–Behnken design-response surface methodology (BBD-RSM) and genetic algorithm-artificial neural network (GA-ANN). BBD-RSM approach disclosed that a quadratic polynomial model fitted well to the experimental data (F-value = 94.596 and R2 = 0.9436), and ANN suggested a three-layer model with test-R2 = 0.9832, the structure of 4-8-1, and learning algorithm type of Levenberg–Marquardt backpropagation. The same optimization results were suggested by BBD-RSM and GA-ANN approaches so that the optimum conditions for RR198 absorption was observed at pH = 3, operating time = 80 min, RR198 = 20 mg L−1 and MSW compost ash dosage = 2 g L−1. The adsorption behavior was appropriately described by Freundlich isotherm, pseudo-second-order kinetic model. Further, the data were found to be better described with the nonlinear when compared to the linear form of these equations. Also, the thermodynamic study revealed the spontaneous and exothermic nature of the adsorption process. In relation to the reuse, a 12.1% reduction in the adsorption efficiency was seen after five successive cycles. The present study showed that MSW compost ash as an economical, reusable, and efficient adsorbent would be desirable for application in the adsorption process to dye wastewater treatment, and both BBD-RSM and GA-ANN approaches are highly potential methods in adsorption modeling and optimization study of the adsorption process. The present work also provides preliminary information, which is helpful for developing the adsorption process on an industrial scale. [ABSTRACT FROM AUTHOR]

This article's innovation and novelty are introducing a new metaheuristic method called mother optimization algorithm (MOA) that mimics the human interaction between a mother and her children. The real inspiration of MOA is to simulate the mother's care of children in three phases education, advice, and upbringing. The mathematical model of MOA used in the search process and exploration is presented. The performance of MOA is assessed on a set of 52 benchmark functions, including unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, and the CEC 2017 test suite. The findings of optimizing unimodal functions indicate MOA's high ability in local search and exploitation. The findings of optimization of high-dimensional multimodal functions indicate the high ability of MOA in global search and exploration. The findings of optimization of fixed-dimension multi-model functions and the CEC 2017 test suite show that MOA with a high ability to balance exploration and exploitation effectively supports the search process and can generate appropriate solutions for optimization problems. The outcomes quality obtained from MOA has been compared with the performance of 12 often-used metaheuristic algorithms. Upon analysis and comparison of the simulation results, it was found that the proposed MOA outperforms competing algorithms with superior and significantly more competitive performance. Precisely, the proposed MOA delivers better results in most objective functions. Furthermore, the application of MOA on four engineering design problems demonstrates the efficacy of the proposed approach in solving real-world optimization problems. The findings of the statistical analysis from the Wilcoxon signed-rank test show that MOA has a significant statistical superiority compared to the twelve well-known metaheuristic algorithms in managing the optimization problems studied in this paper. [ABSTRACT FROM AUTHOR]

Aquaculture effluent treatment is essential to eliminate the undesirable characteristics of water to ensure cleaner production and environmental sustainability. In an effort to develop green coagulant without compromising cost, this research investigated the feasibility of aquaculture effluent (AQEF) pollutant removal using Picralima nitida seeds extract (PNSC) and its bio-coagulation/adsorption kinetic characteristics with the substrate in water. The coagulative decrease was observed in terms of TD (turbidity), TSS (total suspended solids), COD (chemical oxygen demand), BOD (biochemical oxygen demand), and COLR (color) from AQEF. The active coagulant was extracted from the seeds and analyzed for its spectral and morphological characteristics through FTIR and SEM. The influence of PNSC dosage (0.10–0.50 g L−1), pH (2–10), settling time (0–60 min), and temperature (303–323 K) on the removal of contaminants were surveyed. The process kinetics of coagulation–flocculation were also explored. Maximal TD reduction of 90.35%, COD (82.11%), BOD (82.38%); TSS (88.84%), and COLR (65.77%) at 0.2 g PNSC L−1, pH 4, and 303 K was achieved. Analysis of variance (ANOVA) tests proved that pH, temperature, and settling time had a significant effect on pollutant removal. Results fitted Von Smoluchowski's perikinetics theory at the optimum conditions, which gave R2 > 0.900. At perikinetics circumstances, the Kb (reaction rate) and t f 1 2 (half-life) correspond to 0.0635 Lg−1 min−1 and 1.9 min. More so, sorption results fitted the Lagergren over the Ho model. Additionally, the net cost of using PNSC to handle 1 L of AQEF (including electricity, material, and labor costs) was evaluated to be €4.81. Overall, the PNSC appears reliable and useful in pretreating AQEF for improved biodegradability and superior effluent quality. [ABSTRACT FROM AUTHOR]

