Orthogonal Experimental Design Research Articles

Optimization of a Low Surface Energy Coating for Enhanced Water Resistance and Condensation Suppression

This study focuses on formulating a low-surface-energy, water-resistant, and anti-condensation coating utilizing a fluorocarbon and acrylic resins composite (FAC), enhanced by six functional additives: antistatic agents, water-repellent agents, nanofillers, anti-mold and anti-algae agent, leveling agents, and wetting and dispersing agents. An orthogonal experimental design was implemented to systematically investigate the effects of varying concentrations of these additives on the surface tension of the coating. The results show that the optimized combination of fluorocarbon and acrylic resins composite (OFAC)with functional additives significantly reduces the surface tension, thereby improving both water resistance and anti-condensation properties. This research advances the development of more efficient surface treatment technologies, particularly for applications requiring enhanced water resistance and anti-condensation performance.

Optimization of microstructure and mechanical properties of 7075 aluminum alloy by equal channel angular pressing technology

Abstract The equal channel angular pressing technology, as an effective technique for modifying the surface of a material, has had a significant impact on the microstructure and mechanical properties of the 7075 aluminum alloy. The study achieved the refinement of 7075 aluminum alloy grains and the homogenization of the second phase particle distribution through equal channel angular pressing technology, thereby significantly improving the strength and plasticity of the material. Through orthogonal experimental design, the effects of extrusion angle, extrusion speed, and remelting temperature on material properties were studied. The results showed that appropriate ECAP parameters could effectively regulate the microstructure of 7075 aluminum alloy, thereby optimizing its mechanical properties. Especially in the analysis of equivalent strain rate, when the extrusion angle was 50°, the position of the maximum equivalent strain rate at the forefront point remained unchanged, but the equivalent strain rate decreased to 0.107-1. In the aging hardness analysis, when the number of equal channel angular pressing passes was 3, the aging hardness of G group 7075 aluminum alloy increased to 195HV in the first 5 minutes. The equal channel angular pressing technology can optimize the internal structure of 7075 aluminum alloy and improve the material's plasticity and mechanical properties. The research provides important technical support for the application of 7075 aluminum alloy in high-end equipment manufacturing.

Research on the factors affecting the compressive strength of rubber powder modified cement-stabilized crushed stone based on orthogonal experiments

Based on an orthogonal experimental design, this study introduces rubber powder particle size alongside rubber powder content, aggregate gradation, and cement content as factors. A total of 16 mix proportions were formulated. For each mixture, compaction tests and unconfined compressive strength (UCS) tests were conducted. Using Statistical Product and Service Solutions (SPSS) software, a multifactor variance analysis was performed to determine the influence levels of the four factors on maximum dry density and compressive strength. The optimal mix proportion was selected based on compressive strength. The results indicate that aggregate gradation and rubber powder content significantly affect the maximum dry density of the mixture, with aggregate gradation having the greatest impact. Rubber powder content has the most substantial effect on compressive strength, while rubber particle size has the least influence. The optimal formulation is 7% cement content, 30-mesh rubber powder, 0.5% rubber powder content, and a skeletal dense gradation close to the median.

On-site cellulase production by Trichoderma reesei RutC-30 to enhance the enzymatic saccharification of ball-milled corn stover

Cellulases are essential for the enzymatic saccharification of lignocellulose. They play a crucial role in breaking down the structure of lignocellulose to obtain fermentable sugars. In this study, we conducted on-site cellulase production by Trichoderma reesei RutC-30 through submerged fermentation. The effects of carbon source, nitrogen source, KH2PO4, and mineral elements on cellulase production were evaluated using the hydrolyzed total sugar concentration of ball-milled corn stover as an indicator. The optimal fermentation medium conditions for cellulase production were determined through orthogonal experimental design analysis. Additionally, by optimizing culture conditions, including inoculation, pH, and bottling volume, we achieved a total sugar concentration of 92.25 g/L. After the optimization, the FPA, CMCA, protein, and total sugar concentration increased by 75.49 %, 18.43 %, 89.71 %, and 17.83 %, respectively. Furthermore, corn stover pretreated by different methods was applied to induce cellulase production. Ball-milled and steam-exploded corn stover was identified as suitable incubation carbon sources with total sugar concentration up to 94.31 g/L. Our work exploits the cellulase induced by lignocellulose and then applies it to lignocellulose, enabling the customization and providing a reference for the production of cellulase with corn stover as an inducer.

