Statistical optimization and sequential scale-up of α-galactosidase production by Actinoplanes utahensis B1 from shake flask to pilot scale.

α-Galactosidase (α-GAL) is a class of hydrolase that releases galactose from galacto-oligosaccharides and synthetic substrates such as pNPG. In this study, the production of α-GAL by Actinoplanes utahensis B1 in submerged fermentation was enhanced by using statistical methods. The effects of temperature, pH, and inoculum percentage on enzyme secretion were optimized using BBD of RSM. The optimized process was scaled up from the shake flask to the laboratory scale (5 L) and to pilot scale (30 L) using KLa based scale-up strategy. By using BBD, a maximum yield of 62.5 U/mL was obtained at a temperature of 28 °C, a pH of 6.9, and an inoculum of 6.4%. Scale-up was performed successfully and achieved a yield of 74.4 U/mL and 76.8 U/mL in laboratory scale and pilot scale fermenters. The TOST was performed to validate the scale-up strategy and the results showed a confidence level of 95% for both scales indicating the perfect execution of scale-up procedure. Through the implementation of BBD and scale-up strategy, the overall enzyme yield has been significantly increased to 76%. This is the first article to explore the scale-up of α-GAL from the A. utahensis B1 strain and provide valuable insights for industrial applications.

Experimental investigation on utilization of Sesbania grandiflora residues through thermochemical conversion process for the production of value added chemicals and biofuels.

All the countries in the world are now searching for renewable, environmentally friendly alternative fuels due to the shortage and environmental problems related with the usage of conventional fuels. The cultivation of cereal and noncereal crops through agricultural activities produces waste biomasses, which are being evaluated as renewable and viable fossil fuel substitutes. The thermochemical properties and thermal degradation behavior of Sesbania grandiflora residues were investigated for this work. A fluidized bed reactor was used for fast pyrolysis in order to produce pyrolysis oil, char and gas. Investigations were done to analyze the effect of operating parameters such as temperature (350-550 °C), particle size (0.5-2.0 mm), sweeping gas flow rate (1.5-2.25 m3/h). The maximum of pyrolysis oil (44.7 wt%), was obtained at 425 °C for 1.5 mm particle size at the sweep gas flow rate of 2.0 m3/h. Fourier transform infrared spectroscopy and gas chromatography-mass spectrometry methods were used to examine the composition of the pyrolysis oil. The pyrolysis oil is rich with aliphatic, aromatic, phenolic, and some acidic chemicals. The physical characteristics of pyrolysis oil showed higher heating value of 19.76 MJ/kg. The char and gaseous components were also analyzed to find its suitability as a fuel.

The impact of 180° return bend inclination on pressure drop characteristics and phase distribution during oil-water flow.

The present work aims to capture the influence of the inclination of the return bend on flow patterns and pressure drop during oil-water flow. The experiments were carried out for different inclinations (0°, 15°, 30°, and 45°) of return bend for various superficial velocity combinations of oil (kerosene) and water ranging from 0.07 to 0.66 m/s. The experiments showed that pressure drop increases with the increase in inclination. However, the pressure drop at a fixed inclination (say 15°) decreases with the increase in the superficial velocity of the water. Distinct flow patterns observed in the return bend were droplet flow, film inversion, slug flow, plug flow and large slug flow. Droplet flow dominates at the lower range of kerosene (i.e., Usk = 0.07-0.2 m/s) and higher range of water superficial velocity (i.e., Usw = 0.40-0.66 m/s) at all the inclinations considered in this study. Additionally, comparisons between the experimental and numerical simulation results were made. The numerical solution utilized the Euler-Euler approach, considering the different phases as interpenetrating continua. The Volume of Fluid (VOF) model was used within this approach, monitoring the volume fraction of each phase over the domain while calculating one set of momentum equations for each phase. To capture the turbulent effects accurately, the k-ε turbulence model was incorporated. It happened to be found that the numerical findings showed remarkable agreement with the experimental data.