Diclofenac (DCF) as a non-steroidal pharmaceutical has been detected in aquatic samples more than other compounds due to its high consumption and limited biodegradability. In this study, ultrasound waves were applied along with an advanced nano-Fenton process (US/ANF) to remove DCF, and subsequently, the synergistic effect was determined. Before that, the efficiency of the US and ANF processes was separately studied. The central composite design was used as one of the most applicable responses surface method techniques to determine the main and interactive effect of the factors influencing DCF removal efficiency in US/ANF. The mean DCF removal efficiency under different operational conditions and at the time of 1–10 min was obtained to be about 4%, 83%, and 95% for the US, ANF, and US/ANF, respectively. Quadratic regression equations for two frequencies of US were developed using multiple regression analysis involving main, quadratic, and interaction effects. The optimum condition for DCF removal was obtained at time of 8.17 min, H/F of 10.5 and DCF concentration of 10.12 at 130 kHz US frequency. The synergy index values showed a slight synergistic effect for US/ANF (1.1). Although the synergistic effect of US/ANF is not very remarkable, it can be considered as a quick and efficient process for the removal of DCF from wastewater with a significant amount of mineralization. [ABSTRACT FROM AUTHOR]

Considering the destructive effect of stresses on the photosynthetic apparatus of plants and the important role of light in photosynthesis, we investigated the effect of complementary light on the photosynthetic apparatus under salinity and alkalinity stress conditions. Light-emitting diodes (LEDs) in monochromatic blue (460 nm), monochromatic red (660 nm), dichromatic blue/red (1:3), white/yellow (400–700 nm) at 200 μmol m−2 S−1, and without LED treatment were used. The stress treatments were in three stages: Control (no stress), Alkalinity (40 mM NaHCO3), and Salinity (80 mM NaCl). Our results showed that salinity and alkaline stress reduced CO2 assimilation by 62.64% and 40.81%, respectively, compared to the control treatment. The blue light spectrum had the highest increase in water use efficiency (54%) compared to the treatment without supplementary light. Under salinity and alkalinity stress, L, K, and H bands increased and G bands decreased compared to the control treatment, with blue/red light causing the highest increase in L and K bands under both stress conditions. In salinity and alkalinity stress, white/yellow and blue/red spectra caused the highest increase in H bands. Complementary light spectra increased the G band compared to the treatment without complementary light. There was a significant decrease in power indices and quantum power parameters due to salt and alkalinity stress. The use of light spectra, especially blue, red, and blue/red light, increased these parameters compared with treatment without complementary light. Different light spectra have different effects on the photosynthetic apparatus of plants. It can be concluded that using red, blue spectra and their combination can increase the resistance of plants to stress conditions and be adopted as a strategy in planting plants under stress conditions. [ABSTRACT FROM AUTHOR]

Wastewater consisting of different pharmaceuticals and drug residues is quite challenging to treat and dispose of. This situation poses a significant impact on the health aspect of humans and other biotic organisms in the environment. The main concern of hospital wastewater (HWW) is the resistivity towards treatment using the different conventional methods. For the treatment of HWW, this study was performed using an electro bioreactor using hospital wastewater. The electro reduction overcomes the effect of toxic elements in hospital wastewater, and biodegradation removes organic matter and nutrients from wastewater. This study investigated electro bioreactor performance for treating hospital wastewater connected with tubesettler. The parameters of chemical oxygen demand, nitrate, and phosphate concentration were analyzed to evaluate an influent and effluent from electro bioreactor and tubesettler. Also, Kinetic modelling for chemical oxygen demand, nitrate, and phosphate removal was done. The chemical oxygen demand was reduced by 76% in electro bioreactor, and 31% in tubesettler, 84%. The nitrate and phosphate were reduced within permissible discharge limits with a final effluent concentration of 1.4 mg L−1 and 3 mg L−1. Further studies are required to assess the impact of pharmaceutical compounds in hospital wastewater on the system's performance. [ABSTRACT FROM AUTHOR]