Optimizing microplastic treatment in the effluent of biological nutrient removal processes using electrocoagulation: Taguchi experimental design.

Microplastics (MPs) have become one of the most critical environmental pollution problems in recent years. Due to the growing abundance of MPs in aquatic environments, extensive research has been conducted and continues to be ongoing to develop effective treatment methods. In this study, the removal of MPs in the effluent of biological wastewater treatment plant (WWTP) was investigated by electrocoagulation (EC) process with aluminum electrodes. Using Taguchi design, the importance of process variables such as pH, current density, and reaction time were evaluated by Analysis of Variance (ANOVA). Statistically, according to F and p values, the most effective parameter for microplastic (MP) removal was current density, followed by pH and reaction time. The R2 value of the created model was found to be above 98%. According to Taguchi results, the optimum process conditions were determined as pH 9, current density 1.905 mA/cm2, and reaction time 15 min and 99% MP removal efficiency was obtained. Under these optimum conditions, the process cost was calculated as 0.049 $/m3 wastewater, considering energy and electrode consumption. As a result of visual analyses, fiber, film, pellet, amorphous, and undefined forms were dominant in WWTP effluent, while only fiber structures were observed after treatment with EC. In this study, it was concluded that the EC process is an alternative treatment method that can be integrated into wastewater treatment plant effluent to achieve MP removal at very low cost and high efficiency. In addition, as a result of this study, it was observed that the EC process can also be used in MP removal by applying it to real wastewater.

An eco-friendly enzymatic treatment to prepare spinnable banana fibers as an alternative to cotton for textile applications.

Banana fibers are a sustainable material with natural mechanical strength and antibacterial properties. These fibers are extracted from the large amount of waste produced by banana pseudo stems annually. However, despite their numerous advantages, their stiffness and rough texture impede their full use in the textile. This research investigates the degumming treatment of banana fibers using enzyme combination and chemical methods to achieve spinnable soft banana fibers. An L9 orthogonal array was used in a Taguchi design of the experiment to optimize the process parameters. For enzyme combination degumming, the experimental setup comprised different quantities of hemicellulase, laccase, amylase, and pectinase; for chemical degumming, varied amounts of sodium hydroxide (NaOH) were used. The results indicate that enzyme-based degumming procedures produce better results than chemical treatments. Optimum enzyme combinations for various fiber qualities were found using the Taguchi design of experiments. These combinations included Hemicellulase 5 %, Laccase 5 %, Amylase 3 %, and Hemicellulase 5 %, Laccase 3 %, Pectinase 5 %. Without degrading the cellulose structure, these ideal enzyme combinations produced fibers with lower lignin content and higher cellulose percentages, moisture content, and tenacity values. By determining the most efficient enzyme combinations and their effects on fiber qualities, the study offers sustainable fiber processing methods for textile grade banana fiber.

Quality by design steered approach for co-encapsulation of timolol maleate and dorzolamide hydrochloride in injectable liposomes.

Glaucoma is caused by high intraocular pressure, which can causes blindness. Combinations of timolol and dorzolamide are used for its treatment with a requirement of multiple dosing with dosing being twice or four times a day. Conventional eye drops have poor pre-corneal retention and is thus less available for action. This study utilizes principles of Quality by Design for formulation of injectable liposomes coloaded with timolol maleate and dorzolamide HCl, which overcomes limitations of conventional eye drops. For implementation of Quality by Design principles a systematic approach involving defining Quality Target Product Profile, identification of Critical Quality Attributes, mapping Critical Quality Attributes to Critical Process Parameters and Critical Material Attributes, Failure Mode and Effect Analysis based risk assessment, Taguchi screening, and 32 full factorial Design of Experiments design were utilized. A robust model for formulation of coloaded liposomes was successfully developed. Design of Experiments approach allowed to obtain optimized batch having particle size of 116.1 nm, encapsulation efficiency of dorzolamide HCl of 72.12 % and encapsulation efficiency of timolol maleate of 71.94 %. In-vitro drug release showed a sustained release for 4 days. The prepared formulation was in the desired osmolarity range. Biosafety was proved using histopathological characterization. In-vivo studies for assessing the Intra Ocular Pressure reduction showed that there was no significant difference in Intra Ocular Pressure reduction between prepared liposomes and marketed formulation but were superior than marketed formulation because of less fluctuations in Intra Ocular Pressure. Prepared coloaded injectable liposomes lays the foundation for further research in the area and can be translated from to bench side for commercial clinical use.

