Optimization Of Key Process Parameters Research Articles

Nanomaterial-Enhanced Sizings: Design and Optimisation of a Pilot-Scale Fibre Sizing Line

This study focuses on the development of a pilot-scale sizing line, including its initial design and installation, operational phases, and optimization of key process parameters. The primary objective is the identification of critical parameters for achieving a uniform sizing onto the fibres and the determination of optimal conditions for maximum production efficiency. This investigation focused on adjusting the furnace desizing temperature for the removal of commercial sizing, adjusting the drying temperature, as well as optimizing the corresponding residence time of carbon fibres passing through the furnaces. The highest production rate, reaching 1 m sized carbon fibres per minute, was achieved by employing a desizing temperature of 550 °C, a drying temperature of 250 °C, and a residence time of 1 min. Furthermore, a range of sizing solutions was investigated and formulated, exploring carbon-based nanomaterial types with different surface functionalizations and concentrations, to evaluate their impact on the surface morphology and mechanical properties of carbon fibres. In-depth analyses, including scanning electron microscopy and contact angle goniometry, revealed the achievement of a uniform coating on the carbon fibre surface, leading to an enhanced affinity between fibres and the polymeric epoxy matrix. The incorporation of nanomaterials, specifically N2-plasma-functionalized carbon nanotubes and few-layer graphene, demonstrated notable improvements in the interfacial shear properties (90% increase), verified by mechanical and push-out tests.

Multi-objective optimization of key process parameters in laser cladding Stellite12 cobalt-based alloy powder

Laser cladding (LC) process parameters have a substantial influence on coating morphology and mechanical characteristics; it is necessary to optimize key parameters for laser processing. In this study, Stellite12 cobalt-based alloy powder with excellent corrosion and wear resistance was selected as the cladding material. The multi-objective optimization model of the LC process was established by response surface methodology, laser power, scanning speed, and powder feeding rate as input factors, and the target response variables involve dilution, aspect ratio, and microhardness of the single-track cladding. Combined with variance analysis (ANOVA), the multi-objective optimization of laser power, scanning speed, and powder feeding rate was conducted. A single-track cladding layer with a dilution of 18.29%, an aspect ratio of 3.88, and a microhardness of 634.67 HV0.2 was obtained using the optimized process parameters. Errors between the predicted and actual values of single-track cladding dilution, aspect ratio, and microhardness were less than 8%, which verified the accuracy of the established model.

Effective removal of emerging organic pollutants using hybrid Ag@ZnO supported reduced-graphene oxide nanocomposite under visible light

The present study successfully synthesized a rGO-modified Ag@ZnO nanocomposite using simple aqueous solution reactions conducted at low temperatures that successfully removed five emerging pollutants including nonylphenol, roxarsone, ciprofloxacin, rhodamine B, and methylene blue under visible light illumination. A comprehensive analysis encompassing the material optical properties, structure, chemical and surface characteristics was performed, revealing the presence of Ag@ZnO nanoparticles uniformly dispersed on reduced Graphene Oxide (rGO) layers, with particle sizes ranging from 100 to 200 nm. Important features include the material's high purity and a substantial surface area of 636.42 m2/g. The Ag@ZnO@rGO nanocomposite demonstrated significant efficacy of all the mentioned pollutants, achieving an effective removal rate exceeding 85 % after approximately 4 h of visible light irradiation. Systematic optimization of key process parameters, including catalyst dosage, methylene blue (MB) concentration, and pH levels, was performed. Further investigation into MB removal efficiency, using different scavengers, elucidated the crucial role of O2• during photodegradation, providing insightful mechanistic details. Significantly, sustained efficiency over five cycles underlined the reliability of this nanostructure composite.

Optimization of magnetic composite fluid polishing process based on response surface method

ABSTRACT Currently, research on the optimization of key process parameters in the polishing process usually uses orthogonal test method, which needs a lot of experiments. In order to solve this problem, this paper proposes a parameters optimization of magnetic composite fluid polishing process based on response surface method. Firstly, roughness of the polycarbonate microfluidic chip surface polished with magnetic composite fluid was used as the optimization object, while the machining gap, spindle speed and machining time were defined as the key parameters, and experiments were designed and conducted based on response surface method. Subsequently, using the above experimental data, a prediction model for microfluidic chip surface roughness after polishing that depends on key parameters is established. After that, by the application of the prediction model, the key parameters were optimized and verified by experiments. The results indicated that by using the optimized process parameters, the minimum surface roughness of the microfluidic chip reduced from 0.5 μm to 0.0384 μm, while the maximum and minimum errors between measured and predicted results are 0.0052 μm and 0.0007 μm, respectively. The above result has proved the accuracy of the established model and the effectiveness of the proposed parameters optimization method.