Bionic Design and Adsorption Performance Analysis of Vacuum Suckers.

This study addresses the problem that the traditional method is not effective in improving the adsorption performance of vacuum suckers. From the perspective of bionics, the adsorption performance of bionic suckers based on the excellent adsorption of the abalone abdominal foot was studied. A bionic sucker was designed by extracting the sealing ring structure of the abdominal foot tentacle. The bionic sucker was subjected to tensile experiments using an orthogonal experimental design, and the adsorption of the bionic sucker was simulated and analyzed to explore its adsorption mechanism. The results show that the primary and secondary factors affecting the adsorption of the sucker are the number of sealing rings, the width of sealing rings and the spacing of sealing rings. At 60% vacuum, the bionic sucker with two sealing rings, a 1.5 mm sealing ring width and 3 mm sealing ring spacing has the largest adsorption force, and its maximum adsorption force is 15.8% higher than that of the standard sucker. This study shows that the bionic sucker design can effectively improve the adsorption performance of the sucker. The bionic sucker had a different stress distribution on the sucker bottom, which resulted in greater Mises stress in the sealing ring and the surrounding area, while the Mises stress in the central area of the sucker was smaller.

Tuning Na Occupation and Annealing Temperature on Na Diffusion Pathways in P2-Type NaxNi0.25Mn0.75O2 for Sodium-Ion Batteries by Ab Initio Calculations.

The NaxNi0.25Mn0.75O2 cathode material draws ongoing interest owing to its considerable specific capacity along with its elevated average operating voltage. However, its application is limited by weak rate performance and quick capacity fading. In this study, a series of NaxNi0.25Mn0.75O2 (x = 0.65, 0.70, 0.75, 0.80) cathode materials are prepared by the solid-phase method based on an orthogonal experiment. The optimum preparation process is investigated by optimizing factors such as Na content, calcination temperature, calcination time, and heating rate. The Nae/Naf ratio is adjusted, and the Na+ at different sites are rearranged to reduce the Na+/vacancy ordering by changing the Na content on the basis of process optimization. Owing to the fast migration kinetics of Nae sites, experimental results indicate that the P2-Na0.75Ni0.25Mn0.75O2 cathode material, which has Na+ occupying more thermodynamically stable Nae sites, demonstrates superior battery performance. High initial discharge specific capacity (157.2 mAh g-1) along with favorable cycle performance of the Na0.75Ni0.25Mn0.75O2 cathode material can be achieved by modifying the Nae site occupancy and optimizing the experimental conditions. Together with the microscopic control of Na+ occupancy, this novel orthogonal experiment design offers a fresh perspective and approach to a thorough comprehension of cathode materials for sodium-ion batteries.

Investigating Tire Ground Contact Mechanics Under Partial Hydroplaning Conditions: A Multivariate Analysis Approach

Tire hydroplaning is one of the main causes of traffic accidents on slippery roads. In order to study the ground contact mechanical characteristics of tires under partial hydroplaning conditions, 205/55the R16 passenger car tire was taken as the research object, a tire hydroplaning CEL simulation model was established, and the test plan was created using the orthogonal experimental design method. Through range analysis, partial correlation analysis and multivariate linear regression analysis, a tire side slip UA model suitable for different working conditions on slippery roads was established. Comparative analysis shows that the model can accurately predict the tire ground contact mechanical characteristics on slippery roads, and provide a theoretical reference for studying the tire multi-condition hydroplaning performance.