RSM-based modelling for predicting and optimizing the rheological and mechanical properties of fibre-reinforced laterized self-compacting concrete.

A sustainable method to reduce the use of natural resources and the negative effects of the concrete industry on the environment is to use waste lateritic aggregate in self-compacting concrete and evaluate its fresh, mechanical and durability characteristics. Using RSM's central composite design (CCD), Thirteen different SCC mixtures have been designed with varying input factor combinations (LA: 0-100%, PPF: 0-2%) and tested for eight responses (rheological properties, like slump flow, V-funnel time, and T500; mechanical properties, like compressive, split-tensile, and flexural strengths, and durability properties like drying shrinkage and rapid chloride penetration test). The analysis of variance (ANOVA) test was performed to determine the accuracy of the mathematical models developed following the experimental results. ANOVA was used to verify eight response models (seven quadratic and one linear). The inclusion of laterite aggregate has been found to linearly reduce the workability of fresh concrete. Self-compacting concrete will have a lower V-funnel value if any combination of components falls below these two limit values (31% LA and 1.12% PPF). The area bounded by the 760-mm contour line and the graph axes recorded the highest slump flow at (28% LA and 1.26% PPF). Similarly, SCC with a lower T500 value will be produced by any combination of components below these two limit values (25% LA and 1.11% PPF). By replacing 28.5% of the granite aggregate with laterite aggregate and adding 1.24% polypropylene fiber, the compressive strength of M30 grade self-compacting concrete increased by 12.33% after 28 days. A similar strength gain of 7.89% was seen in the splitting tensile by replacing 28% of the granite aggregate with laterite aggregate and adding 1.46% polypropylene fiber over the control mix, and a flexural strength gain of 14.46% was seen by replacing 31.4% of laterite aggregate and adding 1.2% polypropylene fiber, respectively. The low drying shrinkage values are related to a combination of LA concentration (34.4% replacement) and PPF (1.31%) and minimum chloride ingress is located in the region with a LA concentration (30.5% replacement) and a PPF content (1.26%), The projected optimal data were verified experimentally with an error rate of less than 5%. Thus, it is highly recommended that the created model be adequate and capable of optimizing both the experimental and analytical values. It is recommended that the utilization of 25% LA and 1% PPF in lateritic self-compacting concrete provides optimum outcomes for the construction industry in the field of civil engineering.

A comprehensive review of critical analysis of biodegradable waste PCM for thermal energy storage systems using machine learning and deep learning to predict dynamic behavior.

This article explores the use of phase change materials (PCMs) derived from waste, in energy storage systems. It emphasizes the potential of these PCMs in addressing concerns related to fossil fuel usage and environmental impact. This article also highlights the aspects of these PCMs including reduced reliance on renewable resources minimized greenhouse gas emissions and waste reduction. The study also discusses approaches such as integrating nanotechnology to enhance thermal conductivity and utilizing machine learning and deep learning techniques for predicting dynamic behavior. The article provides an overall view of research on biodegradable waste-based PCMs and how they can play a promising role in achieving energy-efficient and sustainable thermal storage systems. However, specific conclusions drawn from the presented results are not explicitly outlined, leaving room, for investigation and exploration in this evolving field. Artificial neural network (ANN) predictive models for thermal energy storage devices perform differently. With a 4% adjusted mean absolute error, the Gaussian radial basis function kernel Support Vector Regression (SVR) model captured heat-related charging and discharging issues. The ANN model predicted finned tube heat and heat flux better than the numerical model. SVM models outperformed ANN and ANFIS in some datasets. Material property predictions favored gradient boosting, but Linear Regression and SVR models performed better, emphasizing application- and dataset-specific model selection. These predictive models provide insights into the complex thermal performance of building structures, aiding in the design and operation of energy-efficient systems. Biodegradable waste-based PCMs' sustainability includes carbon footprint, waste reduction, biodegradability, and circular economy alignment. Nanotechnology, machine learning, and deep learning improve thermal conductivity and prediction. Circular economy principles include waste reduction and carbon footprint reduction. Specific results-based conclusions are not stated. Presenting a comprehensive overview of current research highlights biodegradable waste-based PCMs' potential for energy-efficient and sustainable thermal storage systems.