Process Optimization for Biosurfactant Production by Bacillus aryabhattai SPS1001 using Taguchi Method.

This study employs Taguchi design of experiments (DOE) to optimize biosurfactant yield by analyzing the impact of various input parameters. Signal-to-noise ratio analysis was utilized for optimization, corroborated by ANOVA findings. Regression equations depicted response behaviour and are validated through a confirmation test. Taguchi methodology identified optimal conditions for maximum biosurfactant yield: agitation (180 rpm), inoculum size (2%), beef extract (5 g/L), diesel (20 ml/L), peptone (5 g/L), NaCl (7 g/L), incubation time (4 days), pH (7.9), and yeast extract (6 g/L). This yielded an 8.33% increase to 1.53 g/L, with initial optimum parameters projecting 1.41 g/L. ANOVA ranked and quantified control factor contributions, revealing agitation's significant (31.41%) impact on yield. The study underscores the viability of Taguchi's optimal conditions for substantial yield improvement within specific ranges. The strong alignment between expected and experimental yields affirmed the reliability of developed models for optimal yield selection. This study underscores the power of statistical techniques like Taguchi DOE and ANOVA in systematically enhancing biosurfactant production by Bacillus aryabhattai SPS1001 and paves the way for future advancements in bioprocess optimization.

Utilization of RESOLV with polymer to produce prazosin hydrochloride nanoparticles and optimization of the process parameters.

In this study, rapid expansion of a supercritical solution into a Liquid Solvent (RESOLV) was used for the first time to produce pharmaceutical nanoparticles of Prazosin hydrochloride (PRH). The Taguchi method (robust design) was utilized to design the experiments and ensure obtaining the optimal process conditions. The pressure (15-25 MPa), temperature (308-328 K) and nozzle diameter (300-700 µm) effects on the morphology and size distribution of the resulting particles were also examined. The size of the particles decreased from about 40 µm to the range of (252-418 nm). FTIR, DLS, FESEM, XRD, DSC were used to characterize the primary and processed PRH particles. According to DSC investigations, RESOLV-produced PRH showed lower crystallinity than original PRH.

Taguchi-assisted multi-response improved simultaneous nanocellulose and sugar production from microcrystalline cellulose derived from raw oil palm leaves.

Enzymatic treatment on lignocellulosic biomass has become a trend in preparing nanocellulose (NC), but the process must be optimized to guarantee high production yield and crystallinity. This study offers insights into an innovative protocol using cultivated fungal cellulase and xylanase to improve NC production from raw oil palm leaves (OPL) using five-factor-four-level Taguchi orthogonal design for optimizing parameters, namely substrate and enzyme loading, surfactant concentration, incubation temperature and time. Statistical results revealed the best condition for producing NC (66.06 % crystallinity, 43.59 % yield) required 10 % (w/v) substrate, 1 % (v/v) enzyme, 1.4 % (w/v) Tween-80, with 72-h incubation at 30 °C. Likewise, the highest sugar yield (47.07 %) was obtained using 2.5 % (w/v) substrate, 2.0 % (v/v) enzyme, 2.0 % (w/v) Tween-80, with 72-h incubation at 60 °C. The auxiliary enzymes used in this study, i.e., xylanase, produced higher crystallinity NC, showing widths between 8 and 12 nm and lengths >1 µm and sugars at 47.07 % yield. Thus, our findings proved that optimizing the single-step enzymatic hydrolysis of raw OPL could satisfactorily produce relatively crystalline NC and sugar yield for further transformation into bio-nanocomposites and biofuels. This study presented a simple, innovative protocol for NC synthesis showing characteristics comparable to the traditionally-prepared NC, which is vital for material's commercialization.