Setting of Process Parameters and Effect of Mould Quality on Surface Roughness of Pharmaceutical Cup of Polypropylene

Injection molding and extrusion ranks as one of the key processes for the production of plastics components. It is a rapid process and is bring into utilization to produce bulk of identical products ranging from high precision engineering articles to disposable consumer products. As of late, the production of plastic items has expanded quickly in light of the fact that plastic items are low in price, light in weight and quick for shape framing, along these lines making them more in vogue than the metallic products. As a result of mounting plastics applications, growing customer increased, the quality prerequisites of injection molded items have turned out to be progressively extreme. The setting and optimization of key process parameters are acknowledged as important means to deal with liven up the quality of the molded items. Inappropriate or unseemly settings of parameters normally create quality and production issues. Minute changes of significant molding parameters may give an extensive effect to the plastic product’s characteristics and this research paper is also anticipated to lend a hand to the optimization of parameters of vertical plastic injection molding process. In this study, the plastic product has been fabricated using a mould designed in our labs that shows an improved surface roughness which suggests that molding conditions play a vital role in quality of these products.

Assessment of Trapping Mechanisms of CO2 Sequestration and Optimization of Key Process Parameters in a Deep Saline Aquifer Using Reservoir Simulation and Response Surface Methodology

Deep saline aquifers are perhaps one of the most ubiquitous sites available for CO2 sequestration. In this study, a compositional reservoir simulator was used to evaluate the effects of various CO2 trapping mechanisms in a saline aquifer which includes structural, solubility and mineral trapping. CO2 was injected at a rate of 4000 m3/day at the bottom of the aquifer for 50 years. The results indicate that structural trapping dominated for initial 3 years, after which the maximum gas saturation and pressure was reached. During solubility trapping, the gas saturation decreased by more than 70% of the maximum saturation. Mineral trapping decreased saturation further by another 10%. In another approach, the effect of wastewater injection following injection of CO2 was evaluated. The numerical simulations show reduction in both the amount of mobile CO2 and its upward migration. Among the different trapping mechanisms, solubility trapping is found to be the most vital one. Multivariate analysis and optimization of the responses, viz. average gas saturation and average pressure rise, were carried out using response surface methodology. Optimized results were obtained for efficient CO2 sequestration with lesser mobile CO2 and pressure rise at lower values of injection rate (1000 m3/day), injection time (50 years), vertical-to-horizontal permeability ratio, Kv/Kh (0.016), and residual gas saturation (0.4).

Survey on solid wastes management by composting: Optimization of key process parameters for biofertilizer synthesis from agro wastes using response surface methodology (RSM)

The optimization of key process parameters for the transformation of agro wastes into biofertilizer has been demonstrated using response surface methodology (RSM). Biofertilizer was produced by composting using 120 L capacity drum made of polyethylene as the composter. Composting time (X1), dosage ratio (X2) and moisture content (X3) were the independent factors while percentage nitrogen, phosphorus and potassium (N.P.K) were the response factors. The outcomes exhibited that composting time, dosage ratio and moisture content all significantly affects the mineralization of N.P.K at probability value of 0.0001. The coefficients of determination also called regression coefficients of 98.60%, 99.79% and 97.80% for nitrogen, phosphorus and potassium observed between the predicted and the real value are obvious that the developed regression models can fit the experimental data well. It was seen from the optimization studies that the pinnacle value of N.P.K from the ideal conditions are 9.62%, 8.97% and 5.62. Characterization of the composite uncovered that biofertilizer produced has a high potential for commercial application on agricultural land. It can be concluded that combination of sawdust, sewage sludge and vegetable waste is a good mixture for biofertilizer synthesis. Also, the nutrients release by the compost materials during the process of composting may be maximized when process conditions are circumspectly managed within the reported optimal value.

