An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach.

This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and response surface methodology (RSM). To evaluate the adsorbent's potential, assessments were conducted using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM).

Zeolites in wastewater treatment: A comprehensive review on scientometric analysis, adsorption mechanisms, and future prospects.

Zeolites possess a microporous crystalline structure, a large surface area, and a uniform pore size. Natural or synthetic zeolites are commonly utilized for adsorbing organic and inorganic compounds from wastewater because of their unique physicochemical properties and cost-effectiveness. The present review work comprehensively revealed the application of zeolites in removing a diverse range of wastewater contaminates, such as dyes, heavy metal ions, and phenolic compounds, within the framework of contemporary research.

Machine learning approaches for the treatment of textile wastewater using sugarcane bagasse (Saccharum officinarum) biochar.

Most dyes present in wastewater from the textile industry exhibit toxicity and are resistant to biodegradation. Hence, the imperative arises for the environmentally significant elimination of textile dye by utilising agricultural waste. The achievement of this objective can be facilitated through the utilisation of the adsorption mechanism, which entails the passive absorption of pollutants using biochar.

AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis.

Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity.

Integrating artificial neural networks and response surface methodology for predictive modeling and mechanistic insights into the detoxification of hazardous MB and CV dyes using Saccharum officinarum L. biomass.

The presence of dye pollutants in industrial wastewater poses significant environmental and health risks, necessitating effective treatment methods. The optimal adsorption treatment of methylene blue (MB) and crystal violet (CV) dye-simulated wastewater utilising Saccharum officinarum L presents a key challenge in the selection of appropriate modelling approaches. While RSM and ANN models are frequently used, there is a noticeable knowledge gap when it comes to evaluating their relative strengths and weaknesses in this context.

Introducing machine learning model to response surface methodology for biosorption of methylene blue dye using Triticum aestivum biomass.

A major environmental problem on a global scale is the contamination of water by dyes, particularly from industrial effluents. Consequently, wastewater treatment from various industrial wastes is crucial to restoring environmental quality. Dye is an important class of organic pollutants that are considered harmful to both people and aquatic habitats.

Investigating the Biochemical Responses in Wheat Cultivars Exposed to Thermal Power Plant Emission.

The current study aimed to assess how high concentrations of ozone (O) and suspended particulate matter (SPM) alter biochemical properties of high yielding wheat cultivars (i.e., HD3086 and HD2967) grown under 10 km radius in 8 villages, located around Thermal Power Plant (TPP), Auraiya, Uttar Pradesh, India.

A practical evaluation on integrated role of biochar and nanomaterials in soil remediation processes.

Soil decontamination and restoration continue to be a key environmental concern around the globe. The degradation of soil resources due to the presence of potentially toxic elements (PTEs) has a substantial influence on agricultural production, food security, and human well-being, and as a result, urgent action is required. PTEs pollution is not a threat to the agroecosystems but also a serious concern to human health; thereby, it needs to be addressed timely and effectively.

A systematic approach towards optimization of brackish groundwater treatment using nanofiltration (NF) and reverse osmosis (RO) hybrid membrane filtration system.

This research compares the performance efficiencies of Nanofiltration (NF), Reverse osmosis (RO), and Nanofiltration-Reverse Osmosis (NF-RO) hybrid membrane filtration systems, for treatment of brackish groundwater in Delhi-NCR region. Central composite design (CCD) of response surface methodology (RSM) were applied to formulate predictive models for the optimization and simulation of various responses, viz. Water flux, salt rejection, permeate recovery and specific energy consumption (SEC).

Treatment of textile industry wastewater by using high-performance forward osmosis membrane tailored with alpha-manganese dioxide nanoparticles for fertigation.

Forward osmosis (FO) technology has been acknowledged as an energy-efficient cutting-edge water treatment innovation; however, the inefficient performance of polymer-based membranes remains a tailback in the practical utilization of FO. A significant issue in FO is membrane fouling, which negatively influences the flux efficiency, working expenses and membrane life expectancy. Membranes having high water flux and minimum reverse solute flux at low operating pressures are the ideal membranes for this process.

Fabrication and characterization of high-performance forward-osmosis membrane by introducing manganese oxide incited graphene quantum dots.

Forward osmosis (FO) is the futuristic membrane desalination technology as it transcends the disadvantages of other pressure-driven techniques. But, there still remain critical challenges like fabrication of highly permeable membrane with ideal structures maintaining high rejection rates that need to be addressed for implementation as a practical technology. In this work, novel thin-film composite (TFC) membranes were fabricated by means of incorporating manganese oxide (MnO) incited graphene quantum dots (GQDs) nanocomposite into a cellulose acetate (CA) suspension followed by phase inversion (PI) for enhanced FO performance.

Use of artificial intelligence for optimizing biosorption of textile wastewater using agricultural waste.

Most of the dyes are toxic and non-biodegradable in textile industry wastewaters. Therefore, removal of textile dye using agriculture waste becomes crucial for the environment. This can be accomplished by the biosorption process which is the passive uptake of pollutants by agricultural waste.

Effects of temperature, pH, feed, and fertilizer draw solution concentrations on the performance of forward osmosis process for textile wastewater treatment.

Water is crucial for enhancing the yield of agricultural land to meet the growing demand. Forward osmosis (FO) is a developing technology that utilizes the natural osmotic gradient of solutions. In this study, fertilizer drawn FO setup was considered by using potassium chloride (KCl) as the draw solution (DS) for treating textile wastewater as the feed solution (FS).

Response surface methodology and artificial neural network modelling for the performance evaluation of pilot-scale hybrid nanofiltration (NF) & reverse osmosis (RO) membrane system for the treatment of brackish ground water.

Artificial neural network (ANN) and response surface methodology (RSM) were employed to develop models for process optimisation of pilot scale nanofiltration (NF) and reverse osmosis (RO) membrane filtration system for the treatment of brackish groundwater. The process variables for this study were feed concentration, temperature, pH and pressure. The performance of NF/RO was assessed in terms of permeate flux, water recovery, salt rejection and specific energy consumption, which were considered as responses.

A new approach for optimization of small-scale RO membrane using artificial groundwater.

The present study aims at evaluating a small-scale brackish water reverse osmosis (RO) process using parameter optimization. Experiments were carried out using formulated artificial groundwater, and a predictive model was developed by using response surface methodology (RSM) for the optimization of input process parameters of brackish water RO process to simultaneously maximize water recovery and salt rejection while minimizing energy demand. The result of multiple response optimization along with analysis of variance for RSM predictions showed that the optimal water recovery (19.