Effects of Thermoforming Parameters on Woven Carbon Fiber Thermoplastic Composites.

The quality of woven carbon fiber fabric/polycarbonate thermoplastic composites after thermoforming and demolding was investigated using finite element simulation and the Taguchi orthogonal array. The simulation utilized a discrete approach with a micro-mechanical model to describe the deformation of woven carbon fabric, combined with a resin model. This simulation was validated with bias extension tests at five temperatures. The thermoforming process parameters considered were blank temperature, mold temperature, and blank holding pressure, with three levels for each factor. Optimal values for the fiber-enclosed angle, spring-back angle, mold shape fitness, and the strain of the U-shaped workpiece were desired. The results indicated that the comparison of the stress-displacement curve of bias extension tests verified the application of the discrete finite element method. Results from the Taguchi array indicated that blank holding pressure was the dominant parameter, with the optimal value being 1.18 kPa. Blank temperature was the second most significant factor, effective in the range of 160 °C to 230 °C, while mold temperature had a minor effect. Furthermore, the four quality values are dependent and have a similar trend. The best combination was identified as a blank holding press of 1.18 kPa, a blank temperature of 230 °C, and a mold temperature of 190 °C.

Optimization of Dry Sliding Wear in Hot-Pressed Al/B4C Metal Matrix Composites Using Taguchi Method and ANN.

The presented study investigates the effects of weight percentages of boron carbide reinforcement on the wear properties of aluminum alloy composites. Composites were fabricated via ball milling and the hot extrusion process. During the fabrication of composites, B4C content was varied (0, 5, and 10 wt.%), as well as milling time (0, 10, and 20 h). Microstructural observations with SEM microscopy showed that with an increase in milling time, the distribution of B4C particles is more homogeneous without agglomerates, and that an increase in wt.% of B4C results in a more uniform distribution with distinct grain boundaries. Taguchi and ANOVA analyses are applied in order to investigate how parameters like particle content of B4C, normal load, and milling time affect the wear properties of AA2024-based composites. The ANOVA results showed that the most influential parameters on wear loss and coefficient of friction were the content of B4C with 51.35% and the normal load with 45.54%, respectively. An artificial neural network was applied for the prediction of wear loss and the coefficient of friction. Two separate networks were developed, both having an architecture of 3-10-1 and a tansig activation function. By comparing the predicted values with the experimental data, it was demonstrated that the well-trained feed-forward-back propagation ANN model is a powerful tool for predicting the wear behavior of Al2024-B4C composites. The developed models can be used for predicting the properties of Al2024-B4C composite powders produced with different reinforcement ratios and milling times.

Optimization of Hot Embossing Condition Using Taguchi Method and Evaluation of Microchannels for Flexible On-Chip Proton-Exchange Membrane Fuel Cell.

Hot embossing is a manufacturing technique used to create microchannels on polymer substrates. In recent years, microchannel fabrication technology based on hot embossing has attracted considerable attention due to its convenience and low cost. A new evaluation method of microchannels, as well as an approach to obtaining optimal hot embossing conditions based on the Taguchi method, is proposed in this paper to fabricate precise microchannels for a flexible proton-exchange membrane fuel cell (PEMFC). Our self-made hot embossing system can be used to fabricate assorted types of micro-channel structures on polymer substrates according to various applications, whose bottom width, top width, height and cross-sectional area vary in the aims of different situations. In order to obtain a high effective filling ratio, a new evaluation method is presented based on the four parameters of channel structures, and the Taguchi method is utilized to arrange three main factors (temperature, force and time) affecting the hot embossing in orthogonal arrays, quickly finding the optimal condition for the embossing process. The evaluation method for microchannels proposed in this paper, compared to traditional evaluation methods, incorporates the area factor, providing a more comprehensive assessment of the fabrication completeness of the microchannels. Additionally, it allows for the quick and simple identification of optimal conditions. The experimental results indicate that after determining the optimal embossing temperature, pressure and time using the Taguchi method, the effective filling rate remains above 95%, thereby enhancing the power density. Through variance analysis, it was found that temperature is the most significant factor affecting the hot embossing of microchannels. The high filling rate makes the process suitable for PEMFCs. The results demonstrate that under optimized process conditions, a self-made hot embossing system can effectively fabricate columnar structure microchannels for PEMFCs.

