Optimization Of Process Parameters Research Articles

Fermentation of Biomass and Residues from Brazilian Agriculture for 2G Bioethanol Production.

Brazil is one of the world's leading producers of staple foods and bioethanol. Lignocellulosic residual sources have been proposed as a promising feedstock for 2G bioethanol and to reduce competition between food and fuels. This work aims to discuss residual biomass from Brazilian agriculture as lignocellulosic feedstock for 2G bioethanol production as bagasse, stalk, stem, and peels, using biorefining concepts to increase ethanol yields. Herein, we focused on biomass chemical characteristics, pretreatment, microorganisms, and optimization of process parameters that define ethanol yields for bench-scale fermentation. Although several techniques, such as carbon capture, linking enzymes to supports, and a consortium of microorganisms, emerge as future alternatives in bioethanol synthesis, these technologies entail necessary optimization efforts before commercial availability. Overcoming these challenges is essential to linking technological innovation to synthesizing environmentally friendly fuels and searching other biomass wastes for 2G bioethanol to increase the biofuel industry's potential. Thus, this work is the first to discuss underutilized lignocellulosic feedstock from other agrifoods beyond sugar cane or corn, such as babassu, tobacco, cassava, orange, cotton, soybean, potatoes, and rice. Residual biomasses combined with optimized pretreatment and mixed fermentation increase hydrolysis efficiency, fermentation, and purification. Therefore, more than a product with a high added value, bioethanol synthesis from Brazilian residual biomass prevents waste production.

Biomass hydrothermal gasification characteristics study: based on deep learning for data generation and screening strategies

Hydrothermal gasification is an import-ant way to utilize biomass resources, and accurate prediction of the biomass hydrothermal gasification process is of great significance for the formulation and optimization of process parameters and equipment. Data is a key factor in machine learning, but due to the high experimental costs associated with hydrothermal gasification processes, acquiring a large amount of experimental data for machine learning modeling is a major challenge. To address this issue, this study proposes a data generation and screening strategy based on generative adversarial networks (GAN). The data generation and screening strategy primarily rely on GAN networks trained with real data as data generators and random forest models trained with real data as data screeners. High-quality synthetic data is selected through screening criteria to augment the dataset. Four machine learning models are used to model the biomass hydrothermal gasification process based on synthetic data to validate this strategy. The results show that, compared to the original data, modeling with synthetic data leads to a significant increase in the evaluation metrics for predicting H2, CH4, and CO2, especially during the testing phase. This indicates the rationality of the proposed data generation and screening strategy.

Effect of eco-friendly organic additives on copper electropolishing and process parameter optimization

The type of organic additives significantly affects copper electropolishing performance, including glossiness and surface smoothness. Therefore, developing efficient and eco-friendly additives is crucial for advancing this technology. This study investigates the effects of glycerol and ascorbic acid on copper electrochemical properties and surface morphology. Using the Box- Behnken Design (BBD), we examined the interaction effects of polishing time (A), current density (B), and polishing temperature (C) while optimizing the process parameters. The results demonstrate that glycerol and ascorbic acid additives effectively reduce the critical current density and limiting current density, synergistically enhancing copper surface glossiness and smoothness while slowing down the dissolution rate of the copper. Interaction analysis revealed a prioritized sequence of effects: AB > BC > AC. Under optimal conditions (1.5 min of polishing time, 12 A dm−2 of current density, and 27 °C of polishing temperature), copper surface roughness decreases from 1.2 μm to 37 nm, representing a 97% reduction, along with enhanced corrosion resistance.

Multi-objective optimization of FSW process parameters for AA1100 joints by using a hybrid technique of PCA-TOPSIS

Multi-objective optimization of FSW process parameters for AA1100 joints by using a hybrid technique of PCA-TOPSIS

Optimization of process parameters for preparing AlN nanopowders by combining carbon-containing droplet combustion and carbothermal reduction methods

In this research, AlN nano-powders were prepared by combining carbon-containing droplet combustion (CCDC) and carbothermal reduction (CR) methods. Firstly, Al2O3+C mixed precursor was synthesized by the CCDC method, and then AlN nano-powder was obtained by the carbothermal reduction of this CCDC precursor. The effects of urea and glucose contents, ignition temperature, and combustion atmosphere on precursors and AlN powders were studied in detail. The results showed that with the molar ratio of urea to aluminum nitrate (U/A) of 3, the atomic ratio of carbon to aluminum (C/Al) of 6, and the ignition temperature at 800 °C under air atmosphere, the prepared precursor was composed of hollow and spherical structure particles exhibiting rough and porous surface morphology with high specific surface area (SSA) (41.4 m2/g). AlN nano-powders obtained by the carbothermal reduction of this precursor were found to be consisted of fine particles with the size of 20–30 nm with high SSA of 39.9 m2/g.