Coupling Taguchi experimental designs with deep adaptive learning enhanced AI process models for experimental cost savings in manufacturing process development

The Aluminum alloy AA7075 workpiece material is observed under dry finishing turning operation. This work is an investigation reporting promising potential of deep adaptive learning enhanced artificial intelligence process models for L18 (6133) Taguchi orthogonal array experiments and major cost saving potential in machining process optimization. Six different tool inserts are used as categorical parameter along with three continuous operational parameters i.e., depth of cut, feed rate and cutting speed to study the effect of these parameters on workpiece surface roughness and tool life. The data obtained from special L18 (6133) orthogonal array experimental design in dry finishing turning process is used to train AI models. Multi-layer perceptron based artificial neural networks (MLP-ANNs), support vector machines (SVMs) and decision trees are compared for better understanding ability of low resolution experimental design. The AI models can be used with low resolution experimental design to obtain causal relationships between input and output variables. The best performing operational input ranges are identified for output parameters. AI-response surfaces indicate different tool life behavior for alloy based coated tool inserts and non-alloy based coated tool inserts. The AI-Taguchi hybrid modelling and optimization technique helped in achieving 26% of experimental savings (obtaining causal relation with 26% less number of experiments) compared to conventional Taguchi design combined with two screened factors three levels full factorial experimentation.

Optimization of atomization performance of external mixing air atomizing nozzle based on response surface agent model

Based on the finding that the atomization performance of the nozzle is affected by its structural parameters, a combination of finite element analysis and structural optimization calculations is used. Finite element analysis was performed through the VOF (Volume of Fluid) module of Fluent software to determine the structural parameters affecting the performance of the atomizing nozzle. The liquid cyclone tank inclination, the length of the flat section at the gas outlet, and the mixing chamber outlet diameter were used as optimization factors. The atomization cone angle and fuel atomization circumferential distribution uniformity index were used as the evaluation indexes of atomization performance. An orthogonal experimental design was carried out based on the above. Proxy model for atomization cone angle and fuel atomization circumferential distribution uniformity index based on response surface method. The optimal structure of the nozzle was obtained by optimization calculation of the agent model by genetic algorithm. The results show that the atomization performance is optimal when the inclination angle of the liquid rotating tank is 42.9, the length of the flat section at the gas outlet is 3.3, and the diameter of the mixing chamber outlet is 5.0. The atomization cone angle increased by 23.07 % compared with the original model, and the fuel atomization circumferential distribution uniformity index decreased by 45.06 %. A new solution for the design of externally mixed air atomizing nozzles is provided.

Process optimization and modeling of Acer truncatum seed oil deacidification by molecular distillation

Acer truncatum seed oil (ATO) is a unique woody oil resource, but its high acid value resulting from traditional pressing methods falls short of meeting the necessary food standards. In this research, molecular distillation (MD) was employed for the deacidification of ATO and compared with conventional techniques such as alkali refining and solvent extraction. Predictive models for MD deacidification were established through single-factor experiments, orthogonal experimental design (OED), response surface methodology (RSM), and genetic algorithm - back propagation artificial neural network (GA-BP-ANN). Using RSM, a fitting model was obtained with R = 0.999 and Rpre = 0.990 through multiple linear regression. The constructed BP-ANN model (3-9-1) exhibited R = 0.998 and Rpre = 0.998, indicating slightly higher prediction accuracy. With GA-BP-ANN, the optimal process conditions are an evaporator temperature of 190 °C, condensation temperature of 40 °C, scraper speed of 324 rpm, yielding an acid value of refined ATO at 0.240 ± 0.001 mg g−1. Therefore, under appropriate conditions, the MD deacidification method can produce high-quality refined ATO. The developed orthogonal, RSM, and GA-BP-ANN models demonstrated excellent capabilities in optimizing and predicting the MD deacidification process, meeting even the strictest food regulations.

Study on the response mechanisms and evolution prediction of groundwater microbial-toxicological indicators.