Process parameters optimization of EDM for hybrid aluminum MMC using hybrid optimization technique.

This study explores how machining parameters affect Surface Roughness (SR), Tool Wear Rate (TWR), and Material Removal Rate (MRR) during Electrical Discharge Machining (EDM) of a hybrid aluminum metal matrix composite (AMMC). The composite includes 6 % Silicon carbide (SiC) and 6 % Boron carbide (B4C) in an Aluminum 7075 (Al7075) matrix. A combined optimization approach was used to balance these factors, evaluating Pulse ON time, Current, Voltage, and Pulse OFF time. Response Surface Methodology (RSM) optimized single responses, while multi-response optimization employed a hybrid method combining the Entropy Weight Method (EWM), Taguchi approach, TOPSIS, and GRA. Analysis of Variance (ANOVA) assessed parameter significance, revealing substantial impacts on SR, MRR, and EWR. Based on TOPSIS and GRA, optimized parameters achieved a desirable balance: high MRR (0.4172, 0.5240 mm³/min), minimal EWR (0.0068, 0.0103 mm³/min), and acceptable SR (10.3877, 9.1924 µm) based on EWM-weighted priorities. Confirmation experiments validated a 15 % improvement in the closeness coefficient, and a 16 % improvement in the Grey relational grade, which considers combined SR, MRR, and EWR performance. Scanning Electron Microscope (SEM) analysis of surfaces machined with optimal parameters showed minimal debris, cracks, and no recast layer, indicating high surface integrity. This research enhances EDM optimization for AMMC, achieving efficiency in machining, minimizing tool wear, and meeting surface quality requirements.

Experimental study of diesel engine performance and emissions from microalgae fuel blends with SiO2 nanoadditives: RSM and Taguchi optimization.

In this investigation, the effects of blending microalgae biodiesel with silicon dioxide (SiO2) nanoparticles in a diesel engine are evaluated. For the study, test fuels (diesel, B20, B20n25, B20n50, B20n75, and B20n100) were prepared by blending pure diesel with microalgae biodiesel with the addition of SiO2 nanoparticles having particle sizes ranging from 25 to 100 ppm. A liquid-cooled, two-cylinder, four-stroke, compression ignition engine having a load range between 2 and 12 kW, fuel injection timing of 23° bTDC, and 16.5:1 compression ratio was chosen for this study. The results demonstrated that the test fuels enhanced the engine performance and declined emissions. Performance parameters such as brake thermal efficiency (BTE) and brake-specific fuel consumption (BSFC) were all improved by 1.6-4.8% and 8.55-15.33%, respectively, by the biodiesel containing SiO2 nanoparticles. At full engine load, emissions such as carbon dioxide (CO2), smoke opacity (BSN), and NOx declined by 1.8-9%, 6.2-21.4%, and 19-34% respectively. The study backs the use of SiO2 nanoadditives for better performance and lower emissions in diesel engines. Test results were analyzed by Taguchi and RSM method to find optimized conditions.

Validation of DOE Factorial/Taguchi/Surface Response Models of Mechanical Properties of Synthetic and Natural Fiber Reinforced Epoxy Matrix Hybrid Material.

