Machine Design Sharma Agarwal Pdf 11
Vida Hubbert
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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. The textile industry has become more interested in agricultural-based adsorbents, particularly in adsorption. The biosorption of Methylene blue (MB) dye from aqueous solutions by the wheat straw (T. aestivum) biomass was evaluated in this study. The biosorption process parameters were optimized using the response surface methodology (RSM) approach with a face-centred central composite design (FCCCD). Using a 10 mg/L concentration MB dye, 1.5 mg of biomass, an initial pH of 6, and a contact time of 60 min at 25 C, the maximum MB dye removal percentages (96%) were obtained. Artificial neural network (ANN) modelling techniques are also employed to stimulate and validate the process, and their efficacy and ability to predict the reaction (removal efficiency) were assessed. The existence of functional groups, which are important binding sites involved in the process of MB biosorption, was demonstrated using Fourier Transform Infrared Spectroscopy (FTIR) spectra. Moreover, a scan electron microscope (SEM) revealed that fresh, shiny particles had been absorbed on the surface of the T. aestivum following the biosorption procedure. The bio-removal of MB from wastewater effluents has been demonstrated to be possible using T. aestivum biomass as a biosorbent. It is also a promising biosorbent that is economical, environmentally friendly, biodegradable, and cost-effective.
Textile dyeing plant industries produce a significant amount of waste, 5% of which ends up in wastewater effluents of about 637.3 million cubic metres per year, which contributes significantly to the pollution of water bodies1. Wastewater from industries that make dyes and pigments, as well as many others, is typically rich in colour and organic material. The use of dyes is widespread in sectors like textiles, rubber, paper, plastic, and cosmetics. Textiles are the first among these several industries in the use of dyes to colour fibre. Dye discharge from textile industries causes severe air, water, and soil pollution and thus adversely impacts the environment. The textile industry has recently grown to be a significant problem that has an impact on both people and the environment2. Wastewater that contains dye is hazardous because it contains toxic substances, suspended solids, and other chemicals3,4. A chemical that results from their interaction is extremely dangerous for people, plants, and aquatic life. The result is waterborne diseases5. MB is the most common and popular dye in the textile industry, used to colour wool, silks, and cotton. MB is a positively charged anionic quinonoid structure and the chemical formula of MB is C16H18ClN3S. Methemoglobinemia, tissue necrosis, mental confusion, and vomiting are all possible side effects of MB toxicity6. Limiting oxygen transfer and preventing sunlight from reaching water bodies are two negative effects of dyes on the environment7.
Recently, several reports on dye removal methods have been released8. The three main treatment categories for the methods that were presented are chemical, biological, and physical treatments9,10. Some of the remarkable methods that are typically reported include adsorption, biological treatment, electrochemical treatment, advanced oxidation (AOP), and membrane filtration11,12. Pre-proofing is used to get rid of the dye. Each technique has advantages and disadvantages. The approach that is most frequently used is adsorption13. It enables the removal of pollutants at levels ranging from low to high. As a result, numerous studies have been carried out to create adsorbent materials that are efficient and affordable14. The most adaptable and widely used of these techniques is biosorption it is both affordable and user-friendly15,16. Numerous studies have supported and confirmed the use of a variety of materials for pollutant biosorption to remove contaminants17,18. Popular and highly effective biosorbents such as activated carbon are also more expensive19, which has led leading many researchers to search hunt for biosorbents that were inexpensive and easily accessible locally20,21. To remove the dye MB from textile wastewater, T. aestivum is used as a low-cost biosorbent in this study. It is a frequently discarded agricultural waste product that is readily accessible and can no longer be used for beneficial purposes22,23. Additionally, it is freely available or extremely inexpensive, making it a readily available and cost-effective biosorbent. The disadvantages of the synthesized adsorbents for the treatments of the dye wastewater are regeneration of the biosorbent is expensive and results in the loss of materials, require high dosage, and is economically non-viable for some industries like paper and pulp.