This study aims to investigate the response mechanisms of groundwater microbial-toxicological indicators, specifically total bacteria count (TBC) and total coliform count (TCC), to water quality indicators and environmental conditions. Using data from a water source in the western plateau of China, a predictive model focusing on TBC and TCC was developed. An orthogonal experimental design was employed to manipulate environmental conditions such as temperature, pH, and porosity, facilitating laboratory experiments. These experiments measured pH, chemical oxygen demand (COD), oxidation-reduction potential (ORP), TBC, and TCC at varying depths and environmental conditions. Principal component analysis elucidated the mechanisms by which water quality indicators and environmental conditions affect groundwater microbial-toxicological indicators. A prediction model for these indicators in plateau regions was established based on a backpropagation neural network (BP-NN), using TBC and TCC as target variables and the newly extracted principal components as influencing factors. The results demonstrate that environmental conditions and water quality indicators primarily influence the evolution of groundwater microbial-toxicological indicators by altering the ionic charge quantities, redox conditions, and temperature of the groundwater. The predictive model for groundwater microbial-toxicological indicators shows trends consistent with experimental outcomes, with an average relative error of less than 15%, meeting engineering requirements. PRACTITIONER POINTS: The values of total bacteria count (TBC) and total coliform count (TCC) under different conditions were obtained by column experiments. The influence mechanism of environmental conditions and groundwater indicators on TBC and TCC was elaborated by principal component analysis. TBC and TCC prediction models were established through the investigation of water sources in a plateau area and laboratory experiments.

Reliability analysis of cutting tools using transformed inverse Gaussian process-based wear modelling considering parameter dependence

Reliability analysis is crucial for ensuring the performability of the desired function. The cutting tool performs the machining operation at varied conditions to manufacture diverse products. During operation, the tool degrades stochastically in the form of wear. To avoid the unfavourable consequences occurring from severe tool wear, appropriate formulation of the tool reliability, considering threshold degradation level as the failure criterion, is crucial. However, the degradation of the tool during machining is impacted by the current state of the tool wear and operating conditions. Considering these, the present study proposes a state-dependent transformed inverse Gaussian (TIG) process incorporating the effects of operating conditions to develop the tool wear model. In order to evaluate the proposed method, tool wear experiments are conducted at different operating conditions following the Taguchi orthogonal array experimental design. The experimental data are utilised to estimate the parameters of the developed model using the Bayesian approach. Following the parameter estimation, tool reliability is evaluated under varying operating conditions. The comparison of the estimated median time to failure of the tools with the failure time observed in the validation experiments ensures the effectiveness of the proposed model. The proposed approach has the potential to estimate the reliability of the industrial products subjected to state-dependent degradation under varied operating conditions.

Reconfiguring closed-loop desorption for industrial solvent recycling by retaining post-condensation volatile organic compounds

Massive emissions of volatile organic compounds (VOCs) from industrial solvent evaporation have exacerbated atmospheric pollution. As high-value pollutants, VOCs can be concentrated and condensed into liquid solvents for reuse instead of destructive elimination, minimizing environmental threats and treatment costs. However, classical thermal swing adsorption (TSA) using nitrogen as the purge gas are inclined to be in closed form to economize on nitrogen, which results in desorption residues at the outlet of the adsorber and leads to environmental risks. In this study, one-step closed-loop desorption (OCD) and two-step closed-loop desorption (TCD) were proposed for industrial solvent recovery by reconfiguring TSA and condensation to improve desorption efficiency and recovery performance. A detailed mathematical model was developed and validated experimentally to describe dynamic behaviors of closed-loop desorption. The results showed that the standby adsorber connected to the thermal desorption process effectively retained the post-condensation VOCs and reduced the desorption residual for the re-absorption efficiency. By using the orthogonal experimental design, desorption temperature, condensation temperature, and feed concentration were proved to be more significant on recovery capacity than packing height and purge velocity. A broader feasible recovery domain was achieved by the TCD cycle, with an increase of 48.15 % compared to the OCD cycle at low feed concentrations. The OCD cycle was more energy-efficient, except in scenarios of low adsorption loading and low-grade energy sources. The findings provide new insights and promising approaches for achieving synergies between industrial exhaust treatment and organic solvent recovery.