Development of Regression model and Optimization of Process Parameters for Wire Electrical Discharge of SAE 1010 Steel using Taguchi Grey Approach

Development of Regression model and Optimization of Process Parameters for Wire Electrical Discharge of SAE 1010 Steel using Taguchi Grey Approach

Improving mechanical and electrical properties of Cu-Ni-Si alloy via machine learning assisted optimization of two-stage aging processing

Recent studies have shown that synergistic precipitation of continuous precipitates (CPs) and discontinuous precipitates (DPs) is a promising method to simultaneously improve the strength and electrical conductivity of Cu-Ni-Si alloy. However, the complex relationship between precipitates and two-stage aging process presents a significant challenge for the optimization of process parameters. In this study, machine learning models were established based on orthogonal experiment to mine the relationship between two-stage aging parameters and properties of Cu-5.3Ni-1.3Si-0.12Nb alloy with preferred formation of DPs. Two-stage aging parameters of 400 °C/75 min + 400 °C/30 min were then obtained by multi-objective optimization combined with an experimental iteration strategy, resulting in a tensile strength of 875 MPa and a conductivity of 41.43 %IACS, respectively. Such an excellent comprehensive performance of the alloy is attributed to the combined precipitation of DPs and CPs (with a total volume fraction of 5.4% and a volume ratio of CPs to DPs of 6.7). This study could provide a new approach and insight for improving the comprehensive properties of the Cu-Ni-Si alloys.

An investigation into process parameters optimization for ultimate tensile strength of friction stir welded Al-10Mg-8Ce-3.5Si joint: Numerical and Design of Experiments approach

An investigation into process parameters optimization for ultimate tensile strength of friction stir welded Al-10Mg-8Ce-3.5Si joint: Numerical and Design of Experiments approach

A Comparative Analysis of Single and Multi-objective Process Parameter Optimization of Dissimilar Metal Friction Welds

A Comparative Analysis of Single and Multi-objective Process Parameter Optimization of Dissimilar Metal Friction Welds

Microstructural characterization and wear analysis of AlTiVZrMoCr HEA coating on SS410 steel by atmospheric plasma spraying

In recent research, noteworthy progress is made in developing Lightweight High Entropy Alloy (LWHEA) with superior performance and characteristics. A novel AlTiVZrMoCr LWHEA is synthesized through 50 h of ball milling with a 19 µm mean size of powders. The microstructural analysis of synthesized LWHEA powders revealed minor FCC and predominate BCC phases. The powders are coated on SS410 substrate by Atmospheric Plasma Spraying (APS) and the LWHEA coating significantly improves the structural characteristics of the substrate. Notably, the coated sample exhibits a uniform and homogeneous distribution across the substrate surface with an average grain of 1.71 µm. The microhardness of the coated sample is 1.67 times higher than the substrate, owing to the elemental bonding. Pin-on-disc tribometer is employed to measure the wear rate using different process parameters. Response Surface Methodology (RSM) is employed for the optimization of process parameters to obtain optimum wear rate. The technique of Analysis of Variance (ANOVA) is used to determine the most influential wear process parameter. Among various process parameters, the applied load is found to be the most influential factor in achieving an optimal wear rate. The worn morphology revealed the formation of grooves, micro-cracks, and oxides on the coated sample.