Characteristics estimation of natural fibre reinforced plastic composites using deep multi-layer perceptron (MLP) technique.

Polymer Matrix Composite (PMC/Plastic Composite) often referred to as Plastic Composite with Natural fibre reinforcement has a huge interest in industries to manufacture components for various applications including medical, transportation, sports equipment etc. In the universe, different types of natural fibres are available which can be used for the reinforcement in PMC/Plastic Composite. So, the selection of appropriate fibre for the PMC/Plastic Composite/Plastic composite is a challenging task, but it can be done using an effective metaheuristic or optimization techniques. But in this type of optimal reinforcement fibre or matrix material selection, the optimization is formulated based on any one of the parameters of the composition. Hence to analyse the various parameter of any PMC/Plastic Composite/Plastic Composite without real manufacturing, a machine learning technique is recommended. The conventional simple or single-layer machine learning techniques were not sufficient to emulate the exact real-time performance of the PMC/Plastic Composite. Thus, a deep multi-layer perceptron (Deep MLP) algorithm is proposed to analyse the various parameter of PMC/Plastic Composite with natural fibre reinforcement. In the proposed technique the MLP is modified by including around 50 hidden layers to enhance its performance. In every hidden layer, the basis function is evaluated and subsequently, the sigmodal function-based activation is calculated. The proposed Deep MLP is utilized to evaluate the various parameters of PMC/Plastic Composite Tensile Strength, Tensile Modulus, Flexural Yield Strength, Flexural Yield Modulus, Young's Modulus, Elastic Modulus and Density. Then the obtained parameter is compared with the actual value and the performance of the proposed Deep MLP is evaluated based on the accuracy, precision, and recall. The proposed Deep MLP attained 87.2%, 87.18%, and 87.22% of accuracy, precision, and recall. Ultimately the proposed system proves that the proposed Deep MLP can perform better for the prediction of various parameters of PMC/Plastic Composite with natural fibre reinforcement.

Sustainable wastewater treatment by RO and hybrid organic polyamide membrane nanofiltration system for clean environment.

One of the environmental pollution is happened by the discharge of industrial wastewater that needs to be adequately filtered. Given that the effluent from the leather industry contains high levels of chromium, heavy metals, lipids, and Sulphur, it is one of the wastewater disposals that are most damaging. This experimental study focuses on reverse osmosis and hybrid organic polyimide membrane for nanofiltration for sustainable wastewater treatment. In the RO and organic polyamide Nano-porous membranes, a thin film of polyamide membrane was used for efficient filtration. Taguchi analysis optimized process parameters such as pressure, temperature, pH, and volume reduction factor. The outcome shows an 89% reduction in total wastewater hardness, an 88% reduction in sulfate, and an 89% efficiency reduction in COD. As a result, the proposed technology significantly increased filtration efficiency.

AZ63/Ti/Zr Nanocomposite for Bone-Related Biomedical Applications.

Considering the unique properties of magnesium and its alloy, it has a vast demand in biomedical applications, particularly the implant material in tissue engineering due to its biodegradability. But the fixing spares must hold such implants till the end of the biodegradation of implant material. The composite technology will offer the added benefits of altering the material properties to match the requirements of the desired applications. Hence, this experimental investigation is aimed at developing a composite material for manufacturing fixing spares like a screw for implants in biomedical applications. The matrix of AZ63 magnesium alloy is reinforced with nanoparticles of zirconium (Zr) and titanium (Ti) through the stir casting-type synthesis method. The samples were prepared with equal contributions of zirconium (Zr) and titanium (Ti) nanoparticles in the total reinforcement percentage (3%, 6%, 9%, and 12%). The corrosive and tribological studies were done. In the corrosive study, the process parameters like NaCl concentration, pH value, and exposure time were varied at three levels. In the wear study, the applied Load, speed of sliding, and the distance of the slide were considered at four levels. Taguchi analysis was employed in this investigation to optimize the reinforcement and independent factors to minimize the wear and corrosive losses. The minimum wear rate was achieved in the 12% reinforced sample with the input factor levels of 60 N of load on the pin, 1 m/s of disc speed at a sliding distance was 1500 m, and the 12% reinforce samples also recorded a minimum corrosive rate of 0.0076 mm/year at the operating environment of 5% NaCl-concentrated solution with the pH value of 9 for 24 hrs of exposure. The prediction model was developed based on the experimental results.