Rural residential choices: unravelling the nexus of accessibility and preferences

ABSTRACT As rural residents increasingly migrate to urban areas to improve living conditions, employment, and education, closely examining the relationship between residential choices and accessibility becomes crucial. This study employs a Stated Preference Survey (SP) with an orthogonal experimental design and uses the Multinomial Logit Model (MNL) to explore the preferences of rural residents for accessibility in the built environment when choosing new residences, including potential disparities between residents on the urban fringe and those in suburban areas. The findings reveal a strong inclination among residents for proximity to essential services such as education, transportation, healthcare, markets, recreation, and amenities. Preferred residential locations include within a 2-kilometre radius of schools, 300 metres from bus stops, 500 metres from medical facilities, 2 kilometres from markets, between 500 metres and 1 kilometre from leisure facilities, and within 1 kilometre of supermarkets. Additionally, commuting time preferences vary, with rural residents favoring a 15-minute commute, peri-urban residents preferring 5 minutes, and distant suburban residents showing no clear preference for commute times ranging from 5 to 15 minutes. These insights carry significant implications for urban and rural planning and development.

Efficient production of pullulan by Aureobasidium pullulans using a multi-objective optimization strategy with orthogonal experimental design coupling artificial neural network and genetic algorithm

Efficient pullulan production has long been a central research focus. This study used maltodextrin as the carbon source for pullulan production by Aureobasidium pullulans fermentation. A hybrid optimization approach, integrating orthogonal experimental design (OED), backpropagation artificial neural network (BP-ANN), and elite strategy non-dominated sequential genetic algorithm-II (NSGA-II), was developed. Range analysis based on OED revealed that MgSO4·7H2O significantly affects production but less impacts molecular weight, while pH notably influences molecular weight with a lesser effect on production, underscoring the need for multi-objective optimization. The BP-ANN model showed strong predictive capabilities, with goodness-of-fit values of 0.984 and 0.980 for production and molecular weight, respectively. Using this model as the fitness function for the optimization algorithm enhanced efficiency. Taking cost factors into account, the BP-ANN-NSGA-II algorithm identified the optimal fermentation medium conditions, resulting in a 6.89 % increase in production, a 368.97 % increase in molecular weight, and a 42.49 % reduction in cost. The maximum comprehensive optimization efficiency is 63.73 %, and the multi-objective optimization is better than the single objective optimization. This method significantly guides the improvement of pullulan fermentation optimization efficiency.

Optimization of Consolidated Bioprocessing Fermentation of Uncooked Sweet Potato Residue for Bioethanol Production by Using a Recombinant Amylolytic Saccharomyces cerevisiae Strain via the Orthogonal Experimental Design Method

Optimization of Consolidated Bioprocessing Fermentation of Uncooked Sweet Potato Residue for Bioethanol Production by Using a Recombinant Amylolytic Saccharomyces cerevisiae Strain via the Orthogonal Experimental Design Method

Evaluating and enhancing heat storage in a Ca(OH)2/CaO shell-tube reactor: A numerical study on key factors and performance optimization

Thermochemical energy storage offers a promising solution to addressing the challenges such as the discontinuous operation of concentrating solar power plants. This study develops a comprehensive model to simulate the Ca(OH)2 heat storage process within a shell-tube reactor system by integrating the fluid dynamics, chemical reactions and thermal processes. The influence of operating conditions and geometric characteristics on the reactor's performance is systematically analyzed. Key performance evaluations are evaluated using an orthogonal experimental design. The results reveal that the reactor's reaction time is 11,305 s under baseline conditions. Increasing the HTF temperature from 825 K to 875 K can reduce the reaction time by 47.9 %, while decreasing the reactant porosity from 0.8 to 0.6 leads to an increase of 100 % in energy storage capacity and extends the heat storage duration by 183 %. Additionally, optimizing the tube arrangement by adding 22 tube passes can increase the energy storage efficiency except causing a marginal increase in reaction time. Among the factors analyzed, reactant porosity has the most significant impact on the heat storage time, while the HTF temperature determinates the heat exchange rate and energy storage efficiency.