Algorithmic assessment of drag on thermally cut sheet metal edges

Abstract Drag is a key criterion in assessing the quality of thermally cut sheet metal edges, which is critical to the reliability of the final product. The evaluation of drag has been described qualitatively and quantitatively, but the scientific literature lacks a methodical description of algorithmic tracking of the drag lines themselves. This absence of a standardized approach has hindered the objective determination of drag. With recent advances in the field towards automated quality assessment aimed at autonomous adaptation of process parameters, the need for consistent, fast and reliable assessment of drag lines has become apparent. To address this gap, this study introduces an innovative drag line tracking algorithm, inspired by the behavior of fluid flowing towards the lowest points, to compute a generalized drag line for an edge with a homogeneous cutting pattern. The algorithm utilizes the height data of the measured cut edges as a data base for the assessment of the drag lines. The results indicate that the drag lines identified by the algorithm are not only subjectively accurate, but also show a strong correlation with human-annotated drag lines across several metrics. This work lays the foundation for the objective evaluation of drag by not only describing an algorithm for the consistent determination of drag lines, but also by presenting a tool for human annotation and suitable customized metrics. As a result, it contributes significantly to the comprehensive evaluation of edge quality and represents a step forward in the automatic optimization of process parameters and the improvement of cutting edge quality.

A Modelling Framework for Rapid Evaluation of Speed Limitations during Extrusion of Aluminium Profiles

Hot tearing is a well-known limitation when trying to maximize the throughput rate in aluminium extrusion. In the present work an analytical modelling framework is presented which can be used to predict the maximum extrusion speed that can be applied in production without formation of this type of surface defect. The modelling framework allows almost instantaneous estimates on the resulting productivity in terms of maximum extrusion speed. This is obtained by developing an analytical model for the maximum temperature at the die exit which incorporate the effect of alloy composition and billet processing. The results are consolidated into extrusion limit diagrams, mapping the maximum allowable extrusion speed as a function of billet pre-heat temperature, alloy composition, and homogenisation heat treatment. The calculated temperatures and extrusion limit diagrams obtained from the analytical model are compared with measured temperatures and critical extrusion speeds from extrusion tests of various 6xxx series alloys for a simple rod-shaped geometry. The comparisons indicate that the presented modelling approach gives sufficiently accurate predictions for future application in optimisation of alloy composition and process parameters in extrusion of profiles.

XGBoost-based prediction of electrical properties for anode aluminium foil

Anode aluminum foil (AAF) shows advantages such as high electrical conductivity, high specific capacitance, and low cost, making it a high-quality electrode material for energy storage. However, the formation of AAF is a material process with multiple influencing factors interacted with each other, and it is difficult to analyze the influencing mechanism of each factor and predict the electrical properties. To solve the aforementioned problem, the process data of AAF is collected from a actual production line as the research subject firstly. The correlation coefficient between the process parameters and withstand voltage (WV) is used for feature selection of the data sets. XGBoost-based prediction model is established to predict the electrical properties of AAF, which is evaluated using R2 and root-mean-square error (RMSE). The results indicate that the process parameter X70 (Voltage of repair 3) shows the most significant effect on the WV. The magnitude of the WV shows a stepwise increase with the increase of X70. The result of prediction model is a R2 of 0.990 and a RMSE of 3.888. The XGBoost-based prediction model for the electrical properties (WV) of AAF shows excellent prediction characteristic, which contributes to the optimization of process parameters and the properties prediction.

Statistical modeling and optimization of process parameters for additive manufacturing of styrene–ethylene–butylene–styrene block copolymer parts using solvent cast 3D printing technique

AbstractAdditive manufacturing of thermoplastic elastomers is challenging using fused deposition modeling due to their high melt viscosity, low column strength, and poor fusion among layers. Solvent‐cast 3D printing (SC‐3DP) is an efficient alternative to successfully 3D print such materials. However, selection of suitable 3D printing parameters is crucial to realize parts with optimum physicomechanical properties. In this work, statistical modeling and SC‐3DP parameter optimization for styrene–ethylene–butylene–styrene (SEBS) block copolymer were performed. The effect of 3D printing process parameters on shrinkage, relative density, and tensile strength was analyzed using response surface methodology. Experiments were planned as per central composite design and analysis of variance was performed to evaluate the significant parameters. SEBS content in the polymer solution significantly affected the shrinkage of SC‐3DP samples. Moreover, relative density and tensile strength were significantly affected by print speed and layer height. A significant interaction between print speed and layer height was also noticed for tensile strength and relative density of printed samples. Multi‐objective optimization using genetic algorithm was also performed to minimize shrinkage and maximize relative density and tensile strength. Finally, a case study was conducted comparing the physicomechanical properties of SC‐3DP samples printed at optimized process parameters and compression molded samples.Highlights Statistical models were developed using response surface methodology. Genetic algorithm based multi‐objective optimization was performed. Optimum solvent‐cast 3D printing (SC‐3DP) process parameters were determined.