A novel investigations on medical and non-medical mask performance with influence of marine waste microplastics (polypropylene).

The entire human race is struggling with the spread of COVID-19. Worldwide, the wearing of face masks is indispensable to prevent such spread. Despite numerous studies reporting on the fabrication of face masks and surgical masks to reduce spread and thus human deaths, this novel work is considered the marine waste of microplastics, namely Polypropylene (PP) polymer, used to fabricate non-woven fabric masks through the melt-blown process. This experimental work aims to maximize the mask's quality and minimize its fabrication cost by optimizing the melt-blown process parameters and using microplastics. The melt-blown process was used to make masks. Parameters such as extruder temperature, hot air temperature, melt flow rate, and die-to-collector distance (DCD) were investigated as independent variables. The quality of the mask was investigated in terms of bacterial filtration efficiency (BFE), particle filtration efficiency (PFE), and differential pressure. The Taguchi L16 orthogonal array and Taguchi analysis were employed for experimental design and statistical optimization, respectively. The results reveal that the higher BFE and PFE are recorded at 96.7 % and 98.6 %, respectively. The surface morphological investigation on different layers ensured the fine and uniform porosity of the layers and exhibited minimum breath resistance (a low differential pressure of 0.00152 kPa/cm2). Hence the chemically treated marine waste microplastics improved the masks' performance.

Experimental evaluation of soapberry seed oil biodiesel performance in CRDI diesel engine.

Due to the ongoing demand for alternative fuels for CI engines, biodiesel-based research has received support globally. In this study, soapberry seed oil produced by transesterification process to creates biodiesel. It is referred to as BDSS (Biodiesel of Soapberry Seed). According to criteria, the oil qualities are recognized, hence, three different blends and pure diesel were tested in CRDI (Common Rail Direct Injection) engines. The blends descriptions are: 10BDSS (10% BDSS + 90% diesel), 20BDSS (20% BDSS + 80% diesel), and 30BDSS (30% BDSS + 70% diesel). The outcomes of the related tests for combustion, performance, and pollution were contrasted with those achieved using 100% diesel fuel. In this case, the mixing has resulted in worse braking thermal efficiency than diesel and lower residual emissions with greater NOx emissions. The superior results were obtained by 30BDSS, which had BTE of 27.82%, NOx emissions of 1348 ppm, peak pressure of 78.93 bar, heat release rate (HRR) of 61.15 J/deg, emissions of CO (0.81%), HC (11 ppm), and smoke opacity of 15.38%.

Environmental remediation at vegetable marketplaces through production of biowaste catalysts for biofuel generation.

Large quantities of vegetable biowaste are generated at marketplaces, usually in highly populated locations. On the other hand, nearby markets, hotels, and street shops generate much cooking oil waste and dispose of them in the sewage. Environmental remediation is mandatory at these places. Hence, this experimental work concentrated on preparing biodiesel using green plant wastes and cooking oil. Biowaste catalysts were produced from vegetable wastes and biofuel generated from waste cooking oil using biowaste catalysts to support diesel demand and Environmental remediation. Other organic plant wastes such as bagasse, papaya stem, banana peduncle and moringa oleifera are used as heterogeneous catalysts of this research work. Initially, the plant wastes are independently considered for the catalyst for biodiesel production; secondary, all plant wastes are mixed to form a single catalyst and used to prepare the biodiesel. In the maximum biodiesel yield analysis, the calcination temperature, reaction temperature, methanol/oil ratio, catalyst loading and mixing speed were considered to control the biodiesel production. The results reveal that the catalyst loading of 4.5 wt% with mixed plant waste catalyst offered a maximum biodiesel yield of 95%.