A validation of the factorial, Taguchi and response surface methodology (RSM) statistical models is developed for the analysis of mechanical tests of hybrid materials, with an epoxy matrix reinforced with natural Chambira fiber and synthetic fibers of glass, carbon and Kevlar. These materials present variability in their properties, so for the validation of the models a research methodology with a quantitative approach based on the statistical process of the design of experiments (DOE) was adopted; for which the sampling is in relation to the design matrix using 90 treatments with three replicates for each of the study variables. The analysis of the models reveals that the greatest pressure is obtained by considering only the source elements that are significant; this is reflected in the increase in the coefficient of determination and in the predictive capacity. The modified factorial model is best suited for the research, since it has an R2 higher than 90% in almost all the evaluated mechanical properties of the material; with respect to the combined optimization of the variables, the model showed an overall contribution of 99.73% and global desirability of 0.7537. These results highlight the effectiveness of the modified factorial model in the analysis of hybrid materials.

Efficiency Improvement for Chipless RFID Tag Design Using Frequency Placement and Taguchi-Based Initialized PSO.

Frequency encoding chipless Radio Frequency Identification (RFID) tags have been frequently using the radar cross section (RCS) parameter to determine the resonant frequencies corresponding to the encoded information. Recent advancements in chipless RFID design have focused on the generation of multiple frequencies without considering the frequency position and signal amplitude. This article proposes a novel method for chipless RFID tag design, in which the RCS response can be located at an exact position, corresponding to the desired encoding signal spectrum. To achieve this, the empirical Taguchi method (TM), in combination with particle swarm optimization (PSO), is used to automatically search for optimal design parameters for chipless RFID tags with a fast response time, to comply with the frequency encoding requirements in the presence of the mutual coupling effect. The proposed design method is validated using I-slotted chipless tag structures that are fabricated and measured with different sets of resonant frequencies.

Optimizing germination conditions of Ghaf seed using ZnO nanoparticle priming through Taguchi method analysis.

Ghaf, a resilient tree in arid environments, plays a critical role in ecological restoration, desertification mitigation, and cultural heritage preservation. However, the seeds' inherent challenges, notably their hard outer coating restricting germination, emphasize the pressing need for innovative strategies. This work aimed to investigate the optimization of Ghaf seed germination process through seed priming with ZnO nanoparticles treatment (duration (t), concentration (c), temperature (T), and agitation (a), employing the Taguchi method for efficient experimental design. Furthermore, the study includes Analysis of Variance (ANOVA), analysis for the regression model to assess the significance of predictor variables and their interactions, thereby strengthening the statistical validity of our optimization approach. Notably, it revealed that concentration is a pivotal influencer in optimization of Ghaf seed germination. The results showed that the concentration of ZnO nanoparticles has no linear relation with any other parameters. To verify the outcomes, validation tests were performed utilizing the predicted optimal parameters. The observed low error ratio, falling within the range of 1 to 6%, confirmed the success of the Taguchi methodology in identifying optimal levels of the factors chosen. Significantly, ZnO-primed seeds showcased a remarkable enhancement in Ghaf seed germination, increasing from 15 to 88%. This study introduces a novel approach utilizing ZnO nanoparticle treatment optimized through the Taguchi method, significantly enhancing seed germination rates of Ghaf seeds and offering a promising avenue for sustainable agricultural practices in arid environments.

Failure analysis in predictive maintenance: Belt drive diagnostics with expert systems and Taguchi method for unconventional vibration features.

Predictive maintenance to avoid fatigue and failure enhances the reliability of mechanics, herewith, this paper explores vibrational time-domain data in advancing fault diagnosis of predictive maintenance. This study leveraged a belt-drive system with the properties: operating rotational speeds of 500-2000 RPM, belt pretensions at 70 and 150 N, and three operational cases of healthy, faulty and unbalanced, which leads to 12 studied cases. In this analysis, two one-axis piezoelectric accelerometers were utilized to capture vibration signals near the driver and pulley. Five advanced statistics were calculated during signal processing, namely Variance, Mean Absolute Deviation (MAD), Zero Crossing Rate (ZCR), Autocorrelation Coefficient, and the signal's Energy. The Taguchi method was used to test the five selected features on the basis of Signal-to-Noise (S/N) ratio. For classifications, an expert system was used based on artificial intelligence where a Random Forest (RF) model was trained on untraditional parameters for optimizing the accuracy. The resulted 0.990 and 0.999, accuracy and AUC, demonstrate the RF model's high dependability. Evidently, the methodology highlights the features potential when progressed into expert systems, which advances predictive maintenance strategies for belt-drive systems.