Design of experiments with the support of machine learning for process parameter optimization of all‐small‐molecule organic solar cells

AbstractTraditionally, squaraine dyes have been studied and employed in biomedical research due to their excellent optical properties, and the molecules are being adopted in different research fields such as organic solar cells. In this study, we investigate correlations between solar cell performance and processing parameters of all‐small‐molecule bulk heterojunction solar cells comprising squaraine (SQ) as electron donor (D) and non‐fullerene small molecules (e.g., ITIC) as electron acceptor (A) with the help of machine learning (ML) and design of experiment (DoE) methods. Among the five predictive ML models tested with the selected parameters, the eXtreme gradient boosting model shows the satisfactory results with quite high coefficient of determination of 0.999 and 0.997 in training and testing sets, respectively. By measuring the contribution of each input variable to solar cell efficiency, four process parameters, that is, the total concentration, the ratio of D/A, the rotational speed of spin coating, and the annealing temperature, are found to be the key features strongly correlated to solar cell efficiency. From contour plots in DoE, the highest solar cell efficiency of approximately 5% can be predicted under the conditions of 15 mg mL−1 in solution concentration, a 1:2 mix ratio of D and A, rotational speeds ranging from 800 to 900 rpm, and annealing temperatures within 100–110°C. Using the suggested parameter conditions, we fabricated solar cells, achieving a quite high efficiency of approximately 4%. Besides the global optimization conditions, we also employ the solvent vapor annealing combination to the thermal annealing to facilitate further mobilization of molecules and more optimized microstructure of bulk heterojunction films, resulting in a further enhancement in solar cell efficiency of more than 20%.

Evaluation of Bosch processing and C4F8 plasma deposition at cryogenic temperatures

Abstract The Bosch process was studied at a substrate temperature of −100 °C and compared to etchings performed at room temperature, as in the general case. The tests were realized using an inductively coupled plasma reactor by varying C4F8 passivating gas flow injections both at +20 °C and −100 °C. It was observed that the Bosch process is effectively temperature dependent and that the necessary C4F8 passivating gas flow can be reduced to obtain similar anisotropic profiles at −100 °C compared to the ambient temperature process. For example, in one of the studied cases, a fluorocarbon injection of 8 sccm was sufficient to obtain an anisotropic etch rate of up to 4.4 μm min−1 at −100 °C whereas the profile obtained at +20 °C using the same parameters presents lateral etching defects with a reduced etch rate of 2.4 μm min−1. At this point, the C4F8 flow must be increased to 12 sccm (50% more) to retrieve an anisotropic profile with an etch rate of 4.0 μm min−1. In the case of cryogenic Bosch (cryo-Bosch) processing, C4F8 feed dosing has a greater influence on the passivation regime which affects the subsequent etching result but it can be easily refined through the optimization of process parameters. An in-situ ellipsometry study of the deposition rate of C4F8 on both polycrystalline silicon (p-Si) and SiO2 substrates was realized by varying the gas flow at −100 °C and +20 °C. This study shows that the deposited fluorocarbon material is approximately a hundred times thicker at cryogenic temperatures using the same process parameters. Scanning electron microscopy (SEM) observation of these samples are in adequacy with the ellipsometry results. Cryo–Bosch etching also results in a slightly higher etch rate compared to room temperature processing when analyzing similar anisotropic profiles. Si:SiO2 etching selectivity is significantly increased at −100 °C although the aspect-ratio dependent etching phenomenon is more important.