Hybrid MWCNT and TiO2 Nanoparticle-Suspended Waste Tyre Oil Biodiesel for CI Engines.

Nowadays, scarcity arises in almost all our basic needs, including water, fuel, and food. Recycling used and scrapped things for a valuable commodity is highly appreciable for compensating for the globally fast-growing demand. This paper aims to investigate waste tyre oil for preparing biodiesel for CI engines by enhancing their performance with hybrid nanoparticles for preparing nanofuel and hybrid nanofuel. The nanoparticles (30-40 nm) of MWCNT and TiO2 were utilized to prepare nanofuels with nanoparticle concentrations of MWCNT (300 ppm) and TiO2 (300 ppm), respectively. In the case of hybrid nanofuel, the nanoparticle concentration of MWCNT (150 ppm) and TiO2 (150 ppm) was preferred. The performance of the proposed nanofuel and hybrid nanofuel with pure diesel was evaluated. The proposed fuel performance outperforms the combustion performance, has higher engine efficiency, and has fewer emissions. The best performances were noticed in hybrid nanofuel that has 32% higher brake thermal efficiency than diesel and 24% and 4% lower BSFC and peak pressure than diesel, respectively. The emission performance is also 29%, 50%, and 13% lower in CO, HC, and CO2 emissions than that in pure diesel.

Modelling and optimization of thermophilic anaerobic digestion using biowaste.

Biowaste generation is considerably increasing multiple times recently due to various social and environmental changes like population growth, economic prosperity, globalisation etc. they contain different composition and generated at different stages of their life cycle. Though studies reported for recycle, reproduce and reuse of them, this investigation is unique by focussing to investigate the ideal circumstances for the production of biogas and methane from anaerobic digestion of vegetable waste using response surface methods and artificial neural networks with thermophilic temperature range. Thermophilic temperature of 20.78, organic loading rate of 0.2, pH of 8.81, agitation time of 5.8 and hydro retention time of 3 are the ideal input parameter values for the generation of biogas 3.03 m3 and methane% 186.08 with a desirability of 1. The Response surface model was surpassed by the Artificial Neural Network model.

Magnetic porous Ag2O/Chitin nanostructure adsorbent for eco-friendly effective disposing azo dyes.

Water treatment is as much important as it is to satisfying 11 worldwide sustainable development goals out of 17. The removal of Azo is much important as they are toxic and their existence in water, air and food can easily affect humans by triggering allergies, forming tumours etc. Azo contained Dyes Production was banned in many countries. This research aims to synthesize composite Nanorods and Nanospheres and characterize and test to remove Azo dyes from the wastewater. This research used a previously reported method to rapidly synthesize chitin magnetite nanocomposites (ChM) by co-precipitation while irradiating with ultrasound (US). Detailed structural characterization of ChM revealed a crystalline phase analogous to magnetite and spherical morphologies; extending the reaction time to 8 min yielded a "nanorod" type morphology. Both the morphologies displayed a nanoscale limit with particles averaging between 5 and 30 nm in size, resulting the superparamagnetic performance and saturation magnetization values between 45 and 58 emu/g. The nitrogen adsorption-desorption isotherms showed that the surface modification of ChMs resulted in a rise of specific surface area and pore size. Anionic azo dyes (methyl orange (MO) and reactive black 5 (RB5)) adsorption on the surface of nanocomposites was also demonstrated to be pH-dependent, with the reaction favoured for surface-modified samples at pH 4 and unmodified samples at pH 8. Adsorption capacity studies showed that molecule size effect and electrostatic attraction were two distinct adsorption processes for unmodified and modified ChMs. Chitin Magnetite nanoparticles appear to be a substitute for traditional anionic dye adsorbents. Additionally, the two key materials sources, chitin, and magnetite are inexpensive and easily accessible.