Development, modelling and optimization of process parameters on the tensile strength of aluminum, reinforced with pumice and carbonated coal hybrid composites for brake disc application.

This study focuses on optimizing double stir casting process parameters to enhance the tensile strength of hybrid composites comprising aluminum alloy, brown pumice, and coal ash, intended for brake disc applications. Analytical techniques including X-ray fluorescence, X-ray diffraction, thermogravimetric analysis, and scanning electron microscopy were employed to characterize the composite constituents. The Taguchi method was utilized for experimental design and optimization to determine the optimal weight compositions of brown pumice and coal ash, as well as stir casting parameters (stirrer speed, pouring temperature, and stirring duration). Regression analysis was employed to develop a predictive mathematical model for the tensile strength of the hybrid composites and to assess the significance of process parameters. The optimized composite achieved a predicted tensile strength of 186.81 MPa and an experimental strength of 190.67 MPa using 7.5 vol% brown pumice, 2.5 vol% coal ash, a pouring temperature of 700 °C, stirrer speed of 500 rpm, and stirring duration of 10 min. This represents a 52.23% improvement over the as-cast aluminum alloy's tensile strength. Characterization results revealed that brown pumice and coal ash contain robust minerals (SiO2, Fe2O3, Al2O3) suitable for reinforcing metal matrices like aluminum, titanium, and magnesium. Thermogravimetric and differential thermal analyses demonstrated thermal stability up to 614.01 °C for the optimized composite, making it suitable for brake disc applications.

Optimization of Enterocin Production from Probiotic Enterococcus faecium Using Taguchi Experimental Design.

Objectives: Enterocin is a significant broad-spectrum peptide antibiotic produced by Enterococcus faecium (E. faecium). Enterocin production by E. faecium was investigated using the Taguchi experimental design. The Taguchi models were used to save the time and effort required for optimizing the different conditions affecting its production. They were applied to optimize the conditions for enterocin production using the least number of experiments and the least number of required materials. Materials and Methods: Seven factors i.e., pH, temperature, time of incubation, aeration rate, inoculum size, carbohydrate concentration, and bile salt concentrations, each at three levels were selected and an orthogonal array layout of L273 was performed. Results: The experimental results indicated that the best incubation conditions were; 48 hours incubation on a nutrient medium at pH 6.5, temperature at 25 °C, aeration rate at 0 round per minute, inoculum size 20 mL, and bile salt concentration. It was 5%, and the carbon concentration was 2.0%. All these factors combined led to the best enterocin production by E. faecium. Conclusion: This optimization of enterocin production by the Taguchi experimental models emphasized some important results regarding the interaction of the different driving factors leading to the best enterocin production in one experiment.

Carbon emission reduction and hydrogen production maximization from carbon emission-based hydrogen sources.

This study aims to optimize hydrogen (H2) production via ethanol steam reforming (ESR) and water gas shift reaction (WGSR) pathways, focusing on minimizing CO, CO2, and CH4 emissions while maximizing H2 yield. Employing Taguchi grey relational analysis, we investigate the intricate balance between production conditions and multi-response gas generation. Utilizing Origin Pro software, regression modeling forecasts individual and overall gas generation. Our analysis identifies optimal conditions: a feed liquid flow rate of 2 mL/min, water-to-carbon ratio of 3, ESR temperature of 300 °C, and WGSR temperature of 350 °C. These conditions promise clean, efficient H2 production. Key results show the water-to-carbon ratio and ESR temperature contributing 59.22% and 32.69% to production conditions' impact, respectively. Graphical and mathematical models validate these findings. Moving forward, further experimental validation of optimal conditions for multi-response gas generation is recommended. This study pioneers a transformative approach towards sustainable, environmentally friendly H2 production.