Investigations and Multi-response Optimization of Process Parameters for SS316L Cold Metal Transfer-Wire Arc Additive Manufactured Samples

Investigations and Multi-response Optimization of Process Parameters for SS316L Cold Metal Transfer-Wire Arc Additive Manufactured Samples

Research on the thermal energy environment and process parameters of milling process in ceramic art design

In ceramic art design, milling technology is the key processing technology, which directly affects the quality and artistic expression of ceramic products. However, the thermal energy environment generated in the milling process has a significant impact on the material characteristics and machining results. This study aims to explore the influence of the thermal energy environment on the ceramic art design during the milling process, optimize the process parameters, and improve the processing efficiency and product quality. Experiments with different milling speed, feed rate and cutting depth were designed to compare and analyze the change of thermal environment by combining experiment and simulation. The influence of thermal energy on ceramic machining quality was evaluated by monitoring the temperature distribution in the cutting process with thermal imaging technology and combining with the experimental data of material properties. The experimental results show that the temperature increase in milling process is closely related to the cutting speed and feed rate, and proper control of the thermal energy environment can significantly reduce the crack generation rate and improve the surface finish of ceramic materials. The optimized process parameters make the temperature control in the milling process within the ideal range, thus realizing the processing of high quality ceramic products. Through reasonable optimization of process parameters, the heat energy in the processing process can be effectively controlled, which helps to improve the overall quality and artistic value of ceramic works.

The dependence of Zener-Hollomon parameter on softening behavior and dynamic recrystallization mechanism of a biodegradable Zn-Cu-Mg alloy

The Zn-Cu-Mg alloy exhibits good strength, ductility, anti-aging and antibacterial properties, which lays the foundation for developing high performance Zn-based biodegradable alloys. However, the constitutive equation and dynamic recrystallization (DRX) behavior of this alloy remain unclear, making the optimization of hot processing parameters almost dependent on trial and error. This work aims to address these issues by investigating the hot compression process. The calculated average activation energy Q of this alloy is 141.338 KJ⋅mol-1, exhibiting excellent heat resistance. The deformed microstructure strongly depends on the Zener-Hollomon parameter (Z=ε˙exp(QRT)). Discontinuous DRX (DDRX) dominates at low lnZ, which has a significantly different orientation from the parent grain. Continuous DRX (CDRX) occurs within the grain and at grain boundaries, and is dominant at middle lnZ, mainly through activation 〈a〉 or/and 〈c+a〉 slip systems. Additionally, the activation of prismatic slip further promote CDRX, and most CDRX grains inherit the 30°[0001] orientation from the parent grains. The volume fraction of DRX demonstrates a decreasing trend followed by an increasing trend with increasing lnZ. At high lnZ, the increase of DRX grains is conducive to weakening the texture, and twin-induced DRX (TDRX) is significantly promoted, leading to an increase in both peak stress and strain hardening rate. Furthermore, the grains with c-axis aligned parallel to the compression direction (CD) are more prone to twinning, while the c-axis perpendicular to CD are the hard orientation of basal slip and compression twins. TEM results reveal that a decrease of c/a value promotes the activation of non-basal slip near the twin boundary, and the highly active 〈c〉 and 〈c+a〉 slips contribute to the increase of strain hardening rate. The results of this study are significant for understanding the workability of Zn-Cu-Mg alloys at high lnZ due to its high efficiency and low cost.

An experimental investigation of abrasive jet machining parameters for optimized surface finish of mild steel components

Abrasive jet machining is one of the unconventional machining processes that are initiated for alternations of surface characteristics of ductile materials, like mild steel, due to the entrainment of high-velocity abrasive particles. The present experimental investigation deals with the major process parameters of the AJM process, namely air pressure, standoff distance, and time of machining, on the average surface roughness (Ra) of mild steel specimens. Experiments were conducted on the self-developed AJM setup using aluminum oxide abrasive particles of an average size of 50 µm. The pressure of air is changed in three levels, 6, 7, and 8 bars. The standoff distance is taken as 2 mm constant for all the cases. The surface roughness was measured at machining times of 20, 40, and 45 seconds. The presence of the increase of air pressure from 6 to 8 bars applied for all the machining time showed an increasing influence on roughness, although always significant in Ra. For 8 bars of pressure and 45 s of machining, the minimum resulted in 1.39 µm, compared with 3.47-4.02 µm. The rate of decrease in surface roughness is sharp with increasing machining time from 20 to 40 seconds but marginal beyond that. An optimal machining time of 40–45 seconds was identified to obtain a minimum Ra. Capability of AJM as an effective technique to enhance the surface finish of mild steel components, and practical insight of this study is very useful toward optimization of process parameters for attaining the targeted surface quality. The results obtained may assist in expanding the industrial application of AJM toward the finishing of ductile materials in various manufacturing fields.