Anaerobic co‐digestion of food waste leachate and piggery wastewater for methane production: statistical optimization of key process parameters

AbstractBACKGROUND: Anaerobic co‐digestion of refractory liquid organic wastes is an alternative environmental management strategy with economic benefits arising out of biogas production. Laboratory‐scale experimental investigations were carried out on the anaerobic co‐digestion of two liquid organic wastes, food waste leachate (FWL) and piggery wastewater (PWW). Three important parameters affecting methane yield were chosen for this study, namely, mixing ratio, alkalinity and salinity, which were optimized using response surface methodology.RESULTS: The results were analyzed statistically and the optimum conditions identified as: mixing ratio (FWL: PWW) 33 (in terms of volatile solid, w/w) (2 on v/v), alkalinity 2850 mg CaCO3 L−1, and salinity 3.4 g NaCl L−1. Under the optimum conditions, a cumulative methane yield (CMY) of 310 mL CH4 g−1 VSadded and VS reduction (VSR) of 54% were predicted. Mixing ratio and alkalinity showed the greatest individual and interactive effects on CMY and VSR (P < 0.05). A confirmation experiment under optimum conditions showed a CMY and VSR of 323 mL CH4 g−1 VSadded and 50%, respectively. This was only 1.04% and 1.1%, respectively, different from the predicted values.CONCLUSION: Anaerobic co‐digestion of FWL and PWW carried out under the optimum condition may be a feasible and efficient treatment option for methane production. Copyright © 2012 Society of Chemical Industry

Enhanced Production of Iranian Kefir Grain Biomass by Optimization and Empirical Modeling of Fermentation Conditions Using Response Surface Methodology

This study focused on the optimization of key process parameters for maximizing kefir grain biomass yield using statistical methodology. A response surface methodology (RSM) was developed to describe the effects of whey lactose and yeast concentrations, temperature and pH on increases in kefir grain biomass using cheese whey as a fermentation medium. Initially, one factor at a time was applied to evaluate the effect of different nitrogen sources. The results showed that the concentration of yeast extract significantly influenced the biomass increase. Then, a 24 full-factorial central composite design was used to optimize the process conditions. By using multiple regression analysis, the experimental data were fitted to a second-order polynomial model. RSM analysis indicated good correlation between experimental and predicted values. The most suitable combination of variables for higher biomass increase (76.13%) was 88.4 and 21.3 g/l, 5.2, and 20 °C for concentration of whey lactose, concentration of yeast extract, pH, and temperature, respectively. At these optimal conditions, biomass increased by 81.34%, which was close to the amount predicted by the model.

Identification and optimization of key process parameters in noncontact laser scanning for reverse engineering

This Extended Technical Note shows that the final accuracy level of reverse engineered surfaces depends on scanning distance and scanning angle of the laser beam, hence it also depends on the morphology of the scanned objects. On scanning strongly curved objects, such as the ones with free form surfaces, the distance and impact angle of the laser beam are changing continuously during the scanning process. On the basis of these, two critical parameters are specified for the design model, which make it possible to predict these two factors in advance, depending on the morphology of the scanned object. First, a mathematical–statistical design model of the scanning process is generated, which relies on ANOVA (analysis of variance) and DOE (design of experiments). In the next step, a fitness function is optimized by the genetic algorithm (GA) program. This optimization improves the accuracy of the final scanned surfaces, in terms of the minimum standard deviation values of reverse engineered 3D surface model. The proposed approach was confirmed in a case study, which is presented at the end of this Technical Note.

Optimization and scale-up of a new photobleaching agar extraction process from Gracilaria lemaneiformis

An eco-friendly photobleaching extraction process for agar extraction from the red alga Gracilaria lemaneiformis was developed for the benefit of workers’ health and environmental safety. Here we report the optimization of key process parameters (alkali modification concentration, photobleaching duration, algal length and screen filter opening size) in order to scale up this new technique. The optimal conditions were found to be modification by 3–5% NaOH, photobleaching for 5 h, using algal fragments 2 –4 cm in length, and a filter screen with a 6 μm opening. A 20-L agar extraction reactor was thus constructed, and the scale-up of the agar extraction process was tested in six batch experiments. The resulting agar quality was similar to that of the laboratory-scale extraction. In addition, batch-to-batch reproducibility was excellent. The results demonstrate the excellent scale-up ability and potential application of this new photobleaching agar extraction process on a commercial scale. The agar yield and gel strength for 5% NaOH modified agar were 26.8% and 1,897 g cm−2, while those for 3% NaOH modified agar were 28.2% and 1,287 g cm−2, respectively. It is clear that the agar yield and quality can be manipulated via alkali modification in this new eco-friendly extraction to meet market demands.