Optimization of water supply parameters for enhanced thermal uniformity in aquaculture ponds: An experimental study based on orthogonal experimental design

The temperature distribution in aquaculture ponds plays a crucial role in the rearing of fish larvae. To achieve a more uniform temperature distribution in the pond and alleviate the problem of thermal stratification, precise control of pond temperature is essential. A recirculating aquaculture pond was taken as the test object. Single-factor experiments and orthogonal experiments were conducted to comprehensively test the aquaculture pond's water supply pipe layout, bend angle, perforation rate, and environmental conditions. Three evaluation indicators were used to analyze the temperature distribution of the aquaculture pond under various water supply pipe structures, leading to the identification of optimal structural configurations. The optimal structure I, obtained from single-factor experiments, was determined to be 2.5 m, 50 %, and 60°, while the optimal structure II, derived from orthogonal experiments, was 1.25 m, 75 %, and 30°. Both optimized structures exhibited significant temperature fluctuations in the pond during the summer season. However, Structure II demonstrated the lowest average temperature difference in the winter season, indicating its superior adaptability during winter. Moreover, Structure II could provide higher temperatures for shallow areas with depths greater than 600 mm, making it more suitable for fish larvae cultivation. Additionally, local devices effectively decreased temperature variations by 17.5 % within the pond. Furthermore, energy consumption output was evaluated for different operational conditions. The results showed that Structure II had the lowest energy consumption output during the summer season. In contrast, during the winter season, the energy consumption output increased by approximately 12 %, indicating significant energy-saving potential in aquaculture ponds during winter.

Effect of Vibration Pretreatment-Microwave Curing Process Parameters on the Mechanical Performance of Resin-Based Composites.

The vibration pretreatment-microwave curing process can achieve high-quality molding under low-pressure conditions and is widely used in the curing of resin-based composites. This study investigated the effects of the vibration pretreatment process parameters on the void content and the fiber weight fraction of T700/TRE231; specifically, their influence on the interlaminar shear strength and impact strength of the composite. Initially, an orthogonal experimental design was employed with interlaminar shear strength as the optimization target, where vibration acceleration was determined as the primary factor and dwell time as the secondary factor. Concurrently, thermogravimetric analysis (TGA) was performed based on process parameters that corresponded to the extremum of interlaminar shear strength, revealing a 2.17% difference in fiber weight fraction among specimens with varying parameters, indicating a minimal effect of fiber weight fraction on mechanical properties. Optical digital microscope (ODM) analysis identified interlaminar large-size voids in specimens treated with vibration energy of 5 g and 15 g, while specimens subjected to a vibration energy of 10 g exhibited numerous small-sized voids within layers, suggesting that vibration acceleration influences void escape pathways. Finally, impact testing revealed the effect of the vibration pretreatment process parameters on the impact strength, implying a positive correlation between interlaminar shear strength and impact strength.

Deflection characteristics and influencing factors of projectile oblique impact on concrete targets

The projectile deflects and even ricochets after an oblique impact on the concrete. However, research on the oblique impact of projectiles on concrete targets mainly focuses on oblique penetration and the critical ricochet angle, and there are few experimental studies on ricochets. Deflection and its influencing factors remain undefined. This study conducted experiments and LS-DYNA numerical simulations on projectiles obliquely impacting C60 concrete targets. The experimental research visually revealed deflection and ricochet phenomena after the oblique impact. The ricochet caused by large-angle impacts can effectively reduce the damaged area of concrete targets. Subsequently, the main governing parameters affecting the deflection angle of the projectile were identified through dimensional analysis, and a sensitivity analysis was performed on these parameters using an orthogonal experimental design. On this basis, the influence of the incident angle, impact velocity, and projectile length-to-diameter ratio on the projectile deflection was further clarified. The results showed that the maximum deflection angle was achieved when a 30 mm caliber projectile obliquely impacted a C60 concrete at an incident angle of ∼45°. In the case of ricochets, the deflection angle increased with an increase in the impact velocity and decreased with an increase in the length-to-diameter ratio. This study aids in predicting and controlling projectile deflection and provides a reference for the innovative design of concrete protective structures.