Magnetic Co/CoOx@NCNT catalysts for activation of potassium peroxymonosulfate to deteriorate phenol from wastewater.

Phenols are of much toxicological and they must be effectively removed from the wastewater from industries as well as sewage treatment. Such removal demands a special and strong composite. So, this piece of research aims to activate Potassium peroxymonosulfate (PPMS) with the large surface area of magnetite nitrogen-fixed porous carbon nanotube composites (Co/CoOx@NCNT). Increases in the graphitization degree and structural control brought about by the incorporation of reduced Graphite oxide (rGO) significantly increased the catalyst activity of Co/CoOx@NCNT. It was found that PPMS activation for phenol removal by Co/CoOx@NCNT was nearly as effective as by homogeneous Co2+, with nearly 100% removal efficiency in 10 min. Both high reusability and high recycling of Co/CoOx@NCNT were accomplished simultaneously by proving the technology of viability in practical applications. The PPMS activation mechanism in the Co/CoOx@NCNT/PPMS system was driven by the electron transmission from contaminants to PPMS through the sp2- hybrid carbon nanotubes and nitrogen system. The selectivity of the Co/CoOx@NCNT/PPMS system to remove diverse organic compounds was determined by batch experiments. Due to the insignificant impact of radicals reactive on pollutant breakdown, the ability to inhibit species (such as Cl- and natural organic materials) from a minor role was significantly decreased. These results not only shed light on the process of PPMS heterogeneous activation but also provided a framework for the balanced project of highly effective nanocarbon-based catalysts for PPMS activation.

Use of waste fish oil biodiesel blended with aluminium oxide nanoparticle in IC engines: an experimental on performance, combustion and emission study.

Alternate fuels are in great need as the world's natural resources are depleting with continuous consumption. Furthermore, with a continuous increase in the use of conventional fuel which emits a large number of harmful pollutants to the environment and thus increasing global warming, the need for alternative fuel is in great need. This investigation thus focused on the impact identification on the use of biodiesel from fish waste-based biodiesel [BDWFO (Bio-Diesel of Waste Fish Oil)] with Nanoparticles in single cylinder water cooled IC engine. The fish wastes in fish processing industries/fish markets are used to produce oil and its biodiesel is produced by the transesterification method. The individual BDWFO, Diesel, and blends of 20% of BDWFO were tested with the engine. Then another two combinations of fuel created 200 ppm of 40 nm Aluminium Oxide nanoparticles (AN) mixed with BDWFO, blends of 20% of BDWFO. These five fuels were considered to study the engine performance, combustion, and emissions from the exhaust. The experimental results confirmed the presence of aluminium oxide nanoparticles in BDWFO provides improved engine performance and reduced emissions from exhaust gas except for CO2.

Evaluation of MWCNT Particles-Reinforced Magnesium Composite for Mechanical and Catalytic Applications.

Aluminum, magnesium, and copper materials must have increased mechanical strength with enhanced wear and corrosion resistance. Substantial research focused on reinforcing hard particles into low-strength materials using stir casting or powder metallurgy. This work is intended to develop the magnesium hybrid matrix with the dispersion of boron carbide (B4C) and multiwall carbon nanotubes (MWCNTs). Hybrid magnesium composites are prepared, although the powder metallurgy route considers different process parameters. Statistical analysis such as Taguchi L16 orthogonal array is involved in this work. It is used to find the magnesium hybrid samples' minimum and maximum wear, corrosion, and microhardness levels. Powder metallurgy parameters are B4C (3%, 6%, 9%, and 12%), MWCNT (0.2%, 0.4%, 0.6%, and 0.8%), ball milling (1, 2, 3, and 4 h), and sintering (3, 4, 5, and 6 h). The ball milling parameters are extremely influenced in the wear test analysis. Minimum wear losses are obtained as 0.008 g by influencing the 4 h ball milling process. Similarly, 3 h of sintering time offered a minimum corrosion rate of 0.00078 mm/yr. In microhardness analysis, the percentage of MWCNTs is highly implicated in narrow hardness resulting in the hardness value of 181. The hardness value is recorded using 0.2% MWCNTs in the magnesium alloy AZ80.