A comprehensive study on engineering and sustainability characteristics with emphasizing on 3R's approach in building construction.

The study assesses the mechanical efficiency, long-lasting characteristics, microstructure, and sustainability of sustainable concrete (SC) samples through several optimization methods, emphasizing the significance of the 3Rs (recycle, reuse, reduce) approach in the construction sector. The study uses advanced techniques like the Taguchi method, grey relational analysis (GRA), analysis of variance (ANOVA), and signal-noise ratio (SNR) to optimize parameters affecting the performance of SC. In this study, the properties of SC are assessed by considering various parameters. These parameters include the use of 10 %, 20 %, and 30 % of ground granulated blast furnace slag (GGBFS) as a replacement for fly ash (FA). Additionally, six different binder contents ranging from 300 kg/m3 to 600 kg/m3 are examined. The study also investigates three different molarities of sodium hydroxide (NaOH) (8 M, 12 M, and 16 M), three different ratios of alkaline activators (AA) (1.5, 2.0, and 2.5), three different AA to-binder ratios (0.30, 0.35, and 0.40), and curing temperature (CT) of 30 °C, 60 °C, and 90 °C. The study includes fresh properties such as fresh density (FD) and slump, mechanical properties such as tensile strength (TS), flexural strength (FS), modulus of elasticity (MOE), and compressive strength (CS), and durability studies such as dry density (DD), impact strength, water absorption (WA), and sorptivity. The blended proportions were obtained using the Taguchi method. The study shows that GGBFS accelerates geopolymerization in FA-based concrete, reducing setting time and early-age CS. FA is crucial for setting time, workability, and CS enhancement. GGBFS increases the densities of fresh and hardened concrete, with a highly correlated increase, allowing accurate hardened density prediction with a coefficient of 0.9057. The CS of the cube SC surpassed 40 MPa, irrespective of variables such as the AA ratio, CT, and NaOH molarity. The trail mix with a binder concentration of 600 kg/m3, 30 % GGBFS content, 12 M NaOH molarity, 1.5 AA ratio, 0.35 AA to binder ratio, and 90 °C CT exhibited the greatest strength. Mixtures containing 10 % GGBFS can attain a CS above 30 MPa after 28 days, making them suitable for structural purposes. The T18 mix exhibited a compact Calcium (alumino) silicate hydrate (C-A-S-H) and N-A-S-H gel, whereas the T3 mix displayed a varied and permeable structure. The study used GRA, ANOVA, and SNR methods to analyze properties varying by six variables, finding GGBFS content as the most influencing parameter. The study found that the SC had a lower sustainability score than the OPC mix, but had better energy efficiency.

Effect of heat treatment on mechanical properties and dimensional accuracy of 3D-Printed black carbon fiber HTPLA.

This present study investigated how heat treatment affects the mechanical properties of 3D-printed black carbon fiber HTPLA by manipulating two parameters: heating temperature and holding time. The mechanical properties of 3D-printed black carbon fiber HTPLA components are crucial for assessing their structural integrity and performance. The shrinkage and dimensional accuracy of the 3D-printed parts were also explored using a vernier caliper. The microstructure of both heat-treated and non-heat-treated HTPLA black carbon fiber 3D-printed parts was examined using scanning electron microscopy. Samples were prepared, printed, heat-treated, and mechanically tested, and their microstructure was observed and recorded. The results showed that heat treatment improved the material's strength, hardness, and crystallinity, leading to better mechanical properties. However, statistical analysis indicates no clear evidence that the two factors, optimum heating temperature and holding time, affect the mechanical properties of heat-treated printed parts. Nonetheless, further study suggests that these factors might be important in optimizing the heat treatment process.