Additionally, combining the RSM with machine learning (ANNs) could increase the reliability of the model. The emergence of better modelling methods with improved model performance, such as ANN, offers a substitute for polynomial regression. The output responses obtained by the CCD were compared to the predictions made by machine learning (ANN) using the MATLAB programme. A closer look at the literature, reveals several gaps and shortcomings. Previous research typically focussed on only investigated the thermodynamics, kinetics, and isotherms study of the biosorption process. These studies warrant a better understanding of biosorption, but there is still a great deal of work to be done along with designing of experiments (RSM) and machine modelling. This article provides a new novel approach to optimization modelling using RSM to investigate and examine how biosorbent dosage, initial dye pH, concentration, and temperature affect the critical behaviour of biosorption. The thermodynamic parameters, isotherms, biosorption kinetics and surface modification of T. aestivum were all determined by analysing the biosorption data using Fourier transform infrared spectroscopy. A schematic diagram of the biosorption study is shown in Fig. 1.
It is first divided into smaller pieces and repeatedly rinsed with water to remove residues any. The biosorbent is then placed and exposed to the sun to dry for one week. The dry biosorbent was then mortar-crushed. Using a sieve shaker, the ground biosorbent is sieved to keep uniform-sized particles, prior to being dried for 24 h in a hot air furnace at 70 C, the particles that made it through a 250 m sieve are used deionized water to rinse three times. Biosorption tests are conducted using the prepared biosorbent. The chemical composition of T. aestivum is listed in Table 1.
The requisite quantity of MB dye powder and deionized water are combined to produce the stock solution of MB (500 mg/L). The given concentration levels are produced from the stock MB solution. It was utilised to gather the experimental results.
The biosorption process parameters must be optimised according to variables including the biosorbent dosage, contact time, temperature, pH solution, and initial dye concentration to achieve maximal biosorption. Each experiment involved shaking Erlenmeyer flasks with 100 mL of MB dye solutions at 120 rpm for 60 min. When utilising a pH metre, 1 M NaOH or 1 M HCl solution was added to dye solutions to change the pH. The biosorbent was removed from the mixture using filter paper at intervals of 0.45 m while stirring. An absorption spectrophotometer (Make: Labman Scientific Instruments Pvt. Ltd., Model: LMSP UV1900) is used to compare the dye concentrations before and after treatment. The means of each experiment's two runs were presented as concentrations. To examine the impact on biosorption kinetics, the test solutions' starting colour density and response time were changed. With the use of a pH metre (Make: HANNA instruments, USA, Model: HI 991001), the pH of the dye solution was changed. To determine how temperature affects different thermodynamic parameters, biosorption research was conducted using diluted HCl or NaOH solutions. Biosorption levels (qt) at time t (mg/g) were calculated using Eq. (1)31
Because only one variable is changed while the other variables are kept constant in a standard experiment, the researcher ignores the synergistic effect of the components. A variety of optimization methodologies have been developed in operational analysis over the years, resulting in a long history of optimization studies32. RSM is a methodical statistical methodology that improves the agreement of the minimal test runs when evaluating the relationship between design responses and factors33. The quadrilateral design is provided because the CCD contains only a subset of the experiments required for the five-step factorial and provides schemes with the required statistical properties34,35.
where c is the number of centre-point replicas, n is the number of numerical components, and N is the total number of experiments25. The graphical analysis, regression analysis, and experimental design were all carried out using software from Stat-Ease Inc. known as Design Expert. A total of 30 trials were designed, each containing six repetitions of the centre points, eight replications of the axial points, and sixteen replications of the cubical points, in accordance with Eq. (4). Regression equations were used to determine the variables' ideal circumstances. Using a four-point combination of four variables and three phases, the maximum organic sorbent dosage, pH, initial metal ion concentration, and temperature were all calculated35. This design was chosen because it met most of the criteria for optimizing bio-absorption studies26. Finding ideal process working conditions to meet performance standards is the primary goal of RSM.
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