Synthesis and Workability Behavior of Cu-X wt.% TiC (x = 0, 4, 8, and 12) Powder Metallurgy Composites.

In this work, copper (Cu) matrix composite reinforced with titanium carbide (TiC) was fabricated by powder metallurgy (PM) method with the varying TiC content from 0% to 12% by weight in the step of 4%. The required weight percentage of powders was milled in an indigenously developed ball milling setup. Green compacts were made using a computer-controlled hydraulic press (400 kN) and sintered in a muffle furnace at a temperature of 950°C. Scanning electron microscope (SEM) was used to analyze the distribution of TiC particles in Cu matrix in as-sintered conditions. X-ray diffraction (XRD) analysis resulted in the existence of respective phases in the produced composites. The structural characteristics such as stress, strain, dislocation density, and grain size of the milled composites were evaluated. Cold upsetting was conducted for the sintered composites at room temperature to evaluate the axial (σ z ), hoop (σ Ó© ), hydrostatic (σ m ), and effective (σ eff ) true stresses. These stresses were analyzed against true axial strain (ε z ). Results showed that the increase in the inclusion of weight percentage of TiC into the Cu matrix increases density, hardness, (σ z ), (σ Ó© ), (σ m ), (σ eff ), and stress ratio parameters such as (σ z /σ eff ), (σ θ /σ eff ), (σ m /σ eff ), and (σ z /σ θ ) of the composites.

Novel study on improvement of plastics properties by blending of waste micro plastics into ABS plastics.

Occupancy of waste micro plastic particles in the beach sand is found hazardous sea livings as well as creates the environmental issues. Many research attempts have been made to short out them. This investigation focuses on utilizing such micro plastic to produce cost effective ABS plasticproducts including toys manufacturing. The screened out micro plastic particles were chemically refined to obtain the pro form of Polypropylene (PP) and Polystyrene (PS) and then they used (10 wt%, 15 wt%, 20 wt%and 25 wt%) with raw ABS plastic and prepared billets by injection moulding. The moulding parameters like melting temperature (230 °C, 240 °C, 250 °C and 260 °C), injection pressure (1300 bar, 1400 bar, 1500 bar and 1600 bar) and injection time (0.4s, 0.8s, 1.2s and 1.6s) to maximize the Impact, tensile and flexural strengths of proposed plastic with Taguchi method. The results reveal that 25% micro plastic reinforced specimen prepared at 250 °C, 1400 bar and 1.6s, is outperformed.

Investigations on influences of MWCNT composite membranes in oil refineries waste water treatment with Taguchi route.

Most of the 'oil refineries' severally pollutes the water resources by depleting their untreated waste water like cooling water, storm water and unsanitary sewage water. These wastewaters are to be treated with high care to protect the human, pebbles, plants, fish and other water animals and from harmful effects. The present study focused to treat the oil refinery wastewater by means of Multi wall carbon nanotube (MWCNT) coated Polyvinylidene Fluoride (PVDF) membrane. The main objectives are: to increases the life of filter, reduce the percolation flux and reduce the formation of antifouling in the filter by using MWCNT composite membrane in it. Different process parameters of the proposed water treatment process, like diameter of MWCNT (15 nm, 20 nm, 25 nm and 30 nm), operating pressure (3 bar, 4 bar, 5 bar and 6 bar), pH value (3, 5, 7 and 9) and temperature (25 °C, 30 °C, 35 °C and 40 °C) temperature. Taguchi statistical technique is employed for designing experiments and for optimizing the process parameters of wastewater treatment process of an oil refinery. The proposed filter for wastewater treatment exhibited appreciable performance in removal rate of Percolation flux, percentage of chemical oxygen demand removal and percentage of total carbolic rejection as 27.2 kg/m2h, 78.51% and 95.33% respectively.